Differential Expression Analysis of Dynamical Sequencing Count Data with a Gamma Markov Chain

TL;DR

This paper introduces GMNB, a novel statistical model that captures complex temporal gene expression patterns in sequencing data, improving differential expression analysis without requiring normalization.

Contribution

The paper develops the gamma Markov negative binomial (GMNB) model, which allows flexible temporal variation and handles sequencing depth heterogeneity in NGS data.

Findings

GMNB outperforms existing methods in ROC and PR metrics.

It effectively captures diverse gene expression dynamics.

The model handles sequencing depth variability without normalization.

Abstract

Next-generation sequencing (NGS) to profile temporal changes in living systems is gaining more attention for deriving better insights into the underlying biological mechanisms compared to traditional static sequencing experiments. Nonetheless, the majority of existing statistical tools for analyzing NGS data lack the capability of exploiting the richer information embedded in temporal data. Several recent tools have been developed to analyze such data but they typically impose strict model assumptions, such as smoothness on gene expression dynamic changes. To capture a broader range of gene expression dynamic patterns, we develop the gamma Markov negative binomial (GMNB) model that integrates a gamma Markov chain into a negative binomial distribution model, allowing flexible temporal variation in NGS count data. Using Bayes factors, GMNB enables more powerful temporal gene differential expression analysis across different phenotypes or treatment conditions. In addition, it naturally handles the heterogeneity of sequencing depth in different samples, removing the need for ad-hoc normalization. Efficient Gibbs sampling inference of the GMNB model parameters is achieved by exploiting novel data augmentation techniques. Extensive experiments on both simulated and real-world RNA-seq data show that GMNB outperforms existing methods in both receiver operating characteristic (ROC) and precision-recall (PR) curves of differential expression analysis results.