Overcoming confounding plate effects in differential expression analyses of single-cell RNA-seq data.

ABSTRACT: An increasing number of studies are using single-cell RNA-sequencing (scRNA-seq) to characterize the gene expression profiles of individual cells. One common analysis applied to scRNA-seq data involves detecting differentially expressed (DE) genes between cells in different biological groups. However, many experiments are designed such that the cells to be compared are processed in separate plates or chips, meaning that the groupings are confounded with systematic plate effects. This confounding aspect is frequently ignored in DE analyses of scRNA-seq data. In this article, we demonstrate that failing to consider plate effects in the statistical model results in loss of type I error control. A solution is proposed whereby counts are summed from all cells in each plate and the count sums fo

SUBMITTER: Lun ATL 

PROVIDER: S-EPMC5862359 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature