title: Genomic outlier detection in high-throughput data analysis.
abstract: In the analysis of high-throughput data, a very common goal is the detection of genes or of differential expression between two groups or classes. A recent finding from the scientific literature in prostate cancer demonstrates that by searching for a different pattern of differential expression, new candidate oncogenes might be found. In this chapter, we discuss the statistical problem, termed oncogene outlier detection, and discuss a variety of proposals to this problem. A statistical model in the multiclass situation is described; links with multiple testing concepts are established. Some new nonparametric procedures are described and compared to existing methods using simulation studies.
authors: Departments of Statistics and Public Health Sciences, Penn State University, DuBios, PA, USA. ghoshd@psu.edu
authors: Debashis
authors: D
authors: Ghosh
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D020869:Gene Expression Profiling / Q000379:methods*; D056726:Genetic Association Studies / Q000379:methods; D023281:Genomics; D006801:Humans; D015233:Models, Statistical; D015999:Multivariate Analysis; D009369:Neoplasms / Q000235:genetics / Q000378:metabolism; D016011:Normal Distribution; D020411:Oligonucleotide Array Sequence Analysis; D009857:Oncogenes; D012372:ROC Curve; D018709:Statistics, Nonparametric
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