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Table 1 Statistical algorithms used in analysis of microarray data

From: Microarray analysis of gene expression in lupus

Statistical algorithms Characteristics References Sources
Significance analysis of microarrays (SAM) Identifies differentially expressed genes between sample sets; estimates significance for genes; considers large numbers of genes in array experiments [9] Stanford University, http://www-stat.stanford.edu/~tibs/ x-mine, Brisbane, CA, http://www.x-mine.com/
Hierarchal clustering Unsupervised clustering; clusters genes with similar expression patterns; clusters samples with similar expression patterns [15] University of California, Berkeley, http://rana.lbl.gov/EisenSoftware.htm
Supervised harvesting classification Class prediction; identifies subset of genes that best classify samples as gene sets; estimates accuracy of gene set on prospective population [10] x-mine, Brisbane, CA, http://www.x-mine.com/
Classification and regression trees (CART), multiple additive regression trees (MART) Class prediction; develops decision trees to classify samples using the expression of a subset of genes; estimates accuracy of the gene panel on a prospective set [12, 14] CART: Salford Systems, http://www.salford-systems.com/ MART: Stanford University, http://www-stat.stanford.edu/~jhf/R-MART.html or Salford Systems
Shrunken centroids (prediction analysis for microarrays, PAM) Class prediction; identifies subset of genes that best classify samples as gene sets; estimates accuracy of gene set on prospective population [11] Stanford University, http://www-stat.stanford.edu/~tibs/PAM/index.html
Affymetrix MAS 5.0    Affymetrix
GeneSpring    Silicon Genetics
Pathways 3.0    Research Genetics