iDEG: A single-subject method for assessing gene differential expression from two transcriptomes of an individual

Accurate profiling of gene expression in a single subject has the potential to be a powerful precision medicine tool, useful for unveiling individual disease mechanisms and responses. However, most expression analysis tools for RNA-sequencing (RNA-Seq) data require replicate samples to estimate gene-wise data variability and make inferences, which is costly and not easily obtainable in clinical practice. We propose the iDEG method to identify individualized Differentially Expressed Genes in a single subject sampled under two conditions without replicates, i.e. a baseline sample (unaffected tissue) vs. a case sample (tumor). iDEG borrows information across different genes from the same individual using a partitioned window to strategically bypass the requirement of replicates per condition. It then transforms RNA-Seq data such that, under the null hypothesis, differences of transformed expression counts follow a distribution and variance calculated across a local partition of related transcripts at baseline expression. This transformation enables modeling genes with a two-group mixture model from which the probability of differential expression for each gene is then estimated by an empirical Bayes approach with a local false discovery rate control. Our extensive simulation studies demonstrate iDEGs substantially was the only technique keeping high precision (u003e90%), recall (u003e75%) and low false positive rate (