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

arXiv: https://arxiv.org/abs/1608.03991 · HTML: https://mlsanity.com/paper/1608.03991/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Aug 2016.

## Built on by (most cited first, 2 of 2)

- [SimCD: Simultaneous Clustering and Differential expression analysis for single-cell transcriptomic data](https://mlsanity.com/paper/2104.01512/) (2021)
- [Fisher-Pitman permutation tests based on nonparametric Poisson mixtures with application to single cell genomics](https://mlsanity.com/paper/2106.03022/) (2021)

## Abstract (arXiv)

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.