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Nat Commun DOI:10.1038/s41467-019-10154-8

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

Publication TypeJournal Article
Year of Publication2019
AuthorsRohban, MH, Abbasi, HS, Singh, S, Carpenter, AE
JournalNat Commun
Volume10
Issue1
Pages2082
Date Published2019 May 07
ISSN2041-1723
Abstract

Single-cell resolution technologies warrant computational methods that capture cell heterogeneity while allowing efficient comparisons of populations. Here, we summarize cell populations by adding features' dispersion and covariances to population averages, in the context of image-based profiling. We find that data fusion is critical for these metrics to improve results over the prior alternatives, providing at least ~20% better performance in predicting a compound's mechanism of action (MoA) and a gene's pathway.

DOI10.1038/s41467-019-10154-8
Pubmed

http://www.ncbi.nlm.nih.gov/pubmed/31064985?dopt=Abstract

Alternate JournalNat Commun
PubMed ID31064985
Grant ListR35 GM122547 / / U.S. Department of Health & Human Services | National Institutes of Health (NIH) /