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Analysing gene expression data from DNA microarrays to identify candidate genes
Author(s) -
Wu Thomas D.
Publication year - 2001
Publication title -
the journal of pathology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.964
H-Index - 184
eISSN - 1096-9896
pISSN - 0022-3417
DOI - 10.1002/1096-9896(200109)195:1<53::aid-path891>3.0.co;2-h
Subject(s) - replicate , dna microarray , computational biology , biological data , gene , biology , covariate , microarray analysis techniques , task (project management) , computer science , data mining , genetics , gene expression , statistics , mathematics , machine learning , management , economics
Microarray data analysis can be divided into two tasks: grouping of genes to discover broad patterns of biological behaviour, and filtering of genes to identify specific genes of interest. Whereas the gene‐grouping task is largely addressed by cluster analysis, the gene‐filtering task relies primarily on hypothesis testing. This review article surveys analytical methods for the gene‐filtering task. Various types of data analysis are discussed for four basic types of experimental protocols: a comparison of two biological samples; a comparison of two biological conditions; each represented by a set of replicate samples; a comparison of multiple biological conditions; and analysis of covariate information. Copyright © 2001 John Wiley & Sons, Ltd.