Differential meta-analysis of RNA-seq data from multiple studies
type: Dataset
description: 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.
issued: 2026-05-10T17:20:44.171009
language: en
modified: 2026-05-10T17:20:44.171014
contact point: Organization
  fn: CCSD
keyword: differential-expression
keyword: infoeu-reposemanticspreprint
keyword: meta-analysis
keyword: p-value-combination
keyword: preprints-working-papers-
keyword: rna-seq
keyword: sdvbbmgtplife-sciences-q-biobiochemistry-molecular-biologygenomics-q-biogn
keyword: statapstatistics-statapplications-statap
keyword: statmestatistics-statmethodology-statme
landing page: https://inria.hal.science/hal-00834369

Differential meta-analysis of RNA-seq data from multiple studies
type: Distribution
format: HTML
issued: 2026-05-10T17:20:44.185613
modified: 2026-05-10T17:20:44.160654
access URL: https://inria.hal.science/hal-00834369

test_moissonnage_selune
type: Agent