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Computational Biology Group, CIC bioGUNE-BRTA (Basque Research and Technology Alliance), Parque Cientifico Tecnologico de Bizkaia 801A, 48160 Derio, Spain.
Published: September 2024
AI Article Synopsis
Single-cell RNA sequencing (RNAseq) struggles to accurately determine gene expression due to factors like genetic variation, environmental influences, and gene length.
Existing methods fall short because they overlook the unique expression patterns of different genes.
The new method, scGeneXpress, uses gene-specific reference distributions to effectively identify expressed genes and enhances the analysis of single-cell RNAseq data across various human and mouse tissues.
Article Abstract
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.
