ArticleBMC bioinformatics2019
Investigating skewness to understand gene expression heterogeneity in large patient cohorts.
Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 28 citations in OpenAlex.
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- SIEVEseq: unified differential expression, variability, and skewness analyses using RNA-Seq data.DNA research : an international journal for rapid publication of reports on genes and genomes · 2026Article
- New approaches to meta-analyze differences in skewness, kurtosis, and correlation.PLoS biology · 2026Article
- Sample size requirements for machine learning classification of binary outcomes in bulk RNA-Seq data.BMC bioinformatics · 2026Article
- Incorporating prior information in gene expression network-based cancer heterogeneity analysis.Biostatistics (Oxford, England) · 2025Article
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- Shrinkage estimation of gene interaction networks in single-cell RNA sequencing data.BMC bioinformatics · 2024Article
- Deciphering gene expression patterns using large-scale transcriptomic data and its applications.Briefings in bioinformatics · 2024Article
- Transgressive and parental dominant gene expression and cytosine methylation during seed development in Brassica napus hybrids.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2023Article
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- Statistical Analysis of Rodent Body Weight Data is Robust to Departures from Normality in Historical National Toxicology Program Studies Dated 1980-2013.Journal of young investigators · 2022Article
- Iraqi Parents' Knowledge, Attitudes, and Practices towards Vaccinating Their Children: A Cross-Sectional Study.Vaccines · 2022Article
- The shape of gene expression distributions matter: how incorporating distribution shape improves the interpretation of cancer transcriptomic data.BMC bioinformatics · 2020Article
- Joint learning of multiple gene networks from single-cell gene expression data.Computational and structural biotechnology journal · 2020Article
- The International Conference on Intelligent Biology and Medicine (ICIBM) 2019: bioinformatics methods and applications for human diseases.BMC bioinformatics · 2019Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSkewness is an under-utilized statistical measure that captures the degree of asymmetry in the distribution of any dataset. This study applied a new metric based on skewness to identify regulators or genes that have outlier expression in large patient cohorts.
resultsWe investigated whether specific patterns of skewed expression were related to the enrichment of biological pathways or genomic properties like DNA methylation status. Our study used publicly available datasets that were generated using both RNA-sequencing and microarray technology platforms. For comparison, the datasets selected for this study also included different samples derived from control donors and cancer patients. When comparing the shift in expression skewness between cancer and control datasets, we observed an enrichment of pathways related to the immune function that reflects an increase towards positive skewness in the cancer relative to control datasets. A significant correlation was also detected between expression skewness and the top 500 genes corresponding to the most significant differential DNA methylation occurring in the promotor regions for four Cancer Genome Atlas cancer cohorts.
conclusionsOur results indicate that expression skewness can reveal new insights into transcription based on outlier and asymmetrical behaviour in large patient cohorts.
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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.