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Awesome Cytodata

A curated list of awesome cytodata resources

Here you can see meta information about this topic like the time we last updated this page, the original creator of the awesome list and a link to the original GitHub repository.

Last Update: June 26, 2022, 10:14 p.m.
Thank you cytodata & contributors
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Raw Images

Broad Bioimage Benchmark Collection

https://data.broadinstitute.org/bbbc/

Image Data Resource

https://idr.openmicroscopy.org/

RxRx1

https://www.rxrx.ai/rxrx1

RxRx19

https://www.rxrx.ai/rxrx19

Human Protein Atlas

Raw images or infered protein subcellular locations can be downloaded.

https://www.proteinatlas.org/humanproteome/s…

Chemical Perturbations

Gustafsdottir et al. 2013

https://doi.org/10.1371/journal.pone.0080999

Wawer et al. 2014

Click to download).

https://doi.org/10.1073/pnas.1410933111

Bray et al. 2017

Download from GigaDB | Access from public S3 bucket: s3://cytodata/datasets/CDRPBIO-BBBC036-Bray/profiles_cp/CDRPBIO-BBBC036-Bray/).

https://doi.org/10.1093/gigascience/giw014

Haghighi et al. 2021

Details on GitHub).

https://doi.org/10.1101/2021.09.08.459417

Genetic Perturbations

broadinstitute/cell-health

Predicting Cell Health with Morphological Profiles

17
5
1y 110d
MIT

Singh et al. 2015

Access from GitHub).

https://doi.org/10.1371/journal.pone.0131370

Rohban et al. 2017

https://doi.org/10.7554/eLife.24060.001

Software

cytomining/cytominer

Methods for Image-Based Cell Profiling

33
26
1y 80d
n/a

aoles/EBImage

Image processing toolbox for R

57
27
8m
n/a

menchelab/BioProfiling.jl

A flexible Julia toolkit for high-dimensional cellular profiles

10
1
6m
MIT

cytomining/pycytominer

cytominer python package

16
12
4m
BSD-3-Clause

Advanced Cell Classifier

https://www.cellclassifier.org/

CellProfiler

http://cellprofiler.org/

CellProfiler Analyst

http://cellprofiler.org/cp-analyst/

HTSvis

http://htsvis.dkfz.de/HTSvis/

ImJoy

https://imjoy.io

Reviews

Image-based profiling for drug discovery: due for a machine-learning upgrade?

https://www.nature.com/articles/s41573-020-0…

Data-analysis strategies for image-based cell profiling

https://doi.org/10.1038/nmeth.4397

High-content screening for quantitative cell biology

https://doi.org/10.1016/j.tcb.2016.03.008

Microscopy-based high-content screening

https://doi.org/10.1016/j.cell.2015.11.007

Applications in image-based profiling of perturbations

https://doi.org/10.1016/j.copbio.2016.04.003

Large-scale image-based screening and profiling of cellular phenotypes

https://doi.org/10.1002/cyto.a.22909

How cells explore shape space: A quantitative statistical perspective of cellular morphogenesis

https://dx.doi.org/10.1002%2Fbies.201400011

Machine learning and image-based profiling in drug discovery

https://doi.org/10.1016/J.COISB.2018.05.004

Collections

Deep learning in microscopy

https://www.nature.com/collections/cfcdjceech

High-Content Imaging and Informatics

https://journals.sagepub.com/toc/jbxb/25/7

Applications

Expanding the antibacterial selectivity of polyether ionophore antibiotics through diversity-focused semisynthesis

https://rdcu.be/ccBFH

Image-based multivariate profiling of drug responses from single cells

https://doi.org/10.1038/nmeth1032

Discovering metabolic disease gene interactions by correlated effects on cellular morphology

https://doi.org/10.1016/j.molmet.2019.03.001

Phenotypic profiling of the human genome by time-lapse microscopy reveals cell division genes

https://doi.org/10.1038/nature08869

Bioactivity screening of environmental chemicals using imaging-based high-throughput phenotypic profiling

https://doi.org/10.1016/j.taap.2019.114876

Repurposing High-Throughput Image Assays Enables Biological Activity Prediction for Drug Discovery

https://doi.org/10.1016/j.chembiol.2018.01.0…

Tales of 1,008 Small Molecules: Phenomic Profiling through Live-cell Imaging in a Panel of Reporter Cell Lines

https://doi.org/10.1101/2020.03.13.990093

Mapping the perturbome network of cellular perturbations

https://doi.org/10.1038/s41467-019-13058-9

Morphological profiling of human T and NK lymphocytes by high-content cell imaging

https://doi.org/10.1016/j.celrep.2021.109318

A subcellular map of the human proteome

https://doi.org/10.1126/science.aal3321

A multi-scale map of cell structure fusing protein images and interactions

https://doi.org/10.1038/s41586-021-04115-9

Predicting cell health phenotypes using image-based morphology profiling

https://doi.org/10.1091/mbc.E20-12-0784

Systematic genetics and single‐cell imaging reveal widespread morphological pleiotropy and cell‐to‐cell variability

https://doi.org/10.15252/msb.20199243

Large‐scale image‐based profiling of single‐cell phenotypes in arrayed CRISPR‐Cas9 gene perturbation screens

https://doi.org/10.15252/msb.20178064

Multiparametric phenotyping of compound effects on patient derived organoids

https://doi.org/10.1101/660993

A chemical-genetic interaction map of small molecules using high-throughput imaging in cancer cells

https://doi.org/10.15252/MSB.20156400

Time-resolved mapping of genetic interactions to model rewiring of signaling pathways

https://doi.org/10.7554/eLife.40174

High-Content Imaging of Unbiased Chemical Perturbations Reveals that the Phenotypic Plasticity of the Actin Cytoskeleton Is Constrained

https://doi.org/10.1016/j.cels.2019.09.002

A map of directional genetic interactions in a metazoan cell

https://doi.org/10.7554/eLife.05464

The phenotypic landscape of essential human genes

https://doi.org/10.1101/2021.11.28.470116

Evaluation of Gene Expression and Phenotypic Profiling Data as Quantitative Descriptors for Predicting Drug Targets and Mechanisms of Action

https://doi.org/10.1101/580654

Methods

Cell Painting, a high-content image-based assay for morphological profiling using multiplexed fluorescent dyes

https://doi.org/10.1038/nprot.2016.105

Gustafsdottir et al. 2013

https://doi.org/10.1371/journal.pone.0080999

CIDRE: an illumination-correction method for optical microscopy

https://doi.org/10.1038/nmeth.3323

Retrospective shading correction based entropy minimization

https://doi.org/10.1046/j.1365-2818.2000.006…

Capturing single-cell heterogeneity via data fusion improves image-based profiling

https://doi.org/10.1038/s41467-019-10154-8

Minimum redundancy feature selection from microarray gene expression data

https://doi.org/10.1142/S0219720005001004

Learning unsupervised feature representations for single cell microscopy images with paired cell inpainting

https://doi.org/10.1371/journal.pcbi.1007348

Weakly supervised learning of single-cell feature embeddings

https://doi.org/10.1109/CVPR.2018.00970

Accurate Prediction of Biological Assays with High-Throughput Microscopy Images and Convolutional Networks

https://doi.org/10.1021/acs.jcim.8b00670

Evaluation of Deep Learning Strategies for Nucleus Segmentation in Fluorescence Images

https://doi.org/10.1002/cyto.a.23863

Automating Morphological Profiling with Generic Deep Convolutional Networks

https://doi.org/10.1101/085118

A BaSiC tool for background and shading correction of optical microscopy images

https://doi.org/10.1038/ncomms14836

Cellpose: a generalist algorithm for cellular segmentation

https://doi.org/10.1038/s41592-020-01018-x

Deep Learning Automates the Quantitative Analysis of Individual Cells in Live-Cell Imaging Experiments

https://doi.org/10.1371/journal.pcbi.1005177

The Multidimensional Perturbation Value

https://doi.org/10.1177/1087057112469257

Label-Free Prediction of Cell Painting from Brightfield Images

https://doi.org/10.1101/2021.11.05.467394

CytoGAN: Generative Modeling of Cell Images

https://doi.org/10.1101/227645

Self-supervised feature extraction from image time series in plant phenotyping using triplet networks

https://doi.org/https://doi.org/10.1093/bioi…