Pathway trajectory analysis with tensor imputation reveals drug-induced single-cell transcriptomic landscape.

ABSTRACT: Genome-wide identification of single-cell transcriptomic responses of drugs in various human cells is a challenging issue in medical and pharmaceutical research. Here we present a computational method, tensor-based imputation of gene-expression data at the single-cell level (TIGERS), which reveals the drug-induced single-cell transcriptomic landscape. With this algorithm, we predict missing drug-induced single-cell gene-expression data with tensor imputation, and identify trajectories of regulated pathways considering intercellular heterogeneity. Tensor imputation outperformed existing imputation methods for data completion, and provided cell-type-specific transcriptomic responses for unobserved drugs. For example, TIGERS correctly predicted the cell-type-specific expression of maker genes

SUBMITTER: Iwata M 

PROVIDER: S-EPMC10768635 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature