|DOI / URL||link|
|Title (Primary)||Mining SOM expression portraits: feature selection and integrating concepts of molecular function|
|Author||Wirth, H.; von Bergen, M.; Binder, H.|
|Page From||art. 18|
|Keywords||expression analysis; self organizing maps; feature selection; gene set enrichment analysis; human tissues|
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.
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 different methods of gene set enrichment analysis with the focus on overexpression and/or overrepresentation of predefined sets of genes. Metagene-related overrepresentation of selected gene sets was mapped into the SOM images to assign gene function to different regions. Alternatively we estimated set-related overexpression profiles over all samples studied using a gene set enrichment score. It was also applied to the spot-clusters to generate lists of enriched gene sets. We used the tissue body index data set, a collection of expression data of human tissues as an illustrative example. We found that tissue related spots typically contain enriched populations of gene sets well corresponding to molecular processes in the respective tissues. In addition, we display special sets of housekeeping and of consistently weak and high expressed genes using SOM data filtering.
The presented methods allow the comprehensive downstream analysis of SOM-transformed expression data in terms of cluster-related gene lists and enriched gene sets for functional interpretation. SOM clustering implies the ability to define either new gene sets using selected SOM spots or to verify and/or to amend existing ones.
|Persistent UFZ Identifier||https://www.ufz.de/index.php?en=20939&ufzPublicationIdentifier=13295|
|Wirth, H., von Bergen, M., Binder, H. (2012):
Mining SOM expression portraits: feature selection and integrating concepts of molecular function
BioData Min. 5 (October), art. 18