  High-throughput sequencing is now regularly used for studies of the
transcriptome (RNA-seq), particularly for comparisons among experimental
conditions. For the time being, a limited number of biological replicates are
typically considered in such experiments, leading to low detection power for
differential expression. As their cost continues to decrease, it is likely that
additional follow-up studies will be conducted to re-address the same
biological question. We demonstrate how p-value combination techniques
previously used for microarray meta-analyses can be used for the differential
analysis of RNA-seq data from multiple related studies. These techniques are
compared to a negative binomial generalized linear model (GLM) including a
fixed study effect on simulated data and real data on human melanoma cell
lines. The GLM with fixed study effect performed well for low inter-study
variation and small numbers of studies, but was outperformed by the
meta-analysis methods for moderate to large inter-study variability and larger
numbers of studies. To conclude, the p-value combination techniques illustrated
here are a valuable tool to perform differential meta-analyses of RNA-seq data
by appropriately accounting for biological and technical variability within
studies as well as additional study-specific effects. An R package metaRNASeq
is available on the R Forge.
