Journal
NATURE COMMUNICATIONS
Volume 10, Issue -, Pages -Publisher
NATURE PUBLISHING GROUP
DOI: 10.1038/s41467-019-10154-8
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Funding
- BroadNext10 initiative of the Broad Institute
- National Institute of General Medical Sciences of the National Institutes of Health [R35 GM122547]
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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 similar to 20% better performance in predicting a compound's mechanism of action (MoA) and a gene's pathway.
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