title: Multiple hypothesis testing: a methodological overview.
abstract: The process of screening for differentially expressed genes using microarray samples can usually be reduced to a large set of statistical hypothesis tests. In this situation, statistical issues arise which are not encountered in a single hypothesis test, related to the need to identify the specific hypotheses to be rejected, and to report an associated error. As in any complex testing problem, it is rarely the case that a single method is always to be preferred, leaving the analysts with the problem of selecting the most appropriate method for the particular task at hand. In this chapter, an introduction to current multiple testing methodology was presented, with the objective of clarifying the methodological issues involved, and hopefully providing the reader with some basis with which to compare and select methods.
authors: Department of Biostatistics and Computational Biology, University of Rochester, Rochester, NY, USA. Antony_Almudevar@urmc.rochester.edu
authors: Anthony
authors: A
authors: Almudevar
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D003627:Data Interpretation, Statistical; D020869:Gene Expression Profiling; D006801:Humans; D015233:Models, Statistical; D009010:Monte Carlo Method; D020411:Oligonucleotide Array Sequence Analysis