JOINT for large-scale single-cell RNA-sequencing analysis via soft-clustering and parallel computing.

ABSTRACT:

Background

Single-cell RNA-Sequencing (scRNA-Seq) has provided single-cell level insights into complex biological processes. However, the high frequency of gene expression detection failures in scRNA-Seq data make it challenging to achieve reliable identification of cell-types and Differentially Expressed Genes (DEG). Moreover, with the explosive growth of single-cell data using 10x genomics protocol, existing methods will soon reach the computation limit due to scalability issues. The single-cell transcriptomics field desperately need new tools and framework to facilitate large-scale single-cell analysis.

Results

In order to improve the accuracy, robustness, and speed of scRNA-Seq data processing, we propose a generalized zero-inflated negative binomial mixture model, "JOIN

SUBMITTER: Cui T 

PROVIDER: S-EPMC7798298 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature