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

Qi Liu, Quanhu Sheng, Jie Ping, Marisol Adelina Ramirez, Ken S. Lau, Robert J. Coffey, Yu Shyr

Research output: Contribution to journalArticle

1 Citation (Scopus)

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.

Original languageEnglish
Pages (from-to)5306-5308
Number of pages3
JournalBioinformatics
Volume35
Issue number24
DOIs
Publication statusPublished - 2019 Dec 15

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

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  • Cite this

    Liu, 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, 35(24), 5306-5308. https://doi.org/10.1093/bioinformatics/btz601