BNP-Seq: Bayesian Nonparametric Differential Expression Analysis of Sequencing Count Data Academic Article uri icon

abstract

  • 2018 American Statistical Association. 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. Supplementary materials for this article are available online.

published proceedings

  • Journal of the American Statistical Association

author list (cited authors)

  • Dadaneh, S. Z., Qian, X., & Zhou, M.

citation count

  • 23

complete list of authors

  • Dadaneh, Siamak Zamani||Qian, Xiaoning||Zhou, Mingyuan

publication date

  • January 2018