Dissecting heterogeneous cell populations across drug and disease conditions with PopAlign.

ABSTRACT: Single-cell measurement techniques can now probe gene expression in heterogeneous cell populations from the human body across a range of environmental and physiological conditions. However, new mathematical and computational methods are required to represent and analyze gene-expression changes that occur in complex mixtures of single cells as they respond to signals, drugs, or disease states. Here, we introduce a mathematical modeling platform, PopAlign, that automatically identifies subpopulations of cells within a heterogeneous mixture and tracks gene-expression and cell-abundance changes across subpopulations by constructing and comparing probabilistic models. Probabilistic models provide a low-error, compressed representation of single-cell data that enables efficient large-scale compu

SUBMITTER: Chen S 

PROVIDER: S-EPMC7682438 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature