Biological assessment of robust noise models in microarray data analysis.

ABSTRACT:

Motivation

Although several recently proposed analysis packages for microarray data can cope with heavy-tailed noise, many applications rely on Gaussian assumptions. Gaussian noise models foster computational efficiency. This comes, however, at the expense of increased sensitivity to outlying observations. Assessing potential insufficiencies of Gaussian noise in microarray data analysis is thus important and of general interest.

Results

We propose to this end assessing different noise models on a large number of microarray experiments. The goodness of fit of noise models is quantified by a hierarchical Bayesian analysis of variance model, which predicts normalized expression values as a mixture of a Gaussian density and t-distributions with adjustable degrees of freedom. Inf

SUBMITTER: Posekany A 

PROVIDER: S-EPMC3051324 | biostudies-literature | 2011 Mar

REPOSITORIES: biostudies-literature