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

  • Authors:

  • Bradley M. Broom;Erik P. Sulman;Kim-Anh Do;Mary E. Edgerton;Kenneth D. Aldape

  • Affiliations:

  • Department of Bioinformatics and Computational Biology, UT MD Anderson Cancer Center, Houston, Texas 77030, USA.;Department of Radiation Oncology, UT MD Anderson Cancer Center, Houston, Texas 77030, USA.;Department of Biostatistics, UT MD Anderson Cancer Center, Houston, Texas 77030, USA.;Department of Pathology, UT MD Anderson Cancer Center, Houston, Texas 77030, USA.;Department of Pathology, UT MD Anderson Cancer Center, Houston, Texas 77030, USA

  • Venue:
  • International Journal of Bioinformatics Research and Applications
  • Year:
  • 2010

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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.