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

dc.contributor.authorLiu, Qi
dc.contributor.authorSheng, Quanhu
dc.contributor.authorPing, Jie
dc.contributor.authorRamirez, Marisol Adelina
dc.contributor.authorLau, Ken S.
dc.contributor.authorCoffey, Robert J.
dc.contributor.authorShyr, Yu
dc.identifier.citationLiu, Q., Sheng, Q., Ping, J., Ramirez, M. A., Lau, K. S., Coffey, R. J., & Shyr, Y. (2019). scRNABatchQC: multi-samples quality control for single cell RNA-seq data. Bioinformatics (Oxford, England), 35(24), 5306–5308.
dc.description.abstractA Summary: 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.en_US
dc.description.sponsorshipThis work was supported by the National Cancer Institute grants (U2C CA233291 and U54 CA217450).en_US
dc.rightsCopyright © The Author(s) 2019. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (, which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact
dc.titlescRNABatchQC: multi-samples quality control for single cell RNA-seq dataen_US

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