Initial commit

.envrc

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+use flake

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.lz4 filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+# Audio files - uncompressed
+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
+*.raw filter=lfs diff=lfs merge=lfs -text
+# Audio files - compressed
+*.aac filter=lfs diff=lfs merge=lfs -text
+*.flac filter=lfs diff=lfs merge=lfs -text
+*.mp3 filter=lfs diff=lfs merge=lfs -text
+*.ogg filter=lfs diff=lfs merge=lfs -text
+*.wav filter=lfs diff=lfs merge=lfs -text
+# Image files - uncompressed
+*.bmp filter=lfs diff=lfs merge=lfs -text
+*.gif filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text
+*.tiff filter=lfs diff=lfs merge=lfs -text
+# Image files - compressed
+*.jpg filter=lfs diff=lfs merge=lfs -text
+*.jpeg filter=lfs diff=lfs merge=lfs -text
+*.webp filter=lfs diff=lfs merge=lfs -text

.gitignore

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+.direnv
+
+# https://github.com/github/gitignore/blob/main/Python.gitignore
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/

README.md

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+---
+license: cc-by-4.0
+task_categories:
+- image-classification
+size_categories:
+- 10M<n<100M
+tags:
+- biology
+- drug
+- cells
+---
+
+[![DOI](https://zenodo.org/badge/DOI/10.48550/arXiv.2301.05768.svg)](https://doi.org/10.48550/arXiv.2301.05768)
+
+# RxRx1: A Dataset for Evaluating Experimental Batch Correction Methods
+
+**Homepage**: https://www.rxrx.ai/rxrx1 \
+**Publication Date**: 2019-06 \
+**License**: [Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode) \
+**Citation**:
+```bibtex
+@misc{sypetkowski2023rxrx1,
+  title         = {RxRx1: A Dataset for Evaluating Experimental Batch Correction Methods},
+  author        = {Maciej Sypetkowski and Morteza Rezanejad and Saber Saberian and Oren Kraus and John Urbanik and James Taylor and Ben Mabey and Mason Victors and Jason Yosinski and Alborz Rezazadeh Sereshkeh and Imran Haque and Berton Earnshaw},
+  year          = {2023},
+  eprint        = {2301.05768},
+  archiveprefix = {arXiv},
+  primaryclass  = {cs.CV}
+}
+```
+
+## Description
+
+High-throughput screening techniques are commonly used to obtain large quantities of data in many fields of biology. It is well known that artifacts arising from variability in the technical execution of different experimental batches within such screens confound these observations and can lead to invalid biological conclusions. It is therefore necessary to account for these batch effects when analyzing outcomes. In this paper we describe RxRx1, a biological dataset designed specifically for the systematic study of batch effect correction methods. The dataset consists of 125,510 high-resolution fluorescence microscopy images of human cells under 1,138 genetic perturbations in 51 experimental batches across 4 cell types. Visual inspection of the images alone clearly demonstrates significant batch effects. We propose a classification task designed to evaluate the effectiveness of experimental batch correction methods on these images and examine the performance of a number of correction methods on this task. Our goal in releasing RxRx1 is to encourage the development of effective experimental batch correction methods that generalize well to unseen experimental batches.

RxRx1.py

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+import pathlib
+import datasets
+import pandas as pd
+import numpy as np
+from PIL import Image
+
+_DL_URL = "https://storage.googleapis.com/rxrx/rxrx1"
+
+_BASE_URLS = {
+    "metadata": f"{_DL_URL}/rxrx1-metadata.zip",
+    "embeddings": f"{_DL_URL}/rxrx1-dl-embeddings.zip",
+    "images": f"{_DL_URL}/rxrx1-images.zip",
+}
+
+_HOMEPAGE = "https://www.rxrx.ai/rxrx1"
+
+_DESCRIPTION = """
+RxRx1 is the first dataset released by Recursion in the RxRx.ai series and was the topic of the NeurIPS 2019 CellSignal competition. It contains 125,510 images of 6-channel fluorescent cellular microscopy, taken from four kinds of cells perturbed by 1,138 siRNAs. The goal of the competition was to train models that could identify which siRNA was used in a given image taken from an experimental batch not seen in the training data. For more information about RxRx1 please visit RxRx.ai.
+RxRx1 is part of a larger set of Recursion datasets that can be found at RxRx.ai and on GitHub. For questions about this dataset and others please email info@rxrx.ai.
+"""
+
+_LICENSE = "CC BY NC SA 4.0"
+
+_VERSION = datasets.Version("0.1.0")
+
+_CITATION = """
+@misc{sypetkowski2023rxrx1,
+  title         = {RxRx1: A Dataset for Evaluating Experimental Batch Correction Methods},
+  author        = {Maciej Sypetkowski and Morteza Rezanejad and Saber Saberian and Oren Kraus and John Urbanik and James Taylor and Ben Mabey and Mason Victors and Jason Yosinski and Alborz Rezazadeh Sereshkeh and Imran Haque and Berton Earnshaw},
+  year          = {2023},
+  eprint        = {2301.05768},
+  archiveprefix = {arXiv},
+  primaryclass  = {cs.CV}
+}
+"""
+
+_WELL_TYPES = [
+    "treatment",
+    "positive_control",
+    "negative_control",
+]
+
+_N_FEATURES = 128
+_N_CHANNELS = 6
+
+def get_image_path(image_dir, row, channel):
+    """Returns path to image."""
+    experiment = row["experiment"]
+    plate = row["plate"]
+    well = row["well"]
+    site = row["site"]
+    return image_dir / experiment /f"Plate{plate}" / f"{well}_s{site}_w{channel}.png"
+
+class RxRx1(datasets.GeneratorBasedBuilder):
+    """RxRx1 dataset."""
+
+    DEFAULT_WRITER_BATCH_SIZE = 50
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            homepage=_HOMEPAGE,
+            description=_DESCRIPTION,
+            citation=_CITATION,
+            license=_LICENSE,
+            version=_VERSION,
+            features=datasets.Features(
+                {
+                    "image": datasets.Array3D(shape=(512, 512, _N_CHANNELS), dtype="uint8"),
+                    "site_id": datasets.Value("string"),
+                    "well_id": datasets.Value("string"),
+                    "cell_type": datasets.Value("string"),
+                    "experiment": datasets.Value("string"),
+                    "plate": datasets.Value("int32"),
+                    "well": datasets.Value("string"),
+                    "site": datasets.Value("int32"),
+                    "well_type": datasets.ClassLabel(names=_WELL_TYPES),
+                    "sirna": datasets.Value("string"),
+                    "sirna_id": datasets.Value("int32"),
+                    "embeddings": datasets.Sequence(feature=datasets.Value("float32"), length=_N_FEATURES),
+                }
+            ),
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        # download and extract archives
+        archives = {
+            name: pathlib.Path(dl_manager.download_and_extract(url)) / "rxrx1"
+            for name, url in _BASE_URLS.items()
+        }
+
+        # load dataframes
+        df_metadata = pd.read_csv(archives["metadata"] / "metadata.csv")
+        df_embeddings = pd.read_csv(archives["embeddings"] / "embeddings.csv")
+
+        # merge dataframes
+        df = pd.merge(df_metadata, df_embeddings, on="site_id")
+
+        # split dataframes
+        df_train = df[df["dataset"] == "train"].drop("dataset", axis=1)
+        df_test = df[df["dataset"] == "test"].drop("dataset", axis=1)
+
+        # # get image path
+        image_dir = archives["images"] / "images"
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "dataframe_rows": list(df_train.iterrows()),
+                    "image_dir": image_dir,
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "dataframe_rows": list(df_test.iterrows()),
+                    "image_dir": image_dir,
+                },
+            ),
+        ]
+
+    def _generate_examples(self, dataframe_rows, image_dir):
+        """Generate images and labels for splits."""
+        # loop over rows in dataframe
+        for (i, row) in dataframe_rows:
+            # retreive image from 6 grayscale images
+            image = np.stack([
+                Image.open(get_image_path(image_dir, row, channel))
+                for channel in range(1, _N_CHANNELS + 1)
+            ], axis=-1)
+
+            embeddings = np.array([
+                row[f"feature_{i}"]
+                for i in range(_N_FEATURES)
+            ])
+
+            # yield example: image + embeddings + metadata
+            yield i, {
+                "image": image,
+                "embeddings": embeddings,
+                "site_id": row["site_id"],
+                "well_id": row["well_id"],
+                "cell_type": row["cell_type"],
+                "experiment": row["experiment"],
+                "plate": row["plate"],
+                "well": row["well"],
+                "site": row["site"],
+                "well_type": row["well_type"],
+                "sirna": row["sirna"],
+                "sirna_id": row["sirna_id"],
+            }

flake.lock

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+{
+  "nodes": {
+    "flake-utils": {
+      "inputs": {
+        "systems": "systems"
+      },
+      "locked": {
+        "lastModified": 1694529238,
+        "narHash": "sha256-zsNZZGTGnMOf9YpHKJqMSsa0dXbfmxeoJ7xHlrt+xmY=",
+        "owner": "numtide",
+        "repo": "flake-utils",
+        "rev": "ff7b65b44d01cf9ba6a71320833626af21126384",
+        "type": "github"
+      },
+      "original": {
+        "owner": "numtide",
+        "repo": "flake-utils",
+        "type": "github"
+      }
+    },
+    "nixpkgs": {
+      "locked": {
+        "lastModified": 1694767346,
+        "narHash": "sha256-5uH27SiVFUwsTsqC5rs3kS7pBoNhtoy9QfTP9BmknGk=",
+        "owner": "NixOS",
+        "repo": "nixpkgs",
+        "rev": "ace5093e36ab1e95cb9463863491bee90d5a4183",
+        "type": "github"
+      },
+      "original": {
+        "owner": "NixOS",
+        "ref": "nixos-unstable",
+        "repo": "nixpkgs",
+        "type": "github"
+      }
+    },
+    "root": {
+      "inputs": {
+        "flake-utils": "flake-utils",
+        "nixpkgs": "nixpkgs"
+      }
+    },
+    "systems": {
+      "locked": {
+        "lastModified": 1681028828,
+        "narHash": "sha256-Vy1rq5AaRuLzOxct8nz4T6wlgyUR7zLU309k9mBC768=",
+        "owner": "nix-systems",
+        "repo": "default",
+        "rev": "da67096a3b9bf56a91d16901293e51ba5b49a27e",
+        "type": "github"
+      },
+      "original": {
+        "owner": "nix-systems",
+        "repo": "default",
+        "type": "github"
+      }
+    }
+  },
+  "root": "root",
+  "version": 7
+}

flake.nix

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+{
+  inputs = {
+    nixpkgs.url = "github:NixOS/nixpkgs/nixos-unstable";
+    flake-utils.url = "github:numtide/flake-utils";
+  };
+
+  outputs = { self, nixpkgs, flake-utils }:
+    flake-utils.lib.eachDefaultSystem (system:
+      let pkgs = nixpkgs.legacyPackages.${system};
+      in {
+        devShell = pkgs.mkShell {
+          buildInputs = with pkgs; [
+            python311
+            python311Packages.datasets
+            python311Packages.pillow
+            python311Packages.pandas
+            python311Packages.numpy
+          ];
+        };
+      });
+}

test.py

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+import datasets
+
+ds = datasets.load_dataset('./RxRx1.py', num_proc=16)
+
+print(ds)