Methods for microarray data analysis.

Veronique De Bruyne, Fahd Al-Mulla, Bruno Pot

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


This chapter outlines a typical workflow for micraorray data analysis. It aims at explaining the background of the methods as this is necessary for deciding upon a specific numerical method to use and for understanding and interpreting the outcomes of the analyses. We focus on error handling, various steps during preprocessing (clipping, imputing missing values, normalization, and transformation of data), statistic tests for variable selection and the use of multiple hypothesis testing procedures, various metrics and clustering algorithms for hierarchical clustering, principles, and results from principal components analysis and discriminant analysis, partitioning, self-organizing map, K-nearest neighbor classifier, and the use of a neural network and a support vector machine for classification.

Original languageEnglish
Pages (from-to)373-391
Number of pages19
JournalMethods in molecular biology (Clifton, N.J.)
Publication statusPublished - 1 Jan 2007


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