iDESC: identifying differential expression in single-cell RNA sequencing data with multiple subjects.

Yunqing LiuJiayi ZhaoTaylor S AdamsNingya WangJonas C SchuppWeimiao WuJohn E McDonoughGeoffrey L ChuppNaftali KaminskiZuoheng WangXiting Yan

Published in: BMC bioinformatics (2023)

iDESC was able to achieve more accurate and robust DE analysis results by separating subject effect from disease effect with consideration of dropouts to identify DE genes, suggesting the importance of considering subject effect and dropouts in the DE analysis of scRNA-seq data with multiple subjects.

Keyphrases

single cell
rna seq
electronic health record
gene expression
deep learning
data analysis