ArticleJournal of computational biology : a journal of computational molecular cell biology2023
EnsMOD: A Software Program for Omics Sample Outlier Detection.
Article in Journal of computational biology : a journal of computational molecular cell biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Cross-Study Meta-Analysis of Blood Transcriptomes in Type 2 Diabetes.International journal of molecular sciences · 2025Pooled it
- MCOD: a memory-constrained deep learning framework for robust outlier detection in quantitative proteomics.Briefings in bioinformatics · 2026Article
- Structural diversity of lipid A modulates neutrophil proteome and secretome responses.Journal of proteomics · 2026Article
- Challenges and Opportunities for Statistics in Omics Data Analysis.Advances in experimental medicine and biology · 2026Review
- The Associations of Air Pollution Mixture Exposure with Plasma Proteins in an Elderly U.S. Panel.Environmental science & technology · 2025Article
- Extracellular Vesicle Protein Expression in Doped Bioactive Glasses: Further Insights Applying Anomaly Detection.International journal of molecular sciences · 2024Article
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Authors and funding
3 authors.
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Abstract
Detection of omics sample outliers is important for preventing erroneous biological conclusions, developing robust experimental protocols, and discovering rare biological states. Two recent publications describe robust algorithms for detecting transcriptomic sample outliers, but neither algorithm had been incorporated into a software tool for scientists. Here we describe Ensemble Methods for Outlier Detection (EnsMOD) which incorporates both algorithms. EnsMOD calculates how closely the quantitation variation follows a normal distribution, plots the density curves of each sample to visualize anomalies, performs hierarchical cluster analyses to calculate how closely the samples cluster with each other, and performs robust principal component analyses to statistically test if any sample is an outlier. The probabilistic threshold parameters can be easily adjusted to tighten or loosen the outlier detection stringency. EnsMOD can be used to analyze any omics dataset with normally distributed variance. Here it was used to analyze a simulated proteomics dataset, a multiomic (proteome and transcriptome) dataset, a single-cell proteomics dataset, and a phosphoproteomics dataset. EnsMOD successfully identified all of the simulated outliers, and subsequent removal of a detected outlier improved data quality for downstream statistical analyses.
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