Optimal marker gene selection for cell type discrimination in single cell analyses.

ABSTRACT: Single-cell technologies characterize complex cell populations across multiple data modalities at unprecedented scale and resolution. Multi-omic data for single cell gene expression, in situ hybridization, or single cell chromatin states are increasingly available across diverse tissue types. When isolating specific cell types from a sample of disassociated cells or performing in situ sequencing in collections of heterogeneous cells, one challenging task is to select a small set of informative markers that robustly enable the identification and discrimination of specific cell types or cell states as precisely as possible. Given single cell RNA-seq data and a set of cellular labels to discriminate, scGeneFit selects gene markers that jointly optimize cell label recovery using label-aware co

SUBMITTER: Dumitrascu B 

PROVIDER: S-EPMC7895823 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature