Skip to main content
Article
Discrete nonparametric algorithms for outlier detection with genomic data
Technical Report, Department of Statistics, Penn State University. (2009)
  • Debashis Ghosh, Penn State University
Abstract

In high-throughput studies involving genetic data such as from gene expression microarrays, differential expression analysis between two or more experimental conditions has been a very common analytical task. Much of the resulting literature on multiple comparisons has paid relatively little attention to the choice of test statistic. In this article, we focus on the issue of choice of test statistic based on a special pattern of differential expression. The approach here is based on recasting multiple comparisons procedures for assessing outlying expression values. A major complication is that the resulting p-values are discrete; some theoretical properties of sequential testing procedures in this context are explored. We propose the use of q-value estimation procedures in this setting. Data from a gene expression profiling experiment in prostate cancer are used to illustrate the methodology.

Keywords
  • Bonferroni,
  • Discrete Data,
  • False discovery rate,
  • Multiple Testing,
  • Single Nucleotide Polymorphism.
Publication Date
2009
Citation Information
Debashis Ghosh. "Discrete nonparametric algorithms for outlier detection with genomic data" Technical Report, Department of Statistics, Penn State University. (2009)
Available at: http://works.bepress.com/debashis_ghosh/36/