f-divergence cutoff index to simultaneously identify differential expression in the integrated transcriptome and proteome

Abstract

The ability to integrate 'omics' (i.e., transcriptomics and proteomics) is becoming increasingly important to the understanding of regulatory mechanisms. There are currently no tools available to identify differentially expressed genes (DEGs)across different 'omics'data types or multi-dimensional data including time courses. We present a model capable of simultaneously identifying DEGs from continuous and discrete transcriptomic, proteomic and integrated proteogenomic data. We show that our algorithm can be used across multiple diverse sets of data and can unambiguously find genes that show functional modulation, developmental changes or misregulation. Applying our model to several proteogenomics datasets, we identified a number of important genes that showed distinctive regulation patterns. The package is available at R Bioconductor and also at http://software.steenlab.org/fCI/.