Repository: cellannotation/cap-python-client
Pull request #5: MVP-6449 Interface for MD page
Author: login: rm1113
Created: 2025-02-21T16:05:23Z | Merged: 2025-03-04T14:33:20Z

Fixes MVP-6449


Diff:
diff --git a/.github/workflows/python-app.yml b/.github/workflows/python-app.yml
index 633d629..023cdf6 100644
--- a/.github/workflows/python-app.yml
+++ b/.github/workflows/python-app.yml
@@ -28,8 +28,9 @@ jobs:
     - name: Install dependencies
       run: |
         python -m pip install --upgrade pip
-        pip install flake8 pytest
-        if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
+        python -m pip install --upgrade uv
+        uv pip install --system flake8 pytest
+        if [ -f requirements.txt ]; then uv pip install --system -U -r requirements.txt; fi
     - name: Lint with flake8
       run: |
         # stop the build if there are Python syntax errors or undefined names
diff --git a/.gitignore b/.gitignore
index 9ccdca6..2fce389 100644
--- a/.gitignore
+++ b/.gitignore
@@ -2,4 +2,5 @@ cap_client/__pycache__/*
 cap_client/client/__pycache__/*
 cap_client.egg-info/*
 dist/*
-test/__pycache__
\ No newline at end of file
+test/__pycache__
+tmp/*
diff --git a/cap_client/__init__.py b/cap_client/__init__.py
index 6033884..b7b4619 100644
--- a/cap_client/__init__.py
+++ b/cap_client/__init__.py
@@ -1,5 +1,6 @@
-from .cap import Cap
+from .cap import CapClient, MDSession
 
 __all__ = [
-    "Cap"
+    "CapClient",
+    "MDSession",
 ]
diff --git a/cap_client/cap.py b/cap_client/cap.py
index bb8e558..f4453eb 100644
--- a/cap_client/cap.py
+++ b/cap_client/cap.py
@@ -1,12 +1,14 @@
-from typing import List, Dict
+from typing import List, Dict, Literal
 import time
 import http.client
 import json
 import jwt
 import os
+from uuid import uuid4
+import pandas as pd
 
-from .client.client import Client
-from .client.input_types import (
+from client.client import _Client
+from client.input_types import (
     DatasetSearchOptions,
     LookupDatasetsFiltersInput,
     LookupDatasetsSearchInput,
@@ -17,37 +19,361 @@
     LookupCellsSearch,
     SearchLabelByMetadataArgs,
     CellLabelsSearchSort,
-    GetDatasetClustersDataInput,
     GetDatasetEmbeddingDataInput,
     GetGeneralDiffInput,
-    PostHeatmapInput,
     GetHighlyVariableGenesInput,
     PostSaveEmbeddingSessionInput,
-    DatasetObjectInput
+    PostHeatmapInput
 )
-from .client.search_datasets import SearchDatasets
-from .client.lookup_cells import LookupCells
-from .client.download_urls import DownloadUrls
-from .client.embedding_clusters import EmbeddingClusters
-from .client.embedding_data import EmbeddingData
-from .client.general_de import GeneralDE
-from .client.heatmap import Heatmap
-from .client.highly_variable_genes import HighlyVariableGenes
-from .client.create_session import CreateSession
-
+from client.search_datasets import SearchDatasets
+from client.lookup_cells import LookupCells
+from client.embedding_data import EmbeddingDataDatasetEmbeddingData
+from client.heatmap import HeatmapDatasetEmbeddingDiffHeatMap
 
 CAP_API_URL = "https://celltype.info/graphql"
 CAP_AUTHENTICATE_URL = "us-central1-capv2-gke-prod.cloudfunctions.net" # https://${var.gcp_region}-${var.gcp_project_id}.cloudfunctions.net/authenticate-token
-class Cap(Client):
-    def __init__(
+
+SESSION_ID = str
+DIFF_KEY = str
+SELECTION_KEY = str
+CELL_LABELS_MODE = "cell-labels"
+
+
+class MDSession:
+    def __init__(self, dataset_id: str, _client: _Client):
+        self.__client: _Client = _client
+        self._dataset_id: str = dataset_id
+        self._session_id: str = None
+        self._dataset_snapshot = None
+        self._embeddings: list[str] = None
+        self._labelsets: list[str] = None
+        self._clusterings: list[str] = None
+        self._metadata: list[str] = None
+    
+    def __repr__(self) -> str:
+        return f"Molecular Data page session for dataset id: {self.dataset_id}"
+    
+    def __str__(self) -> str:
+        return self.__repr__()
+    
+    @property
+    def dataset_id(self) -> str:
+        return self._dataset_id
+    
+    @property
+    def dataset_snapshot(self):
+        return self._dataset_snapshot
+    
+    @property
+    def embeddings(self) -> list[str]:
+        return self._embeddings
+    
+    @property
+    def clusterings(self) -> list[str]:
+        return self._clusterings
+    
+    @property
+    def labelsets(self) -> list[str]:
+        return self._labelsets
+    
+    @property
+    def session_id(self) -> str:
+        return self._session_id
+
+    def _check_md_ready(self):
+        ready = self.__client.dataset_ready(self.dataset_id)
+        if not ready.dataset.is_embeddings_up_to_date:
+            raise RuntimeError(f"The Molecular Data for the dataset {self.dataset_id} is not ready!")
+        
+    def _get_clusterings(self) -> list[str]:
+        res = self.__client.cluster_types(self.dataset_id)
+        res = res.dataset
+        clusters = res.embedding_cluster_types
+        cluster_names = [cl.name for cl in clusters]
+        return cluster_names
+    
+    def _get_embeddings(self) -> list[str]:
+        res = self.__client.md_commons_query(self.dataset_id)
+        res = res.dataset
+        embeddings = res.embeddings
+        emb_names = [e.name for e in embeddings]
+        return emb_names
+
+    def _get_cell_type_labelsets(self) -> list[str]:
+        if self.dataset_snapshot is None:
+            raise RuntimeError("The dataset snapshot is not ready, call MDSession.create_session first!")
+        
+        labelsets = []
+        for lbst in self.dataset_snapshot.labelsets:
+            if lbst.mode == CELL_LABELS_MODE:
+                labelsets.append(lbst.name)
+        return labelsets
+
+    def create_session(
+            self,
+        ) -> SESSION_ID:
+        """
+        Creates a new session for embedding processing.
+
+        This method performs a sanity check, retrieves the initial state of the dataset, 
+        fetches clusterings and embeddings, and then initializes a new session with a 
+        unique session ID. The session information is saved via the client.
+
+        Returns:
+            str: The unique session ID of the newly created embedding session.
+        """
+
+        self._check_md_ready()
+
+        ds = self.__client.dataset_initial_state_query(self.dataset_id)
+        self._dataset_snapshot = ds.dataset
+        self._clusterings = self._get_clusterings()
+        self._embeddings = self._get_embeddings()
+        self._labelsets = self._get_cell_type_labelsets()
+
+        session_id = str(uuid4())
+ 
+        data = PostSaveEmbeddingSessionInput(
+            session_id = session_id,
+            dataset = self._dataset_snapshot.model_dump()
+        )
+        response = self.__client.create_session(
+            data = data
+        )
+        self._dataset_snapshot = response.save_embedding_session
+        self._session_id = session_id
+        return self.session_id
+    
+    def embedding_data(
             self, 
+            embedding: str,
+            max_points: int,
+            labelsets: List[str] = None,
+            selection_gene: str = None,
+            selection_key_major: str = None,
+            selection_key_minor: str = None,
+        ) -> EmbeddingDataDatasetEmbeddingData:
+        """
+        Retrieves embedding data for the specified embedding type, with optional filtering and downsampling.
+
+        Parameters:
+        -----------
+        embedding : str
+            The name of the embedding to retrieve. Must be present in `self.embeddings`.
+        max_points : int
+            The maximum number of points to include in the response. Data may be downsampled to meet this limit.
+        labelsets : List[str], optional
+            A list of label sets to include in the embedding data. Defaults to None.
+        selection_gene : str, optional
+            If provided, returns a list of expression values for the specified gene. Defaults to None.
+        selection_key_major : str, optional
+            If provided, returns a list of boolean markers indicating whether each point is within the major selection. Defaults to None.
+        selection_key_minor : str, optional
+            If provided, returns a list of boolean markers indicating whether each point is within the minor selection. Defaults to None.
+
+        Returns:
+        --------
+        EmbeddingDataDatasetEmbeddingData
+            An object containing the embedding data, including observation IDs, selections, embeddings, annotations, 
+            and gene expression values.
+
+        Raises:
+        -------
+        ValueError
+            If the specified embedding is not found in `self.embeddings`.
+        """
+
+        if embedding not in self.embeddings:
+            raise ValueError(f"Embedding '{embedding}' is not found in the list of '{self.embeddings}'")
+        
+        options =  GetDatasetEmbeddingDataInput(
+            embedding = embedding,
+            scale_max_plan = max_points,
+            session_id = self.session_id,
+            labelsets = labelsets,
+            selection_gene = selection_gene,
+            selection_key_major = selection_key_major,
+            selection_key_minor = selection_key_minor,
+        )
+        
+        # TODO: is not workgin with new rc1
+        # update request later https://capdevelopment.atlassian.net/browse/MVP-6489
+        response = self.__client.embedding_data(
+            dataset_id = self.dataset_id,
+            options = options
+        )
+        data = response.dataset.embedding_data
+        return data
+
+    def _labelset_id_from_name(self, labelset_name) -> str:
+        if self.dataset_snapshot is None:
+            raise RuntimeError("Dataset snapshot is not ready, create session first!")
+        
+        for lbst in self.dataset_snapshot.labelsets:
+            if lbst.name == labelset_name:
+                return lbst.id
+        
+        raise ValueError(f"Can't find labelset '{labelset_name}' in dataset snapshot!")
+
+    def general_de(
+            self, 
+            labelset: str,
+            random_seed: int = 42,
+        ) -> DIFF_KEY:
+        """
+        Performs a general differential expression (DE) analysis.
+
+        This method conducts a differential expression analysis, comparing each of the 
+        top 10 largest labels within the specified label set against all other data points.
+
+        Parameters:
+        -----------
+        labelset : str
+            The name of the label set to use for differential expression analysis. 
+            Must be present in `self.labelsets`.
+        random_seed : int, optional
+            The random seed for reproducibility. Defaults to 42.
+
+        Returns:
+        --------
+        DIFF_KEY
+            A string key associated with the results of the differential expression analysis.
+
+        Raises:
+        -------
+        ValueError
+            If the specified label set is not found in `self.labelsets`.
+        """
+        if labelset not in self.labelsets:
+            raise ValueError(f"Labelset '{labelset}' is not found in the list of '{self.labelsets}'")
+        
+        labelset_id = self._labelset_id_from_name(labelset)
+
+        options = GetGeneralDiffInput(
+            random_seed = random_seed,
+            session_id = self.session_id,
+            labelset_id = labelset_id
+        )
+        response = self.__client.general_de(
+            dataset_id = self.dataset_id,
+            options = options,
+        )
+        diff_key = response.dataset.general_diff
+        return diff_key
+    
+    def highly_variable_genes(
+            self,
+            gene_name_filter: str = None,
+            pseudogenes_filter: bool = True,
+            offset: int = 0,
+            limit: int = 50,
+            sort_order: Literal["desc", "asc"] = "desc"
+        ) -> pd.DataFrame:
+        """
+        Retrieves a list of highly variable genes from the specified dataset.
+
+        This method queries the dataset for highly variable genes based on dispersion values.
+        It supports filtering by gene name, excluding pseudogenes, and sorting the results.
+        The retrieved genes are returned as a Pandas DataFrame with columns for gene names
+        and their respective dispersion values.
+
+        Args:
+            dataset_id (str): The unique identifier of the dataset.
+            gene_name_filter (str, optional): A filter to include only genes matching a given prefix.
+            pseudogenes_filter (bool, optional): If True, filters out genes which are often
+                over-expressed but biologically non-informative. Defaults to True.
+                See https://github.com/cellannotation/cap-gene-filtering for details.
+            offset (int, optional): The starting index for pagination. Defaults to 0.
+            limit (int, optional): The maximum number of genes to return. Defaults to 50.
+            sort_order (Literal["desc", "asc"], optional): The sorting order for dispersion values.
+                Defaults to "desc" (descending).
+
+        Returns:
+            pd.DataFrame: A DataFrame containing highly variable genes with two columns:
+                - "gene_symbol" (str): The gene symbol.
+                - "dispersion" (float): The dispersion value of the gene. Initially, the gene
+                    dispersion values are calculated over the log-transformed count matrix,
+                    these dispersion values are then log-transformed again before being displayed
+                    in the gene table.
+        """
+ 
+        options = GetHighlyVariableGenesInput(
+            offset = offset,
+            limit = limit,
+            gene_name_filter = gene_name_filter,
+            use_genes_pattern = pseudogenes_filter,
+            sort_by = "dispersion",
+            sort_order = sort_order
+        )
+        res = self.__client.highly_variable_genes(
+            dataset_id = self.dataset_id,
+            options = options
+        )
+        hvg_list = res.dataset.embedding_highly_variable_genes
+        
+        df = pd.DataFrame({
+            "gene_symbol": [g.name for g in hvg_list],
+            "dispersion": [g.dispersion for g in hvg_list],
+        })
+        return df
+
+    def is_md_cache_ready(self) -> bool:
+        """
+        Checks whether the molecular data cache is ready.
+
+        This method queries the dataset's file status and determines if the 
+        molecular data page files are fully prepared and available.
+
+        Returns:
+        --------
+        bool
+            True if the metadata cache is ready, otherwise False.
+        """
+        res = self.__client.files_status(self.dataset_id)
+        status = res.dataset.get_md_files_status
+        return status == "ready"
+
+    def heatmap(
+            self,
+            diff_key: DIFF_KEY,
+            n_top_genes: int = 3,
+            max_cells_displayed: int = 1000,
+            gene_name_filter: str = None,
+            pseudogenes_filter: bool = True,
+            selection_key: SELECTION_KEY = None,
+            include_reference: bool = True
+        ) -> HeatmapDatasetEmbeddingDiffHeatMap:
+        
+        options=PostHeatmapInput(
+            diff_key = diff_key,
+            n_genes = n_top_genes,
+            scale_max_plan = max_cells_displayed,
+            genes_filter = gene_name_filter,
+            use_genes_pattern = pseudogenes_filter,
+            session_id = self.session_id,
+            include_reference_selection = include_reference,
+            selection_key = selection_key,
+        )
+
+        # TODO: update api, it was changed on rc1 https://capdevelopment.atlassian.net/browse/MVP-6489
+        res = self.__client.heatmap(
+            dataset_id=self.dataset_id,
+            options=options,
+        )
+        heatmap = res.dataset.embedding_diff_heat_map
+        return heatmap
+
+
+class CapClient:
+    def __init__(
+            self,
             url: str = CAP_API_URL, 
             auth_url: str = CAP_AUTHENTICATE_URL,
             login: str = None,
             pwd: str = None,
             custom_token: str = None  
         ) -> None:
-        super().__init__(url)
+        self.__client = _Client(url)
         self._login = login if login is not None else os.environ.get('CAP_LOGIN')
         self._pwd = pwd if pwd is not None else os.environ.get('CAP_PWD')
         self._custom_token = custom_token if custom_token is not None else os.environ.get('CAP_TOKEN')
@@ -56,7 +382,7 @@ def __init__(
         self._error_status: str = None
         self.auth_url = auth_url
 
-    def _request (
+    def _auth_request (
             self,
             base_url: str, 
             url: str,
@@ -70,27 +396,27 @@ def _request (
             try:
                 response = response.read().decode()
                 self._token = json.loads(response)['idToken']
-                # TODO : Add signature verification 
+                # TODO : Add signature verification https://capdevelopment.atlassian.net/browse/MVP-6392
                 self._token_expiry_time = jwt.decode(self._token, options={"verify_signature": False})['exp']
                 self._error_status = None
                 return True
-            except: 
+            except: # TODO: implement appropriate error handling https://capdevelopment.atlassian.net/browse/MVP-6489
                 self._error_status = "Failed to parse 200 OK response to get ID token"
-                return False  
+                return False
         self._error_status = "Failed to get ID token " + response.reason 
         return False
     
-    def _authenticate(
+    def authenticate(
         self
      ) -> bool:
         # try authenticate by custom token first
         if self._custom_token is not None:
             body = {'token':self._custom_token}
-            if (self._request(base_url= self.auth_url, url = "/authenticate-token", body = body)):
+            if (self._auth_request(base_url= self.auth_url, url = "/authenticate-token", body = body)):
                 return True
         if self._login is not None and self._pwd is not None:    
             body = {'email':self._login, 'password': self._pwd}
-            if (self._request(base_url = self.auth_url, url = "/authenticate-user", body = body)):
+            if (self._auth_request(base_url = self.auth_url, url = "/authenticate-user", body = body)):
                 return True
         self._error_status = "Missing CAP client authetication settings. Check CAP_LOGIN, CAP_PWD or CAP_TOKEN enviroment variables."
         return False
@@ -106,7 +432,7 @@ def id_token(self) -> str:
     @property
     def token_expiry_time(self) -> time:
         return self._token_expiry_time
-    
+
     def search_datasets(
         self,
         search: List[str] = None,
@@ -137,32 +463,11 @@ def search_datasets(
         if search:
             search_input = LookupDatasetsSearchInput(name=search)
 
-        response = super().search_datasets(
+        response = self.__client.search_datasets(
             options=search_options, filter=search_filter, search=search_input
         )
         return response
 
-    def search_datasets_json(
-        self,
-        search: List[str] = None,
-        organism: List[str] = None,
-        tissue: List[str] = None,
-        assay: List[str] = None,
-        limit: int = 50,
-        offset: int = 0,
-        sort: List[Dict[str, str]] = []
-    ) -> str:
-        response = self.search_datasets(
-            search,
-            organism,
-            tissue,
-            assay,
-            limit,
-            offset,
-            sort
-        )
-        return response.model_dump_json()
-
     def search_cell_labels(
         self,
         search: List[str] = None,
@@ -197,300 +502,10 @@ def search_cell_labels(
         if search:
             search_input = LookupCellsSearch(name=search)
 
-        response = super().lookup_cells(
+        response = self.__client.lookup_cells(
             options=search_options, filter=search_filter, search=search_input
         )
         return response
     
-    def search_cell_labels_json(
-        self,
-        search: List[str] = None,
-        organism: List[str] = None,
-        tissue: List[str] = None,
-        assay: List[str] = None,
-        limit: int = 50,
-        offset: int = 0,
-        sort: List[Dict[str, str]] = [],
-    ) -> str:
-        response = self.search_cell_labels(
-            self,
-            search,
-            organism,
-            tissue,
-            assay,
-            limit,
-            offset,
-            sort
-        )
-        return response.model_dump_json()
-    
-    def download_urls_json(self, dataset_id: str) -> str:
-        response = super().download_urls(dataset_id)
-        return response.model_dump_json()
-    
-    def files_status_json(self, dataset_id: str) -> str:
-        response = super().files_status(dataset_id)
-        return response.model_dump_json()
-    
-    def md_commons_query_json(self, dataset_id: str) -> str:
-        response = super().md_commons_query(dataset_id)
-        return response.model_dump_json()
-    
-    def dataset_initial_state_query_json(self, dataset_id: str) -> str:
-        response = super().dataset_initial_state_query(dataset_id)
-        return response.model_dump_json()
-    
-    def cluster_types_json(self, dataset_id: str) -> str:
-        response = super().cluster_types(dataset_id)
-        return response.model_dump_json()
-    
-    def dataset_ready_json(self, dataset_id: str) -> str:
-        response = super().dataset_ready(dataset_id)
-        return response.model_dump_json()
-    
-    def md_ready_json(self, dataset_id: str) -> str:
-        response = super().md_ready(dataset_id)
-        return response.model_dump_json()
-    
-    def embeddings_clusters(
-            self, 
-            dataset_id: str, 
-            cluster: str
-        ) -> EmbeddingClusters:
-
-        response = super().embedding_clusters(
-            dataset_id = dataset_id, 
-            cluster = GetDatasetClustersDataInput(cluster)
-        )
-        return response
-    
-    def embeddings_clusters_json(
-            self, 
-            dataset_id: str, 
-            cluster: str
-        ) -> str:
-
-        response = self.embedding_clusters(
-            dataset_id = dataset_id, 
-            cluster = GetDatasetClustersDataInput(cluster) 
-        )
-        return response.model_dump_json()
-
-    def embedding_data(
-            self, 
-            dataset_id: str,
-            embedding: str,
-            scale_max_plan: float,
-            session_id: str = None,
-            labelsets: List[str] = None,
-            selection_gene: str = None,
-            selection_key_major: str = None,
-            selection_key_minor: str = None
-        ) -> EmbeddingData:
- 
-        options = GetDatasetEmbeddingDataInput(
-            embedding = embedding,
-            selection_gene = selection_gene,
-            scale_max_plan = scale_max_plan,
-            selection_key_major = selection_key_major,
-            selection_key_minor = selection_key_minor,
-            session_id = session_id,
-            labelsets = labelsets
-        )
-        response = super().embedding_data(
-            dataset_id = dataset_id,
-            options = options
-        )
-        return response
-    
-    def embedding_data_json(
-            self, 
-            dataset_id: str,
-            embedding: str,
-            scale_max_plan: float,
-            session_id: str = None,
-            labelsets: List[str] = None,
-            selection_gene: str = None,
-            selection_key_major: str = None,
-            selection_key_minor: str = None
-        ) -> str:
- 
-        response = self.embedding_data(
-            dataset_id = dataset_id,
-            embedding = embedding,
-            scale_max_plan = scale_max_plan,
-            session_id = session_id,
-            labelsets = labelsets,
-            selection_gene = selection_gene,
-            selection_key_major = selection_key_major,
-            selection_key_minor = selection_key_minor
-        )
-        return response.model_dump_json()
-    
-    def general_de(
-            self, 
-            dataset_id: str,
-            labelset_id: str,
-            random_seed: float = 123,
-            session_id: str = None
-        ) -> GeneralDE:
- 
-        options = GetGeneralDiffInput(
-            random_seed = random_seed,
-            session_id = session_id,
-            labelset_id = labelset_id
-        )
-        response = super().general_de(
-            dataset_id = dataset_id,
-            options = options
-        )
-        return response
-
-    def general_de_json(
-            self, 
-            dataset_id: str,
-            labelset_id: str,
-            random_seed: float = 123,
-            session_id: str = None
-        ) -> GeneralDE:
- 
-        response = self.general_de(
-            dataset_id = dataset_id,
-            labelset_id = labelset_id,
-            random_seed = random_seed,
-            session_id = session_id
-        )
-        return response.model_dump_json()
-    
-    def heatmap(
-            self, 
-            dataset_id: str,
-            diff_key: str,
-            n_genes: int = None,
-            scale_max_plan: float = None,
-            genes_filter: List[str] = None,
-            use_genes_pattern: bool = None,
-            session_id: str = None,
-            include_reference_selection: bool = None,
-            selection_key: str = None
-        ) -> Heatmap:
- 
-        options = PostHeatmapInput(
-            diff_key = diff_key,
-            n_genes = n_genes,
-            scale_max_plan = scale_max_plan,
-            genes_filter = genes_filter,
-            use_genes_pattern = use_genes_pattern,
-            session_id = session_id,
-            include_reference_selection = include_reference_selection,
-            selection_key = selection_key
-        )
-        response = super().heatmap(
-            dataset_id = dataset_id,
-            options = options
-        )
-        return response
-
-    def heatmap_json(
-            self, 
-            dataset_id: str,
-            diff_key: str,
-            n_genes: int = None,
-            scale_max_plan: float = None,
-            genes_filter: List[str] = None,
-            use_genes_pattern: bool = None,
-            session_id: str = None,
-            include_reference_selection: bool = None,
-            selection_key: str = None
-        ) -> Heatmap:
- 
-        response = self.heatmap(
-            dataset_id = dataset_id,
-            diff_key = diff_key,
-            n_genes = n_genes,
-            scale_max_plan = scale_max_plan,
-            genes_filter = genes_filter,
-            use_genes_pattern = use_genes_pattern,
-            session_id = session_id,
-            include_reference_selection = include_reference_selection,
-            selection_key = selection_key
-        )
-        
-        return response.model_dump_json()
-    
-    def highly_variable_genes(
-            self, 
-            dataset_id: str,
-            offset: float,
-            limit: float,
-            gene_name_filter: str = None,
-            use_genes_pattern:bool = None,
-            sort_by: str = None,
-            sort_order:str = None
-        ) -> HighlyVariableGenes:
- 
-        options = GetHighlyVariableGenesInput(
-            offset = offset,
-            limit = limit,
-            gene_name_filter = gene_name_filter,
-            use_genes_pattern = use_genes_pattern,
-            sort_by = sort_by,
-            sort_order = sort_order
-        )
-        response = super().highly_variable_genes(
-            dataset_id = dataset_id,
-            options = options
-        )
-        return response
-    
-    def highly_variable_genes_json(
-            self, 
-            dataset_id: str,
-            offset: float,
-            limit: float,
-            gene_name_filter: str = None,
-            use_genes_pattern:bool = None,
-            sort_by: str = None,
-            sort_order:str = None
-        ) -> HighlyVariableGenes:
- 
-        response = self.highly_variable_genes(
-            dataset_id = dataset_id,
-            offset = offset,
-            limit = limit,
-            gene_name_filter = gene_name_filter,
-            use_genes_pattern = use_genes_pattern,
-            sort_by = sort_by,
-            sort_order = sort_order
-        )
-    
-        return response.model_dump_json()
-    
-    def create_session(
-            self, 
-            session_id: str,
-            dataset: DatasetObjectInput
-        ) -> CreateSession:
- 
-        data = PostSaveEmbeddingSessionInput(
-            session_id = session_id,
-            dataset = dataset
-        )
-        response = super().create_session(
-            data = data
-        )
-        return response
-    
-    def create_session_json(
-            self, 
-            session_id: str,
-            dataset: dict = None
-        ) -> CreateSession:
- 
-        response = self.create_session(
-            session_id = session_id,
-            dataset = dataset
-        )
-        
-        return response.model_dump_json()
-    
\ No newline at end of file
+    def md_session(self, dataset_id: str) -> MDSession:
+        return MDSession(dataset_id=dataset_id, _client=self.__client)
diff --git a/cap_client/client/__init__.py b/cap_client/client/__init__.py
index baa4f98..8cbb962 100644
--- a/cap_client/client/__init__.py
+++ b/cap_client/client/__init__.py
@@ -2,7 +2,7 @@
 
 from .base_client import BaseClient
 from .base_model import BaseModel, Upload
-from .client import Client
+from .client import _Client
 from .cluster_types import (
     ClusterTypes,
     ClusterTypesDataset,
@@ -142,7 +142,7 @@
     "CellLabelResultLabelsetDatasetProject",
     "CellLabelsSearchOptions",
     "CellLabelsSearchSort",
-    "Client",
+    "_Client",
     "ClusterTypes",
     "ClusterTypesDataset",
     "ClusterTypesDatasetEmbeddingClusterTypes",
diff --git a/cap_client/client/client.py b/cap_client/client/client.py
index 007489e..522028a 100644
--- a/cap_client/client/client.py
+++ b/cap_client/client/client.py
@@ -40,7 +40,7 @@ def gql(q: str) -> str:
     return q
 
 
-class Client(BaseClient):
+class _Client(BaseClient):
     def search_datasets(
         self,
         options: Union[Optional[DatasetSearchOptions], UnsetType] = UNSET,
diff --git a/example.py b/example.py
deleted file mode 100644
index 92113ac..0000000
--- a/example.py
+++ /dev/null
@@ -1,45 +0,0 @@
-import json
-from cap_client import Cap
-from cap_client.client.input_types import DatasetObjectInput
-
-cap = Cap(
-    url = "https://rc1.celltype.info/graphql", 
-    auth_url = "us-central1-capv2-gke-rc1.cloudfunctions.net",
-    login = "<login>",
-    pwd = "<password>" 
-)
-
-# public API 
-
-datasets = cap.search_datasets()
-
-# for dataset in datasets.results:
-dataset  = datasets.results[1]
-print("Processing dataset id: ", dataset.id) 
-
-print("Files status")
-cap.files_status(dataset.id)
-
-print("Dataset initial query")
-snapshot = cap.dataset_initial_state_query(dataset.id)
-
-print("Create session")
-session_id = "123qwerty"
-# TODO DatasetIntialStateQuery should be compatible with DatasetObjectInput 
-# OR should be the endpoint to get a snapshot for session creation
-snapshot = snapshot.model_dump()
-embeddings = cap.create_session(session_id = session_id, dataset = DatasetObjectInput(**snapshot))
-
-print("General DE")
-labelset = dataset.labelsets[1]    
-cap.general_de(dataset_id = dataset.id, labelset_id = labelset.id, session_id = session_id)
-
-# TODO: Cover full MD page cycle '''
-
-# authorized API 
-
-'''
-if cap._authenticate() == True:
-    # authorized API call
-
-'''    
\ No newline at end of file
diff --git a/examples/md_page.ipynb b/examples/md_page.ipynb
new file mode 100644
index 0000000..efe692a
--- /dev/null
+++ b/examples/md_page.ipynb
@@ -0,0 +1,381 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Molecular Data page API\n",
+    "\n",
+    "In this example, we will show how to use `CapClient` to run **MD-page** operations, such as getting an embeddings plot or running **DE** analysis. All the actions here are performed with public datasets, so the user doesn't need to be authorized. To perform the same operations with private datasets, the user must first pass authentication."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# uncomment if working from source repo\n",
+    "# import sys\n",
+    "# sys.path.append(\"../cap_client\")\n",
+    "\n",
+    "from cap_client import CapClient\n",
+    "import numpy as np\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# First we need to create a CapClient instance \n",
+    "client = CapClient()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'4594afa8-0915-4848-86ab-8f618e342c9c'"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Create an instance of MDSession class\n",
+    "# NOTE: the session is not started yet\n",
+    "md = client.md_session(dataset_id=\"825\")\n",
+    "md.create_session()  # Now, session is actually created\n",
+    "md.session_id"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "('4594afa8-0915-4848-86ab-8f618e342c9c',\n",
+       " '825',\n",
+       " ['cell_type_azimuth'],\n",
+       " ['umap'],\n",
+       " [])"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# When session is opened we have access to general information important for this MD session\n",
+    "md.session_id, md.dataset_id, md.labelsets, md.embeddings, md.clusterings"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "CreateSessionSaveEmbeddingSession(id='825', name='An Atlas of Cells in the Human Tonsil', dataset_type='integrated', description=\"Tonsils are constantly exposed to antigens through the upper respiratory tract, making them a valuable secondary lymphoid organ (SLO) for studying the interaction between innate and adaptive immune cells during germinal center (GC) development, which is crucial for building adaptive immunity. This reference includes 377,963 cells (10x Genomics 3' v3) from 17 healthy human donors spanning various age groups, including children, young adults, and older adults.\\n\\nThe annotation provided in this first version comprises 42 categories that provide a stable categories to classify single-cell transcriptomes of SLOs, useful for tools like Azimuth (see external URLs). In the next version, we will add a more detailed classification, encompassing all cell types and states identified in the tonsil atlas. A validation cohort was included to confirm the presence and accuracy of each annotation this latter level, using criteria such as cell neighborhood preservation, conservation of bona fide marker genes, and annotation confidence derived from the KNN classifier. For a full description and interpretation of this validation cohort, please refer to the final section of the manuscript.\\n\\nThe tonsil atlas is a FAIR resource—findable, accessible, interoperable, and reusable. The raw data has been deposited in ArrayExpress and can be remapped and reanalyzed by following the instructions provided in the TonsilAtlas and TonsilAtlasCAP GitHub repositories. The resulting expression matrices and Seurat objects are available on Zenodo, and the data can be accessed, explored, interpreted, and reused via the HCATonsilAtlas Bioconductor package and the Azimuth web interface. Additionally, we provide a detailed glossary of the marker genes, rationales, and publications used to annotate each cell type and state. The external URLs section contains links to all these resources.\", cell_count=377963.0, labelsets=[CreateSessionSaveEmbeddingSessionLabelsets(id='10911', name='cell_type_azimuth', mode='cell-labels', description='The annotation provided in this first version comprises 42 categories that provide a stable categories to classify single-cell transcriptomes of SLOs, useful for tools like Azimuth (see external URLs). In the next version, we will add a more detailed classification, encompassing all cell types and states identified in the tonsil atlas. A validation cohort was included to confirm the presence and accuracy of each annotation this latter level, using criteria such as cell neighborhood preservation, conservation of bona fide marker genes, and annotation confidence derived from the KNN classifier. For a full description and interpretation of this validation cohort, please refer to the final section of the manuscript.', annotation_method='manual', algorithm_name='NA', algorithm_version='NA', algorithm_repo_url='NA', reference_location=None, reference_description='NA', labels=[CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135086', name='LZ GCB', count=7884.0, color='#db5788', full_name='centrocyte', ontology_term_exists=True, ontology_term='centrocyte', ontology_term_id='CL:0009111', category_full_name='germinal center B cell', category_ontology_term_exists=True, category_ontology_term='germinal center B cell', category_ontology_term_id='CL:0000844', marker_genes=['CD83', 'LMO2', 'RGS13', 'MEF2B', 'BCL2A1'], canonical_marker_genes=['CD83', 'LMO2', 'RGS13', 'BCL2A1'], synonyms=['lymph node centrocyte', 'Light Zone Germinal Center B cell'], rationale='Cells in this cluster show expression of well-known light zone markers, i.e. CD83, LMO2, and downregulation of DZ genes such as CXCR4 or AICDA. Within this cluster, genes related to CD40 signaling and response to BCR stimulation are starting to be expressed (e.g. BCL2A1, BCL2L1, TRAF1/4 or EBI3). However, the expression levels of these genes were not homogeneous, suggesting the presence of different levels of activation of LZ cells.', rationale_dois=['10.1126/sciimmunol.abe6291', '10.1016/j.it.2022.01.003', '10.1016/j.immuni.2020.06.013'], ontology_assessment='In the literature one can found these cells as \"LZ GCB\" or \"centrocytes\". The community needs to come to an agreement on which term to use.', typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135085', name='non-TRDV2+ gdT', count=743.0, color='#57db85', full_name='non-TRDV2+ gamma-delta T cell', ontology_term_exists=False, ontology_term='gamma-delta T cell', ontology_term_id='CL:0000798', category_full_name='T cell', category_ontology_term_exists=True, category_ontology_term='T cell', category_ontology_term_id='CL:0000084', marker_genes=['TRDC', 'TRGC1', 'TRGC2', 'CCL5', 'KLRK1', 'NKG7'], canonical_marker_genes=['TRDC', 'TRGC1', 'TRGC2', 'CCL5', 'KLRK1', 'NKG7'], synonyms=['non-TRDV2+ gamma-delta T-lymphocyte', 'non-TRDV2+ gamma-delta T-cell', 'non-TRDV2+ gammadelta T cell', 'non-TRDV2+ gd T cell', 'non-TRDV2+ gamma-delta T lymphocyte'], rationale='γδ T cells are a distinct subgroup of T cells containing T cell receptors (TCR) γ and δ chains. In this cluster we identified a clear signature of gd T cells: KLRD1, KLRC2, TRGC1, TRGC2 and TRDC (Pizzolato et al., 2019)', rationale_dois=['10.1073/pnas.1818488116'], ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135091', name='prePB', count=1033.0, color='#8557db', full_name='precursor plasmablast', ontology_term_exists=False, ontology_term='plasmablast', ontology_term_id='CL:0000980', category_full_name='plasma cell', category_ontology_term_exists=True, category_ontology_term='plasma cell', category_ontology_term_id='CL:0000786', marker_genes=['RGS13', 'MEF2B', 'PAX5', 'MS4A1', 'XBP1', 'VDR', 'PRDM1'], canonical_marker_genes=['RGS13', 'MEF2B', 'PAX5', 'MS4A1', 'XBP1', 'VDR', 'PRDM1'], synonyms=['precursor PB'], rationale='This cluster still has phenotypic markers of LZ GCBC (centrocytes, such as RGS13, MEF2B), as well as pan-B cell markers (PAX5, MS4A1). However, it already expresses PC-specific markers, such as XBP1, VDR and PRDM1.', rationale_dois=['10.1038/nri3795', '10.1126/sciimmunol.abe6291'], ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135110', name='Cycling myeloid', count=159.0, color='#dbbd57', full_name='Cycling myeloid', ontology_term_exists=False, ontology_term='macrophage', ontology_term_id='CL:0000235', category_full_name='macrophage', category_ontology_term_exists=True, category_ontology_term='macrophage', category_ontology_term_id='CL:0000235', marker_genes=['LYZ', 'CST3', 'TOP2A', 'MKI67', 'PCLAF'], canonical_marker_genes=['LYZ', 'CST3', 'TOP2A', 'MKI67', 'PCLAF'], synonyms=['unknown'], rationale='These cells express canonical markers of the myeloid lineage (e.g. LYZ) and cell cycling genes (TOP2A, MKI67, PCLAF).', rationale_dois=[], ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135112', name='FDC', count=1061.0, color='#c0db57', full_name='follicular dendritic cell', ontology_term_exists=True, ontology_term='follicular dendritic cell', ontology_term_id='CL:0000442', category_full_name='follicular dendritic cell', category_ontology_term_exists=True, category_ontology_term='follicular dendritic cell', category_ontology_term_id='CL:0000442', marker_genes=['CLU', 'FDCSP', 'CXCL13', 'CR2'], canonical_marker_genes=['CLU', 'FDCSP', 'CXCL13', 'CR2'], synonyms=['unknown'], rationale='This cluster expresses canonical markers of follicular dendritic cells, such as FDCSP, CLU, CR2, and CXCL13.\\n\\nVersion 2.0 will provide a more granular annotation of this compartment, including CD14+CD55+ FDC, COL27A1+ FDC, Fibroblastic Reticular Cells (FRC) and marginal reticular cells (MRC). Several of these clusters expressed multiple collagens, such as COL1A1, COL1A2, and COL6A3.', rationale_dois=['10.1084/jem.20210790', '10.1016/j.immuni.2018.04.006', '10.1158/1078-0432.CCR-13-2367'], ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135105', name='aDC', count=457.0, color='#dbaa57', full_name='activated dendritic cell', ontology_term_exists=False, ontology_term='dendritic cell', ontology_term_id='CL:0000451', category_full_name='dendritic cell', category_ontology_term_exists=True, category_ontology_term='dendritic cell', category_ontology_term_id='CL:0000451', marker_genes=['LAMP3', 'CCR7', 'PRDM16', 'CCL19', 'TREML1', 'CCL22'], canonical_marker_genes=['LAMP3', 'CCR7', 'PRDM16', 'CCL19', 'TREML1', 'CCL22'], synonyms=['mregDCs', 'CCR7+ DC', 'migratory dendritic cell'], rationale=\"The hallmark of activated DC (aDC) is the high expression of CCR7, the chemokine needed to migrate to the T cell zone (that's why they are commonly referred to as migratory DC). They can produce cytokines that attract other CCR7-expressing cells to the T cell zone (such as CCL19).\\n\\nWe could define 3 subsets of aDC in line with the thymus atlas, which will become available in version 2.0. These subsets, however, require validation.\", rationale_dois=['10.1126/science.aay3224', '10.1002/ctm2.1199', '10.1126/sciimmunol.abl5053'], ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135100', name='CD4 Non-TFH', count=5908.0, color='#57addb', full_name='CD4 Non-T follicular helper cell', ontology_term_exists=False, ontology_term='helper T cell', ontology_term_id='CL:0000912', category_full_name='T cell', category_ontology_term_exists=True, category_ontology_term='T cell', category_ontology_term_id='CL:0000084', marker_genes=['CD3D', 'CD3E', 'CD3G', 'CD4'], canonical_marker_genes=['CD3D', 'CD3E', 'CD3G', 'CD4'], synonyms=['T-helper cell', 'helper T-lymphocyte', 'helper T lymphocyte', 'helper T-cell', 'CD4 Non-Tfh'], rationale='After antigen recognition in the T cell zone, CD4 T cells can either differentiate into non-T follicular helper (non-Tfh, regulated by PRDM1) or Tfh (regulated by BCL6). Non-Tfh cells represent a broad category -introduced in the review articles from Dr. Shane Crotty- that encompasses effector memory CD4 T cells, Th1, Th2, Th17 and Th22 cells. In version 2.0, we will provide all such categories, consistent with Figure 2 of our publication.', rationale_dois=['10.1146/annurev-immunol-031210-101400', '10.1016/j.immuni.2019.04.011', '10.1002/eji.201343751'], ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135098', name='CD4 TFH Mem', count=3491.0, color='#5788db', full_name='CD4 T follicular helper memory cells', ontology_term_exists=False, ontology_term='T follicular helper cell', ontology_term_id='CL:0002038', category_full_name='T cell', category_ontology_term_exists=True, category_ontology_term='T cell', category_ontology_term_id='CL:0000084', marker_genes=['CD3D', 'CD3E', 'KLRB1', 'MAF', 'TOX2', 'ICOS', 'IL21'], canonical_marker_genes=['CD3D', 'CD3E', 'KLRB1', 'MAF', 'TOX2', 'ICOS', 'IL21', 'CD4'], synonyms=['T(FH) memory', 'follicular helper memory T cell', 'Tfh memory'], rationale='Upon leaving a GC, the Tfh cell acquires a less activated, less polarized Tfh phenotype and can upregulate KLRB1 and develop into resting memory Tfh cells. This cluster had the expression of follicular markers PDCD1, MAF, and higher expression of memory markers such as KLRB1, ITGB1, and CD28', rationale_dois=['10.1016/j.immuni.2014.10.004', '10.4049/jimmunol.1002828', '10.1016/j.immuni.2013.08.031', '10.1002/eji.201242540'], ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135092', name='B activated', count=14777.0, color='#d057db', full_name='Activated B cell', ontology_term_exists=False, ontology_term='B cell', ontology_term_id='CL:0000236', category_full_name='B cell', category_ontology_term_exists=True, category_ontology_term='B cell', category_ontology_term_id='CL:0000236', marker_genes=['FCER2', 'IGHD', 'NFKB2', 'REL', 'CD69', 'MYC', 'CD83', 'EGR3', 'CCL3'], canonical_marker_genes=['FCER2', 'IGHD', 'NFKB2', 'REL', 'CD69', 'MYC', 'CD83', 'EGR3', 'CCL3'], synonyms=['B-cell', 'B-lymphocyte', 'B lymphocyte'], rationale='Cells in this cluster express typical NBC markers such as FCER2 (CD23), TCLA1, IGHM and IGHD. This cluster is characterized by the expression of very specific genes that seem to correspond to activation and early GC commitment due to the transient MYC upregulation (Dominguez-Sola et al., 2012). For instance, we observed high expression of EGR family (i.e. EGR1, EGR2, EGR3), transcription factors that are rapidly induced upon stimulation of the BCR genes (Gururajan et al., 2008) related to NF-kB pathway (i.e. NFKB1, NFKB2, NFKBIA, NFKBID, RELB) (Burger and Chiorazzi, 2013); or proinflammatory chemokines i.e. CCL3 and CCL4 (Takahashi et al., 2015) . Furthermore, in this cluster we identified high levels of bone-fide activation marker, i.e. CD69 (Cibrián and Sánchez-Madrid, 2017).', rationale_dois=['10.1038/ni.2428', '10.4049/jimmunol.181.7.4590', '10.1016/j.it.2013.07.002', '10.1111/bjh.13659', '10.1002/eji.201646837', '10.1016/j.it.2022.01.003', '10.1126/sciimmunol.abe6291', '10.1016/j.immuni.2020.06.013'], ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135117', name='B memory', count=51387.0, color='#9757db', full_name='memory B cell', ontology_term_exists=True, ontology_term='memory B cell', ontology_term_id='CL:0000787', category_full_name='B cell', category_ontology_term_exists=True, category_ontology_term='B cell', category_ontology_term_id='CL:0000236', marker_genes=['CD27', 'BANK1', 'MARCH1', 'TNFRSF13B', 'BLK', 'CD24'], canonical_marker_genes=['CD19', 'CD27', 'TNFRSF13B', 'CD24'], synonyms=['memory B-cell', 'memory B-lymphocyte', 'memory B lymphocyte'], rationale='Memory B cells are a vital subset of the adaptive immune system, distinguished by their long-lived nature and ability to persist for decades after initial antigen exposure. These cells circulate in a quiescent state and rapidly respond to re-exposure to their specific antigen, quickly differentiating into antibody-producing plasma cells, thereby ensuring a faster and more robust secondary immune response compared to naive B cells. They retain antigen specificity and can express various immunoglobulin isotypes, often originating from germinal centers in secondary lymphoid organs. Memory B cells are essential for long-term immunity and the effectiveness of vaccines.\\n\\nThey can be further stratified into class-switched (IgG+ or IgA+) or non-class switch (IgD or IgM), and they differ from naive B cells by the expression of CD27 and negativity for TCL1A and FCER2', rationale_dois=['10.1016/j.it.2022.01.003', '10.1016/j.immuni.2020.06.013'], ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135093', name='B naive', count=85755.0, color='#bd57db', full_name='naive B cell', ontology_term_exists=True, ontology_term='naive B cell', ontology_term_id='CL:0000788', category_full_name='B cell', category_ontology_term_exists=True, category_ontology_term='B cell', category_ontology_term_id='CL:0000236', marker_genes=['FCER2', 'TCL1A', 'IGHM', 'IGHD', 'MS4A1', 'CD79A'], canonical_marker_genes=['FCER2', 'TCL1A', 'IGHM', 'MS4A1', 'CD79A'], synonyms=['naive B-cell', 'naive B-lymphocyte', 'naive B lymphocyte'], rationale='The \"B Naive” annotation represents the main cluster of naive (antigen-inexperienced) B cells, characterized by a clear and homogeneous expression of key markers including FCER2 (CD23), TCLA1, IGHM, and IGHD. In the “Cell types level 3 20230508” annotation set, this cluster will be further refined and subclustered into 8 distinct subpopulations.', rationale_dois=['10.1016/j.it.2022.01.003', '10.1016/j.immuni.2020.06.013'], ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135119', name='CD4 naive', count=26279.0, color='#57d3db', full_name='naive CD4 T cell', ontology_term_exists=False, ontology_term='naive T cell', ontology_term_id='CL:0000898', category_full_name='T cell', category_ontology_term_exists=True, category_ontology_term='T cell', category_ontology_term_id='CL:0000084', marker_genes=['CD3D', 'CD3E', 'CD3G', 'LEF1', 'CCR7', 'NOSIP', 'SELL', 'BACH2'], canonical_marker_genes=['CD3D', 'CD3E', 'CD3G', 'LEF1', 'CCR7', 'NOSIP', 'SELL', 'BACH2'], synonyms=['naive T lymphocyte', 'naive T-lymphocyte', 'naive T-cell', 'CD4 naive T cell'], rationale='Cluster of naive (antigen-inexperienced) CD4+ T cells that expresses canonical markers of T cells (CD3D, CD3E, CD3G), markers of homing into the T cell zone of secondary lymphoid organs (CCR7), and markers typical of naive T cells (SELL, LEF1). At the protein level, these cells expressed CD45RA.', rationale_dois=['10.1038/s41467-020-15543-y', '10.1002/eji.201343751'], ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135099', name='CD4 TCM', count=18809.0, color='#57c0db', full_name='CD4 central memory T cell', ontology_term_exists=False, ontology_term='memory T cell', ontology_term_id='CL:0000813', category_full_name='T cell', category_ontology_term_exists=True, category_ontology_term='T cell', category_ontology_term_id='CL:0000084', marker_genes=['CD3D', 'CD3G', 'IL7R', 'TCF7', 'ANXA1', 'ANK3', 'PASK', 'S100A4'], canonical_marker_genes=['CD3D', 'CD3G', 'IL7R', 'TCF7', 'ANXA1', 'PASK', 'S100A4'], synonyms=['memory T lymphocyte', 'memory T-lymphocyte', 'memory T-cell', 'CM CD4 T cell'], rationale='After antigen encounter T cells acquire memory and effector functions maintaining the lymph node homing. CM showed an upregulation of IL7R compared to naïve T cells, and higher expression of PRDM1, PASK, ANXA1 and S100A4. They had an intermediate expression of CCR7 and higher expression of CD28 confirming their migratory capacity to secondary lymph nodes and their memory profile based on their effector function. This cluster expressed CD45RO at the protein level.', rationale_dois=['10.1038/s41467-020-15543-y', '10.1002/eji.201343751'], ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135120', name='CD4 TFH', count=25880.0, color='#57dbd0', full_name='T follicular helper cell', ontology_term_exists=True, ontology_term='T follicular helper cell', ontology_term_id='CL:0002038', category_full_name='T cell', category_ontology_term_exists=True, category_ontology_term='T cell', category_ontology_term_id='CL:0000084', marker_genes=['CD3D', 'CD3E', 'CD2', 'TIGIT', 'IL6ST', 'PDCD1', 'TOX2', 'ICOS', 'CXCR5', 'CXCL13'], canonical_marker_genes=['CD3D', 'CD3E', 'CD2', 'TIGIT', 'IL6ST', 'PDCD1', 'TOX2', 'ICOS', 'CXCR5', 'CXCL13'], synonyms=['T(FH)', 'follicular helper T cell', 'follicular T-helper cell', 'follicular T-cell', 'follicular T cell', 'follicular B helper T cell', 'follicular helper T-cell', 'Tfh'], rationale='After antigen recognition in the T cell zone, CD4 T cells can either differentiate into non-T follicular helper (non-Tfh, regulated by PRDM1) or Tfh (regulated by BCL6). Tfh cells migrate to the follicles by upregulating CXCR5 (which binds to CXCL13) and downregulting CCR7. Tfh cells select centrocytes (LZ B cells) that have the highest affinity towards the antigen during the germinal center reaction. Their canonical markers are CXCR5, CXCL13, ICOS, PDCD1 and IL21.', rationale_dois=['10.1016/j.it.2021.02.002', '10.1146/annurev-immunol-031210-101400', '10.1016/j.immuni.2019.04.011', '10.1016/j.immuni.2014.10.004'], ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135126', name='CD4 TREG', count=7678.0, color='#579bdb', full_name='regulatory T cell', ontology_term_exists=True, ontology_term='regulatory T cell', ontology_term_id='CL:0000815', category_full_name='T cell', category_ontology_term_exists=True, category_ontology_term='T cell', category_ontology_term_id='CL:0000084', marker_genes=['CD3D', 'CD3E', 'CD2', 'CYTOR', 'IL10', 'FOXP3', 'CTLA4', 'IL2RA'], canonical_marker_genes=['CD3D', 'CD3E', 'CD2', 'CD4', 'IL10', 'FOXP3', 'CTLA4', 'IL2RA'], synonyms=['regulatory T-lymphocyte', 'regulatory T-cell', 'regulatory T lymphocyte', 'suppressor T cell', 'suppressor T lymphocyte', 'suppressor T-lymphocyte', 'Treg', 'suppressor T-cell', 'regulatory CD4 T cell'], rationale='CD4 T regulatory (Treg) cells are vital for the maintenance of immune tolerance, and are characterized by the expression of cytokine IL10, the transcription factor FOXP3 and the expression of IL2RA (CD25) and CTLA4.\\n\\nIn version 2.0, we will further subdivide this cluster into effector Tregs and T follicular regulatory cells.', rationale_dois=['10.1007/978-981-15-6407-9_1', '10.1073/pnas.1705551114'], ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135101', name='CD8 naive', count=7594.0, color='#57db97', full_name='naive T cell', ontology_term_exists=True, ontology_term='naive T cell', ontology_term_id='CL:0000898', category_full_name='T cell', category_ontology_term_exists=True, category_ontology_term='T cell', category_ontology_term_id='CL:0000084', marker_genes=['CD3D', 'CD3E', 'CD8A', 'CD8B', 'CCR7', 'LEF1', 'SELL', 'NOSIP'], canonical_marker_genes=['CD3D', 'CD3E', 'CD8A', 'CD8B', 'CCR7', 'LEF1', 'SELL', 'NOSIP'], synonyms=['CD8 naive T lymphocyte', 'CD8 naive T-lymphocyte', 'CD8 naive T-cell'], rationale='Cluster of naive (antigen-inexperienced) CD4+ T cells that expresses canonical markers of T cells (CD3D, CD3E,), markers of homing into the T cell zone of secondary lymphoid organs (CCR7), and markers typical of naive T cells (SELL, LEF1, NOSIP). At the protein level, these cells expressed CD45RA.\\n\\nThis cluster has a very similar gene expression profile to naive CD4 T cell. While CD4 transcripts are usually not detected in scRNA-seq data, both CD8A and CD8B are detectable and used to differentiate both cell types.', rationale_dois=['10.1002/eji.201343751'], ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135121', name='CD8 T', count=6865.0, color='#57db5f', full_name='CD8 T cell', ontology_term_exists=False, ontology_term='CD8-positive, alpha-beta cytotoxic T cell', ontology_term_id='CL:0000794', category_full_name='T cell', category_ontology_term_exists=True, category_ontology_term='T cell', category_ontology_term_id='CL:0000084', marker_genes=['CD3D', 'CD3G', 'CD8A', 'CD8B', 'CCL5', 'GZMK', 'NKG7', 'KLRG1', 'PRF1', 'KLRK1', ''], canonical_marker_genes=['CD3D', 'CD3G', 'CD8A', 'CD8B', 'CCL5', 'GZMK', 'NKG7', 'KLRG1', 'PRF1', 'KLRK1'], synonyms=['CD8-positive, alpha-beta cytotoxic T-lymphocyte', 'cytotoxic T cell', 'killer T-cell', 'killer T lymphocyte', 'killer T cell', 'cytotoxic T lymphocyte', 'CD8-positive, alpha-beta cytotoxic T lymphocyte', 'killer T-lymphocyte', 'cytotoxic T-lymphocyte', 'cytotoxic T-cell', 'CD8-positive, alpha-beta cytotoxic T-cell'], rationale='This is a cluster of non-naive, non-central memory CD8 T cells. It is rather a broad annotation, which in version 2.0 we will split into resident memory CD8 T, resident memory activated T, effector memory CD8 T, CD8 T follicular, DC-recruiter CD8 T, and IFN+ CD8 T cells, in line with figure 3 of our paper.', rationale_dois=['10.1002/eji.201343751'], ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135102', name='CD8 TCM', count=2099.0, color='#57db72', full_name='CD8 central memory T cell', ontology_term_exists=False, ontology_term='CD4-positive, alpha-beta memory T cell, CD45RO-positive', ontology_term_id='CL:0001204', category_full_name='T cell', category_ontology_term_exists=True, category_ontology_term='T cell', category_ontology_term_id='CL:0000084', marker_genes=['CD3D', 'CD3E', 'CD8A', 'CD8B', 'IL7R', 'ANXA1', 'S100A4'], canonical_marker_genes=['CD3D', 'CD3E', 'CD8A', 'CD8B', 'IL7R', 'ANXA1', 'S100A4'], synonyms=['CD4-positive, alpha-beta memory T-cell, CD45RO-positive', 'CD4-positive, alpha-beta memory T lymphocyte, CD45RO-positive', 'CD4-positive, alpha-beta memory T-lymphocyte, CD45RO-positive'], rationale='CM CD8 T cells acquire memory (marked by IL7R+) while maintaining lymph node homing capacity (CCR7+). CM CD8 T cells expressed higher levels of IL7R compared to naïve T cells, and higher expression of ANXA1 and S100A4.  They had an intermediate expression of CCR7 and a higher expression of CD28. At the protein level, this cluster was CD45RO+, and CD28+.\\n\\nThis cluster has a very similar gene expression profile to CD4 TCM. While CD4 transcripts are usually not detected in scRNA-seq data, both CD8A and CD8B are detectable and used to differentiate both cell types.\\n\\n', rationale_dois=['10.1002/eji.201343751'], ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135118', name='Cycling DZ GCB', count=29390.0, color='#db57ad', full_name='Cycling Dark Zone Germinal Center B cell', ontology_term_exists=False, ontology_term='centroblast', ontology_term_id='CL:0009112', category_full_name='germinal center B cell', category_ontology_term_exists=True, category_ontology_term='germinal center B cell', category_ontology_term_id='CL:0000844', marker_genes=['AICDA', 'MME', 'MKI67', 'PCLAF', 'TOP2A', 'CXCR4'], canonical_marker_genes=['AICDA', 'MME', 'MKI67', 'PCLAF', 'TOP2A', 'CXCR4'], synonyms=['lymph node centroblast', 'cycling centroblast', ''], rationale='Cluster with high expression of CXCR4 (a marker defining DZ GCB cells) and additional expression of DZ markers, such as AICDA and MME, as well as moderate expression of AICDA. This cluster also has a clear expression of cell cycle genes, including MKI67, TOP2A, and PCLAF. (Bar-Joseph et al., 2008; Holmes et al., 2020; King et al., 2021; Milpied et al., 2018)', rationale_dois=['10.1073/pnas.0704723105', '10.1084/jem.20200483', '10.1126/sciimmunol.abe6291', '10.1038/s41590-018-0181-4'], ontology_assessment='These cells are found in the literature as \"DZ B cells\" and \"centroblasts\"', typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135125', name='Cycling FDC', count=248.0, color='#addb57', full_name='Cycling Follicular Dendritic Cell', ontology_term_exists=False, ontology_term='follicular dendritic cell', ontology_term_id='CL:0000442', category_full_name='follicular dendritic cell', category_ontology_term_exists=True, category_ontology_term='follicular dendritic cell', category_ontology_term_id='CL:0000442', marker_genes=['FDCSP', 'CLU', 'CXCL13', 'TOP2A', 'MKI67', 'PCLAF'], canonical_marker_genes=['FDCSP', 'CLU', 'CXCL13', 'TOP2A', 'MKI67', 'PCLAF'], synonyms=['unknown'], rationale='These cells express canonical markers of FDC (FDCSP, CLU) and cell cycle genes (TOP2A, MKI67).', rationale_dois=['10.1084/jem.20210790'], ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135095', name='Cycling T', count=2580.0, color='#5775db', full_name='Cycling T cell', ontology_term_exists=False, ontology_term='T cell', ontology_term_id='CL:0000084', category_full_name='T cell', category_ontology_term_exists=True, category_ontology_term='T cell', category_ontology_term_id='CL:0000084', marker_genes=['CD3D', 'CD3E', 'CD3G', 'CD2', 'TOP2A', 'MKI67', 'PCLAF', 'PCNA'], canonical_marker_genes=['CD3D', 'CD3E', 'CD3G', 'CD2', 'TOP2A', 'MKI67', 'PCLAF', 'PCNA'], synonyms=['Cycling T-lymphocyte', 'Cycling T-cell', 'Cycling T lymphocyte', 'Proliferating T-lymphocyte', 'Proliferating T-cell', 'Proliferating  T lymphocyte'], rationale='This cluster is a T cell cluster (CD3D, CD3E, CD3G) that had a high cell cycle score and expressed cell cycle genes: STMN1, HIST1H4C, TUBA1B, HMGB2, MKI67, TOP2A and TUBB.', rationale_dois=['10.1126/sciimmunol.abe6291'], ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135114', name='DC', count=1583.0, color='#db7257', full_name='dendritic cell', ontology_term_exists=True, ontology_term='dendritic cell', ontology_term_id='CL:0000451', category_full_name='dendritic cell', category_ontology_term_exists=True, category_ontology_term='dendritic cell', category_ontology_term_id='CL:0000451', marker_genes=['LYZ', 'CST3', 'LGALS3', 'FCER1A', 'CLEC9A', 'SIGLEC10'], canonical_marker_genes=['LYZ', 'CST3', 'LGALS3', 'FCER1A', 'CLEC9A', 'SIGLEC10'], synonyms=['unknown'], rationale='The dendritic cell subsets described in the tonsil atlas overlap with the ones provided by Villani, et al. (2017) in peripheral blood:  DC1, conventional DC1 (cDC1, e.g. CLEC9A), divided into precursor and mature states on\\nthe basis of XCR1 expression (Balan et al., 2018); (2) DC2 and DC3, corresponding to cDC2\\n(CD1C, FCER1A, CLEC10A) and differing in their antigen-presenting capacity (e.g. HLA-DQB1)\\nand inflammatory signatures (S100A8, S100A9), respectively; (3) DC4 (FCGR3A, SERPINA1 and\\nFTL) putatively derive from non-classical monocytes; and (4) DC5 expressing AXL and SIGLEC6\\n(AS), the hallmark markers of AS DCs \\n\\nThese subsets will be available in version 2.0.\\n\\n', rationale_dois=['10.1126/science.aah4573', '10.1016/j.celrep.2018.07.033'], ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135111', name='dnT', count=1017.0, color='#57dbbd', full_name='double negative thymocyte', ontology_term_exists=True, ontology_term='double negative thymocyte', ontology_term_id='CL:0002489', category_full_name='T cell', category_ontology_term_exists=True, category_ontology_term='T cell', category_ontology_term_id='CL:0000084', marker_genes=['CD3G', 'CD3D', 'CD3E', 'HAVCR2', 'GPR183', 'ISG20'], canonical_marker_genes=['CD3G', 'CD3D', 'CD3E', 'HAVCR2', 'GPR183', 'ISG20'], synonyms=['CD4-CD8- T cell', 'double negative T cell'], rationale='Double negative T cells express the αβ T cell receptor but do not express CD4, CD8, or natural killer cell markers. This cluster had a profile of inflammatory and activated DN T cells expressing HLA-DRB1, HLA-DPA1, CD38 and GPR183.', rationale_dois=['10.3389/fimmu.2022.816005', '10.1126/sciimmunol.abe6291'], ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135087', name='DZ GCB', count=7902.0, color='#db57c0', full_name='centroblast', ontology_term_exists=True, ontology_term='centroblast', ontology_term_id='CL:0009112', category_full_name='germinal center B cell', category_ontology_term_exists=True, category_ontology_term='germinal center B cell', category_ontology_term_id='CL:0000844', marker_genes=['SUGCT', 'AICDA', 'CXCR4', 'MME', 'CD38'], canonical_marker_genes=['CXCR4', 'AICDA', 'MME'], synonyms=['lymph node centroblast', 'DZ GCB', 'DZ Germinal Center B cells'], rationale='Cluster with high expression of CXCR4 (a marker defining DZ GCB cells) and additional expression of DZ markers, such as FOXP1 and MME, as well as moderate expression of AICDA. Cells from these clusters are clearly non-proliferative, lacking the detectable expression of any cell cycle gene. Therefore, we hypothesize that cells in this cluster, although they maintain the expression of dark zone-related genes, have already finished the cell cycle program and are progressing towards the next maturation stage. (Bar-Joseph et al., 2008; Holmes et al., 2020; King et al., 2021; Milpied et al., 2018)', rationale_dois=['10.1073/pnas.0704723105', '10.1084/jem.20200483', '10.1126/sciimmunol.abe6291', '10.1038/s41590-018-0181-4'], ontology_assessment='These cells are found in the literature as \"DZ B cells\" and \"centroblasts\"', typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135094', name='DZtoLZ GCB transition', count=20878.0, color='#db579b', full_name='DZtoLZ GCB transition', ontology_term_exists=False, ontology_term='centroblast', ontology_term_id='CL:0009112', category_full_name='germinal center B cell', category_ontology_term_exists=True, category_ontology_term='germinal center B cell', category_ontology_term_id='CL:0000844', marker_genes=['CXCR4', 'CD83', 'LMO2'], canonical_marker_genes=['CXCR4', 'CD83', 'LMO2'], synonyms=['lymph node centroblast', 'recycling centroblast'], rationale='This relatively large cluster is characterized by rather intermediate or low expression of DZ markers, such as CXCR4. The cells start to express some LZ markers such as CD83 or LMO2, but still are missing other LZ-genes such as BCL2A1 or DUSP2. Furthermore, we could not define clear markers specific and unique for this cluster.', rationale_dois=['10.1073/pnas.0704723105', '10.1084/jem.20200483', '10.1126/sciimmunol.abe6291', ' 10.1038/s41590-018-0181-4'], ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135106', name='Epithelial', count=396.0, color='#dbd057', full_name='tonsil epithelial cell', ontology_term_exists=False, ontology_term='tonsil squamous cell', ontology_term_id='CL:1001577', category_full_name='epithelial cell', category_ontology_term_exists=True, category_ontology_term='epithelial cell', category_ontology_term_id='CL:0000066', marker_genes=['KRT19', 'KRT13', 'KRT18', 'KRT14', 'KRT5', 'LCE3D', 'SPINK5', 'SPRR3', 'SPRR1B'], canonical_marker_genes=['KRT19', 'KRT13', 'KRT18', 'KRT14', 'KRT5', 'LCE3D', 'SPINK5', 'SPRR3', 'SPRR1B'], synonyms=['tonsil squamous epithelial cells', 'tonsillar squamous cell', 'tonsil squamous epithelial cell', 'tonsillar squamous epithelial cell', 'tonsillar epithelial cell'], rationale='This cluster represents the epithelial cells of the tonsil, characterized by the expression of several cytokeratins (e.g. KRT5, KRT19).\\n\\nIn version 2.0, we will further stratify it into basal, surface, outer surface and crypt layers.\\n\\nOne of these clusters expressed FDCSP (FDCSP epithelium). FDCSP was first described in FDC and in ‘‘leukocyte-infiltrated tonsillar crypts,’’ although the specific population within the crypts remained unknown. We provide evidence that FDSCP-expressing cells represent a specific subpopulation of the tonsillar epithelium, in line with a cluster observed in the oral mucosa cell atlas.', rationale_dois=['10.1016/j.cell.2021.05.013', '10.1084/jem.20210790', '10.3389/fimmu.2019.02345'], ontology_assessment='Cells in one of the epithelial clusters expressed SPIB and MARCKSL1, two genes associated with microfold cells (M cells), which are responsible for the uptake of antigens in the tonsillar lumen (Kobayashi et al., 2019).', typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135090', name='FCRL4/5+ B memory', count=14693.0, color='#aa57db', full_name='FCRL4/5+ B memory', ontology_term_exists=False, ontology_term='memory B cell', ontology_term_id='CL:0000787', category_full_name='B cell', category_ontology_term_exists=True, category_ontology_term='B cell', category_ontology_term_id='CL:0000236', marker_genes=['CD27', 'TNFRSF13B', 'FCRL4', 'FCRL5', 'BANK1', 'ITGAX', 'IFI30'], canonical_marker_genes=['CD274', 'FCRL4', 'FCRL5', 'ITGAX', 'TBX21'], synonyms=['memory B-cell', 'memory B-lymphocyte', 'memory B lymphocyte'], rationale='This cluster expresses markers consistent with FCRL4/5+ atypical memory B cells (CD27, TNFRSF13B, FCRL4, FCRL5) as described by Li et al, 2020. Consistent with the paper, these cells express IFI30, suggesting that they are responding to interferon gamma.\\n\\n', rationale_dois=['10.1093/intimm/dxaa053', '10.1182/blood.2023021990', '10.1146/annurev-immunol-092419-031130', '10.1126/sciimmunol.abe6291', '10.1158/2159-8290.CD-22-0561'], ontology_assessment='In follow-up work, we noticed that these cells also express TBET (TBX21, see Roessner et al., 2024), which is consistent with age-associated B cells (ABC). These ABC accumulate with age and are increased in autoimmune diseases (see Cancro et al., 2020).\\n\\nThe community needs to conclude whether these cells are referred to as \"FCRL4+ memory B cell\", \"atypical B cells\", \"age-associated B cells\" or \"autoimmune B cells\"', typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135109', name='Granulocytes', count=207.0, color='#db8557', full_name='granulocyte', ontology_term_exists=True, ontology_term='granulocyte', ontology_term_id='CL:0000094', category_full_name='granulocyte', category_ontology_term_exists=True, category_ontology_term='granulocyte', category_ontology_term_id='CL:0000094', marker_genes=['SOD2', 'NAMPT', 'CXCL8', 'S100A8', 'CXCR2'], canonical_marker_genes=['SOD2', 'NAMPT', 'CXCL8', 'S100A8', 'CXCR2'], synonyms=['granular leukocyte', 'polymorphonuclear leukocyte', 'granular leucocyte'], rationale='This cluster expressed specific markers previously described for neutrophils.', rationale_dois=['10.1038/s41467-021-22973-9', '10.1038/s41590-020-0736-z'], ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135116', name='ILC', count=718.0, color='#75db57', full_name='innate lymphoid cell', ontology_term_exists=True, ontology_term='innate lymphoid cell', ontology_term_id='CL:0001065', category_full_name='innate lymphoid cell', category_ontology_term_exists=True, category_ontology_term='innate lymphoid cell', category_ontology_term_id='CL:0001065', marker_genes=['IL7R', 'FCER1G', 'GNLY', 'CD200R1', 'RORC', 'IL23R'], canonical_marker_genes=['IL7R', 'FCER1G', 'GNLY', 'CD200R1', 'RORC', 'IL23R'], synonyms=['ILC', 'CD127+ ILC'], rationale='This cluster contains ILC1, NKp44+ ILC3 and NKp44- ILC3.\\n\\nILC1 are characterized by their capacity to produce IFNγ and they depend on the transcription factor T-bet. Contrary to NK cells, ILC1 express IL7R (CD127) and CD200R; and do not express KLRF1 (NKp80). Accordingly, this cluster expressed high levels of IL7R, CD200R1, CD69 and ANXA1; and did not express KLRF1 (Boldt et al., 2014; Vivier et al., 2018)\\n\\n\\nILC3s depend on RORC for their development and function, and comprise subsets that can be distinguished on the basis of expression of the natural cytotoxicity receptors NKp46 and NKp44. The main cytokine produced by ILC3s is IL-22 and a proportion of ILC3s also produce IL-17. We identified two subtypes of ILC3s that expressed RORC and IL23R; and were NKp44 (NCR2) positive and negative (Bal et al., 2020; Vivier et al., 2018)\\n\\nThis -more granular- classification will be provided in version 2.0, in line with our paper', rationale_dois=['10.1002/cyto.b.21162', '10.1016/j.cell.2018.07.017', '10.1038/s41577-020-0282-9'], ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135122', name='LZtoDZ GCB transition', count=6086.0, color='#db5775', full_name='LZtoDZ GCB transition', ontology_term_exists=False, ontology_term='centrocyte', ontology_term_id='CL:0009111', category_full_name='germinal center B cell', category_ontology_term_exists=True, category_ontology_term='germinal center B cell', category_ontology_term_id='CL:0000844', marker_genes=['BATF', 'MYC', 'NME1', 'EBI3', 'LMO2'], canonical_marker_genes=['BATF', 'MYC', 'NME1', 'EBI3', 'LMO2'], synonyms=['lymph node centrocyte', 'LZ-DZ GCBC', 'recycling centrocyte'], rationale='Cluster with high expression of CD83 and low expression of CXCR4, as well as positive for LMO2, which indicate that this cluster contains LZ-GCBC cells. Within this cluster, apart from the high expression of genes associated with the previously described LZ cluster, the cells start to express very specific genes such as MYC, MIR155HG or genes related to NF-kB pathway (i.e. NFKB1, NFKB2, NFKBIA, NFKBID, RELB). This transient expression of MYC and MYC targets (NME1) has been described in LZ cells re-entering into the DZ, to continue with additional rounds of affinity maturation (Dominguez-Sola et al., 2012). Additionally, as compared to the LZ cluster, this cluster shows higher expression of BATF, a gene known to be involved in regulating AIDCA (a DZ marker).', rationale_dois=['10.1038/ni.2428', '10.1016/j.ccell.2024.02.010'], ontology_assessment='This cell state is reffered to as \"LZ-to-DZ GCBC\" in the original publication by Dominguez-Sola, et al., 2012. In recent work (see Barisic et al, 2024), this cell state is referred to as \"recycling centrocytes\".', typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135103', name='MAIT/TRDV2+ gdT', count=571.0, color='#57dbaa', full_name='mucosal invariant T cell /TRDV2+ gamma-delta T cell', ontology_term_exists=False, ontology_term='mucosal invariant T cell', ontology_term_id='CL:0000940', category_full_name='T cell', category_ontology_term_exists=True, category_ontology_term='T cell', category_ontology_term_id='CL:0000084', marker_genes=['KLRB1', 'TRDC', 'TRGC1', 'TRGC2', 'IL18RAP', 'CEBPD', 'ZBTB16', 'RORA', 'CCR6', 'IL23R'], canonical_marker_genes=['KLRB1', 'TRDC', 'TRGC1', 'TRGC2', 'IL18RAP', 'CEBPD', 'ZBTB16', 'RORA', 'CCR6', 'IL23R'], synonyms=['mucosal invariant T-cell / TRDV2+ gamma-delta T cells', 'mucosal invariant T lymphocyte / TRDV2+ gamma-delta T cells', 'mucosal invariant T-lymphocyte / TRDV2+ gamma-delta T cells'], rationale='This cluster is a mix of MAIT cells (KLRB1, SLC4A10, ZBTB16, IL23R, IL18R1, CEPBD, CCR6, DPP4) and Vδ2Vγ9+ γδ T cells (TRDV2, TRDC, TRGC1, TRGC2, ZBTB16, KLRB1, IL18R1, DPP4) which share a similar phenotype and transcriptional profile (Provine et al., 2018; Wragg et al., 2020).', rationale_dois=['10.3389/fimmu.2018.00756', '10.1016/j.celrep.2020.107773', '10.1038/s41590-023-01575-1'], ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135108', name='Mast', count=78.0, color='#db9757', full_name='mast cell', ontology_term_exists=True, ontology_term='mast cell', ontology_term_id='CL:0000097', category_full_name='mast cell', category_ontology_term_exists=True, category_ontology_term='mast cell', category_ontology_term_id='CL:0000097', marker_genes=['TPSAB1', 'TPSB2', 'MS4A2'], canonical_marker_genes=['TPSAB1', 'TPSB2', 'MS4A2'], synonyms=['tissue basophil', 'histaminocyte', 'mastocyte', 'labrocyte'], rationale='This cluster has expressions of classical markers for Mast cells KIT, TPSAB1 and TPSB2. Top markers of this cluster were: TPSAB1, TPSB2, CPA3, MS4A2, LTC4S, HPGD, HPGDS and LMO4.', rationale_dois=['10.1182/bloodadvances.2022006969'], ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135104', name='MBC-derived PC / doublets', count=584.0, color='#5f57db', full_name='Memory B cell-derived plasma cell / doublets', ontology_term_exists=False, ontology_term='plasma cell', ontology_term_id='CL:0000786', category_full_name='plasma cell', category_ontology_term_exists=True, category_ontology_term='plasma cell', category_ontology_term_id='CL:0000786', marker_genes=['PRDM1', 'XBP1', 'FCMR', 'CCDC50'], canonical_marker_genes=['PRDM1', 'XBP1', 'FCMR', 'CCDC50'], synonyms=['MBC-derived PC'], rationale='This cluster expresses both markers of markers of memory B cells (FCMR, CCDC50) and plasma cells (PRDM1, XBP1). Thus, it could represent a cluster of reactivated MBC that differentiated into PC. However, this population had a low annotation confidence in the validation cohort. Because it is composed of markers of two different cell types, we cannot rule out that they correspond to doublets.', rationale_dois=['10.1038/nri3795'], ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135096', name='Mono/Macro', count=2225.0, color='#db5f57', full_name='Monocytes/Macrophages', ontology_term_exists=False, ontology_term='macrophage', ontology_term_id='CL:0000235', category_full_name='macrophage', category_ontology_term_exists=True, category_ontology_term='macrophage', category_ontology_term_id='CL:0000235', marker_genes=['LYZ', 'CST3', 'CD68', 'S100A8', 'S100A9'], canonical_marker_genes=['LYZ', 'CST3', 'CD68', 'S100A8', 'S100A9'], synonyms=['unknown'], rationale='This cluster expresses markers of monocytes and macrophages.\\n\\nSurprisingly, these cells also expressed markers specific of tonsillar slan+ cells. We identified several slan-like clusters, which we will provide in version 2.0.', rationale_dois=['10.1096/fj.202000181R'], ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135123', name='NK', count=369.0, color='#88db57', full_name='natural killer cell', ontology_term_exists=True, ontology_term='natural killer cell', ontology_term_id='CL:0000623', category_full_name='natural killer cell', category_ontology_term_exists=True, category_ontology_term='natural killer cell', category_ontology_term_id='CL:0000623', marker_genes=['KLRF1', 'KLRD1', 'KLRK1', 'NKG7', 'GNLY', 'FCGR3A', 'PRF1', 'CCL5', 'CX3CR1', 'GZMA', 'TBX21'], canonical_marker_genes=['KLRF1', 'KLRD1', 'KLRK1', 'NKG7', 'GNLY', 'FCGR3A', 'PRF1', 'CCL5', 'CX3CR1', 'GZMA', 'TBX21'], synonyms=['large granular lymphocyte', 'NK cell', 'null cell'], rationale='Fully differentiated CD16+ NK cells with high cytotoxic capacity.', rationale_dois=['10.1002/cyto.b.21162', '10.3389/fimmu.2020.00812', '10.1101/630657'], ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135124', name='NK_CD56bright', count=312.0, color='#62db57', full_name='CD16-negative, CD56-bright natural killer cell, human', ontology_term_exists=True, ontology_term='CD16-negative, CD56-bright natural killer cell, human', ontology_term_id='CL:0000938', category_full_name='natural killer cell', category_ontology_term_exists=True, category_ontology_term='natural killer cell', category_ontology_term_id='CL:0000623', marker_genes=['KLRF1', 'GNLY', 'KLRD1', 'NKG7', 'NCAM1', 'EOMES', 'AREG'], canonical_marker_genes=['KLRF1', 'GNLY', 'KLRD1', 'NKG7', 'NCAM1', 'EOMES', 'AREG'], synonyms=['CD56-bright cytokine secreting NK cell', 'CD16-negative, CD56-bright NK cell', 'CD16-CD56bright NK cell', 'CD56-bright cytokine secreting natural killer cell'], rationale='These cells have an immunoregulatory function, possess a strong ability to react to cytokine stimulation, are mainly found in secondary lymphoid organs, and have limited cytotoxic capacity. They are the precursors of CD16+CD56- NK cells.', rationale_dois=['10.1002/cyto.b.21162', '10.3389/fimmu.2020.00812', '10.1101/630657'], ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135089', name='PB', count=955.0, color='#7257db', full_name='plasmablast', ontology_term_exists=True, ontology_term='plasmablast', ontology_term_id='CL:0000980', category_full_name='plasmablast', category_ontology_term_exists=True, category_ontology_term='plasmablast', category_ontology_term_id='CL:0000980', marker_genes=['XBP1', 'MZB1', 'DERL3', 'CENPA', 'PCLAF', 'JCHAIN', 'TOP2A'], canonical_marker_genes=['XBP1', 'MZB1', 'TOP2A', 'DERL3'], synonyms=['CD27-positive, CD38-positive, CD20-negative B cell', 'CD20-negative B cell'], rationale='Cells within this cluster are clearly proliferative plasmablasts with high expression levels of proliferation-related genes (H2AFZ, TUBA1B, MKI67 and TOP2A, among others). Otherwise have an expression pattern of PCs (the 3 transcription factors PRDM1, IRF4 and XBP1, as well as phenotypic markers MZB1, SLAMF7, SSR4, CREB3L2 and FKBP11).', rationale_dois=['10.1038/nri3795', '10.1126/sciimmunol.abe6291'], ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135088', name='PC', count=10131.0, color='#5762db', full_name='plasma cell', ontology_term_exists=True, ontology_term='plasma cell', ontology_term_id='CL:0000786', category_full_name='plasma cell', category_ontology_term_exists=True, category_ontology_term='plasma cell', category_ontology_term_id='CL:0000786', marker_genes=['XBP1', 'IRF4', 'PRDM1', 'DERL3', 'FKBP11', 'MZB1'], canonical_marker_genes=['XBP1', 'IRF4', 'PRDM1', 'DERL3', 'FKBP11', 'MZB1'], synonyms=['plasma B cell', 'plasmocyte', 'plasma B-cell', 'plasmacyte'], rationale='This cluster corresponds to fully-differentiated plasma cells. These cells express high levels of the 3 bona-fide PC transcription factors (XBP1, IRF4, PRDM1), as well as PC phenotypic markers (MZB1, DERL3). They do not express any cell cycle gene.', rationale_dois=['10.1038/nri3795', '10.1126/sciimmunol.abe6291'], ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135113', name='PDC', count=915.0, color='#d3db57', full_name='plasmacytoid dendritic cell', ontology_term_exists=True, ontology_term='plasmacytoid dendritic cell', ontology_term_id='CL:0000784', category_full_name='plasmacytoid dendritic cell', category_ontology_term_exists=True, category_ontology_term='plasmacytoid dendritic cell', category_ontology_term_id='CL:0000784', marker_genes=['LILRA4', 'IL3RA', 'IRF8', 'CST3'], canonical_marker_genes=['LILRA4', 'IL3RA', 'IRF8', 'CST3'], synonyms=['IPC', 'pDC', 'type 2 DC'], rationale='pDCs were previously identified by the standard protein marker CD123 (IL3RA). This cluster had higher expression of IRF8, IL3RA and LILRA4.\\n\\nOne subcluster additionally expressed interferon-related genes (IFI44, IFI6).', rationale_dois=['10.1038/s41467-018-05784-3'], ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135107', name='preB/T', count=89.0, color='#9bdb57', full_name='precursor B/T cell', ontology_term_exists=False, ontology_term='precursor B cell', ontology_term_id='CL:0000817', category_full_name='precursor B cell', category_ontology_term_exists=True, category_ontology_term='precursor B cell', category_ontology_term_id='CL:0000817', marker_genes=['VPREB1', 'RAG1', 'RAG2', 'DNTT', 'CD1A', 'CD1B', 'CD1C', 'CD1E'], canonical_marker_genes=['VPREB1', 'RAG1', 'RAG2', 'DNTT', 'CD1A', 'CD1B', 'CD1C', 'CD1E'], synonyms=['pre-B cell'], rationale='These cluster expressed members of the VDJ recombinase (RAG1, RAG2, DNTT), and markers of B and T cells. The expression of CD1A is consistent with a publication describing extrathymyc development of T cells in human tonsils.', rationale_dois=['10.1172/JCI46125'], ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135115', name='preGCB', count=3820.0, color='#db57d3', full_name='precursor germinal center B cell', ontology_term_exists=False, ontology_term='tonsil germinal center B cell', ontology_term_id='CL:2000006', category_full_name='B cell', category_ontology_term_exists=True, category_ontology_term='B cell', category_ontology_term_id='CL:0000236', marker_genes=['BATF', 'MEF2B', 'RGS13', 'BCL2A1', 'TCL1A', 'LMO2', 'NME2', 'NPM1', 'FCER2'], canonical_marker_genes=['BATF', 'MEF2B', 'RGS13', 'BCL2A1', 'TCL1A', 'LMO2', 'NME2', 'NPM1', 'FCER2'], synonyms=['preGCB', 'precursor tonsil germinal center B cell'], rationale='Cells in this cluster express typical NBC markers such as FCER2 (CD23) and. As compared to \"B activated cells\", within this cluster we could detect the expression of GC-specific genes, such as RGS13, MEF2B, LMO2. (King et al., 2021). However, these cells still partially maintain the NBC transcriptome and transcriptional programs started in the previous steps of NBC activation process (i.e. GC-committed NBC) such as the BATF expression and MYC targets (NPM1, NME2). Additionally, this cluster still lacks other GC phenotypic genes, such as AICDA or BCL6. Therefore, we conclude that this subcluster may represent the last step of NBC activation before entering into the DZ and starting a proper GC reaction.', rationale_dois=['10.1126/sciimmunol.abe6291'], ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135097', name='preMBC/doublet', count=4357.0, color='#db5762', full_name='precursor memory B cell', ontology_term_exists=False, ontology_term='memory B cell', ontology_term_id='CL:0000787', category_full_name='memory B cell', category_ontology_term_exists=True, category_ontology_term='memory B cell', category_ontology_term_id='CL:0000787', marker_genes=['CCR6', 'MEF2B', 'CD22', 'CELF2'], canonical_marker_genes=['CCR6', 'MEF2B', 'CD22', 'CELF2'], synonyms=['unknown'], rationale='This cluster was characterized by high expression of CCR6 or CELF2, genes that are commonly used in literature to define memory B cell precursors (Holmes et al., 2020). Although this cluster likely represents early MBCs derived from the LZ, we cannot exclude some technical issues or even MBC-GC doublet cells.', rationale_dois=['10.1084/jem.20200483'], ontology_assessment=None, typename__='LabelModel')], typename__='LabelsetModel'), CreateSessionSaveEmbeddingSessionLabelsets(id='10919', name='10X_channel', mode='unused', description=None, annotation_method=None, algorithm_name=None, algorithm_version=None, algorithm_repo_url=None, reference_location=None, reference_description=None, labels=[CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135211', name='bz5rpwtv_kg7w108r', count=8458.0, color='#db7357', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135218', name='dvdzq8et_eot75su8', count=7071.0, color='#dbae57', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135214', name='ejto2bae_y5mydeam', count=7804.0, color='#dbc257', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135220', name='et5kifg3_0ufc6p4w', count=5273.0, color='#5784db', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135221', name='hg9au5oq_9ih1c0vd', count=5172.0, color='#5770db', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135213', name='i5udk3x0_57gv6ncx', count=7816.0, color='#a5db57', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135219', name='indj51br_urakqcmi', count=5804.0, color='#57bfdb', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135222', name='izi9unx1_8qdzhivu', count=4602.0, color='#91db57', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135228', name='jb6vuao4_g4vi9ur0', count=971.0, color='#57dbcf', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135224', name='lf02okcv_j85nmx31', count=3820.0, color='#5798db', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135216', name='n1b3su0a_l7shyi35', count=7401.0, color='#57db58', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135215', name='odyctre0_4qtf9h1z', count=7719.0, color='#57acdb', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135217', name='TIP02-1_fresh', count=7231.0, color='#db57c6', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135226', name='TIP04-1_fresh', count=3574.0, color='#db5777', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135227', name='TIP04-2_fresh', count=2408.0, color='#db5763', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135210', name='wf4su8ny_h4yj8bv7', count=8569.0, color='#57db94', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135223', name='wkup7hvl_reo7jg84', count=4029.0, color='#57d3db', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135229', name='x739d5z1_dsamhgey', count=6022.0, color='#57dba8', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135225', name='y7qn780g_p6jkgk63', count=3803.0, color='#575cdb', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135212', name='z3of7uaq_mzbhy4tt', count=7982.0, color='#57dbbb', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135202', name='md651vbh_eymr91s7', count=10420.0, color='#69db57', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135190', name='nqyw13tk_o7guqugw', count=21505.0, color='#8d57db', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135191', name='p4b145yq_4z8crwqq', count=21462.0, color='#a157db', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135194', name='rfs8oamh_0obpdn7k', count=16152.0, color='#b557db', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135199', name='TIP01-1_fresh', count=12114.0, color='#db57da', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135205', name='TIP02-2_fresh', count=9603.0, color='#db57b2', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135206', name='TIP03-1_fresh', count=9256.0, color='#db579e', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135200', name='TIP03-2_fresh', count=12037.0, color='#db578a', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135207', name='umt51kfr_p8ei65ms', count=8943.0, color='#57db6c', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135209', name='v8g80gtx_ps9bamz7', count=8632.0, color='#57db80', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135195', name='xxfne43y_x9dh95oz', count=16017.0, color='#c957db', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135193', name='altbaco5_45sf3wul', count=18173.0, color='#6557db', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135201', name='bw94nf57_vm85woki', count=11786.0, color='#db5f57', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135192', name='c3ftguo2_xxwxs507', count=18679.0, color='#7957db', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135197', name='d8kwy76j_7blj9otf', count=12789.0, color='#db8657', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135204', name='dvcbn9p8_ix0j3k8b', count=9985.0, color='#db9a57', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135203', name='ff8s19u3_7e96iusr', count=10402.0, color='#dbd657', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135198', name='ggq3nifm_jkilwp1x', count=12568.0, color='#cddb57', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135196', name='giz3qso4_783dbpu6', count=13237.0, color='#b9db57', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135208', name='kjzv2rwx_sfomyxok', count=8674.0, color='#7ddb57', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel')], typename__='LabelsetModel'), CreateSessionSaveEmbeddingSessionLabelsets(id='10920', name='age_group', mode='metadata', description=None, annotation_method=None, algorithm_name=None, algorithm_version=None, algorithm_repo_url=None, reference_location=None, reference_description=None, labels=[CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135231', name='child', count=149463.0, color='#db5f57', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135232', name='old_adult', count=74594.0, color='#5f57db', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135230', name='young_adult', count=153906.0, color='#57db5f', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel')], typename__='LabelsetModel'), CreateSessionSaveEmbeddingSessionLabelsets(id='10921', name='hospital', mode='unused', description=None, annotation_method=None, algorithm_name=None, algorithm_version=None, algorithm_repo_url=None, reference_location=None, reference_description=None, labels=[CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135233', name='CIMA', count=186358.0, color='#db5f57', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135234', name='Clinic', count=135382.0, color='#57db5f', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135235', name='Newcastle', count=56223.0, color='#5f57db', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel')], typename__='LabelsetModel'), CreateSessionSaveEmbeddingSessionLabelsets(id='10912', name='tissue', mode='metadata', description=None, annotation_method=None, algorithm_name=None, algorithm_version=None, algorithm_repo_url=None, reference_location=None, reference_description=None, labels=[CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135127', name='palatine tonsil', count=377963.0, color='#db5f57', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id='UBERON:0002373', category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel')], typename__='LabelsetModel'), CreateSessionSaveEmbeddingSessionLabelsets(id='10913', name='organism', mode='metadata', description=None, annotation_method=None, algorithm_name=None, algorithm_version=None, algorithm_repo_url=None, reference_location=None, reference_description=None, labels=[CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135128', name='Homo sapiens', count=377963.0, color='#db5f57', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id='NCBITaxon:9606', category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel')], typename__='LabelsetModel'), CreateSessionSaveEmbeddingSessionLabelsets(id='10914', name='donor_id', mode='unused', description=None, annotation_method=None, algorithm_name=None, algorithm_version=None, algorithm_repo_url=None, reference_location=None, reference_description=None, labels=[CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135132', name='BCLL-10-T', count=32073.0, color='#db5f57', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135134', name='BCLL-11-T', count=24207.0, color='#ccdb57', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135139', name='BCLL-12-T', count=18366.0, color='#9ddb57', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135136', name='BCLL-13-T', count=22199.0, color='#db8d57', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135138', name='BCLL-14-T', count=21109.0, color='#57dbcc', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135142', name='BCLL-15-T', count=15786.0, color='#6edb57', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135143', name='BCLL-20-T', count=12114.0, color='#ad57db', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135141', name='BCLL-21-T', count=16834.0, color='#db57db', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135137', name='BCLL-22-T', count=21293.0, color='#db57ac', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135130', name='BCLL-24-T', count=36852.0, color='#578ddb', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135129', name='BCLL-25-T', count=42967.0, color='#575edb', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135131', name='BCLL-26-T', count=32169.0, color='#7e57db', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135144', name='BCLL-28-T', count=5982.0, color='#db577e', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135145', name='BCLL-2-T', count=5573.0, color='#57db9d', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135140', name='BCLL-6-T', count=17821.0, color='#57bcdb', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135133', name='BCLL-8-T', count=30055.0, color='#dbbc57', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135135', name='BCLL-9-T', count=22563.0, color='#57db6e', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel')], typename__='LabelsetModel'), CreateSessionSaveEmbeddingSessionLabelsets(id='10915', name='libraryID', mode='unused', description=None, annotation_method=None, algorithm_name=None, algorithm_version=None, algorithm_repo_url=None, reference_location=None, reference_description=None, labels=[CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135165', name='BCLL_10_T_1_hashing', count=11786.0, color='#db5f57', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135162', name='BCLL_10_T_2_hashing', count=12568.0, color='#cddb57', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135179', name='BCLL_10_T_5K', count=7719.0, color='#57acdb', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135168', name='BCLL_11_T_1_hashing', count=9985.0, color='#db9a57', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135167', name='BCLL_11_T_2_hashing', count=10402.0, color='#dbd657', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135154', name='BCLL_11_T_5K', count=3820.0, color='#5798db', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135149', name='BCLL_12_T_1_hashing', count=6022.0, color='#57dba8', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135148', name='BCLL_12_T_2_hashing', count=7071.0, color='#dbae57', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135151', name='BCLL_12_T_5K', count=5273.0, color='#5784db', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135174', name='BCLL_13_T_1_hashing', count=8569.0, color='#57db94', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135175', name='BCLL_13_T_2_hashing', count=8458.0, color='#db7357', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135146', name='BCLL_13_T_5K', count=5172.0, color='#5770db', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135173', name='BCLL_14_T_1_hashing', count=8632.0, color='#57db80', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135172', name='BCLL_14_T_2_hashing', count=8674.0, color='#7ddb57', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135155', name='BCLL_14_T_5K', count=3803.0, color='#575cdb', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135176', name='BCLL_15_T_1_hashing', count=7982.0, color='#57dbbb', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135178', name='BCLL_15_T_2_hashing', count=7804.0, color='#dbc257', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135184', name='BCLL_24_T_1_hashing', count=18173.0, color='#6557db', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135183', name='BCLL_24_T_2_hashing', count=18679.0, color='#7957db', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135182', name='BCLL_25_T_1_hashing', count=21462.0, color='#a157db', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135152', name='BCLL_25_T_2_hashing', count=21505.0, color='#8d57db', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135159', name='BCLL_26_T_GEX_1', count=16017.0, color='#c957db', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135185', name='BCLL_26_T_GEX_2', count=16152.0, color='#b557db', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135158', name='BCLL_2_T_5K', count=971.0, color='#57dbcf', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135147', name='BCLL_2_T_hashing', count=4602.0, color='#91db57', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135180', name='BCLL_6_T_1_hashing', count=7401.0, color='#57db58', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135166', name='BCLL_6_T_2_hashing', count=10420.0, color='#69db57', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135161', name='BCLL_8_T_1_hashing', count=12789.0, color='#db8657', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135160', name='BCLL_8_T_2_hashing', count=13237.0, color='#b9db57', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135153', name='BCLL_8_T_5K', count=4029.0, color='#57d3db', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135171', name='BCLL_9_T_1_hashing', count=8943.0, color='#57db6c', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135177', name='BCLL_9_T_2_hashing', count=7816.0, color='#a5db57', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135150', name='BCLL_9_T_5K', count=5804.0, color='#57bfdb', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135163', name='TIP01-1_fresh', count=12114.0, color='#db57da', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135181', name='TIP02-1_fresh', count=7231.0, color='#db57c6', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135169', name='TIP02-2_fresh', count=9603.0, color='#db57b2', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135170', name='TIP03-1_fresh', count=9256.0, color='#db579e', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135164', name='TIP03-2_fresh', count=12037.0, color='#db578a', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135156', name='TIP04-1_fresh', count=3574.0, color='#db5777', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135157', name='TIP04-2_fresh', count=2408.0, color='#db5763', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel')], typename__='LabelsetModel'), CreateSessionSaveEmbeddingSessionLabelsets(id='10916', name='sex', mode='metadata', description=None, annotation_method=None, algorithm_name=None, algorithm_version=None, algorithm_repo_url=None, reference_location=None, reference_description=None, labels=[CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135187', name='female', count=145866.0, color='#57d3db', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel'), CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135186', name='male', count=232097.0, color='#db5f57', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id=None, category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel')], typename__='LabelsetModel'), CreateSessionSaveEmbeddingSessionLabelsets(id='10917', name='assay', mode='metadata', description=None, annotation_method=None, algorithm_name=None, algorithm_version=None, algorithm_repo_url=None, reference_location=None, reference_description=None, labels=[CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135188', name=\"10x 3' v3\", count=377963.0, color='#db5f57', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id='EFO:0009922', category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel')], typename__='LabelsetModel'), CreateSessionSaveEmbeddingSessionLabelsets(id='10918', name='disease', mode='metadata', description=None, annotation_method=None, algorithm_name=None, algorithm_version=None, algorithm_repo_url=None, reference_location=None, reference_description=None, labels=[CreateSessionSaveEmbeddingSessionLabelsetsLabels(id='135189', name='tonsillitis', count=377963.0, color='#db5f57', full_name=None, ontology_term_exists=None, ontology_term=None, ontology_term_id='MONDO:0001039', category_full_name=None, category_ontology_term_exists=None, category_ontology_term=None, category_ontology_term_id=None, marker_genes=[], canonical_marker_genes=None, synonyms=[], rationale=None, rationale_dois=None, ontology_assessment=None, typename__='LabelModel')], typename__='LabelsetModel')], typename__='DatasetModel')"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# The snapshot of the dataset is stored in the class as a Pydantic Model\n",
+    "md.dataset_snapshot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MD page is not ready, let's wait 1 sec\n",
+      "MD page is not ready, let's wait 1 sec\n",
+      "MD page is not ready, let's wait 1 sec\n",
+      "MD page is ready!\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Before working with MD page it is recommended to check whether\n",
+    "# the MD-page cache files are ready\n",
+    "from time import sleep\n",
+    "\n",
+    "while not md.is_md_cache_ready():\n",
+    "    print(\"MD page is not ready, let's wait 1 sec\")\n",
+    "    sleep(1)\n",
+    "print(\"MD page is ready!\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "EmbeddingDataDatasetEmbeddingData(obs_ids=[2, 8, 18, 20, 23, 29, 34, 55, 58, 66, 68, 83, 88, 101, 102, 108, 119, 121, 130, 159, 160, 172, 174, 179, 198, 202, 206, 208, 211, 219, 229, 236, 258, 260, 270, 280, 285, 306, 313, 314, 324, 341, 342, 343, 352, 356, 362, 363, 364, 365, 375, 382, 394, 397, 404, 405, 406, 410, 411, 414, 417, 418, 421, 430, 441, 451, 453, 454, 476, 481, 502, 519, 523, 524, 531, 550, 558, 572, 579, 580, 588, 594, 595, 598, 608, 615, 625, 637, 650, 690, 698, 704, 705, 708, 715, 721, 731, 732, 741, 743, 745, 751, 767, 769, 773, 774, 780, 785, 790, 792, 797, 809, 813, 817, 821, 831, 839, 859, 860, 861, 869, 871, 872, 874, 889, 902, 920, 924, 935, 942, 945, 956, 958, 965, 968, 970, 987, 988, 1006, 1015, 1020, 1023, 1024, 1031, 1035, 1039, 1040, 1041, 1049, 1051, 1053, 1057, 1059, 1061, 1064, 1065, 1069, 1070, 1074, 1082, 1084, 1090, 1093, 1097, 1102, 1104, 1106, 1110, 1112, 1113, 1125, 1126, 1131, 1133, 1136, 1140, 1141, 1155, 1160, 1167, 1180, 1184, 1186, 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'Cycling DZ GCB', 'Cycling DZ GCB', 'Cycling DZ GCB', 'Cycling DZ GCB', 'Cycling DZ GCB', 'Cycling DZ GCB', 'B memory', 'DZ GCB', 'LZ GCB', 'Cycling DZ GCB', 'Cycling DZ GCB', 'Cycling DZ GCB', 'Cycling DZ GCB', 'LZ GCB', 'DZtoLZ GCB transition', 'Cycling DZ GCB', 'DZtoLZ GCB transition', 'Cycling DZ GCB', 'Cycling DZ GCB', 'preMBC/doublet', 'LZ GCB', 'DZtoLZ GCB transition', 'Cycling DZ GCB', 'DZ GCB', 'prePB', 'DZ GCB', 'DZ GCB', 'LZ GCB', 'Cycling DZ GCB', 'preMBC/doublet', 'Cycling DZ GCB', 'LZtoDZ GCB transition', 'Cycling DZ GCB', 'Cycling DZ GCB', 'Cycling DZ GCB', 'DZtoLZ GCB transition', 'DZ GCB', 'Cycling DZ GCB', 'DZtoLZ GCB transition', 'LZ GCB', 'Cycling DZ GCB', 'Cycling DZ GCB', 'DZ GCB', 'Cycling DZ GCB', 'DZtoLZ GCB transition', 'Cycling DZ GCB', 'preMBC/doublet', 'Cycling DZ GCB', 'B memory', 'DZtoLZ GCB transition', 'Cycling DZ GCB', 'Cycling DZ GCB', 'Cycling DZ GCB', 'DZ GCB', 'Cycling DZ GCB', 'DZtoLZ GCB transition', 'Cycling DZ GCB', 'Cycling DZ GCB', 'LZtoDZ GCB transition', 'LZ GCB', 'DZ GCB', 'B memory', 'DZ GCB', 'preMBC/doublet', 'Cycling DZ GCB', 'Cycling DZ GCB', 'preMBC/doublet', 'B memory', 'B memory', 'LZ GCB', 'B memory', 'Cycling DZ GCB', 'B memory', 'DZ GCB', 'Cycling DZ GCB', 'DZ GCB', 'Cycling DZ GCB', 'Cycling DZ GCB', 'preMBC/doublet', 'LZ GCB', 'Cycling DZ GCB', 'Cycling DZ GCB', 'Cycling DZ GCB', 'DZtoLZ GCB transition', 'Cycling DZ GCB', 'Cycling DZ GCB', 'DZtoLZ GCB transition', 'LZ GCB', 'Cycling DZ GCB', 'DZtoLZ GCB transition', 'DZ GCB', 'LZtoDZ GCB transition', 'Cycling DZ GCB', 'DZ GCB', 'LZ GCB', 'Cycling DZ GCB', 'Cycling DZ GCB', 'Cycling DZ GCB', 'DZtoLZ GCB transition', 'DZ GCB', 'DZ GCB', 'LZtoDZ GCB transition', 'LZtoDZ GCB transition', 'LZ GCB', 'Cycling DZ GCB', 'LZ GCB', 'Cycling DZ GCB', 'DZtoLZ GCB transition', 'Cycling DZ GCB', 'Cycling DZ GCB', 'DZtoLZ GCB transition', 'Cycling DZ GCB', 'Cycling DZ GCB', 'LZ GCB', 'Cycling DZ GCB', 'Cycling DZ GCB', 'Cycling DZ GCB', 'DZtoLZ GCB transition', 'preMBC/doublet', 'Cycling DZ GCB', 'LZtoDZ GCB transition', 'B memory', 'DZtoLZ GCB transition', 'Cycling DZ GCB', 'Cycling DZ GCB', 'B memory', 'Cycling DZ GCB', 'Cycling DZ GCB', 'LZtoDZ GCB transition', 'LZtoDZ GCB transition', 'preMBC/doublet', 'DZtoLZ GCB transition', 'LZtoDZ GCB transition', 'DZtoLZ GCB transition', 'Cycling DZ GCB', 'LZtoDZ GCB transition', 'DZ GCB', 'DZtoLZ GCB transition', 'LZtoDZ GCB transition', 'Cycling DZ GCB', 'Cycling DZ GCB', 'B memory', 'Cycling DZ GCB', 'B memory', 'B memory', 'LZtoDZ GCB transition', 'Cycling DZ GCB', 'Cycling DZ GCB', 'Cycling DZ GCB', 'DZtoLZ GCB transition', 'Cycling DZ GCB', 'LZ GCB', 'B memory', 'LZ GCB', 'Cycling DZ GCB', 'B memory', 'DZtoLZ GCB transition', 'Cycling DZ GCB', 'Cycling DZ GCB', 'Cycling DZ GCB', 'LZ GCB', 'Cycling DZ GCB', 'LZ GCB', 'B memory', 'DZtoLZ GCB transition', 'Cycling DZ GCB', 'DZ GCB', 'Cycling DZ GCB', 'Cycling DZ GCB', 'Cycling DZ GCB', 'Cycling DZ GCB', 'Cycling DZ GCB', 'Cycling DZ GCB', 'Cycling DZ GCB', 'DZ GCB', 'Cycling DZ GCB', 'Cycling DZ GCB', 'Cycling DZ GCB', 'LZtoDZ GCB transition', 'DZ GCB', 'DZtoLZ GCB transition', 'preMBC/doublet', 'LZtoDZ GCB transition', 'LZtoDZ GCB transition', 'LZtoDZ GCB transition', 'Cycling DZ GCB', 'DZ GCB', 'Cycling DZ GCB', 'Cycling DZ GCB', 'Cycling DZ GCB', 'Cycling DZ GCB', 'Cycling DZ GCB', 'Cycling DZ GCB', 'LZtoDZ GCB transition', 'LZtoDZ GCB transition', 'Cycling DZ GCB', 'Cycling DZ GCB', 'Cycling DZ GCB', 'Cycling DZ GCB', 'Cycling DZ GCB', 'DZ GCB', 'Cycling DZ GCB', 'Cycling DZ GCB', 'DZtoLZ GCB transition', 'DZ GCB', 'DZ GCB', 'Cycling DZ GCB', 'DZ GCB', 'Cycling DZ GCB', 'preMBC/doublet', 'Cycling DZ GCB', 'DZtoLZ GCB transition', 'DZtoLZ GCB transition', 'DZ GCB', 'LZtoDZ GCB transition', 'Cycling DZ GCB', 'preMBC/doublet', 'preMBC/doublet', 'Cycling DZ GCB', 'DZtoLZ GCB transition', 'Cycling DZ GCB', 'DZtoLZ GCB transition', 'Cycling DZ GCB', 'DZtoLZ GCB transition', 'preMBC/doublet', 'Cycling DZ GCB', 'LZtoDZ GCB transition', 'Cycling DZ GCB', 'Cycling DZ GCB', 'DZ GCB', 'LZ GCB', 'LZtoDZ GCB transition', 'Cycling DZ GCB', 'Cycling DZ GCB', 'Cycling DZ GCB', 'Cycling DZ GCB', 'Cycling DZ GCB', 'LZ GCB', 'B memory', 'Cycling DZ GCB', 'Cycling DZ GCB', 'Cycling DZ GCB', 'Cycling DZ GCB', 'Cycling DZ GCB', 'Cycling DZ GCB', 'Cycling DZ GCB', 'LZtoDZ GCB transition', 'Cycling DZ GCB', 'DZtoLZ GCB transition', 'Cycling DZ GCB', 'DZtoLZ GCB transition', 'DZ GCB', 'Cycling DZ GCB', 'Cycling DZ GCB', 'Cycling DZ GCB', 'Cycling DZ GCB', 'Cycling DZ GCB', 'preMBC/doublet', 'DZ GCB', 'preMBC/doublet', 'Cycling DZ GCB', 'Cycling DZ GCB', 'Cycling DZ GCB', 'LZtoDZ GCB transition', 'Cycling DZ GCB', 'Cycling DZ GCB', 'DZtoLZ GCB transition', 'LZ GCB', 'LZtoDZ GCB transition', 'B memory', 'LZ GCB', 'Cycling DZ GCB', 'Cycling DZ GCB', 'Cycling DZ GCB', 'DZtoLZ GCB transition', 'preMBC/doublet', 'Cycling DZ GCB', 'LZtoDZ GCB transition', 'Cycling DZ GCB', 'DZtoLZ GCB transition', 'Cycling DZ GCB', 'Cycling DZ GCB', 'DZtoLZ GCB transition', 'Cycling DZ GCB', 'DZtoLZ GCB transition', 'DZ GCB', 'B memory', 'Cycling DZ GCB', 'Cycling DZ GCB', 'Cycling DZ GCB', 'DZtoLZ GCB transition', 'Cycling DZ GCB', 'DZtoLZ GCB transition', 'Cycling DZ GCB', 'DZtoLZ GCB transition', 'DZ GCB', 'preMBC/doublet', 'Cycling DZ GCB', 'DZ GCB', 'Cycling DZ GCB', 'DZtoLZ GCB transition', 'Cycling DZ GCB', 'DZ GCB', 'Cycling DZ GCB', 'B memory', 'DZtoLZ GCB transition', 'B memory', 'DZtoLZ GCB transition', 'Cycling DZ GCB', 'DZtoLZ GCB transition', 'LZ GCB', 'DZ GCB', 'preMBC/doublet', 'B memory', 'DZtoLZ GCB transition', 'Cycling DZ GCB', 'preMBC/doublet', 'DZtoLZ GCB transition', 'LZtoDZ GCB transition', 'Cycling DZ GCB', 'Cycling DZ GCB', 'DZtoLZ GCB transition', 'DZ GCB', 'DZ GCB', 'Cycling DZ GCB', 'Cycling DZ GCB', 'DZtoLZ GCB transition', 'preMBC/doublet', 'DZtoLZ GCB transition', 'Cycling DZ GCB', 'preMBC/doublet', 'LZtoDZ GCB transition', 'DZtoLZ GCB transition', 'DZtoLZ GCB transition', 'LZtoDZ GCB transition', 'LZtoDZ GCB transition', 'Cycling DZ GCB', 'DZ GCB', 'DZtoLZ GCB transition', 'DZtoLZ GCB transition', 'LZtoDZ GCB transition', 'LZtoDZ GCB transition', 'Cycling DZ GCB', 'Cycling DZ GCB', 'Cycling DZ GCB', 'Cycling DZ GCB', 'Cycling DZ GCB', 'DZtoLZ GCB transition', 'Cycling DZ GCB', 'DZ GCB', 'LZ GCB', 'Cycling DZ GCB', 'LZtoDZ GCB transition', 'DZtoLZ GCB transition', 'Cycling DZ GCB', 'B memory', 'Cycling DZ GCB', 'LZtoDZ GCB transition', 'DZtoLZ GCB transition', 'Cycling DZ GCB', 'Cycling DZ GCB', 'DZ GCB', 'DZtoLZ GCB transition', 'Cycling DZ GCB', 'Cycling DZ GCB', 'Cycling DZ GCB', 'Cycling DZ GCB', 'DZtoLZ GCB transition', 'DZtoLZ GCB transition', 'DZtoLZ GCB transition', 'DZ GCB', 'DZtoLZ GCB transition', 'Cycling DZ GCB', 'DZ GCB', 'Cycling DZ GCB', 'Cycling DZ GCB', 'DZtoLZ GCB transition', 'Cycling DZ GCB', 'Cycling DZ GCB', 'LZ GCB', 'DZtoLZ GCB transition', 'Cycling DZ GCB', 'DZ GCB', 'LZ GCB', 'Cycling DZ GCB', 'LZ GCB', 'LZ GCB', 'Cycling DZ GCB', 'DZtoLZ GCB transition', 'LZ GCB', 'Cycling DZ GCB', 'DZ GCB', 'Cycling DZ GCB', 'Cycling DZ GCB', 'Cycling DZ GCB', 'Cycling DZ GCB', 'preMBC/doublet', 'preMBC/doublet', 'B memory', 'Cycling DZ GCB', 'DZtoLZ GCB transition', 'DZtoLZ GCB transition', 'DZ GCB', 'Cycling DZ GCB', 'Cycling DZ GCB', 'DZtoLZ GCB transition', 'Cycling DZ GCB', 'Cycling DZ GCB', 'LZ GCB', 'DZ GCB', 'Cycling DZ GCB', 'Cycling DZ GCB', 'DZtoLZ GCB transition', 'Cycling DZ GCB', 'Cycling DZ GCB', 'DZtoLZ GCB transition', 'Cycling DZ GCB', 'Cycling DZ GCB', 'Cycling DZ GCB', 'Cycling DZ GCB', 'Cycling DZ GCB', 'LZtoDZ GCB transition', 'Cycling DZ GCB', 'Cycling DZ GCB', 'Cycling DZ GCB', 'Cycling DZ GCB', 'Cycling DZ GCB', 'Cycling DZ GCB', 'Cycling DZ GCB', 'Cycling DZ GCB', 'Cycling DZ GCB', 'Cycling DZ GCB', 'Cycling DZ GCB', 'LZtoDZ GCB transition', 'DZtoLZ GCB transition', 'DZ GCB', 'DZtoLZ GCB transition', 'Cycling DZ GCB', 'Cycling DZ GCB', 'Cycling DZ GCB', 'DZtoLZ GCB transition', 'Cycling DZ GCB', 'DZ GCB', 'Cycling DZ GCB', 'LZ GCB', 'Cycling DZ GCB', 'DZ GCB', 'Cycling DZ GCB', 'Cycling DZ GCB', 'DZ GCB', 'Cycling DZ GCB', 'Cycling DZ GCB', 'Cycling DZ GCB', 'DZtoLZ GCB transition', 'LZtoDZ GCB transition', 'DZ GCB', 'DZtoLZ GCB transition', 'B memory', 'Cycling DZ GCB', 'Cycling DZ GCB', 'DZtoLZ GCB transition', 'DZ GCB', 'LZtoDZ GCB transition', 'Cycling DZ GCB', 'DZtoLZ GCB transition', 'DZ GCB', 'preMBC/doublet', 'Cycling DZ GCB'], typename__='EmbeddingDataAnnotationItem')], gene_expression=[], typename__='EmbeddingData')"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Embeddings data\n",
+    "emb_data = md.embedding_data(\n",
+    "    embedding=md.embeddings[0],\n",
+    "    max_points=10_000,\n",
+    "    labelsets=[md.labelsets[0]],\n",
+    ")\n",
+    "emb_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "embeddings = emb_data.embeddings\n",
+    "labelset_name = emb_data.annotations[0].name\n",
+    "labels = emb_data.annotations[0].items"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "sns.scatterplot(x=[e.x for e in embeddings], y=[e.y for e in embeddings], hue=labels);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Y2VsbF90eXBlX2F6aW11dGg7Tm9uZTs0MjtiNDgyZjk4NTc5ZTUyZjIwZDUyZmU4MDNmYTk4NjRmNw=='"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Running general DE will produce the diff_key which will\n",
+    "# be used later for getting heatmaps\n",
+    "\n",
+    "diff_key = md.general_de(\n",
+    "    labelset=md.labelsets[0]\n",
+    ")\n",
+    "diff_key"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "heatmap = md.heatmap(\n",
+    "    diff_key=diff_key,\n",
+    "    n_top_genes=3,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['obs_ids', 'annotations', 'is_in_selections', 'genes', 'scores', 'top_genes_by_selection', 'typename__'])"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "heatmap.model_dump().keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>gene_symbol</th>\n",
+       "      <th>dispersion</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>HBB</td>\n",
+       "      <td>7.861795</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>IGLL5</td>\n",
+       "      <td>6.832189</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>SPRR3</td>\n",
+       "      <td>6.516472</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>CXCL8</td>\n",
+       "      <td>6.487165</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>FDCSP</td>\n",
+       "      <td>6.388253</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>HBA2</td>\n",
+       "      <td>6.278853</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>PI3</td>\n",
+       "      <td>5.952504</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>S100A7</td>\n",
+       "      <td>5.928604</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>CXCL5</td>\n",
+       "      <td>5.920533</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>CCL4L2</td>\n",
+       "      <td>5.825043</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  gene_symbol  dispersion\n",
+       "0         HBB    7.861795\n",
+       "1       IGLL5    6.832189\n",
+       "2       SPRR3    6.516472\n",
+       "3       CXCL8    6.487165\n",
+       "4       FDCSP    6.388253\n",
+       "5        HBA2    6.278853\n",
+       "6         PI3    5.952504\n",
+       "7      S100A7    5.928604\n",
+       "8       CXCL5    5.920533\n",
+       "9      CCL4L2    5.825043"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Highly variable genes list could get with:\n",
+    "md.highly_variable_genes(limit=10)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "cap_pyent",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/pyproject.toml b/pyproject.toml
index 6b07d83..b6d9099 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -37,3 +37,9 @@ include_all_inputs=false
 include_all_enums=false
 target_package_path="./cap_client/"
 target_package_name="client"
+client_name="_Client"
+base_client_name="_BaseClient"
+
+[tool.pytest.ini_options]
+pythonpath = ["cap_client"]
+log_level = "DEBUG"
diff --git a/requirements.txt b/requirements.txt
index eb39733..1e678b7 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,4 +1,5 @@
 httpx>=0.27.2
 pydantic>=2.10.3
 pydantic_core>=2.27.1
-PyJWT>=2.10.1
\ No newline at end of file
+PyJWT>=2.10.1
+pandas>=2.0.0
diff --git a/test/cap_auth_test.py b/test/cap_auth_test.py
index 0ab6f0d..002d23c 100644
--- a/test/cap_auth_test.py
+++ b/test/cap_auth_test.py
@@ -1,16 +1,16 @@
 import pytest
 from unittest.mock import patch, Mock
-from cap_client import Cap
+from cap_client import CapClient
 
 CAP_AUTHENTICATE_URL = "us-central1-capv2-gke-prod.cloudfunctions.net" 
 
 def test_authenticate_with_custom_token_success():
-    cap = Cap()
-    with patch.object(Cap, '_request') as mock_request:
+    cap = CapClient()
+    with patch.object(CapClient, '_auth_request') as mock_request:
         mock_request.return_value = True
         cap._custom_token = "dummy_token"
 
-        result = cap._authenticate()
+        result = cap.authenticate()
 
         assert result is True
         mock_request.assert_called_once_with(
@@ -20,19 +20,19 @@ def test_authenticate_with_custom_token_success():
         )
 
 def test_authenticate_with_custom_token_failure():
-    cap = Cap()
-    with patch.object(Cap, '_request') as mock_request:
+    cap = CapClient()
+    with patch.object(CapClient, '_auth_request') as mock_request:
         mock_request.return_value = False
         cap._custom_token = "dummy_token"
 
-        result = cap._authenticate()
+        result = cap.authenticate()
 
         assert result is False
         mock_request.assert_called_once()
 
 def test_authenticate_with_credentials_success():
-    cap = Cap()
-    with patch.object(Cap, '_request') as mock_request:
+    cap = CapClient()
+    with patch.object(CapClient, '_auth_request') as mock_request:
         # Arrange
         mock_request.return_value = True
         cap._custom_token = None
@@ -40,7 +40,7 @@ def test_authenticate_with_credentials_success():
         cap._pwd = "password123"
 
         # Act
-        result = cap._authenticate()
+        result = cap.authenticate()
 
         # Assert
         assert result is True
@@ -51,8 +51,8 @@ def test_authenticate_with_credentials_success():
         )
 
 def test_authenticate_with_credentials_failure():
-    cap = Cap()
-    with patch.object(Cap, '_request') as mock_request:
+    cap = CapClient()
+    with patch.object(CapClient, '_auth_request') as mock_request:
         # Arrange
         mock_request.return_value = False
         cap._custom_token = None
@@ -60,21 +60,21 @@ def test_authenticate_with_credentials_failure():
         cap._pwd = "password123"
 
         # Act
-        result = cap._authenticate()
+        result = cap.authenticate()
 
         # Assert
         assert result is False
         mock_request.assert_called_once()
 
 def test_authenticate_with_no_credentials():
-    cap = Cap()
+    cap = CapClient()
     # Arrange
     cap._custom_token = None
     cap._login = None
     cap._pwd = None
 
     # Act
-    result = cap._authenticate()
+    result = cap.authenticate()
 
     # Assert
     assert result is False
diff --git a/test/public_api_test.py b/test/public_api_test.py
index 64f8acb..a0b1bf0 100644
--- a/test/public_api_test.py
+++ b/test/public_api_test.py
@@ -1,369 +1,280 @@
 import pytest
-import json
-from unittest.mock import patch, Mock
-from cap_client import Cap
+from unittest.mock import patch, MagicMock, ANY
+from cap_client import CapClient, MDSession
+import pandas as pd
+
 
 CAP_AUTHENTICATE_USER_URL  = "authenticate-user-wg6qkl5yea-uc.a.run.app"
 CAP_AUTHENTICATE_TOKEN_URL = "authenticate-token-wg6qkl5yea-uc.a.run.app"
 
+# ------------------------------
+# Tests for CapClient methods
+# ------------------------------
+
 def test_search_datasets():
-    # Arrange
-    cap = Cap()
-    sample_dataset_response = '{"results": [{"id": "123", "name": "Test Dataset"}]}'
-    with patch.object(Cap, 'search_datasets_json') as search_request_mock:
-        search_request_mock.return_value = sample_dataset_response
-
-        # Act
-        result = cap.search_datasets_json(
-            search=["blood"],
+    cap = CapClient()
+    dummy_response = MagicMock()
+    # Patch the internal client (name-mangled as _CapClient__client)
+    with patch.object(cap, "_CapClient__client") as mock_client:
+        mock_client.search_datasets.return_value = dummy_response
+
+        response = cap.search_datasets(
+            search="name",
             organism=["Homo sapiens"],
             tissue=["stomach"],
             assay=["10x 3' v1"],
-            sort=[{"name":"ASC"}],
             limit=10,
-            offset=0
+            offset=0,
+            sort=[{"name": "ASC"}]
         )
+        assert response == dummy_response
+        mock_client.search_datasets.assert_called_once()
 
-        # Assert
-        assert result == '{"results": [{"id": "123", "name": "Test Dataset"}]}'
-        search_request_mock.assert_called_once()
-
-def test_search_datasets_no_params():
-    # Arrange
-    cap = Cap()
-    sample_dataset_response = '{"results": [{"id": "123", "name": "Test Dataset"}]}'
-    with patch.object(Cap, 'search_datasets_json') as search_request_mock:
-        search_request_mock.return_value = sample_dataset_response
-
-        # Act
-        result = cap.search_datasets_json()
-
-        # Assert
-        assert result == '{"results": [{"id": "123", "name": "Test Dataset"}]}'
-        search_request_mock.assert_called_once()
-
-def test_download_urls():
-    # Arrange
-    cap = Cap()
-    sample_url_response = '{"download_urls": [{"annDataUrl": "gs://test_bucket/dataset.h5ad"}]}'
-    with patch.object(Cap, 'download_urls_json') as download_request_mock:
-        download_request_mock.return_value = sample_url_response
-
-        # Act
-        result = cap.download_urls_json(1)
-
-        # Assert
-        assert result == '{"download_urls": [{"annDataUrl": "gs://test_bucket/dataset.h5ad"}]}'
-        download_request_mock.assert_called_once()
-
-def test_download_urls_no_params():
-    # Arrange
-    cap = Cap()
-    sample_url_response = '{"errors": [{"message": "Failed to get download urls"}]}'
-    with patch.object(Cap, 'download_urls_json') as download_request_mock:
-        download_request_mock.return_value = sample_url_response
-
-        # Act
-        result = cap.download_urls_json()
-
-        # Assert
-        assert result == '{"errors": [{"message": "Failed to get download urls"}]}'
-        download_request_mock.assert_called_once()
-
-def test_search_cells():
-    cap = Cap()
-    sample_cell_labels_response = '{"lookup_cells": [{"id": "123", "name": "Test Cell Label"}]}'
-    with patch.object(Cap, 'search_cell_labels_json') as search_request_mock:
-        search_request_mock.return_value = sample_cell_labels_response
-
-        # Act
-        result = cap.search_cell_labels_json(
-            search="blood", 
-            organism=["Homo sapiens"], 
-            tissue=["parietal cortex"],
-            assay=["10x 3' v1"], 
-            sort=[{"name":"ASC"}],
-            limit=10,
-            offset=0
+
+def test_search_cell_labels():
+    cap = CapClient()
+    dummy_response = MagicMock()
+    with patch.object(cap, "_CapClient__client") as mock_client:
+        mock_client.lookup_cells.return_value = dummy_response
+
+        response = cap.search_cell_labels(
+            search="name",
+            organism=["Homo sapiens"],
+            tissue=["brain"],
+            assay=["10x 3' v1"],
+            limit=5,
+            offset=0,
+            sort=[{"name": "ASC"}]
         )
+        assert response == dummy_response
+        mock_client.lookup_cells.assert_called_once()
+
+
+def test_open_md_session():
+    cap = CapClient()
+    dataset_id = "1234"
+    md_session = cap.md_session(dataset_id)
+    assert isinstance(md_session, MDSession)
+    assert md_session.dataset_id == dataset_id
+
+
+# ------------------------------
+# Tests for MDSession methods
+# ------------------------------
+
+@pytest.fixture
+def dummy_md_session():
+    """
+    Returns an MDSession instance along with a dummy client (a MagicMock)
+    so we can patch its methods.
+    """
+    dummy_client = MagicMock()
+    md_session = MDSession(dataset_id="1234", _client=dummy_client)
+    return md_session, dummy_client
+
+
+@pytest.mark.parametrize("ready", [True, False])
+def test_check_md_ready(ready, dummy_md_session):
+    md_session, client = dummy_md_session
+    dataset_mock = MagicMock()
+    dataset_mock.is_embeddings_up_to_date = ready
+    client.dataset_ready.return_value = MagicMock(dataset=dataset_mock)
+
+    if ready:
+        md_session._check_md_ready()
+    else:
+        with pytest.raises(RuntimeError):
+            md_session._check_md_ready()
+
+
+def test_create_session(dummy_md_session):
+    md_session, dummy_client = dummy_md_session
+
+    # Setup dummy response for dataset_ready (should be "ready")
+    dummy_ready = MagicMock()
+    dummy_ready.dataset = MagicMock(is_embeddings_up_to_date=True)
+    dummy_client.dataset_ready.return_value = dummy_ready
+
+    # Setup dummy response for dataset_initial_state_query
+    dummy_initial_state = MagicMock()
+    dummy_dataset = MagicMock()
+    dummy_labelset = MagicMock()
+    dummy_labelset.mode = "cell-labels"
+    dummy_labelset.name = "Test Labelset"
+    dummy_labelset.id = "label1"
+    dummy_dataset.labelsets = [dummy_labelset]
+    dummy_dataset.model_dump.return_value = dict(dummy_dataset)
+    dummy_initial_state.dataset = dummy_dataset
+    dummy_client.dataset_initial_state_query.return_value = dummy_initial_state
+
+    # Setup dummy response for cluster_types
+    dummy_cluster_types = MagicMock()
+    dummy_cluster_dataset = MagicMock()
+    dummy_cluster = MagicMock()
+    dummy_cluster.name = "cluster1"
+    dummy_cluster_dataset.embedding_cluster_types = [dummy_cluster]
+    dummy_cluster_types.dataset = dummy_cluster_dataset
+    dummy_client.cluster_types.return_value = dummy_cluster_types
+
+    # Setup dummy response for md_commons_query (for embeddings)
+    dummy_embeddings = MagicMock()
+    dummy_embeddings_dataset = MagicMock()
+    dummy_embedding = MagicMock()
+    dummy_embedding.name = "embedding1"
+    dummy_embeddings_dataset.embeddings = [dummy_embedding]
+    dummy_embeddings.dataset = dummy_embeddings_dataset
+    dummy_client.md_commons_query.return_value = dummy_embeddings
+
+    # Setup dummy response for create_session
+    dummy_create_session = MagicMock()
+    dummy_create_session.save_embedding_session = "snapshot_updated"
+    dummy_client.create_session.return_value = dummy_create_session
+
+    session_id = md_session.create_session()
+
+    # Check that the returned session_id matches the session property
+    assert md_session.session_id == session_id
+
+    # Verify that the expected internal client calls were made
+    dummy_client.dataset_ready.assert_called_once_with(md_session.dataset_id)
+    dummy_client.dataset_initial_state_query.assert_called_once_with(md_session.dataset_id)
+    dummy_client.cluster_types.assert_called_once_with(md_session.dataset_id)
+    dummy_client.md_commons_query.assert_called_once_with(md_session.dataset_id)
+    dummy_client.create_session.assert_called_once()
+
+
+def test_create_session_not_ready(dummy_md_session):
+    md_session, dummy_client = dummy_md_session
+
+    # Simulate dataset not ready (is_embeddings_up_to_date is False)
+    dummy_ready = MagicMock()
+    dummy_ready.dataset = MagicMock(is_embeddings_up_to_date=False)
+    dummy_client.dataset_ready.return_value = dummy_ready
+
+    with pytest.raises(RuntimeError, match="is not ready"):
+        md_session.create_session()
+
+
+def test_embedding_data_success(dummy_md_session):
+    md_session, dummy_client = dummy_md_session
+    # Set available embeddings so that "embedding1" is valid.
+    md_session._embeddings = ["embedding1"]
+
+    # Setup dummy response for embedding_data
+    dummy_embedding_data = MagicMock()
+    dummy_embedding_data.dataset = MagicMock(embedding_data="embedding_data_response")
+    dummy_client.embedding_data.return_value = dummy_embedding_data
+
+    result = md_session.embedding_data(
+        embedding="embedding1",
+        max_points=1000,
+        labelsets=["label1"],
+        selection_gene="geneA",
+        selection_key_major="sel_major",
+        selection_key_minor="sel_minor"
+    )
+    assert result == "embedding_data_response"
+    dummy_client.embedding_data.assert_called_once_with(
+        dataset_id=md_session.dataset_id,
+        options=ANY  # We use ANY because the input options object is complex
+    )
+
+
+def test_embedding_data_invalid_embedding(dummy_md_session):
+    md_session, dummy_client = dummy_md_session
+    md_session._embeddings = ["embedding1"]
+
+    with pytest.raises(ValueError, match="is not found"):
+        md_session.embedding_data(
+            embedding="nonexistent",
+            max_points=1000
+        )
+
 
-        # Assert
-        assert result == '{"lookup_cells": [{"id": "123", "name": "Test Cell Label"}]}'
-        search_request_mock.assert_called_once()
- 
-def test_search_cells_no_params():
-    cap = Cap()
-    sample_dataset_response = '{"lookup_cells": [{"id": "123", "name": "Test Cell Label"}]}'
-    with patch.object(Cap, 'search_cell_labels_json') as search_request_mock:
-        search_request_mock.return_value = sample_dataset_response
-
-        # Act
-        result = cap.search_cell_labels_json()
-
-        # Assert
-        assert result == '{"lookup_cells": [{"id": "123", "name": "Test Cell Label"}]}'
-        search_request_mock.assert_called_once()
-
-def test_files_status_json():
-    cap = Cap()
-    sample_dataset_response = '{"data": { "dataset": { "id": "123", "getMdFilesStatus": "ready" } } }'
-    with patch.object(Cap, 'files_status_json') as files_status_request_mock:
-        files_status_request_mock.return_value = sample_dataset_response
-
-        # Act
-        result = cap.files_status_json("1")
-
-        # Assert
-        assert result == '{"data": { "dataset": { "id": "123", "getMdFilesStatus": "ready" } } }'
-        files_status_request_mock.assert_called_once()
-
-def test_files_status_json_no_params():
-    cap = Cap()
-    try:
-        # Act and Assert
-        cap.files_status_json()
-        assert False
-    except TypeError as e:
-       assert True
-
-def test_md_commons_query_json():
-    cap = Cap()
-    sample_dataset_response = '{"data": { "dataset": { "id": "123", "embeddings": [] } } }'
-    with patch.object(Cap, 'md_commons_query_json') as md_commons_query_request_mock:
-        md_commons_query_request_mock.return_value = sample_dataset_response
-
-        # Act
-        result = cap.md_commons_query_json("1")
-
-        # Assert
-        assert result == '{"data": { "dataset": { "id": "123", "embeddings": [] } } }'
-        md_commons_query_request_mock.assert_called_once()
-
-def test_md_commons_query_json_no_params():
-    cap = Cap()
-    try:
-        # Act and Assert
-        cap.md_commons_query_json()
-        assert False
-    except TypeError as e:
-       assert True
-    
-def test_dataset_initial_state_query_json():
-    cap = Cap()
-    sample_dataset_response = '{"data": { "dataset": { "id": "123", "name": "dataset"} } }'
-    with patch.object(Cap, 'dataset_initial_state_query_json') as dataset_initial_state_query_request_mock:
-        dataset_initial_state_query_request_mock.return_value = sample_dataset_response
-
-        # Act
-        result = cap.dataset_initial_state_query_json("1")
-
-        # Assert
-        assert result == '{"data": { "dataset": { "id": "123", "name": "dataset"} } }'
-        dataset_initial_state_query_request_mock.assert_called_once()
-
-def test_dataset_initial_state_query_json_no_params():
-    cap = Cap()
-    try:
-        # Act and Assert
-        cap.dataset_initial_state_query_json()
-        assert False
-    except TypeError as e:
-       assert True
-    
-def test_cluster_types_json():
-    cap = Cap()
-    sample_dataset_response = '{"data": { "dataset": { "id": "123", "embeddingClusterTypes": [] } } }'
-    with patch.object(Cap, 'cluster_types_json') as cluster_types_request_mock:
-        cluster_types_request_mock.return_value = sample_dataset_response
-
-        # Act
-        result = cap.cluster_types_json("1")
-
-        # Assert
-        assert result == '{"data": { "dataset": { "id": "123", "embeddingClusterTypes": [] } } }'
-        cluster_types_request_mock.assert_called_once()
-
-def test_cluster_types_json_no_params():
-    cap = Cap()
-    try:
-        # Act and Assert
-        cap.cluster_types_json()
-        assert False
-    except TypeError as e:
-       assert True
-
-def test_embeddings_clusters_json():
-    cap = Cap()
-    sample_dataset_response = '{"data": { "dataset": { "id": "123", "embeddingCluster": [] } } }'
-    with patch.object(Cap, 'embeddings_clusters_json') as embeddings_clusters_request_mock:
-        embeddings_clusters_request_mock.return_value = sample_dataset_response
-
-        # Act
-        result = cap.embeddings_clusters_json(dataset_id = "1", cluster = "cluster")
-
-        # Assert
-        assert result == '{"data": { "dataset": { "id": "123", "embeddingCluster": [] } } }'
-        embeddings_clusters_request_mock.assert_called_once()
-
-def test_embeddings_clusters_json_no_params():
-    cap = Cap()
-    try:
-        # Act and Assert
-        cap.embeddings_clusters_json()
-        assert False
-    except TypeError as e:
-       assert True
-
-def test_embedding_data_json():
-    cap = Cap()
-    sample_dataset_response = '{"data": { "dataset": { "id": "123", "embeddingData": {} } } } }'
-    with patch.object(Cap, 'embedding_data_json') as embedding_data_request_mock:
-        embedding_data_request_mock.return_value = sample_dataset_response
-
-        # Act
-        result = cap.embedding_data_json(dataset_id = "1", embedding = "embedding", scale_max_plan = 1000, session_id = "123")
-
-        # Assert
-        assert result == '{"data": { "dataset": { "id": "123", "embeddingData": {} } } } }'
-        embedding_data_request_mock.assert_called_once()
-
-def test_embedding_data_json_no_params():
-    cap = Cap()
-    try:
-        # Act and Assert
-        cap.embedding_data_json()
-        assert False
-    except TypeError as e:
-       assert True
-
-def test_general_de_json():
-    cap = Cap()
-    sample_dataset_response = '{"data": { "dataset": { "id": "123", "generalDiff": "" } } } }'
-    with patch.object(Cap, 'general_de_json') as general_de_request_mock:
-        general_de_request_mock.return_value = sample_dataset_response
-
-        # Act
-        result = cap.general_de_json(dataset_id = "1", labelset_id = "1", random_seed = 1, session_id = "123")
-
-        # Assert
-        assert result == '{"data": { "dataset": { "id": "123", "generalDiff": "" } } } }'
-        general_de_request_mock.assert_called_once()
-
-def test_general_de_json_no_params():
-    cap = Cap()
-    try:
-        # Act and Assert
-        cap.general_de_json()
-        assert False
-    except TypeError as e:
-       assert True
-
-def test_heatmap_json():
-    cap = Cap()
-    sample_dataset_response = '{"data": { "dataset": { "id": "123", "embeddingDiffHeatMap": {} } } } }'
-    with patch.object(Cap, 'heatmap_json') as heatmap_request_mock:
-        heatmap_request_mock.return_value = sample_dataset_response
-
-        # Act
-        result = cap.heatmap_json(dataset_id = "1", diff_key = "kgjgjgk")
-
-        # Assert
-        assert result == '{"data": { "dataset": { "id": "123", "embeddingDiffHeatMap": {} } } } }'
-        heatmap_request_mock.assert_called_once()
-
-def test_heatmap_json_no_params():
-    cap = Cap()
-    try:
-        # Act and Assert
-        cap.heatmap_json()
-        assert False
-    except TypeError as e:
-       assert True
-
-def test_highly_variable_genes_json():
-    cap = Cap()
-    sample_dataset_response = '{"data": { "dataset": { "id": "123", "embeddingHighlyVariableGenes": [] } } } }'
-    with patch.object(Cap, 'highly_variable_genes_json') as highly_variable_genes_request_mock:
-        highly_variable_genes_request_mock.return_value = sample_dataset_response
-
-        # Act
-        result = cap.highly_variable_genes_json(dataset_id = "1", offset = 1, limit = 10)
-
-        # Assert
-        assert result == '{"data": { "dataset": { "id": "123", "embeddingHighlyVariableGenes": [] } } } }'
-        highly_variable_genes_request_mock.assert_called_once()
-
-def test_highly_variable_genes_json_no_params():
-    cap = Cap()
-    try:
-        # Act and Assert
-        cap.highly_variable_genes_json()
-        assert False
-    except TypeError as e:
-       assert True
-
-def test_dataset_ready_json():
-    cap = Cap()
-    sample_dataset_response = '{"data": { "dataset": { "id": "123" } } }'
-    with patch.object(Cap, 'dataset_ready_json') as dataset_ready_request_mock:
-        dataset_ready_request_mock.return_value = sample_dataset_response
-
-        # Act
-        result = cap.dataset_ready_json("1")
-
-        # Assert
-        assert result == '{"data": { "dataset": { "id": "123" } } }'
-        dataset_ready_request_mock.assert_called_once()
-
-def test_dataset_ready_json_no_params():
-    cap = Cap()
-    try:
-        # Act and Assert
-        cap.dataset_ready_json()
-        assert False
-    except TypeError as e:
-       assert True
-
-def test_md_ready_json():
-    cap = Cap()
-    sample_dataset_response = '{"data": { "dataset": { "id": "123" } } }'
-    with patch.object(Cap, 'md_ready_json') as md_ready_request_mock:
-        md_ready_request_mock.return_value = sample_dataset_response
-
-        # Act
-        result = cap.md_ready_json("1")
-
-        # Assert
-        assert result == '{"data": { "dataset": { "id": "123" } } }'
-        md_ready_request_mock.assert_called_once()
-
-def test_md_ready_json_no_params():
-    cap = Cap()
-    try:
-        # Act and Assert
-        cap.md_ready_json()
-        assert False
-    except TypeError as e:
-       assert True
-
-def test_create_session_json():
-    cap = Cap()
-    sample_dataset_response = '{"data": { "saveEmbeddingSession": { "id": "123", "name": "test" } } }'
-    with patch.object(Cap, 'create_session_json') as create_session_request_mock:
-        create_session_request_mock.return_value = sample_dataset_response
-
-        # Act
-        result = cap.create_session_json(session_id = "1")
-
-        # Assert
-        assert result == '{"data": { "saveEmbeddingSession": { "id": "123", "name": "test" } } }'
-        create_session_request_mock.assert_called_once()
-
-def test_create_session_json_no_params():
-    cap = Cap()
-    try:
-        # Act and Assert
-        cap.create_session_json()
-        assert False
-    except TypeError as e:
-       assert True
\ No newline at end of file
+def test_general_de_success(dummy_md_session):
+    md_session, dummy_client = dummy_md_session
+    # Set available labelsets so that "Test Label" is valid.
+    md_session._labelsets = ["Test Label"]
+
+    # Patch the internal helper to return a dummy labelset id.
+    md_session._labelset_id_from_name = MagicMock(return_value="label1")
+
+    dummy_general_de = MagicMock()
+    dummy_general_de.dataset = MagicMock(general_diff="diff_key")
+    dummy_client.general_de.return_value = dummy_general_de
+
+    md_session._session_id = "session-id"
+    result = md_session.general_de(labelset="Test Label", random_seed=42)
+    assert result == "diff_key"
+    md_session._labelset_id_from_name.assert_called_once_with("Test Label")
+    dummy_client.general_de.assert_called_once_with(
+        dataset_id=md_session.dataset_id,
+        options=ANY
+    )
+
+
+def test_general_de_invalid_labelset(dummy_md_session):
+    md_session, dummy_client = dummy_md_session
+    md_session._labelsets = ["Test Label"]
+
+    with pytest.raises(ValueError, match="is not found"):
+        md_session.general_de(labelset="Nonexistent")
+
+
+def test_highly_variable_genes(dummy_md_session):
+    md_session, dummy_client = dummy_md_session
+
+    # Setup dummy response for highly_variable_genes
+    dummy_gene = MagicMock()
+    dummy_gene.name = "gene1"
+    dummy_gene.dispersion = 0.5
+    dummy_hvg = MagicMock()
+    dummy_hvg.dataset = MagicMock(embedding_highly_variable_genes=[dummy_gene])
+    dummy_client.highly_variable_genes.return_value = dummy_hvg
+
+    df = md_session.highly_variable_genes(
+        gene_name_filter="g",
+        pseudogenes_filter=True,
+        offset=0,
+        limit=10,
+        sort_order="desc"
+    )
+
+    # Check that the returned DataFrame contains the expected columns and values.
+    assert isinstance(df, pd.DataFrame)
+    assert "gene_symbol" in df.columns
+    assert "dispersion" in df.columns
+    assert df.iloc[0]["gene_symbol"] == "gene1"
+    assert df.iloc[0]["dispersion"] == 0.5
+
+    dummy_client.highly_variable_genes.assert_called_once_with(
+        dataset_id=md_session.dataset_id,
+        options=ANY
+    )
+
+
+def test_is_md_cache_ready(dummy_md_session):
+    md_session, dummy_client = dummy_md_session
+    dummy_files_status = MagicMock()
+    dummy_files_status.dataset = MagicMock(get_md_files_status="ready")
+    dummy_client.files_status.return_value = dummy_files_status
+
+    assert md_session.is_md_cache_ready() is True
+    dummy_client.files_status.assert_called_once_with(md_session.dataset_id)
+
+
+def test_heatmap(dummy_md_session):
+    md_session, dummy_client = dummy_md_session
+    # Assume that create_session was already called so session_id is set.
+    md_session._session_id = "session123"
+
+    dummy_heatmap = MagicMock()
+    dummy_heatmap.dataset = MagicMock(embedding_diff_heat_map="heatmap_response")
+    dummy_client.heatmap.return_value = dummy_heatmap
+
+    result = md_session.heatmap(diff_key="diff_key", n_top_genes=3, max_cells_displayed=1000)
+    assert result == "heatmap_response"
+    dummy_client.heatmap.assert_called_once_with(
+        dataset_id=md_session.dataset_id,
+        options=ANY
+    )
\ No newline at end of file


Reviews:
user: login: siberianisaev
state: APPROVED
author_association: CONTRIBUTOR
submitted_at: 2025-03-04T13:23:16Z
user: login: sokolov-maxim
state: CHANGES_REQUESTED
author_association: CONTRIBUTOR
submitted_at: 2025-03-04T13:24:41Z
user: login: rm1113
state: COMMENTED
author_association: CONTRIBUTOR
submitted_at: 2025-03-04T14:27:28Z
user: login: rm1113
state: COMMENTED
author_association: CONTRIBUTOR
submitted_at: 2025-03-04T14:28:09Z
user: login: rm1113
state: COMMENTED
author_association: CONTRIBUTOR
submitted_at: 2025-03-04T14:32:19Z
user: login: rm1113
state: COMMENTED
author_association: CONTRIBUTOR
submitted_at: 2025-03-04T14:32:35Z
user: login: rm1113
state: COMMENTED
author_association: CONTRIBUTOR
submitted_at: 2025-03-04T14:32:42Z
user: login: rm1113
state: COMMENTED
author_association: CONTRIBUTOR
submitted_at: 2025-03-04T14:32:56Z

Review comments:
diff_hunk: @@ -74,23 +400,23 @@ def _request (
                 self._token_expiry_time = jwt.decode(self._token, options={"verify_signature": False})['exp']
                 self._error_status = None
                 return True
-            except: 
+            except: # TODO: implement appropriate error handling
path: cap_client/cap.py
user: login: siberianisaev
body: please fill jira for it
created_at: 2025-03-04T13:16:01Z
author_association: CONTRIBUTOR
path: cap_client/cap.py
user: login: sokolov-maxim
body: I think it's better to have a dedicated file for MD session class 
created_at: 2025-03-04T13:18:40Z
author_association: CONTRIBUTOR
diff_hunk: @@ -1,4 +1,5 @@
 httpx>=0.27.2
 pydantic>=2.10.3
 pydantic_core>=2.27.1
-PyJWT>=2.10.1
\ No newline at end of file
+PyJWT>=2.10.1
path: requirements.txt
user: login: siberianisaev
body: probably it could be better to fix versions
created_at: 2025-03-04T13:21:42Z
author_association: CONTRIBUTOR
diff_hunk: @@ -17,37 +19,361 @@
     LookupCellsSearch,
     SearchLabelByMetadataArgs,
     CellLabelsSearchSort,
-    GetDatasetClustersDataInput,
     GetDatasetEmbeddingDataInput,
     GetGeneralDiffInput,
-    PostHeatmapInput,
     GetHighlyVariableGenesInput,
     PostSaveEmbeddingSessionInput,
-    DatasetObjectInput
+    PostHeatmapInput
 )
-from .client.search_datasets import SearchDatasets
-from .client.lookup_cells import LookupCells
-from .client.download_urls import DownloadUrls
-from .client.embedding_clusters import EmbeddingClusters
-from .client.embedding_data import EmbeddingData
-from .client.general_de import GeneralDE
-from .client.heatmap import Heatmap
-from .client.highly_variable_genes import HighlyVariableGenes
-from .client.create_session import CreateSession
-
+from client.search_datasets import SearchDatasets
+from client.lookup_cells import LookupCells
+from client.embedding_data import EmbeddingDataDatasetEmbeddingData
+from client.heatmap import HeatmapDatasetEmbeddingDiffHeatMap
 
 CAP_API_URL = "https://celltype.info/graphql"
 CAP_AUTHENTICATE_URL = "us-central1-capv2-gke-prod.cloudfunctions.net" # https://${var.gcp_region}-${var.gcp_project_id}.cloudfunctions.net/authenticate-token
-class Cap(Client):
-    def __init__(
+
+SESSION_ID = str
+DIFF_KEY = str
+SELECTION_KEY = str
+CELL_LABELS_MODE = "cell-labels"
+
+
+class MDSession:
+    def __init__(self, dataset_id: str, _client: _Client):
+        self.__client: _Client = _client
+        self._dataset_id: str = dataset_id
+        self._session_id: str = None
+        self._dataset_snapshot = None
+        self._embeddings: list[str] = None
+        self._labelsets: list[str] = None
+        self._clusterings: list[str] = None
+        self._metadata: list[str] = None
+    
+    def __repr__(self) -> str:
+        return f"Molecular Data page session for dataset id: {self.dataset_id}"
+    
+    def __str__(self) -> str:
+        return self.__repr__()
+    
+    @property
+    def dataset_id(self) -> str:
+        return self._dataset_id
+    
+    @property
+    def dataset_snapshot(self):
+        return self._dataset_snapshot
+    
+    @property
+    def embeddings(self) -> list[str]:
+        return self._embeddings
+    
+    @property
+    def clusterings(self) -> list[str]:
+        return self._clusterings
+    
+    @property
+    def labelsets(self) -> list[str]:
+        return self._labelsets
+    
+    @property
+    def session_id(self) -> str:
+        return self._session_id
+
+    def _check_md_ready(self):
+        ready = self.__client.dataset_ready(self.dataset_id)
+        if not ready.dataset.is_embeddings_up_to_date:
+            raise RuntimeError(f"The Molecular Data for the dataset {self.dataset_id} is not ready!")
+        
+    def _get_clusterings(self) -> list[str]:
+        res = self.__client.cluster_types(self.dataset_id)
+        res = res.dataset
+        clusters = res.embedding_cluster_types
+        cluster_names = [cl.name for cl in clusters]
+        return cluster_names
+    
+    def _get_embeddings(self) -> list[str]:
+        res = self.__client.md_commons_query(self.dataset_id)
+        res = res.dataset
+        embeddings = res.embeddings
+        emb_names = [e.name for e in embeddings]
+        return emb_names
+
+    def _get_cell_type_labelsets(self) -> list[str]:
+        if self.dataset_snapshot is None:
+            raise RuntimeError("The dataset snapshot is not ready, call MDSession.create_session first!")
+        
+        labelsets = []
+        for lbst in self.dataset_snapshot.labelsets:
+            if lbst.mode == CELL_LABELS_MODE:
+                labelsets.append(lbst.name)
+        return labelsets
+
+    def create_session(
+            self,
+        ) -> SESSION_ID:
+        """
+        Creates a new session for embedding processing.
+
+        This method performs a sanity check, retrieves the initial state of the dataset, 
+        fetches clusterings and embeddings, and then initializes a new session with a 
+        unique session ID. The session information is saved via the client.
+
+        Returns:
+            str: The unique session ID of the newly created embedding session.
+        """
+
+        self._check_md_ready()
+
+        ds = self.__client.dataset_initial_state_query(self.dataset_id)
+        self._dataset_snapshot = ds.dataset
+        self._clusterings = self._get_clusterings()
+        self._embeddings = self._get_embeddings()
+        self._labelsets = self._get_cell_type_labelsets()
+
+        session_id = str(uuid4())
+ 
+        data = PostSaveEmbeddingSessionInput(
+            session_id = session_id,
+            dataset = self._dataset_snapshot.model_dump()
+        )
+        response = self.__client.create_session(
+            data = data
+        )
+        self._dataset_snapshot = response.save_embedding_session
+        self._session_id = session_id
+        return self.session_id
+    
+    def embedding_data(
             self, 
+            embedding: str,
+            max_points: int,
+            labelsets: List[str] = None,
+            selection_gene: str = None,
+            selection_key_major: str = None,
+            selection_key_minor: str = None,
+        ) -> EmbeddingDataDatasetEmbeddingData:
+        """
+        Retrieves embedding data for the specified embedding type, with optional filtering and downsampling.
+
+        Parameters:
+        -----------
+        embedding : str
+            The name of the embedding to retrieve. Must be present in `self.embeddings`.
+        max_points : int
+            The maximum number of points to include in the response. Data may be downsampled to meet this limit.
+        labelsets : List[str], optional
+            A list of label sets to include in the embedding data. Defaults to None.
+        selection_gene : str, optional
+            If provided, returns a list of expression values for the specified gene. Defaults to None.
+        selection_key_major : str, optional
+            If provided, returns a list of boolean markers indicating whether each point is within the major selection. Defaults to None.
+        selection_key_minor : str, optional
+            If provided, returns a list of boolean markers indicating whether each point is within the minor selection. Defaults to None.
+
+        Returns:
+        --------
+        EmbeddingDataDatasetEmbeddingData
+            An object containing the embedding data, including observation IDs, selections, embeddings, annotations, 
+            and gene expression values.
+
+        Raises:
+        -------
+        ValueError
+            If the specified embedding is not found in `self.embeddings`.
+        """
+
+        if embedding not in self.embeddings:
+            raise ValueError(f"Embedding '{embedding}' is not found in the list of '{self.embeddings}'")
+        
+        options =  GetDatasetEmbeddingDataInput(
+            embedding = embedding,
+            scale_max_plan = max_points,
+            session_id = self.session_id,
+            labelsets = labelsets,
+            selection_gene = selection_gene,
+            selection_key_major = selection_key_major,
+            selection_key_minor = selection_key_minor,
+        )
+        
+        # TODO: is not workgin with new rc1
+        # update request later
+        response = self.__client.embedding_data(
+            dataset_id = self.dataset_id,
+            options = options
+        )
+        data = response.dataset.embedding_data
+        return data
+
+    def _labelset_id_from_name(self, labelset_name) -> str:
+        if self.dataset_snapshot is None:
+            raise RuntimeError("Dataset snapshot is not ready, create session first!")
+        
+        for lbst in self.dataset_snapshot.labelsets:
+            if lbst.name == labelset_name:
+                return lbst.id
+        
+        raise ValueError(f"Can't find labelset '{labelset_name}' in dataset snapshot!")
+
+    def general_de(
+            self, 
+            labelset: str,
+            random_seed: int = 42,
+        ) -> DIFF_KEY:
+        """
+        Performs a general differential expression (DE) analysis.
+
+        This method conducts a differential expression analysis, comparing each of the 
+        top 10 largest labels within the specified label set against all other data points.
+
+        Parameters:
+        -----------
+        labelset : str
+            The name of the label set to use for differential expression analysis. 
+            Must be present in `self.labelsets`.
+        random_seed : int, optional
+            The random seed for reproducibility. Defaults to 42.
+
+        Returns:
+        --------
+        DIFF_KEY
+            A string key associated with the results of the differential expression analysis.
+
+        Raises:
+        -------
+        ValueError
+            If the specified label set is not found in `self.labelsets`.
+        """
+        if labelset not in self.labelsets:
+            raise ValueError(f"Labelset '{labelset}' is not found in the list of '{self.labelsets}'")
+        
+        labelset_id = self._labelset_id_from_name(labelset)
+
+        options = GetGeneralDiffInput(
+            random_seed = random_seed,
+            session_id = self.session_id,
+            labelset_id = labelset_id
+        )
+        response = self.__client.general_de(
+            dataset_id = self.dataset_id,
+            options = options,
+        )
+        diff_key = response.dataset.general_diff
+        return diff_key
+    
+    def highly_variable_genes(
+            self,
+            gene_name_filter: str = None,
+            pseudogenes_filter: bool = True,
+            offset: int = 0,
+            limit: int = 50,
+            sort_order: Literal["desc", "asc"] = "desc"
+        ) -> pd.DataFrame:
+        """
+        Retrieves a list of highly variable genes from the specified dataset.
+
+        This method queries the dataset for highly variable genes based on dispersion values.
+        It supports filtering by gene name, excluding pseudogenes, and sorting the results.
+        The retrieved genes are returned as a Pandas DataFrame with columns for gene names
+        and their respective dispersion values.
+
+        Args:
+            dataset_id (str): The unique identifier of the dataset.
+            gene_name_filter (str, optional): A filter to include only genes matching a given prefix.
+            pseudogenes_filter (bool, optional): If True, filters out genes which are often
+                over-expressed but biologically non-informative. Defaults to True.
+                See https://github.com/cellannotation/cap-gene-filtering for details.
+            offset (int, optional): The starting index for pagination. Defaults to 0.
+            limit (int, optional): The maximum number of genes to return. Defaults to 50.
+            sort_order (Literal["desc", "asc"], optional): The sorting order for dispersion values.
+                Defaults to "desc" (descending).
+
+        Returns:
+            pd.DataFrame: A DataFrame containing highly variable genes with two columns:
+                - "gene_symbol" (str): The gene symbol.
+                - "dispersion" (float): The dispersion value of the gene. Initially, the gene
+                    dispersion values are calculated over the log-transformed count matrix,
+                    these dispersion values are then log-transformed again before being displayed
+                    in the gene table.
+        """
+ 
+        options = GetHighlyVariableGenesInput(
+            offset = offset,
+            limit = limit,
+            gene_name_filter = gene_name_filter,
+            use_genes_pattern = pseudogenes_filter,
+            sort_by = "dispersion",
+            sort_order = sort_order
+        )
+        res = self.__client.highly_variable_genes(
+            dataset_id = self.dataset_id,
+            options = options
+        )
+        hvg_list = res.dataset.embedding_highly_variable_genes
+        
+        df = pd.DataFrame({
+            "gene_symbol": [g.name for g in hvg_list],
+            "dispersion": [g.dispersion for g in hvg_list],
+        })
+        return df
+
+    def is_md_cache_ready(self) -> bool:
+        """
+        Checks whether the molecular data cache is ready.
+
+        This method queries the dataset's file status and determines if the 
+        molecular data page files are fully prepared and available.
+
+        Returns:
+        --------
+        bool
+            True if the metadata cache is ready, otherwise False.
+        """
+        res = self.__client.files_status(self.dataset_id)
+        status = res.dataset.get_md_files_status
+        return status == "ready"
+
+    def heatmap(
+            self,
+            diff_key: DIFF_KEY,
+            n_top_genes: int = 3,
+            max_cells_displayed: int = 1000,
+            gene_name_filter: str = None,
+            pseudogenes_filter: bool = True,
+            selection_key: SELECTION_KEY = None,
+            include_reference: bool = True
+        ) -> HeatmapDatasetEmbeddingDiffHeatMap:
+        
+        options=PostHeatmapInput(
+            diff_key = diff_key,
+            n_genes = n_top_genes,
+            scale_max_plan = max_cells_displayed,
+            genes_filter = gene_name_filter,
+            use_genes_pattern = pseudogenes_filter,
+            session_id = self.session_id,
+            include_reference_selection = include_reference,
+            selection_key = selection_key,
+        )
+
+        # TODO: update api, it was changed on rc1
path: cap_client/cap.py
user: login: siberianisaev
body: please fill jira
created_at: 2025-03-04T13:22:11Z
author_association: CONTRIBUTOR
diff_hunk: @@ -17,37 +19,361 @@
     LookupCellsSearch,
     SearchLabelByMetadataArgs,
     CellLabelsSearchSort,
-    GetDatasetClustersDataInput,
     GetDatasetEmbeddingDataInput,
     GetGeneralDiffInput,
-    PostHeatmapInput,
     GetHighlyVariableGenesInput,
     PostSaveEmbeddingSessionInput,
-    DatasetObjectInput
+    PostHeatmapInput
 )
-from .client.search_datasets import SearchDatasets
-from .client.lookup_cells import LookupCells
-from .client.download_urls import DownloadUrls
-from .client.embedding_clusters import EmbeddingClusters
-from .client.embedding_data import EmbeddingData
-from .client.general_de import GeneralDE
-from .client.heatmap import Heatmap
-from .client.highly_variable_genes import HighlyVariableGenes
-from .client.create_session import CreateSession
-
+from client.search_datasets import SearchDatasets
+from client.lookup_cells import LookupCells
+from client.embedding_data import EmbeddingDataDatasetEmbeddingData
+from client.heatmap import HeatmapDatasetEmbeddingDiffHeatMap
 
 CAP_API_URL = "https://celltype.info/graphql"
 CAP_AUTHENTICATE_URL = "us-central1-capv2-gke-prod.cloudfunctions.net" # https://${var.gcp_region}-${var.gcp_project_id}.cloudfunctions.net/authenticate-token
-class Cap(Client):
-    def __init__(
+
+SESSION_ID = str
+DIFF_KEY = str
+SELECTION_KEY = str
+CELL_LABELS_MODE = "cell-labels"
+
+
+class MDSession:
+    def __init__(self, dataset_id: str, _client: _Client):
+        self.__client: _Client = _client
+        self._dataset_id: str = dataset_id
+        self._session_id: str = None
+        self._dataset_snapshot = None
+        self._embeddings: list[str] = None
+        self._labelsets: list[str] = None
+        self._clusterings: list[str] = None
+        self._metadata: list[str] = None
+    
+    def __repr__(self) -> str:
+        return f"Molecular Data page session for dataset id: {self.dataset_id}"
+    
+    def __str__(self) -> str:
+        return self.__repr__()
+    
+    @property
+    def dataset_id(self) -> str:
+        return self._dataset_id
+    
+    @property
+    def dataset_snapshot(self):
+        return self._dataset_snapshot
+    
+    @property
+    def embeddings(self) -> list[str]:
+        return self._embeddings
+    
+    @property
+    def clusterings(self) -> list[str]:
+        return self._clusterings
+    
+    @property
+    def labelsets(self) -> list[str]:
+        return self._labelsets
+    
+    @property
+    def session_id(self) -> str:
+        return self._session_id
+
+    def _check_md_ready(self):
+        ready = self.__client.dataset_ready(self.dataset_id)
+        if not ready.dataset.is_embeddings_up_to_date:
+            raise RuntimeError(f"The Molecular Data for the dataset {self.dataset_id} is not ready!")
+        
+    def _get_clusterings(self) -> list[str]:
+        res = self.__client.cluster_types(self.dataset_id)
+        res = res.dataset
+        clusters = res.embedding_cluster_types
+        cluster_names = [cl.name for cl in clusters]
+        return cluster_names
+    
+    def _get_embeddings(self) -> list[str]:
+        res = self.__client.md_commons_query(self.dataset_id)
+        res = res.dataset
+        embeddings = res.embeddings
+        emb_names = [e.name for e in embeddings]
+        return emb_names
+
+    def _get_cell_type_labelsets(self) -> list[str]:
+        if self.dataset_snapshot is None:
+            raise RuntimeError("The dataset snapshot is not ready, call MDSession.create_session first!")
+        
+        labelsets = []
+        for lbst in self.dataset_snapshot.labelsets:
+            if lbst.mode == CELL_LABELS_MODE:
+                labelsets.append(lbst.name)
+        return labelsets
+
+    def create_session(
+            self,
+        ) -> SESSION_ID:
+        """
+        Creates a new session for embedding processing.
+
+        This method performs a sanity check, retrieves the initial state of the dataset, 
+        fetches clusterings and embeddings, and then initializes a new session with a 
+        unique session ID. The session information is saved via the client.
+
+        Returns:
+            str: The unique session ID of the newly created embedding session.
+        """
+
+        self._check_md_ready()
+
+        ds = self.__client.dataset_initial_state_query(self.dataset_id)
+        self._dataset_snapshot = ds.dataset
+        self._clusterings = self._get_clusterings()
+        self._embeddings = self._get_embeddings()
+        self._labelsets = self._get_cell_type_labelsets()
+
+        session_id = str(uuid4())
+ 
+        data = PostSaveEmbeddingSessionInput(
+            session_id = session_id,
+            dataset = self._dataset_snapshot.model_dump()
+        )
+        response = self.__client.create_session(
+            data = data
+        )
+        self._dataset_snapshot = response.save_embedding_session
+        self._session_id = session_id
+        return self.session_id
+    
+    def embedding_data(
             self, 
+            embedding: str,
+            max_points: int,
+            labelsets: List[str] = None,
+            selection_gene: str = None,
+            selection_key_major: str = None,
+            selection_key_minor: str = None,
+        ) -> EmbeddingDataDatasetEmbeddingData:
+        """
+        Retrieves embedding data for the specified embedding type, with optional filtering and downsampling.
+
+        Parameters:
+        -----------
+        embedding : str
+            The name of the embedding to retrieve. Must be present in `self.embeddings`.
+        max_points : int
+            The maximum number of points to include in the response. Data may be downsampled to meet this limit.
+        labelsets : List[str], optional
+            A list of label sets to include in the embedding data. Defaults to None.
+        selection_gene : str, optional
+            If provided, returns a list of expression values for the specified gene. Defaults to None.
+        selection_key_major : str, optional
+            If provided, returns a list of boolean markers indicating whether each point is within the major selection. Defaults to None.
+        selection_key_minor : str, optional
+            If provided, returns a list of boolean markers indicating whether each point is within the minor selection. Defaults to None.
+
+        Returns:
+        --------
+        EmbeddingDataDatasetEmbeddingData
+            An object containing the embedding data, including observation IDs, selections, embeddings, annotations, 
+            and gene expression values.
+
+        Raises:
+        -------
+        ValueError
+            If the specified embedding is not found in `self.embeddings`.
+        """
+
+        if embedding not in self.embeddings:
+            raise ValueError(f"Embedding '{embedding}' is not found in the list of '{self.embeddings}'")
+        
+        options =  GetDatasetEmbeddingDataInput(
+            embedding = embedding,
+            scale_max_plan = max_points,
+            session_id = self.session_id,
+            labelsets = labelsets,
+            selection_gene = selection_gene,
+            selection_key_major = selection_key_major,
+            selection_key_minor = selection_key_minor,
+        )
+        
+        # TODO: is not workgin with new rc1
+        # update request later
path: cap_client/cap.py
user: login: siberianisaev
body: need jira number
created_at: 2025-03-04T13:22:45Z
author_association: CONTRIBUTOR
path: pyproject.toml
user: login: sokolov-maxim
body: Ariadne configuration is invalid `assert_path_is_valid_file(self.base_client_file_path)`
created_at: 2025-03-04T13:24:13Z
author_association: CONTRIBUTOR
path: pyproject.toml
user: login: rm1113
body: fixed
created_at: 2025-03-04T14:27:28Z
author_association: CONTRIBUTOR
path: cap_client/cap.py
user: login: rm1113
body: don't need to overcomplicate the package, it already too many modules in here
created_at: 2025-03-04T14:28:09Z
author_association: CONTRIBUTOR
diff_hunk: @@ -17,37 +19,361 @@
     LookupCellsSearch,
     SearchLabelByMetadataArgs,
     CellLabelsSearchSort,
-    GetDatasetClustersDataInput,
     GetDatasetEmbeddingDataInput,
     GetGeneralDiffInput,
-    PostHeatmapInput,
     GetHighlyVariableGenesInput,
     PostSaveEmbeddingSessionInput,
-    DatasetObjectInput
+    PostHeatmapInput
 )
-from .client.search_datasets import SearchDatasets
-from .client.lookup_cells import LookupCells
-from .client.download_urls import DownloadUrls
-from .client.embedding_clusters import EmbeddingClusters
-from .client.embedding_data import EmbeddingData
-from .client.general_de import GeneralDE
-from .client.heatmap import Heatmap
-from .client.highly_variable_genes import HighlyVariableGenes
-from .client.create_session import CreateSession
-
+from client.search_datasets import SearchDatasets
+from client.lookup_cells import LookupCells
+from client.embedding_data import EmbeddingDataDatasetEmbeddingData
+from client.heatmap import HeatmapDatasetEmbeddingDiffHeatMap
 
 CAP_API_URL = "https://celltype.info/graphql"
 CAP_AUTHENTICATE_URL = "us-central1-capv2-gke-prod.cloudfunctions.net" # https://${var.gcp_region}-${var.gcp_project_id}.cloudfunctions.net/authenticate-token
-class Cap(Client):
-    def __init__(
+
+SESSION_ID = str
+DIFF_KEY = str
+SELECTION_KEY = str
+CELL_LABELS_MODE = "cell-labels"
+
+
+class MDSession:
+    def __init__(self, dataset_id: str, _client: _Client):
+        self.__client: _Client = _client
+        self._dataset_id: str = dataset_id
+        self._session_id: str = None
+        self._dataset_snapshot = None
+        self._embeddings: list[str] = None
+        self._labelsets: list[str] = None
+        self._clusterings: list[str] = None
+        self._metadata: list[str] = None
+    
+    def __repr__(self) -> str:
+        return f"Molecular Data page session for dataset id: {self.dataset_id}"
+    
+    def __str__(self) -> str:
+        return self.__repr__()
+    
+    @property
+    def dataset_id(self) -> str:
+        return self._dataset_id
+    
+    @property
+    def dataset_snapshot(self):
+        return self._dataset_snapshot
+    
+    @property
+    def embeddings(self) -> list[str]:
+        return self._embeddings
+    
+    @property
+    def clusterings(self) -> list[str]:
+        return self._clusterings
+    
+    @property
+    def labelsets(self) -> list[str]:
+        return self._labelsets
+    
+    @property
+    def session_id(self) -> str:
+        return self._session_id
+
+    def _check_md_ready(self):
+        ready = self.__client.dataset_ready(self.dataset_id)
+        if not ready.dataset.is_embeddings_up_to_date:
+            raise RuntimeError(f"The Molecular Data for the dataset {self.dataset_id} is not ready!")
+        
+    def _get_clusterings(self) -> list[str]:
+        res = self.__client.cluster_types(self.dataset_id)
+        res = res.dataset
+        clusters = res.embedding_cluster_types
+        cluster_names = [cl.name for cl in clusters]
+        return cluster_names
+    
+    def _get_embeddings(self) -> list[str]:
+        res = self.__client.md_commons_query(self.dataset_id)
+        res = res.dataset
+        embeddings = res.embeddings
+        emb_names = [e.name for e in embeddings]
+        return emb_names
+
+    def _get_cell_type_labelsets(self) -> list[str]:
+        if self.dataset_snapshot is None:
+            raise RuntimeError("The dataset snapshot is not ready, call MDSession.create_session first!")
+        
+        labelsets = []
+        for lbst in self.dataset_snapshot.labelsets:
+            if lbst.mode == CELL_LABELS_MODE:
+                labelsets.append(lbst.name)
+        return labelsets
+
+    def create_session(
+            self,
+        ) -> SESSION_ID:
+        """
+        Creates a new session for embedding processing.
+
+        This method performs a sanity check, retrieves the initial state of the dataset, 
+        fetches clusterings and embeddings, and then initializes a new session with a 
+        unique session ID. The session information is saved via the client.
+
+        Returns:
+            str: The unique session ID of the newly created embedding session.
+        """
+
+        self._check_md_ready()
+
+        ds = self.__client.dataset_initial_state_query(self.dataset_id)
+        self._dataset_snapshot = ds.dataset
+        self._clusterings = self._get_clusterings()
+        self._embeddings = self._get_embeddings()
+        self._labelsets = self._get_cell_type_labelsets()
+
+        session_id = str(uuid4())
+ 
+        data = PostSaveEmbeddingSessionInput(
+            session_id = session_id,
+            dataset = self._dataset_snapshot.model_dump()
+        )
+        response = self.__client.create_session(
+            data = data
+        )
+        self._dataset_snapshot = response.save_embedding_session
+        self._session_id = session_id
+        return self.session_id
+    
+    def embedding_data(
             self, 
+            embedding: str,
+            max_points: int,
+            labelsets: List[str] = None,
+            selection_gene: str = None,
+            selection_key_major: str = None,
+            selection_key_minor: str = None,
+        ) -> EmbeddingDataDatasetEmbeddingData:
+        """
+        Retrieves embedding data for the specified embedding type, with optional filtering and downsampling.
+
+        Parameters:
+        -----------
+        embedding : str
+            The name of the embedding to retrieve. Must be present in `self.embeddings`.
+        max_points : int
+            The maximum number of points to include in the response. Data may be downsampled to meet this limit.
+        labelsets : List[str], optional
+            A list of label sets to include in the embedding data. Defaults to None.
+        selection_gene : str, optional
+            If provided, returns a list of expression values for the specified gene. Defaults to None.
+        selection_key_major : str, optional
+            If provided, returns a list of boolean markers indicating whether each point is within the major selection. Defaults to None.
+        selection_key_minor : str, optional
+            If provided, returns a list of boolean markers indicating whether each point is within the minor selection. Defaults to None.
+
+        Returns:
+        --------
+        EmbeddingDataDatasetEmbeddingData
+            An object containing the embedding data, including observation IDs, selections, embeddings, annotations, 
+            and gene expression values.
+
+        Raises:
+        -------
+        ValueError
+            If the specified embedding is not found in `self.embeddings`.
+        """
+
+        if embedding not in self.embeddings:
+            raise ValueError(f"Embedding '{embedding}' is not found in the list of '{self.embeddings}'")
+        
+        options =  GetDatasetEmbeddingDataInput(
+            embedding = embedding,
+            scale_max_plan = max_points,
+            session_id = self.session_id,
+            labelsets = labelsets,
+            selection_gene = selection_gene,
+            selection_key_major = selection_key_major,
+            selection_key_minor = selection_key_minor,
+        )
+        
+        # TODO: is not workgin with new rc1
+        # update request later
path: cap_client/cap.py
user: login: rm1113
body: done
created_at: 2025-03-04T14:32:19Z
author_association: CONTRIBUTOR
diff_hunk: @@ -17,37 +19,361 @@
     LookupCellsSearch,
     SearchLabelByMetadataArgs,
     CellLabelsSearchSort,
-    GetDatasetClustersDataInput,
     GetDatasetEmbeddingDataInput,
     GetGeneralDiffInput,
-    PostHeatmapInput,
     GetHighlyVariableGenesInput,
     PostSaveEmbeddingSessionInput,
-    DatasetObjectInput
+    PostHeatmapInput
 )
-from .client.search_datasets import SearchDatasets
-from .client.lookup_cells import LookupCells
-from .client.download_urls import DownloadUrls
-from .client.embedding_clusters import EmbeddingClusters
-from .client.embedding_data import EmbeddingData
-from .client.general_de import GeneralDE
-from .client.heatmap import Heatmap
-from .client.highly_variable_genes import HighlyVariableGenes
-from .client.create_session import CreateSession
-
+from client.search_datasets import SearchDatasets
+from client.lookup_cells import LookupCells
+from client.embedding_data import EmbeddingDataDatasetEmbeddingData
+from client.heatmap import HeatmapDatasetEmbeddingDiffHeatMap
 
 CAP_API_URL = "https://celltype.info/graphql"
 CAP_AUTHENTICATE_URL = "us-central1-capv2-gke-prod.cloudfunctions.net" # https://${var.gcp_region}-${var.gcp_project_id}.cloudfunctions.net/authenticate-token
-class Cap(Client):
-    def __init__(
+
+SESSION_ID = str
+DIFF_KEY = str
+SELECTION_KEY = str
+CELL_LABELS_MODE = "cell-labels"
+
+
+class MDSession:
+    def __init__(self, dataset_id: str, _client: _Client):
+        self.__client: _Client = _client
+        self._dataset_id: str = dataset_id
+        self._session_id: str = None
+        self._dataset_snapshot = None
+        self._embeddings: list[str] = None
+        self._labelsets: list[str] = None
+        self._clusterings: list[str] = None
+        self._metadata: list[str] = None
+    
+    def __repr__(self) -> str:
+        return f"Molecular Data page session for dataset id: {self.dataset_id}"
+    
+    def __str__(self) -> str:
+        return self.__repr__()
+    
+    @property
+    def dataset_id(self) -> str:
+        return self._dataset_id
+    
+    @property
+    def dataset_snapshot(self):
+        return self._dataset_snapshot
+    
+    @property
+    def embeddings(self) -> list[str]:
+        return self._embeddings
+    
+    @property
+    def clusterings(self) -> list[str]:
+        return self._clusterings
+    
+    @property
+    def labelsets(self) -> list[str]:
+        return self._labelsets
+    
+    @property
+    def session_id(self) -> str:
+        return self._session_id
+
+    def _check_md_ready(self):
+        ready = self.__client.dataset_ready(self.dataset_id)
+        if not ready.dataset.is_embeddings_up_to_date:
+            raise RuntimeError(f"The Molecular Data for the dataset {self.dataset_id} is not ready!")
+        
+    def _get_clusterings(self) -> list[str]:
+        res = self.__client.cluster_types(self.dataset_id)
+        res = res.dataset
+        clusters = res.embedding_cluster_types
+        cluster_names = [cl.name for cl in clusters]
+        return cluster_names
+    
+    def _get_embeddings(self) -> list[str]:
+        res = self.__client.md_commons_query(self.dataset_id)
+        res = res.dataset
+        embeddings = res.embeddings
+        emb_names = [e.name for e in embeddings]
+        return emb_names
+
+    def _get_cell_type_labelsets(self) -> list[str]:
+        if self.dataset_snapshot is None:
+            raise RuntimeError("The dataset snapshot is not ready, call MDSession.create_session first!")
+        
+        labelsets = []
+        for lbst in self.dataset_snapshot.labelsets:
+            if lbst.mode == CELL_LABELS_MODE:
+                labelsets.append(lbst.name)
+        return labelsets
+
+    def create_session(
+            self,
+        ) -> SESSION_ID:
+        """
+        Creates a new session for embedding processing.
+
+        This method performs a sanity check, retrieves the initial state of the dataset, 
+        fetches clusterings and embeddings, and then initializes a new session with a 
+        unique session ID. The session information is saved via the client.
+
+        Returns:
+            str: The unique session ID of the newly created embedding session.
+        """
+
+        self._check_md_ready()
+
+        ds = self.__client.dataset_initial_state_query(self.dataset_id)
+        self._dataset_snapshot = ds.dataset
+        self._clusterings = self._get_clusterings()
+        self._embeddings = self._get_embeddings()
+        self._labelsets = self._get_cell_type_labelsets()
+
+        session_id = str(uuid4())
+ 
+        data = PostSaveEmbeddingSessionInput(
+            session_id = session_id,
+            dataset = self._dataset_snapshot.model_dump()
+        )
+        response = self.__client.create_session(
+            data = data
+        )
+        self._dataset_snapshot = response.save_embedding_session
+        self._session_id = session_id
+        return self.session_id
+    
+    def embedding_data(
             self, 
+            embedding: str,
+            max_points: int,
+            labelsets: List[str] = None,
+            selection_gene: str = None,
+            selection_key_major: str = None,
+            selection_key_minor: str = None,
+        ) -> EmbeddingDataDatasetEmbeddingData:
+        """
+        Retrieves embedding data for the specified embedding type, with optional filtering and downsampling.
+
+        Parameters:
+        -----------
+        embedding : str
+            The name of the embedding to retrieve. Must be present in `self.embeddings`.
+        max_points : int
+            The maximum number of points to include in the response. Data may be downsampled to meet this limit.
+        labelsets : List[str], optional
+            A list of label sets to include in the embedding data. Defaults to None.
+        selection_gene : str, optional
+            If provided, returns a list of expression values for the specified gene. Defaults to None.
+        selection_key_major : str, optional
+            If provided, returns a list of boolean markers indicating whether each point is within the major selection. Defaults to None.
+        selection_key_minor : str, optional
+            If provided, returns a list of boolean markers indicating whether each point is within the minor selection. Defaults to None.
+
+        Returns:
+        --------
+        EmbeddingDataDatasetEmbeddingData
+            An object containing the embedding data, including observation IDs, selections, embeddings, annotations, 
+            and gene expression values.
+
+        Raises:
+        -------
+        ValueError
+            If the specified embedding is not found in `self.embeddings`.
+        """
+
+        if embedding not in self.embeddings:
+            raise ValueError(f"Embedding '{embedding}' is not found in the list of '{self.embeddings}'")
+        
+        options =  GetDatasetEmbeddingDataInput(
+            embedding = embedding,
+            scale_max_plan = max_points,
+            session_id = self.session_id,
+            labelsets = labelsets,
+            selection_gene = selection_gene,
+            selection_key_major = selection_key_major,
+            selection_key_minor = selection_key_minor,
+        )
+        
+        # TODO: is not workgin with new rc1
+        # update request later
+        response = self.__client.embedding_data(
+            dataset_id = self.dataset_id,
+            options = options
+        )
+        data = response.dataset.embedding_data
+        return data
+
+    def _labelset_id_from_name(self, labelset_name) -> str:
+        if self.dataset_snapshot is None:
+            raise RuntimeError("Dataset snapshot is not ready, create session first!")
+        
+        for lbst in self.dataset_snapshot.labelsets:
+            if lbst.name == labelset_name:
+                return lbst.id
+        
+        raise ValueError(f"Can't find labelset '{labelset_name}' in dataset snapshot!")
+
+    def general_de(
+            self, 
+            labelset: str,
+            random_seed: int = 42,
+        ) -> DIFF_KEY:
+        """
+        Performs a general differential expression (DE) analysis.
+
+        This method conducts a differential expression analysis, comparing each of the 
+        top 10 largest labels within the specified label set against all other data points.
+
+        Parameters:
+        -----------
+        labelset : str
+            The name of the label set to use for differential expression analysis. 
+            Must be present in `self.labelsets`.
+        random_seed : int, optional
+            The random seed for reproducibility. Defaults to 42.
+
+        Returns:
+        --------
+        DIFF_KEY
+            A string key associated with the results of the differential expression analysis.
+
+        Raises:
+        -------
+        ValueError
+            If the specified label set is not found in `self.labelsets`.
+        """
+        if labelset not in self.labelsets:
+            raise ValueError(f"Labelset '{labelset}' is not found in the list of '{self.labelsets}'")
+        
+        labelset_id = self._labelset_id_from_name(labelset)
+
+        options = GetGeneralDiffInput(
+            random_seed = random_seed,
+            session_id = self.session_id,
+            labelset_id = labelset_id
+        )
+        response = self.__client.general_de(
+            dataset_id = self.dataset_id,
+            options = options,
+        )
+        diff_key = response.dataset.general_diff
+        return diff_key
+    
+    def highly_variable_genes(
+            self,
+            gene_name_filter: str = None,
+            pseudogenes_filter: bool = True,
+            offset: int = 0,
+            limit: int = 50,
+            sort_order: Literal["desc", "asc"] = "desc"
+        ) -> pd.DataFrame:
+        """
+        Retrieves a list of highly variable genes from the specified dataset.
+
+        This method queries the dataset for highly variable genes based on dispersion values.
+        It supports filtering by gene name, excluding pseudogenes, and sorting the results.
+        The retrieved genes are returned as a Pandas DataFrame with columns for gene names
+        and their respective dispersion values.
+
+        Args:
+            dataset_id (str): The unique identifier of the dataset.
+            gene_name_filter (str, optional): A filter to include only genes matching a given prefix.
+            pseudogenes_filter (bool, optional): If True, filters out genes which are often
+                over-expressed but biologically non-informative. Defaults to True.
+                See https://github.com/cellannotation/cap-gene-filtering for details.
+            offset (int, optional): The starting index for pagination. Defaults to 0.
+            limit (int, optional): The maximum number of genes to return. Defaults to 50.
+            sort_order (Literal["desc", "asc"], optional): The sorting order for dispersion values.
+                Defaults to "desc" (descending).
+
+        Returns:
+            pd.DataFrame: A DataFrame containing highly variable genes with two columns:
+                - "gene_symbol" (str): The gene symbol.
+                - "dispersion" (float): The dispersion value of the gene. Initially, the gene
+                    dispersion values are calculated over the log-transformed count matrix,
+                    these dispersion values are then log-transformed again before being displayed
+                    in the gene table.
+        """
+ 
+        options = GetHighlyVariableGenesInput(
+            offset = offset,
+            limit = limit,
+            gene_name_filter = gene_name_filter,
+            use_genes_pattern = pseudogenes_filter,
+            sort_by = "dispersion",
+            sort_order = sort_order
+        )
+        res = self.__client.highly_variable_genes(
+            dataset_id = self.dataset_id,
+            options = options
+        )
+        hvg_list = res.dataset.embedding_highly_variable_genes
+        
+        df = pd.DataFrame({
+            "gene_symbol": [g.name for g in hvg_list],
+            "dispersion": [g.dispersion for g in hvg_list],
+        })
+        return df
+
+    def is_md_cache_ready(self) -> bool:
+        """
+        Checks whether the molecular data cache is ready.
+
+        This method queries the dataset's file status and determines if the 
+        molecular data page files are fully prepared and available.
+
+        Returns:
+        --------
+        bool
+            True if the metadata cache is ready, otherwise False.
+        """
+        res = self.__client.files_status(self.dataset_id)
+        status = res.dataset.get_md_files_status
+        return status == "ready"
+
+    def heatmap(
+            self,
+            diff_key: DIFF_KEY,
+            n_top_genes: int = 3,
+            max_cells_displayed: int = 1000,
+            gene_name_filter: str = None,
+            pseudogenes_filter: bool = True,
+            selection_key: SELECTION_KEY = None,
+            include_reference: bool = True
+        ) -> HeatmapDatasetEmbeddingDiffHeatMap:
+        
+        options=PostHeatmapInput(
+            diff_key = diff_key,
+            n_genes = n_top_genes,
+            scale_max_plan = max_cells_displayed,
+            genes_filter = gene_name_filter,
+            use_genes_pattern = pseudogenes_filter,
+            session_id = self.session_id,
+            include_reference_selection = include_reference,
+            selection_key = selection_key,
+        )
+
+        # TODO: update api, it was changed on rc1
path: cap_client/cap.py
user: login: rm1113
body: done
created_at: 2025-03-04T14:32:35Z
author_association: CONTRIBUTOR
diff_hunk: @@ -74,23 +400,23 @@ def _request (
                 self._token_expiry_time = jwt.decode(self._token, options={"verify_signature": False})['exp']
                 self._error_status = None
                 return True
-            except: 
+            except: # TODO: implement appropriate error handling
path: cap_client/cap.py
user: login: rm1113
body: done
created_at: 2025-03-04T14:32:42Z
author_association: CONTRIBUTOR
diff_hunk: @@ -1,4 +1,5 @@
 httpx>=0.27.2
 pydantic>=2.10.3
 pydantic_core>=2.27.1
-PyJWT>=2.10.1
\ No newline at end of file
+PyJWT>=2.10.1
path: requirements.txt
user: login: rm1113
body: not really needed as it is a public package
created_at: 2025-03-04T14:32:56Z
author_association: CONTRIBUTOR

PR comments:
user: login: sokolov-maxim
created_at: 2025-03-03T08:51:46Z
body: Please avoid changing `/cap_client/client/*` - it's code is generated based on `queries.graphql` and toml files
Once we would like to add support for new endpoint requests your changes will be lost
Use `client_name="_Client"` setting instead 
author_association: CONTRIBUTOR
user: login: sokolov-maxim
created_at: 2025-03-05T07:23:48Z
body: @rm1113 there are still following issues
1. cap.py in your edition should be split to cap_client.py and md_session.py to follow conventions used in the project (see /cap_client/client/*)
2. Ariadne configuration is still invalid. Run `ariadne-codegen` to get errors 

NOTE that you can control imports on top package (`cap_client`) level, no need to mess up with _BaseClient, _Client
author_association: CONTRIBUTOR