AutoSOME: a clustering method for identifying gene expression modules without prior knowledge of cluster number.

ABSTRACT:

Background

Clustering the information content of large high-dimensional gene expression datasets has widespread application in "omics" biology. Unfortunately, the underlying structure of these natural datasets is often fuzzy, and the computational identification of data clusters generally requires knowledge about cluster number and geometry.

Results

We integrated strategies from machine learning, cartography, and graph theory into a new informatics method for automatically clustering self-organizing map ensembles of high-dimensional data. Our new method, called AutoSOME, readily identifies discrete and fuzzy data clusters without prior knowledge of cluster number or structure in diverse datasets including whole genome microarray data. Visualization of AutoSOME output using n

SUBMITTER: Newman AM 

PROVIDER: S-EPMC2846907 | biostudies-literature | 2010 Mar

REPOSITORIES: biostudies-literature