Motivation: The differential expression analysis focusing on inter-group comparison can capture only differentially expressed genes (DE genes) at the population level, which may mask the heterogeneity of differential expression in individuals. Thus, to provide patient-specific information for personalized medicine, it is necessary to conduct differential expression analysis at the individual level.
Results: We proposed a method to detect DE genes in individual disease samples by using the disrupted ordering in individual disease samples. In both simulated data and real paired cancer-normal sample data, this method showed excellent performance. It was found to be insensitive to experimental batch effects and data normalization. The landscape of stable gene pairs in a particular type of normal tissue could be predetermined using previously accumulated data, based on which dysregulated genes and pathways for any disease sample can be readily detected. The usefulness of the RankComp method in clinical settings was exemplified by the identification and application of prognostic markers for lung cancer.
Availability and implementation: RankComp is implemented in R script that is freely available from Supplementary Materials.
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