Identification of differentially expressed spatial clusters using humoral response microarray data

  • Authors:

  • Jincao Wu;Tasneem H. Patwa;David M. Lubman;Debashis Ghosh

  • Affiliations:

  • Department of Biostatistics, University of Michigan, United States;Department of Chemistry, University of Michigan, United States;Department of Surgery, University of Michigan, United States;Departments of Statistics and Public Health Sciences, Penn State University, United States

  • Venue:
  • Computational Statistics & Data Analysis
  • Year:
  • 2009

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Abstract

The protein microarray is a powerful chip-based technology for profiling hundreds of proteins simultaneously and is being increasingly used. To study humoral response in pancreatic cancers, scientists have developed a two-dimensional liquid separation technique and built a two-dimensional protein microarray. However, identifying regions of differential expression on the protein microarray requires the use of appropriate statistical methods to assess the large amounts of data generated. A permutation-based test is proposed that incorporates spatial information of the two-dimensional antibody microarray. By borrowing strength from neighboring differentially expressed spots, the procedure is able to detect differentially expressed regions with high power while controlling the familywise type I error at 0.05 in simulation studies. The proposed methodology is also applied to a real microarray dataset.