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Awesome Cytodata

A curated list of awesome cytodata resources

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Raw Images

The Broad Bioimage Benchmark Collection (BBBC) is a collection of freely downloadable microscopy image sets. In addition to the images themselves, each set includes a description of the biological application and some type of "ground truth" (expected results).

Public repository of image datasets from published scientific studies.

RxRx1 is a set of 125,514 high-resolution 512x512 6-channel fluorescence microscopy images of human cells under 1,108 genetic perturbations in 51 experimental batches across four cell types. The images were produced by Recursion Pharmaceuticals in their labs in Salt Lake City, Utah. Researchers will use this dataset for studying and benchmarking methods for dealing with biological batch effects, as well as areas in machine learning such as domain adaptation, transfer learning, and k-shot learning.

RxRx19 is the first morphological dataset that demonstrates the rescue of morphological effects of COVID-19.

Chemical Perturbations

Cell painting profiles from 1,600 bioactive compounds in U2OS cells (Access from public S3 bucket: s3://cytodata/datasets/Bioactives-BBBC022-Gustafsdottir/profiles/Bioactives-BBBC022-Gustafsdottir/).

Cell painting profiles from 31,770 compounds in U2OS cells (Click to download).

Cell painting profiles from 30,616 compounds in U2OS cells (Center Driven Research Project CDRP) (Download from GigaDB | Access from public S3 bucket: s3://cytodata/datasets/CDRPBIO-BBBC036-Bray/profiles_cp/CDRPBIO-BBBC036-Bray/).

Genetic Perturbations

Predicting Cell Health with Morphological Profiles

1y 66d

3,072 cell painting profiles from 41 genes knocked down with RNA interference (RNAi) in U2OS cells (Access from GitHub).

Cell painting data from 220 overexpressed genes in U2OS cells (Access from public S3 bucket: s3://cytodata/datasets/TA-ORF-BBBC037-Rohban/profiles_cp/TA-ORF-BBBC037-Rohban/).


Methods for Image-Based Cell Profiling

1y 5m

Image processing toolbox for R

1y 6m

A software package for exploration, annotation and classification of cells within large datasets using machine learning.

CellProfiler is a free open-source software for measuring and analyzing cell images.

Interactive data exploration, analysis, and classification of large biological image sets.

A web app for exploratory data analysis and visualization of arrayed high-throughput screens.


Introduce the steps required to create high-quality image-based (i.e., morphological) profiles from a collection of microscopy images.

Describe some recent applications of HCS, ranging from the identification of genes required for specific biological processes to the characterization of genetic interactions.

Describe the state of the art for image-based screening experiments and delineate experimental approaches and image-analysis approaches as well as discussing challenges and future directions, including leveraging CRISPR/Cas9-mediated genome engineering.

Describes applications of image-based profiling including target and MOA identification, lead hopping, library enrichment, gene annotation and identification of disease-specific phenotypes


A multivariate method for classifying untreated and treated human cancer cells based on โˆผ300 single-cell phenotypic measurements.

This study provides an in-depth analysis of cell division phenotypes and makes the entire high-content data set available as a resource to the community.

Using image-based profiles to predict the bioactivity of small molecules in other unrelated assays.

Demonstrating the effects of polypharmacology in MOA prediction while offering solutions for overcoming it in future image-based profiling studies.


Cell painting profiles from 1,600 bioactive compounds in U2OS cells (Access from public S3 bucket: s3://cytodata/datasets/Bioactives-BBBC022-Gustafsdottir/profiles/Bioactives-BBBC022-Gustafsdottir/).

Retrospective method for illumination-correction based on energy minimization.

Method for retrospective shading correction based on entropy minimization.

Adds dispersion and covariances to population averages to capture single-cell heterogeneity.

Selfsupervised method to learn feature representations of single cells in microscopy images without labelled training data.

Training CNNs using a weakly supervised approach for feature learning.

End-to-end learning with CNNs to predict bioactivity of small molecules in unrelated assays using image-based profiles.

Transfer of activation features of generic CNNs to extract features for image-based profiling.