ArticlePloS one2010
Should we abandon the t-test in the analysis of gene expression microarray data: a comparison of variance modeling strategies.
Article in PloS one, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 61 papers.
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Who cites it
61 citing papers in PubMed.
- Confidence: a web app for cross-platform differential gene expression analysis, gene scoring, and enrichment analysis.Scientific reports · 2026Article
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- Chronic arsenic exposure induces malignant transformation of human HaCaT cells through both deterministic and stochastic changes in transcriptome expression.Toxicology and applied pharmacology · 2024Article
- KLB and NOX4 expression levels as potential blood-based transcriptional biomarkers of physical activity in children.Scientific reports · 2023Article
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- Advances in proteomics: characterization of the innate immune system after birth and during inflammation.Frontiers in immunology · 2023Review
- A pilot investigation of differential hydroxymethylation levels in patient-derived neural stem cells implicates altered cortical development in bipolar disorder.Frontiers in psychiatry · 2023Article
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- Finding associations in a heterogeneous setting: statistical test for aberration enrichment.Genome medicine · 2021Article
- Preparing for Life: Plasma Proteome Changes and Immune System Development During the First Week of Human Life.Frontiers in immunology · 2020Article
- Merging microarray studies to identify a common gene expression signature to several structural heart diseases.BioData mining · 2020Article
- Newly identified LMO3-BORCS5 fusion oncogene in Ewing sarcoma at relapse is a driver of tumor progression.Oncogene · 2019Article
- Gene shaving using a sensitivity analysis of kernel based machine learning approach, with applications to cancer data.PloS one · 2019Article
- Fold change based approach for identification of significant network markers in breast, lung and prostate cancer.IET systems biology · 2018Article
- Identification of common key genes in breast, lung and prostate cancer and exploration of their heterogeneous expression.Oncology letters · 2018Article
- Single subject transcriptome analysis to identify functionally signed gene set or pathway activity.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2018Article
1 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
6 authors.
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No grant is acknowledged in the PubMed record.
Abstract
High-throughput post-genomic studies are now routinely and promisingly investigated in biological and biomedical research. The main statistical approach to select genes differentially expressed between two groups is to apply a t-test, which is subject of criticism in the literature. Numerous alternatives have been developed based on different and innovative variance modeling strategies. However, a critical issue is that selecting a different test usually leads to a different gene list. In this context and given the current tendency to apply the t-test, identifying the most efficient approach in practice remains crucial. To provide elements to answer, we conduct a comparison of eight tests representative of variance modeling strategies in gene expression data: Welch's t-test, ANOVA [1], Wilcoxon's test, SAM [2], RVM [3], limma [4], VarMixt [5] and SMVar [6]. Our comparison process relies on four steps (gene list analysis, simulations, spike-in data and re-sampling) to formulate comprehensive and robust conclusions about test performance, in terms of statistical power, false-positive rate, execution time and ease of use. Our results raise concerns about the ability of some methods to control the expected number of false positives at a desirable level. Besides, two tests (limma and VarMixt) show significant improvement compared to the t-test, in particular to deal with small sample sizes. In addition limma presents several practical advantages, so we advocate its application to analyze gene expression data.
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