Summary: 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.#R##N##R##N#Availability and Implementation: SinQC software and documents are available at http://www.morgridge.net/SinQC.html#R##N##R##N#Contacts: gro.egdirgrom@gnaiJP or gro.egdirgrom@trawetSR#R##N##R##N#Supplementary information: Supplementary data are available at Bioinformatics online.
['Peng Jiang', 'James A. Thomson', 'Ron Stewart']
Quality control of single-cell RNA-seq by SinQC.