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Identification of differentially expressed spatial clusters using humoral response microarray data

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

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.

Suggested Citation

  • Wu, Jincao & Patwa, Tasneem H. & Lubman, David M. & Ghosh, Debashis, 2009. "Identification of differentially expressed spatial clusters using humoral response microarray data," Computational Statistics & Data Analysis, Elsevier, vol. 53(8), pages 3094-3102, June.
    • Handle: RePEc:eee:csdana:v:53:y:2009:i:8:p:3094-3102
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    References listed on IDEAS

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    1. Toshiro Tango, 2007. "A Class of Multiplicity Adjusted Tests for Spatial Clustering Based on Case–Control Point Data," Biometrics, The International Biometric Society, vol. 63(1), pages 119-127, March.
    2. Youngchao Ge & Sandrine Dudoit & Terence Speed, 2003. "Resampling-based multiple testing for microarray data analysis," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 12(1), pages 1-77, June.
    3. John D. Storey, 2002. "A direct approach to false discovery rates," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(3), pages 479-498, August.
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    Cited by:

    1. LeSage, James & Banerjee, Sudipto & Fischer, Manfred M. & Congdon, Peter, 2009. "Spatial statistics: Methods, models & computation," Computational Statistics & Data Analysis, Elsevier, vol. 53(8), pages 2781-2785, June.

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