Differentially expressed heterogeneous overdispersion genes testing for count data
Yubai Yuan, Qi Xu, Agaz Wani, Jan Dahrendorff, Chengqi Wang, Arlina Shen, Janelle Donglasan, Sarah Burgan, Zachary Graham, Monica Uddin, Derek Wildman, Annie Qu
TL;DR
This paper introduces a new method for identifying differentially expressed genes in RNA-seq data that improves detection power when sample sizes are small.
Contribution
The novel DEHOGT method uses heterogeneous overdispersion modeling to better detect differentially expressed genes in RNA-seq data.
Findings
DEHOGT outperforms DESeq2 and EdgeR in detecting differentially expressed genes in synthetic RNA-seq data.
DEHOGT detects more differentially expressed genes in microglial cells under stress hormone treatments.
The method enhances detection power when the number of replicates is limited but the number of conditions is large.
Abstract
The mRNA-seq data analysis is a powerful technology for inferring information from biological systems of interest. Specifically, the sequenced RNA fragments are aligned with genomic reference sequences, and we count the number of sequence fragments corresponding to each gene for each condition. A gene is identified as differentially expressed (DE) if the difference in its count numbers between conditions is statistically significant. Several statistical analysis methods have been developed to detect DE genes based on RNA-seq data. However, the existing methods could suffer decreasing power to identify DE genes arising from overdispersion and limited sample size, where overdispersion refers to the empirical phenomenon that the variance of read counts is larger than the mean of read counts. We propose a new differential expression analysis procedure: heterogeneous overdispersion genes…
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Taxonomy
TopicsMetabolomics and Mass Spectrometry Studies · Statistical Methods and Inference · Cancer-related molecular mechanisms research