EnClaSC: a novel ensemble approach for accurate and robust cell-type classification of single-cell transcriptomes.

ABSTRACT:

Background

In recent years, the rapid development of single-cell RNA-sequencing (scRNA-seq) techniques enables the quantitative characterization of cell types at a single-cell resolution. With the explosive growth of the number of cells profiled in individual scRNA-seq experiments, there is a demand for novel computational methods for classifying newly-generated scRNA-seq data onto annotated labels. Although several methods have recently been proposed for the cell-type classification of single-cell transcriptomic data, such limitations as inadequate accuracy, inferior robustness, and low stability greatly limit their wide applications.

Results

We propose a novel ensemble approach, named EnClaSC, for accurate and robust cell-type classification of single-cell transcriptomic d

SUBMITTER: Chen X 

PROVIDER: S-EPMC7496207 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature