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scPerturb: harmonized single-cell perturbation data
by
Yuan, Bo
, Sander, Chris
, Garda, Samuele
, Luna, Augustin
, Taylor-King, Jake P.
, Min, Joseph
, Schumacher, Linus J.
, Blüthgen, Nils
, Shen, Ciyue
, Marks, Debora S.
, Gross, Torsten
, Peidli, Stefan
, Green, Tessa D.
in
631/114/2163
/ 631/114/2401
/ 631/337/2019
/ 631/61/514/1949
/ Annotations
/ Bioinformatics
/ Biological Microscopy
/ Biological Techniques
/ Biomedical and Life Sciences
/ Biomedical Engineering/Biotechnology
/ Computer applications
/ Data analysis
/ Datasets
/ Design of experiments
/ Epigenomics
/ Experimental design
/ Gene Expression Profiling - methods
/ Information resources
/ Information sources
/ Life Sciences
/ Perturbation
/ Proteomics
/ Quality control
/ Resource
/ Single-Cell Analysis
/ Software
/ Statistical analysis
/ Statistics
/ Transcriptomics
2024
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scPerturb: harmonized single-cell perturbation data
by
Yuan, Bo
, Sander, Chris
, Garda, Samuele
, Luna, Augustin
, Taylor-King, Jake P.
, Min, Joseph
, Schumacher, Linus J.
, Blüthgen, Nils
, Shen, Ciyue
, Marks, Debora S.
, Gross, Torsten
, Peidli, Stefan
, Green, Tessa D.
in
631/114/2163
/ 631/114/2401
/ 631/337/2019
/ 631/61/514/1949
/ Annotations
/ Bioinformatics
/ Biological Microscopy
/ Biological Techniques
/ Biomedical and Life Sciences
/ Biomedical Engineering/Biotechnology
/ Computer applications
/ Data analysis
/ Datasets
/ Design of experiments
/ Epigenomics
/ Experimental design
/ Gene Expression Profiling - methods
/ Information resources
/ Information sources
/ Life Sciences
/ Perturbation
/ Proteomics
/ Quality control
/ Resource
/ Single-Cell Analysis
/ Software
/ Statistical analysis
/ Statistics
/ Transcriptomics
2024
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scPerturb: harmonized single-cell perturbation data
by
Yuan, Bo
, Sander, Chris
, Garda, Samuele
, Luna, Augustin
, Taylor-King, Jake P.
, Min, Joseph
, Schumacher, Linus J.
, Blüthgen, Nils
, Shen, Ciyue
, Marks, Debora S.
, Gross, Torsten
, Peidli, Stefan
, Green, Tessa D.
in
631/114/2163
/ 631/114/2401
/ 631/337/2019
/ 631/61/514/1949
/ Annotations
/ Bioinformatics
/ Biological Microscopy
/ Biological Techniques
/ Biomedical and Life Sciences
/ Biomedical Engineering/Biotechnology
/ Computer applications
/ Data analysis
/ Datasets
/ Design of experiments
/ Epigenomics
/ Experimental design
/ Gene Expression Profiling - methods
/ Information resources
/ Information sources
/ Life Sciences
/ Perturbation
/ Proteomics
/ Quality control
/ Resource
/ Single-Cell Analysis
/ Software
/ Statistical analysis
/ Statistics
/ Transcriptomics
2024
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Journal Article
scPerturb: harmonized single-cell perturbation data
2024
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Overview
Analysis across a growing number of single-cell perturbation datasets is hampered by poor data interoperability. To facilitate development and benchmarking of computational methods, we collect a set of 44 publicly available single-cell perturbation–response datasets with molecular readouts, including transcriptomics, proteomics and epigenomics. We apply uniform quality control pipelines and harmonize feature annotations. The resulting information resource, scPerturb, enables development and testing of computational methods, and facilitates comparison and integration across datasets. We describe energy statistics (E-statistics) for quantification of perturbation effects and significance testing, and demonstrate E-distance as a general distance measure between sets of single-cell expression profiles. We illustrate the application of E-statistics for quantifying similarity and efficacy of perturbations. The perturbation–response datasets and E-statistics computation software are publicly available at scperturb.org. This work provides an information resource for researchers working with single-cell perturbation data and recommendations for experimental design, including optimal cell counts and read depth.
scPerturb is an information resource for single-cell perturbation data analysis and comparison.
Publisher
Nature Publishing Group US,Nature Publishing Group
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