  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.
