ScPred: accurate supervised method for cell-type classification from single-cell RNA-seq data.

ABSTRACT: Single-cell RNA sequencing has enabled the characterization of highly specific cell types in many tissues, as well as both primary and stem cell-derived cell lines. An important facet of these studies is the ability to identify the transcriptional signatures that define a cell type or state. In theory, this information can be used to classify an individual cell based on its transcriptional profile. Here, we present scPred, a new generalizable method that is able to provide highly accurate classification of single cells, using a combination of unbiased feature selection from a reduced-dimension space, and machine-learning probability-based prediction method. We apply scPred to scRNA-seq data from pancreatic tissue, mononuclear cells, colorectal tumor biopsies, and circulating dendritic cell

SUBMITTER: Alquicira-Hernandez J 

PROVIDER: S-EPMC6907144 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature