Dataset Information

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

ABSTRACT:

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.

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.

SUBMITTER: Liu Q

PROVIDER: S-EPMC6954654 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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Publications

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

Liu Qi Q Sheng Quanhu Q Ping Jie J Ramirez Marisol Adelina MA Lau Ken S KS Coffey Robert J RJ Shyr Yu Y

Bioinformatics (Oxford, England) 20191201 24

<h4>Summary</h4>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 cons ...[more]

PMID: 31373345

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