Non-specific filtering of beta-distributed data.

ABSTRACT:

Background

Non-specific feature selection is a dimension reduction procedure performed prior to cluster analysis of high dimensional molecular data. Not all measured features are expected to show biological variation, so only the most varying are selected for analysis. In DNA methylation studies, DNA methylation is measured as a proportion, bounded between 0 and 1, with variance a function of the mean. Filtering on standard deviation biases the selection of probes to those with mean values near 0.5. We explore the effect this has on clustering, and develop alternate filter methods that utilize a variance stabilizing transformation for Beta distributed data and do not share this bias.

Results

We compared results for 11 different non-specific filters on eight Infinium HumanMet

SUBMITTER: Wang X 

PROVIDER: S-EPMC4230495 | biostudies-literature | 2014 Jun

REPOSITORIES: biostudies-literature