ArticleBMC bioinformatics2018
Gene length corrected trimmed mean of M-values (GeTMM) processing of RNA-seq data performs similarly in intersample analyses while improving intrasample comparisons.
Article in BMC bioinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 118 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
118 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A meta-analysis of RNA-Seq studies to identify novel genes that regulate aging.Experimental gerontology · 2023Pooled it
- Metabolic Task Analysis Reveals Distinct Metabotypes in End-Stage Dilated Cardiomyopathy.Circulation. Genomic and precision medicine · 2026Article
- Preparation method shapes the recovery and ecological interpretation of DNA and RNA soil viral communities.Nature communications · 2026Article
- Viruses help shape microbiome response to polyphenol rewiring of methane-suppressed peat microcosms.PLoS biology · 2026Article
- Integration of GWAS and eQTL analysis reveals regulatory CNVs for fatty acid traits in cattle.iScience · 2026Article
- A multigene model for response stratification to neoadjuvant chemotherapy in triple negative breast cancer.Breast (Edinburgh, Scotland) · 2026Article
- Large-scale analysis of temporal gene expression variation in peripheral blood.Nature communications · 2026Article
- Genome-scale metabolic modeling uncovers cell-type specific signatures associated with APOE variants.iScience · 2026Article
- CXCR2 blockade overcomes the NETosis-mediated resistance to MEK inhibition in pancreatic cancer models.The Journal of clinical investigation · 2026Article
- Article
- Increased FICD-mediated protein AMPylation triggers conserved endoplasmic reticulum stress signaling across species.Cell stress & chaperones · 2026Article
- Transcriptional Adaptations to Muscle Loading in a Murine Model of Achilles Tendinopathy.Journal of orthopaedic research : official publication of the Orthopaedic Research Society · 2026Article
- Multicondition expression profiling reveals limitations of canonical housekeeping genes.BMC genomics · 2026Article
- Commensal microbe-derived butyrate enhances T follicular helper cell function to boost mucosal vaccine efficacy.Microbiome · 2026Article
- Calm in the midst of the storm: inferring gene expression stability in flowering plants.Frontiers in plant science · 2026Article
- The HIV-1 Vpr R77Q mutant alters host apoptotic gene regulation in CD4+ T cells.Frontiers in cellular and infection microbiology · 2026Article
- A Co-essentiality Network of Cancer Driver Genes Better Prioritizes Anticancer Drugs.Genomics, proteomics & bioinformatics · 2025Article
- A new algorithm Precision OncoPanels (PrOPs) identifies short individualized actionable panels that can guide cancer treatment: a pan-cancer analysis of TCGA cohorts.NAR genomics and bioinformatics · 2025Article
- Article
- Low-flow in aortic valve stenosis patients with reduced ejection fraction does not depend on left ventricular function.Clinical research in cardiology : official journal of the German Cardiac Society · 2025Article
58 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
16 authors.
Funding
Abstract
backgroundCurrent normalization methods for RNA-sequencing data allow either for intersample comparison to identify differentially expressed (DE) genes or for intrasample comparison for the discovery and validation of gene signatures. Most studies on optimization of normalization methods typically use simulated data to validate methodologies. We describe a new method, GeTMM, which allows for both inter- and intrasample analyses with the same normalized data set. We used actual (i.e. not simulated) RNA-seq data from 263 colon cancers (no biological replicates) and used the same read count data to compare GeTMM with the most commonly used normalization methods (i.e. TMM (used by edgeR), RLE (used by DESeq2) and TPM) with respect to distributions, effect of RNA quality, subtype-classification, recurrence score, recall of DE genes and correlation to RT-qPCR data.
resultsWe observed a clear benefit for GeTMM and TPM with regard to intrasample comparison while GeTMM performed similar to TMM and RLE normalized data in intersample comparisons. Regarding DE genes, recall was found comparable among the normalization methods, while GeTMM showed the lowest number of false-positive DE genes. Remarkably, we observed limited detrimental effects in samples with low RNA quality.
conclusionsWe show that GeTMM outperforms established methods with regard to intrasample comparison while performing equivalent with regard to intersample normalization using the same normalized data. These combined properties enhance the general usefulness of RNA-seq but also the comparability to the many array-based gene expression data in the public domain.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.