A probabilistic gene expression barcode for annotation of cell types from single-cell RNA-seq data.

ABSTRACT: Single-cell RNA sequencing (scRNA-seq) quantifies gene expression for individual cells in a sample, which allows distinct cell-type populations to be identified and characterized. An important step in many scRNA-seq analysis pipelines is the annotation of cells into known cell types. While this can be achieved using experimental techniques, such as fluorescence-activated cell sorting, these approaches are impractical for large numbers of cells. This motivates the development of data-driven cell-type annotation methods. We find limitations with current approaches due to the reliance on known marker genes or from overfitting because of systematic differences, or batch effects, between studies. Here, we present a statistical approach that leverages public data sets to combine information acro

SUBMITTER: Grabski IN 

PROVIDER: S-EPMC9802389 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature