BCseq: accurate single cell RNA-seq quantification with bias correction.

ABSTRACT: With rapid technical advances, single cell RNA-seq (scRNA-seq) has been used to detect cell subtypes exhibiting distinct gene expression profiles and to trace cell transitions in development and disease. However, the potential of scRNA-seq for new discoveries is constrained by the robustness of subsequent data analysis. Here we propose a robust model, BCseq (bias-corrected sequencing analysis), to accurately quantify gene expression from scRNA-seq. BCseq corrects inherent bias of scRNA-seq in a data-adaptive manner and effectively removes technical noise. BCseq rescues dropouts through weighted consideration of similar cells. Cells with higher sequencing depths contribute more to the quantification nonlinearly. Furthermore, BCseq assigns a quality score for the expression of each gene in e

SUBMITTER: Chen L 

PROVIDER: S-EPMC6101504 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature