Cancer Informatics

Motivation: RNA-Seq is a novel technology that provides read counts of RNA fragments in each gene, including the mapped positions of each read within each gene. Besides many other applications it can be used to detect differentially expressed genes. Most published methods collapse the position-level read data into a single gene-specific expression measurement. Statistical inference proceeds by modeling these gene-level expression measurements.

Results: We present a Bayesian method of calling differential expression (BM-DE) that directly models the position-level read counts. We demonstrate the potential advantage of the BM-DE method compared to existing approaches that rely on gene-level aggregate data. An important additional feature of the proposed approach is that BM-DE can be used to analyze RNA-Seq data from experiments without biological replicates. This becomes possible since the approach works with multiple position-level read counts for each gene. We demonstrate the importance of modeling for position-level read counts with a yeast data set and a simulation study.

Availability: A public domain R package is available from http://odin.mdacc.tmc.edu/~ylji/BMDE/.