Mining SOM expression portraits: feature selection and integrating concepts of molecular function.

ABSTRACT:

Background

Self organizing maps (SOM) enable the straightforward portraying of high-dimensional data of large sample collections in terms of sample-specific images. The analysis of their texture provides so-called spot-clusters of co-expressed genes which require subsequent significance filtering and functional interpretation. We address feature selection in terms of the gene ranking problem and the interpretation of the obtained spot-related lists using concepts of molecular function.

Results

Different expression scores based either on simple fold change-measures or on regularized Student's t-statistics are applied to spot-related gene lists and compared with special emphasis on the error characteristics of microarray expression data. The spot-clusters are analyzed using di

SUBMITTER: Wirth H 

PROVIDER: S-EPMC3599960 | biostudies-literature | 2012 Oct

REPOSITORIES: biostudies-literature