scRNABatchQC: multi-samples quality control for single cell RNA-seq data.

Bioinformatics
Qi LiuYu Shyr

Abstract

Single cell RNA sequencing is a revolutionary technique to characterize inter-cellular transcriptomics heterogeneity. However, the data are noise-prone because gene expression is often driven by both technical artifacts and genuine biological variations. Proper disentanglement of these two effects is critical to prevent spurious results. While several tools exist to detect and remove low-quality cells in one single cell RNA-seq dataset, there is lack of approach to examining consistency between sample sets and detecting systematic biases, batch effects and outliers. We present scRNABatchQC, an R package to compare multiple sample sets simultaneously over numerous technical and biological features, which gives valuable hints to distinguish technical artifact from biological variations. scRNABatchQC helps identify and systematically characterize sources of variability in single cell transcriptome data. The examination of consistency across datasets allows visual detection of biases and outliers. scRNABatchQC is freely available at https://github.com/liuqivandy/scRNABatchQC as an R package. Supplementary data are available at Bioinformatics online.

References

Jun 28, 2005·Nucleic Acids Research·Bing ZhangJay Snoddy
Feb 19, 2016·Genome Biology·Tomislav IlicicSarah A Teichmann
May 7, 2016·Bioinformatics·Peng JiangRon Stewart
Aug 11, 2018·PLoS Computational Biology·Luyi TianMatthew E Ritchie
Sep 6, 2018·Nature Communications·Mihriban KaraayvazLeif W Ellisen
Sep 18, 2018·Current Biology : CB·Robert A CarterCharles Gawad

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Methods Mentioned

BETA
scRNA-seq
RNA-seq

Software Mentioned

scPipe
scRNABatchQC
SinQC
R package
SingleCellExperiment
Seurat
Scater
WebGestalt

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