Covariate Selection for RNA-Seq Differential Expression Analysis with Hidden Factor Adjustment

In RNA-seq data analysis, a primary objective is the identification of differentially expressed genes, which are genes that exhibit varying expression levels across different conditions of interest. It is widely known that hidden factors, such as batch effects, can substantially influence the differential expression analysis. Furthermore, apart from the primary factor of interest and unforeseen artifacts, an RNA-seq experiment typically contains multiple measured covariates, some of which may significantly affect gene expression levels, while others may not. Existing methods either address the covariate selection or the unknown artifacts separately. In this study, we investigate two integrated strategies, FSR_sva and SVAall_FSR, for jointly addressing covariate selection and hidden factors through simulations based on a real RNA-seq dataset. Our results show that when no available relevant covariates are strongly associated with the main factor of interest, FSR_sva performs comparably to existing methods. However, when some available relevant covariates are strongly correlated with the primary factor of interest–SVAall_FSR achieves the best performance among the compared methods.