Unsupervised assessment of microarray data quality using a Gaussian mixture model.

ABSTRACT:

Background

Quality assessment of microarray data is an important and often challenging aspect of gene expression analysis. This task frequently involves the examination of a variety of summary statistics and diagnostic plots. The interpretation of these diagnostics is often subjective, and generally requires careful expert scrutiny.

Results

We show how an unsupervised classification technique based on the Expectation-Maximization (EM) algorithm and the naïve Bayes model can be used to automate microarray quality assessment. The method is flexible and can be easily adapted to accommodate alternate quality statistics and platforms. We evaluate our approach using Affymetrix 3' gene expression and exon arrays and compare the performance of this method to a similar supervised app

SUBMITTER: Howard BE 

PROVIDER: S-EPMC2717951 | biostudies-literature | 2009 Jun

REPOSITORIES: biostudies-literature