ark_example.py

# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""
This dataset contains example data for running through the multiplexed imaging data pipeline in
Ark Analysis: https://github.com/angelolab/ark-analysis.


Dataset Fov renaming:

TMA2_R8C3 -> fov0
TMA6_R4C5 -> fov1
TMA7_R5C4 -> fov2
TMA10_R7C3 -> fov3
TMA11_R9C6 -> fov4
TMA13_R8C5 -> fov5
TMA17_R9C2 -> fov6
TMA18_R9C2 -> fov7
TMA21_R2C5 -> fov8
TMA21_R12C6 -> fov9
TMA24_R9C1 -> fov10

"""

import datasets
import pathlib

# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@InProceedings{huggingface:dataset,
title = {Ark Analysis Example Dataset},
author={Angelo Lab},
year={2022}
}
"""

# TODO: Add description of the dataset here
# You can copy an official description
_DESCRIPTION = """\
This dataset contains 11 Field of Views (FOVs), each with 22 channels. 
"""

_HOMEPAGE = "https://github.com/angelolab/ark-analysis"

_LICENSE = "https://github.com/angelolab/ark-analysis/blob/main/LICENSE"

# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)

_URL_DATA = {
    "image_data": "./data/image_data.zip",
    "cell_table": "./data/segmentation/cell_table.zip",
    "deepcell_output": "./data/segmentation/deepcell_output.zip",
    "example_pixel_output_dir": "./data/pixie/example_pixel_output_dir.zip",
    "example_cell_output_dir": "./data/pixie/example_cell_output_dir.zip",
    "spatial_lda": "./data/spatial_analysis/spatial_lda.zip",
    "post_clustering": "./data/post_clustering.zip",
    "ome_tiff": "./data/ome_tiff.zip",
    "ez_seg_data": "./data/ez_seg_data.zip"
}

_URL_DATASET_CONFIGS = {
    "segment_image_data": {"image_data": _URL_DATA["image_data"]},
    "cluster_pixels": {
        "image_data": _URL_DATA["image_data"],
        "cell_table": _URL_DATA["cell_table"],
        "deepcell_output": _URL_DATA["deepcell_output"],
    },
    "cluster_cells": {
        "image_data": _URL_DATA["image_data"],
        "cell_table": _URL_DATA["cell_table"],
        "deepcell_output": _URL_DATA["deepcell_output"],
        "example_pixel_output_dir": _URL_DATA["example_pixel_output_dir"],
    },
    "post_clustering": {
        "image_data": _URL_DATA["image_data"],
        "cell_table": _URL_DATA["cell_table"],
        "deepcell_output": _URL_DATA["deepcell_output"],
        "example_cell_output_dir": _URL_DATA["example_cell_output_dir"],
    },
    "fiber_segmentation": {
        "image_data": _URL_DATA["image_data"],
    },
    "LDA_preprocessing": {
        "image_data": _URL_DATA["image_data"],
        "cell_table": _URL_DATA["cell_table"],
    },
    "LDA_training_inference": {
        "image_data": _URL_DATA["image_data"],
        "cell_table": _URL_DATA["cell_table"],
        "spatial_lda": _URL_DATA["spatial_lda"],
    },
    "neighborhood_analysis": {
        "image_data": _URL_DATA["image_data"],
        "cell_table": _URL_DATA["cell_table"],
        "deepcell_output": _URL_DATA["deepcell_output"],
    },
    "pairwise_spatial_enrichment": {
        "image_data": _URL_DATA["image_data"],
        "cell_table": _URL_DATA["cell_table"],
        "deepcell_output": _URL_DATA["deepcell_output"],
        "post_clustering": _URL_DATA["post_clustering"],
    },
    "ome_tiff": {
        "ome_tiff": _URL_DATA["ome_tiff"],
    },
    "ez_seg_data": {
        "ez_seg_data": _URL_DATA["ez_seg_data"]
    }
}


# Note: Name of the dataset usually match the script name with CamelCase instead of snake_case
class ArkExample(datasets.GeneratorBasedBuilder):
    """The Dataset consists of 11 FOVs"""

    VERSION = datasets.Version("0.0.5")

    # You will be able to load one or the other configurations in the following list with
    BUILDER_CONFIGS = [
        datasets.BuilderConfig(
            name="segment_image_data",
            version=VERSION,
            description="This configuration contains data used by notebook 1 - Segment Image Data.",
        ),
        datasets.BuilderConfig(
            name="cluster_pixels",
            version=VERSION,
            description="This configuration contains data used by notebook 2 - Pixel Clustering (Pixie Pipeline #1).",
        ),
        datasets.BuilderConfig(
            name="cluster_cells",
            version=VERSION,
            description="This configuration contains data used by notebook 3 - Cell Clustering (Pixie Pipeline #2).",
        ),
        datasets.BuilderConfig(
            name="post_clustering",
            version=VERSION,
            description="This configuration contains data used by notebook 4 - Post Clustering.",
        ),
        datasets.BuilderConfig(
            name="fiber_segmentation",
            version=VERSION,
            description="This configuration contains data used by the Fiber Segmentation Notebook.",
        ),
        datasets.BuilderConfig(
            name="LDA_preprocessing",
            version=VERSION,
            description="This configuration contains data used by the Spatial LDA - Preprocessing Notebook."
        ),
        datasets.BuilderConfig(
            name="LDA_training_inference",
            version=VERSION,
            description="This configuration contains data used by the Spatial LDA - Training and Inference Notebook."
        ),
        datasets.BuilderConfig(
            name="neighborhood_analysis",
            version=VERSION,
            description="This configuration contains data used by the Neighborhood Analysis Notebook."
        ),
        datasets.BuilderConfig(
            name="pairwise_spatial_enrichment",
            version=VERSION,
            description="This configuration contains data used by the Pairwise Spatial Enrichment Notebook."
        ),
        datasets.BuilderConfig(
            name="ome_tiff",
            version=VERSION,
            description="This configuration contains an OME-TIFF format of FOV1. Intended to be used with the OME-TIFF Conversion Notebook."
        ),
        datasets.BuilderConfig(
            name="ez_seg_data",
            version=VERSION,
            description="This configuration contains the data used by the ezSegmenter notebook."
        )
    ]

    def _info(self):
        # This is the name of the configuration selected in BUILDER_CONFIGS above
        if self.config.name in list(_URL_DATASET_CONFIGS.keys()):
            features = datasets.Features(
                {f: datasets.Value("string") for f in _URL_DATASET_CONFIGS[self.config.name].keys()}
            )
        else:
            ValueError(f"Dataset name is incorrect, options include {list(_URL_DATASET_CONFIGS.keys())}")
        return datasets.DatasetInfo(
            # This is the description that will appear on the datasets page.
            description=_DESCRIPTION,
            # This defines the different columns of the dataset and their types
            features=features,  # Here we define them above because they are different between the two configurations
            # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
            # specify them. They'll be used if as_supervised=True in builder.as_dataset.
            # supervised_keys=("sentence", "label"),
            # Homepage of the dataset for documentation
            homepage=_HOMEPAGE,
            # License for the dataset if available
            license=_LICENSE,
            # Citation for the dataset
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        # This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
        urls = _URL_DATASET_CONFIGS[self.config.name]
        data_dirs = {}
        for data_name, url in urls.items():
            dl_path = pathlib.Path(dl_manager.download_and_extract(url))
            data_dirs[data_name] = dl_path

        return [
            datasets.SplitGenerator(
                name=self.config.name,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={"dataset_paths": data_dirs},
            ),
        ]

    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
    def _generate_examples(self, dataset_paths):
        yield self.config.name, dataset_paths