Investigating skewness to understand gene expression heterogeneity in large patient cohorts

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.

The shape of gene expression distributions matter: how incorporating distribution shape improves the interpretation of cancer transcriptomic data

“…The search for other non-Normal distributions in the transcriptome remains limited despite the fact that these distributions have the potential to model rare regulatory events in large patient cohorts with more flexibility than a Normal distribution. Non-Normal distributions that are asymmetric and skewed can more accurately model genes spanning a range of aberrant expression for an extreme group of individuals than a symmetric distribution can [ 22 ]. Such long-tailed aberrations could reflect DNA sequence or copy number variation, different isoforms or alternative splicing patterns.…”

Joint learning of multiple gene networks from single-cell gene expression data

“…However, Gaussian graphical models require that the data should follow a normal distribution, which limits their applications on scRNA-seq data [31] , as RNA sequencing data can be considered to obey negative binomial or Poisson distribution [32] , [33] . In order to model the graph under non-Gaussian conditions, Liu et al [34] proposed a nonparanormal distribution and introduced semiparametric Gaussian copula graphical models to infer the conditional dependence between random variables that do not follow normal distribution.…”

The International Conference on Intelligent Biology and Medicine (ICIBM) 2019: bioinformatics methods and applications for human diseases

“…The next paper by Church et al [12] tried to understand gene expression heterogeneity by investigating skewness in large patient cohorts. Skewness is a statistical measure that captures the degree of asymmetry in the distribution of any dataset.…”