{"doi":"10.1093/nar/gkw797","title":"Methods to increase reproducibility in differential gene expression via meta-analysis","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.","journal":"Nucleic Acids Research","year":2016,"id":5564,"datarank":3.5615497799093867,"base_score":5.043425116919247,"endowment":5.043425116919247,"self_citation_contribution":0.7565137675378871,"citation_network_contribution":2.8050360123714997,"self_endowment_contribution":0.7565137675378871,"citer_contribution":2.8050360123714997,"corpus_percentile":null,"corpus_rank":null,"citation_count":154,"citer_count":93,"citers_with_citation_signal":79,"citers_with_endowment":79,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.0489,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2016-09-14","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":54522,"name":"Winston A. Haynes","orcid":null,"position":1,"is_corresponding":false},{"id":54523,"name":"Francesco Vallania","orcid":"0000-0003-0098-249X","position":2,"is_corresponding":false},{"id":4851,"name":"Purvesh Khatri","orcid":"0000-0002-4143-4708","position":4,"is_corresponding":false},{"id":54524,"name":"Winston Haynes","orcid":"0000-0002-2376-0630","position":5,"is_corresponding":false},{"id":148,"name":"John P. A. Ioannidis","orcid":"0000-0003-3118-6859","position":6,"is_corresponding":false},{"id":54521,"name":"Timothy E. Sweeney","orcid":"0000-0002-3596-1093","position":0,"is_corresponding":true}],"reference_count":88,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-03-01T18:20:47.508186Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}