|Publication Type||Journal Article|
|Year of Publication||2019|
|Authors||Rohban, MH, Abbasi, HS, Singh, S, Carpenter, AE|
|Date Published||2019 May 07|
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.
|Alternate Journal||Nat Commun|
|Grant List||R35 GM122547 / / U.S. Department of Health & Human Services | National Institutes of Health (NIH) /|