title: Quality control of single-cell RNA-seq by SinQC.
abstract: 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.
  
  
  
  SinQC software and documents are available at http://www.morgridge.net/SinQC.html
  
  
  
  : PJiang@morgridge.org or RStewart@morgridge.org
  
  
  
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Regenerative Biology Laboratory, Morgridge Institute for Research, Madison, WI 53707, USA.
authors.forename: Peng
authors.initials: P
authors.lastname: Jiang
authors.affiliation: Regenerative Biology Laboratory, Morgridge Institute for Research, Madison, WI 53707, USA Department of Cell and Regenerative Biology, University of Wisconsin, Madison, WI 53706, USA Department of Molecular, Cellular and Developmental Biology, University of California, Santa Barbara, CA 93106, USA.
authors.forename: James A
authors.initials: JA
authors.lastname: Thomson
authors.affiliation: Regenerative Biology Laboratory, Morgridge Institute for Research, Madison, WI 53707, USA.
authors.forename: Ron
authors.initials: R
authors.lastname: Stewart
mesh_terms: D020869:Gene Expression Profiling; D011786:Quality Control; D012313:RNA; D017423:Sequence Analysis, RNA; D059010:Single-Cell Analysis; D012984:Software
chemical_list: D012313:RNA