Estimation and control of multiple testing error rates for microarray studies.
The analysis of microarray data often involves performing a large number of statistical tests, usually at least one test per queried gene. Each test has a certain probability of reaching an incorrect inference; therefore, it is crucial to estimate or control error rates that measure the occurrence of erroneous conclusions in reporting and interpreting the results of a microarray study. In recent years, many innovative statistical methods have been developed to estimate or control various error rates for microarray studies. Researchers need guidance choosing the appropriate statistical methods for analysing these types of data sets. This review describes a family of methods that use a set of P-values to estimate or control the false discovery rate and similar error rates. Finally, these methods are classified in a manner that suggests the appropriate method for specific applications and diagnostic procedures that can identify problems in the analysis are described.
Pounds
Stanley B
SB
Department of Biostatistics, MS 768, St Jude Children's Research Hospital, Memphis, TN 38105, USA. stanley.pounds@stjude.org
D019295:Computational Biology; D020869:Gene Expression Profiling / Q000706:statistics & numerical data*; D020411:Oligonucleotide Array Sequence Analysis / Q000706:statistics & numerical data*; D040081:Protein Array Analysis / Q000706:statistics & numerical data*; D015203:Reproducibility of Results