Bias correction for single cell RNA-seq quantification

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. USC researchers 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 each cell, providing users an objective measure to select genes for downstream analysis. In comparison to existing scRNA-seq methods, BCseq demonstrates increased robustness in detection of differentially expressed (DE) genes and cell subtype classification.

Schematic diagram of BCseq


Availability – The software tool BCseq is available at∼liangche/software.html.

Chen L, Zheng S. (2018) BCseq: accurate single cell RNA-seq quantification with bias correction. Nucleic Acids Res [Epub ahead of print]. [article]

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