Cell Health - Cell Painting Single Cell Profiles
View DatasetDescription
Single Cell Databases of Cell Painting Profiles for the Cell Health Project. These data are used to aggregate profiles in a CRISPR knockout experiment. The data are used to predict cell health assays.DataWe collected Cell Painting measurements on a CRISPR experiment. The experiment targeted 59 genes, which included 119 unique guides (~2 per gene), across 3 cell lines. The cell lines included A549, ES2, and HCC44.About 40% of all CRISPR guides were reproducible. This is ok since we are not actually interested in the CRISPR treatment specifically, but instead, just its corresponding readout in each cell health assay.ApproachWe performed the following approach:Split data into 85% training and 15% test sets.Normalized data by plate (z-score).Selected optimal hyperparamters using 5-fold cross-validationTrained elastic net regression models to predict each of the 70 cell health assay readouts, independently.Trained using shuffled data as well.Report performance on training and test sets.We also trained logistic regression classifiers using the same approach above
See https://github.com/broadinstitute/cell-health for more details.
Citations (2)
Cited on 01 January 2026
Weight: 1.00
Cited on 19 April 2021
Weight: 1.23
Mentions (7)
- https://github.com/fabiodorazio/cell-healthSoftware Heritage
Mentioned on 15 February 2025
Weight: 1.59
- https://github.com/gwaygenomics/cell-healthSoftware Heritage
Mentioned on 11 July 2024
Weight: 1.53
- https://github.com/hgomz/cell-healthSoftware Heritage
Mentioned on 14 December 2023
Weight: 1.46
- https://github.com/mbergins/cell-healthSoftware Heritage
Mentioned on 28 November 2023
Weight: 1.46
- https://github.com/axiomcura/cell-healthSoftware Heritage
Mentioned on 22 November 2023
Weight: 1.46
- https://github.com/aissatech/cell-healthSoftware Heritage
Mentioned on 12 November 2023
Weight: 1.46
- https://github.com/broadinstitute/cell-healthSoftware Heritage
Mentioned on 13 April 2023
Weight: 1.46
Metrics Over Time
Publication Details
Subfield
Immunology
Field
Immunology and Microbiology
Domain
Life Sciences
Confidence Score
54%
Source
Scholar Data Model