Work

Bagged gene shaving for the robust clustering of high-throughput data

Year: 2010

Type: article

Abstract: The analysis of high-throughput data sets, such as microarray data, often requires that individual variables (genes, for example) be grouped into clusters of variables with highly correlated values across all samples. Gene shaving is an established method for generating such clusters, but is overly sensitive to the input data: changing just one sample can determine whether or not an entire cluster is found. This paper describes a clustering method based on the bootstrap aggregation of gene shaving clusters, which overcomes this and other problems, and applies the new method to a large gene expression microarray dataset from brain tumour samples.

Source: International Journal of Bioinformatics Research and Applications

DOI: 10.1504/ijbra.2010.035997

Authors: Bradley M. Broom, Erik P. Sulman, Kim Anh Do, Mary E. Edgerton, Kenneth D. Aldape

Institution: The University of Texas MD Anderson Cancer Center

Language: English

Topic: Gene expression and cancer classification

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: green

Funders: University of Texas MD Anderson Cancer Center, National Cancer Institute

Award: SPORE in Brain Cancer