Most published methods of detecting differentially expressed genes in RNA-Seq data collapse the position-level read data into a single gene-specific expression measurement. Statistical inference proceeds by modeling these gene-level expression measurements.
Researchers at UTMDACC and UT Austin present a Bayesian method of calling differential expression (BM-DE) that directly models the position-level read counts.
They 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. They further 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/
- Lee J, Ji Y, Liang S, Cai G, Müller P. (2011) On Differential Gene Expression Using RNA-Seq Data. Cancer Inform 10, 205-15, [abstract]