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Quality control of single-cell RNA-seq by SinQC.

ABSTRACT:

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Single-cell RNA-seq (scRNA-seq) is emerging as a promising technology for profiling cell-to-cell variability in cell populations. However, the combination of technical noise and intrinsic biological variability makes detecting technical artifacts in scRNA-seq samples particularly challenging. Proper detection of technical artifacts is critical to prevent spurious results during downstream analysis. In this study, we present 'Single-cell RNA-seq Quality Control' (SinQC), a method and software tool to detect technical artifacts in scRNA-seq samples by integrating both gene expression patterns and data quality information. We apply SinQC to nine different scRNA-seq datasets, and show that SinQC is a useful tool for controlling scRNA-seq data quality.

Availability and implementation

SinQC software and documents are available at http://www.morgridge.net/SinQC.html

Contacts

: PJiang@morgridge.org or RStewart@morgridge.org

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Jiang P

PROVIDER: S-EPMC4978927 | biostudies-literature |

REPOSITORIES: biostudies-literature

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