RNAseqCovarImpute: a multiple imputation procedure that outperforms complete case and single imputation differential expression analysis

Missing covariate data is a common problem that has not been addressed in observational studies of gene expression. Here we present a multiple imputation (MI) method that accommodates high dimensional transcriptomic data by binning genes, creating separate MI datasets and differential expression models within each bin, and pooling results with Rubins rules. Simulation studies using real and synthetic data show that this method outperforms complete case and single imputation analyses at uncovering true positive differentially expressed genes, limiting false discovery rates, and minimizing bias. This method is easily implemented via an R package, "RNAseqCovarImpute" that integrates with the limma-voom pipeline.