Document Type
Article
Journal Title
Journal of Biomedical Analytics
Publication Date
2018
Volume
1
Abstract
One of the main objectives of many biological studies is to explore differential gene expression profiles between samples. Genes are referred to as differentially expressed (DE) if the read counts change across treatments or conditions systematically. Poisson and negative binomial (NB) regressions are widely used methods for non-over-dispersed (NOD) and over-dispersed (OD) count data respectively. However, in the presence of excessive number of zeros, these methods need adjustments. In this paper, we consider a zero-inflated Poisson mixed effects model (ZIPMM) and zero-inflated negative binomial mixed effects model (ZINBMM) to address excessive zero counts in the NOD and OD RNA-seq data respectively in the presence of random effects. We apply these methods to both simulated and real RNA-seq datasets. The ZIPMM and ZINBMM perform better on both simulated and real datasets.
DOI Link
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Alam, Morshed; Al Mahi, Naim; and Begum, Munni, "Zero-Inflated Models for RNA-Seq Count Data" (2018). Journal Articles: Biostatistics. 4.
https://digitalcommons.unmc.edu/coph_biostats_articles/4