ArticlePloS one2012
Non-gaussian distributions affect identification of expression patterns, functional annotation, and prospective classification in human cancer genomes.
Article in PloS one, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 30 citations in OpenAlex.
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- Fuelling Neuroblastoma: Genomic Analysis of Ketolytic and Glycolytic Gene Expression in Relation to MYCN Oncogene Amplification, Stage and Prognosis.Cancer reports (Hoboken, N.J.) · 2025Article
- Deciphering gene expression patterns using large-scale transcriptomic data and its applications.Briefings in bioinformatics · 2024Article
- Learning massive interpretable gene regulatory networks of the human brain by merging Bayesian networks.PLoS computational biology · 2023Article
- XGboost Prediction Model Based on 3.0T Diffusion Kurtosis Imaging Improves the Diagnostic Accuracy of MRI BiRADS 4 Masses.Frontiers in oncology · 2022Article
- LINC00460 Is a Dual Biomarker That Acts as a Predictor for Increased Prognosis in Basal-Like Breast Cancer and Potentially Regulates Immunogenic and Differentiation-Related Genes.Frontiers in oncology · 2021Article
- A prognostic model for overall survival of patients with early-stage non-small cell lung cancer: a multicentre, retrospective study.The Lancet. Digital health · 2020Article
- Nonstandard conditionally specified models for nonignorable missing data.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
- Investigating skewness to understand gene expression heterogeneity in large patient cohorts.BMC bioinformatics · 2019Article
- Improving patient classification and biomarker assessment using Gaussian Mixture Models and Bayes' rule.Oncoscience · 2019Article
- The rise of the distributions: why non-normality is important for understanding the transcriptome and beyond.Biophysical reviews · 2019Review
- Inferring network structure in non-normal and mixed discrete-continuous genomic data.Biometrics · 2018Article
- Cancer Classification in Microarray Data using a Hybrid Selective Independent Component Analysis and υ-Support Vector Machine Algorithm.Journal of medical signals and sensors · 2014Article
Corrections and comments
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Authors and funding
2 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionGene expression data is often assumed to be normally-distributed, but this assumption has not been tested rigorously. We investigate the distribution of expression data in human cancer genomes and study the implications of deviations from the normal distribution for translational molecular oncology research.
methodsWe conducted a central moments analysis of five cancer genomes and performed empiric distribution fitting to examine the true distribution of expression data both on the complete-experiment and on the individual-gene levels. We used a variety of parametric and nonparametric methods to test the effects of deviations from normality on gene calling, functional annotation, and prospective molecular classification using a sixth cancer genome.
resultsCentral moments analyses reveal statistically-significant deviations from normality in all of the analyzed cancer genomes. We observe as much as 37% variability in gene calling, 39% variability in functional annotation, and 30% variability in prospective, molecular tumor subclassification associated with this effect.
conclusionsCancer gene expression profiles are not normally-distributed, either on the complete-experiment or on the individual-gene level. Instead, they exhibit complex, heavy-tailed distributions characterized by statistically-significant skewness and kurtosis. The non-Gaussian distribution of this data affects identification of differentially-expressed genes, functional annotation, and prospective molecular classification. These effects may be reduced in some circumstances, although not completely eliminated, by using nonparametric analytics. This analysis highlights two unreliable assumptions of translational cancer gene expression analysis: that "small" departures from normality in the expression data distributions are analytically-insignificant and that "robust" gene-calling algorithms can fully compensate for these effects.
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