ArticleNucleic acids research2016
Differential distribution improves gene selection stability and has competitive classification performance for patient survival.
Article in Nucleic acids research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Improving data interpretability with new differential sample variance gene set tests.BMC bioinformatics · 2025Article
- clrDV: a differential variability test for RNA-Seq data based on the skew-normal distribution.PeerJ · 2023Article
- Computational approaches for direct cell reprogramming: from the bulk omics era to the single cell era.Briefings in functional genomics · 2022Article
- Identification of differentially distributed gene expression and distinct sets of cancer-related genes identified by changes in mean and variability.NAR genomics and bioinformatics · 2022Article
- scClassify: sample size estimation and multiscale classification of cells using single and multiple reference.Molecular systems biology · 2020Article
- Adaptively capturing the heterogeneity of expression for cancer biomarker identification.BMC bioinformatics · 2018Article
- Novel CSF biomarkers to discriminate FTLD and its pathological subtypes.Annals of clinical and translational neurology · 2018Article
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Authors and funding
4 authors.
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Abstract
A consistent difference in average expression level, often referred to as differential expression (DE), has long been used to identify genes useful for classification. However, recent cancer studies have shown that when transcription factors or epigenetic signals become deregulated, a change in expression variability (DV) of target genes is frequently observed. This suggests that assessing the importance of genes by either differential expression or variability alone potentially misses sets of important biomarkers that could lead to improved predictions and treatments. Here, we describe a new approach for assessing the importance of genes based on differential distribution (DD), which combines information from differential expression and differential variability into a unified metric. We show that feature ranking and selection stability based on DD can perform two to three times better than DE or DV alone, and that DD yields equivalent error rates to DE and DV. Finally, assessing genes via differential distribution produces a complementary set of selected genes to DE and DV, potentially opening up new categories of biomarkers.
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