ArticlePloS one2018
Variable selection in omics data: A practical evaluation of small sample sizes.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
40 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- Transfer of motor skill between virtual reality viewed using a head-mounted display and conventional screen environments.Journal of neuroengineering and rehabilitation · 2020Trial
- Exploratory study on blood and urine transcriptomes for identifying miRNA biomarkers in pediatric febrile bacterial and viral infections.Scientific reports · 2026Article
- Machine Learning to Identify Physician Actions Associated with Patient Experience of Compassion.Journal of general internal medicine · 2026Article
- Fast autoregressive model for multivariate dependent outcomes with application to lipidomics analysis for Alzheimer's disease and APOE-Computational statistics & data analysis · 2026Article
- Multimodal AI fuses proteomic and EHR data for rational prioritization of protein biomarkers in diabetic retinopathy.medRxiv : the preprint server for health sciences · 2026Article
- Uncovering the molecular landscape of young-onset diffuse gastric cancer: A relieff-based feature selection analysis on RNA-Seq data.PloS one · 2026Article
- Bioactive compound identification without fractionation: an Ocimum spp. case study.Metabolomics : Official journal of the Metabolomic Society · 2025Article
- GRASS-NB: Group-structured variable selection for spatial negative binomial data with applications to cancer registry and spatial omics.bioRxiv : the preprint server for biology · 2025Article
- Modelling of immune infiltration in prostate cancer treated with HDR-brachytherapy using Raman spectroscopy and machine learning.Scientific reports · 2025Article
- How thoughtful experimental design can empower biologists in the omics era.Nature communications · 2025Review
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- Deep learning and radiomics-based system for early diagnosis of hip synovitis in juvenile idiopathic arthritis.Frontiers in immunology · 2025Article
- A machine learning approach to leveraging electronic health records for enhanced omics analysis.Nature machine intelligence · 2025Article
- Identifying and overcoming COVID-19 vaccination impediments using Bayesian data mining techniques.Scientific reports · 2024Article
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- Methods for multi-omic data integration in cancer research.Frontiers in genetics · 2024Review
- Text-mining-based feature selection for anticancer drug response prediction.Bioinformatics advances · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
In omics experiments, variable selection involves a large number of metabolites/ genes and a small number of samples (the n < p problem). The ultimate goal is often the identification of one, or a few features that are different among conditions- a biomarker. Complicating biomarker identification, the p variables often contain a correlation structure due to the biology of the experiment making identifying causal compounds from correlated compounds difficult. Additionally, there may be elements in the experimental design (blocks, batches) that introduce structure in the data. While this problem has been discussed in the literature and various strategies proposed, the over fitting problems concomitant with such approaches are rarely acknowledged. Instead of viewing a single omics experiment as a definitive test for a biomarker, an unrealistic analytical goal, we propose to view such studies as screening studies where the goal of the study is to reduce the number of features present in the second round of testing, and to limit the Type II error. Using this perspective, the performance of LASSO, ridge regression and Elastic Net was compared with the performance of an ANOVA via a simulation study and two real data comparisons. Interestingly, a dramatic increase in the number of features had no effect on Type I error for the ANOVA approach. ANOVA, even without multiple test correction, has a low false positive rates in the scenarios tested. The Elastic Net has an inflated Type I error (from 10 to 50%) for small numbers of features which increases with sample size. The Type II error rate for the ANOVA is comparable or lower than that for the Elastic Net leading us to conclude that an ANOVA is an effective analytical tool for the initial screening of features in omics experiments.
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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.