Non-parametric Bayesian modelling of digital gene expression data

TL;DR

This paper introduces a non-parametric Bayesian method for analyzing digital gene expression data, effectively handling low biological replicates by clustering genes and inferring the number of clusters directly from the data.

Contribution

A novel hierarchical Bayesian model with a clustering approach that adapts to low-replicate RNA-seq data, improving analysis accuracy over existing methods.

Findings

Successfully models gene expression with high fidelity

Effectively compensates for low biological replicates

Infers the number of gene clusters from data

Abstract

Next-generation sequencing technologies provide a revolutionary tool for generating gene expression data. Starting with a fixed RNA sample, they construct a library of millions of differentially abundant short sequence tags or "reads", which constitute a fundamentally discrete measure of the level of gene expression. A common limitation in experiments using these technologies is the low number or even absence of biological replicates, which complicates the statistical analysis of digital gene expression data. Analysis of this type of data has often been based on modified tests originally devised for analysing microarrays; both these and even de novo methods for the analysis of RNA-seq data are plagued by the common problem of low replication. We propose a novel, non-parametric Bayesian approach for the analysis of digital gene expression data. We begin with a hierarchical model for modelling over-dispersed count data and a blocked Gibbs sampling algorithm for inferring the posterior distribution of model parameters conditional on these counts. The algorithm compensates for the problem of low numbers of biological replicates by clustering together genes with tag counts that are likely sampled from a common distribution and using this augmented sample for estimating the parameters of this distribution. The number of clusters is not decided a priori, but it is inferred along with the remaining model parameters. We demonstrate the ability of this approach to model biological data with high fidelity by applying the algorithm on a public dataset obtained from cancerous and non-cancerous neural tissues.