Methods to increase reproducibility in differential gene expression via meta-analysis.
Nucleic Acids Res
; 45(1): e1, 2017 01 09.
Article
em En
| MEDLINE
| ID: mdl-27634930
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.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Genoma Humano
/
Regulação da Expressão Gênica
/
Metanálise como Assunto
/
Modelos Estatísticos
/
Estudo de Associação Genômica Ampla
Tipo de estudo:
Etiology_studies
/
Guideline
/
Incidence_studies
/
Observational_studies
/
Prognostic_studies
/
Risk_factors_studies
/
Systematic_reviews
Limite:
Humans
Idioma:
En
Ano de publicação:
2017
Tipo de documento:
Article