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Bioinformatics. 2019 Aug 2. pii: btz601. doi: 10.1093/bioinformatics/btz601. [Epub ahead of print]

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

Liu Q1,2, Sheng Q1,2, Ping J1,2, Ramirez MA1,2, Lau KS2,3, Coffey R3, Shyr Y1,2.

Author information

1
Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA.
2
Center for Quantitative Sciences, Vanderbilt University Medical Center, Nashville, TN, USA.
3
Department of Cell and Developmental Biology, Vanderbilt University School of Medicine, Nashville, TN, USA.

Abstract

SUMMARY:

Single cell RNA sequencing is a revolutionary technique to characterize inter-cellular transcriptomics heterogeneity. However, the data is 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.

AVAILABILITY AND IMPLEMENTATION:

scRNABatchQC is freely available at https://github.com/liuqivandy/scRNABatchQC as an R package.

SUPPLEMENTARY INFORMATION:

Supplementary data are available at Bioinformatics online.

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