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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: Nov. 29, 2021, 7:05 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

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

Genetic Perturbations

broadinstitute/cell-health

Predicting Cell Health with Morphological Profiles

17
5
8m
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
7m
n/a

aoles/EBImage

Image processing toolbox for R

57
27
48d
n/a

Advanced Cell Classifier

https://www.cellclassifier.org/

CellProfiler

http://cellprofiler.org/

CellProfiler Analyst

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

HTSvis

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

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

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

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 painting

https://doi.org/10.1101/395954

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