BNP-Seq: Bayesian Nonparametric Differential Expression Analysis of Sequencing Count Data

TL;DR

BNP-Seq introduces a Bayesian nonparametric method for differential expression analysis of sequencing data, effectively accounting for sequencing depth and improving detection accuracy without complex pre-processing.

Contribution

It employs gamma and beta negative binomial processes to model sequencing counts, enabling more accurate detection of differentially expressed genes by sharing information across genes and samples.

Findings

Outperforms existing methods in ROC and PR curve metrics

Effectively models sequencing depth variations

Demonstrates robustness on simulated and real data

Abstract

We perform differential expression analysis of high-throughput sequencing count data under a Bayesian nonparametric framework, removing sophisticated ad-hoc pre-processing steps commonly required in existing algorithms. We propose to use the gamma (beta) negative binomial process, which takes into account different sequencing depths using sample-specific negative binomial probability (dispersion) parameters, to detect differentially expressed genes by comparing the posterior distributions of gene-specific negative binomial dispersion (probability) parameters. These model parameters are inferred by borrowing statistical strength across both the genes and samples. Extensive experiments on both simulated and real-world RNA sequencing count data show that the proposed differential expression analysis algorithms clearly outperform previously proposed ones in terms of the areas under both the receiver operating characteristic and precision-recall curves.