ArticleNucleic acids research2017
Methods to increase reproducibility in differential gene expression via meta-analysis.
Article in Nucleic acids research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 85 papers, 12 of them syntheses that pooled it.
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Who cites it
85 citing papers in PubMed, 12 syntheses or guidelines pooled it.
- Inflamed Yet Immune-Evasive? A Transcriptomic Meta-Analysis Identifies Conserved Inflammatory, Developmental, and Neuronal Signatures Associated with Polyploid Giant Cancer Cells.International journal of molecular sciences · 2026Pooled it
- Comparative meta-analysis of transcriptomic studies in spinal muscular atrophy: comparison between tissues and mouse models.BMC medical genomics · 2024Pooled it
- Meta-analysis of Transcriptomic Data from Lung Autopsy and Cellular Models of SARS-CoV-2 Infection.Biochemical genetics · 2024Pooled it
- Global transcriptome analysis reveals fungal disease responsive core gene regulatory landscape in tea.Scientific reports · 2023Pooled it
- Comprehensive multi-cohort transcriptional meta-analysis of muscle diseases identifies a signature of disease severity.Scientific reports · 2022Pooled it
- Increasing reproducibility, robustness, and generalizability of biomarker selection from meta-analysis using Bayesian methodology.PLoS computational biology · 2022Pooled it
- Time trajectories in the transcriptomic response to exercise - a meta-analysis.Nature communications · 2021Pooled it
- Multisystem inflammatory syndrome in children: a microcosm of challenges and opportunities for translational bioinformatics in pediatric research.Current opinion in pediatrics · 2021Pooled it
- Gene set meta-analysis with Quantitative Set Analysis for Gene Expression (QuSAGE).PLoS computational biology · 2019Pooled it
- Transcriptomic metaanalyses of autistic brains reveals shared gene expression and biological pathway abnormalities with cancer.Molecular autism · 2019Pooled it
- Transcriptomic responses to biotic stresses in Malus x domestica: a meta-analysis study.Scientific reports · 2018Pooled it
- Meta-Analysis of Maternal and Fetal Transcriptomic Data Elucidates the Role of Adaptive and Innate Immunity in Preterm Birth.Frontiers in immunology · 2018Pooled it
- A 6-mRNA host response classifier in whole blood predicts outcomes in COVID-19 and other acute viral infections.Scientific reports · 2022Trial
- Molecular Transducers of Human Skeletal Muscle Remodeling under Different Loading States.Cell reports · 2020Trial
- An integrative multi-project transcriptomic and structural prediction framework identifies candidate cold-responsive transcription factors in Medicago sativa.Functional & integrative genomics · 2026Article
- Lipidomic profiling reveals age-dependent changes in plasma membrane lipids that affect neural stem cell aging.Science advances · 2026Article
- MCVAE-based multi-omic anomaly detection in Fragile X Syndrome.NAR molecular medicine · 2026Article
- hStouffer: the enhanced meta-analysis method for the comprehensive analysis of large-scale RNA-seq data.BMC bioinformatics · 2026Article
- Machine learning prioritization identifies PANX1 as an inflammation-associated candidate regulator in lung adenocarcinoma.Frontiers in genetics · 2026Article
- Peripheral blood cellular dynamics during the progression of human tuberculosis.Scientific reports · 2025Article
25 more citing papers are in PubMed but not listed here.
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Authors and funding
5 authors.
Funding
Abstract
Findings from clinical and biological studies are often not reproducible when tested in independent cohorts. Due to the testing of a large number of hypotheses and relatively small sample sizes, results from whole-genome expression studies in particular are often not reproducible. Compared to single-study analysis, gene expression meta-analysis can improve reproducibility by integrating data from multiple studies. However, there are multiple choices in designing and carrying out a meta-analysis. Yet, clear guidelines on best practices are scarce. Here, we hypothesized that studying subsets of very large meta-analyses would allow for systematic identification of best practices to improve reproducibility. We therefore constructed three very large gene expression meta-analyses from clinical samples, and then examined meta-analyses of subsets of the datasets (all combinations of datasets with up to N/2 samples and K/2 datasets) compared to a 'silver standard' of differentially expressed genes found in the entire cohort. We tested three random-effects meta-analysis models using this procedure. We showed relatively greater reproducibility with more-stringent effect size thresholds with relaxed significance thresholds; relatively lower reproducibility when imposing extraneous constraints on residual heterogeneity; and an underestimation of actual false positive rate by Benjamini-Hochberg correction. In addition, multivariate regression showed that the accuracy of a meta-analysis increased significantly with more included datasets even when controlling for sample size.
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