Detecting expressed genes in cell populations at the single-cell level with scGeneXpress

Determining whether genes are expressed or not remains a challenge in single-cell RNAseq experiments due to their different expression spectra, which are influenced by genetics, the microenvironment and gene length. Current approaches for addressing this issue fail to provide a comprehensive landscape of expressed genes, since they neglect the inherent differences in the expression ranges and distributions of genes. Here, we present scGeneXpress, a method for detecting expressed genes in cell populations of single-cell RNAseq samples based on gene-specific reference distributions. We demonstrate that scGeneXpress accurately detects expressed cell markers and identity genes in 34 human and mouse tissues and can be employed to improve differential expression analysis of single-cell RNAseq data.