The Annals of Applied Statistics

Classification and clustering of sequencing data using a Poisson model

Daniela M. Witten

Full-text: Open access

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.

Article information

Source
Ann. Appl. Stat. Volume 5, Number 4 (2011), 2493-2518.

Dates
First available in Project Euclid: 20 December 2011

Permanent link to this document
https://projecteuclid.org/euclid.aoas/1324399604

Digital Object Identifier
doi:10.1214/11-AOAS493

Mathematical Reviews number (MathSciNet)
MR2907124

Zentralblatt MATH identifier
1234.62150

Keywords
Classification clustering genomics gene expression Poisson sequencing

Citation

Witten, Daniela M. Classification and clustering of sequencing data using a Poisson model. Ann. Appl. Stat. 5 (2011), no. 4, 2493--2518. doi:10.1214/11-AOAS493. https://projecteuclid.org/euclid.aoas/1324399604.


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