Classification and clustering of sequencing data using a Poisson model

TL;DR

This paper introduces new classification and clustering methods tailored for sequencing data, using a Poisson model to better handle the discrete count nature of such data, improving analysis accuracy.

Contribution

It develops a Poisson-based analog of linear discriminant analysis and a novel clustering dissimilarity measure for sequencing data, addressing limitations of Gaussian-based methods.

Findings

Poisson-based methods outperform Gaussian-based methods on sequencing data

The approaches work well on real RNA sequencing datasets

The methods are validated through simulation and real data analysis

Abstract

In recent years, advances in high throughput sequencing technology have led to a need for specialized methods for the analysis of digital gene expression data. While gene expression data measured on a microarray take on continuous values and can be modeled using the normal distribution, RNA sequencing data involve nonnegative counts and are more appropriately modeled using a discrete count distribution, such as the Poisson or the negative binomial. Consequently, analytic tools that assume a Gaussian distribution (such as classification methods based on linear discriminant analysis and clustering methods that use Euclidean distance) may not perform as well for sequencing data as methods that are based upon a more appropriate distribution. Here, we propose new approaches for performing classification and clustering of observations on the basis of sequencing data. Using a Poisson log linear model, we develop an analog of diagonal linear discriminant analysis that is appropriate for sequencing data. We also propose an approach for clustering sequencing data using a new dissimilarity measure that is based upon the Poisson model. We demonstrate the performances of these approaches in a simulation study, on three publicly available RNA sequencing data sets, and on a publicly available chromatin immunoprecipitation sequencing data set.