A rank-based marker selection method for high throughput scRNA-seq data.

ABSTRACT:

Background

High throughput microfluidic protocols in single cell RNA sequencing (scRNA-seq) collect mRNA counts from up to one million individual cells in a single experiment; this enables high resolution studies of rare cell types and cell development pathways. Determining small sets of genetic markers that can identify specific cell populations is thus one of the major objectives of computational analysis of mRNA counts data. Many tools have been developed for marker selection on single cell data; most of them, however, are based on complex statistical models and handle the multi-class case in an ad-hoc manner.

Results

We introduce RANKCORR, a fast method with strong mathematical underpinnings that performs multi-class marker selection in an informed manner. RANKCORR proce

SUBMITTER: Vargo AHS 

PROVIDER: S-EPMC7585212 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature