![]() Whereas one might anticipate that some algorithms are inherently better for cluster analysis of 'typical' gene-expression data, nearly every software vendor is compelled to provide access to most published methods. There are numerous algorithms and associated programs to perform cluster analysis (for example, hierarchical methods, self-organizing maps, k-means and model-based approaches ) and many of these techniques have been applied to expression data (for example ). ![]() ![]() , cluster analysis was used to identify subsets of genes that show different expression patterns between different types of cancers. Often, there is the additional goal of identifying a small subset of genes that are most diagnostic of sample differences. In this case, the expression pattern is effectively a complex phenotype and cluster analysis is used to identify samples with similar and different phenotypes. Hence, in these examples, the function(s) of gene(s) could be inferred through 'guilt by association' or appearance in the same cluster(s).Īnother common use of cluster analysis is to group samples by relatedness in expression patterns. Such genes are typically involved in related functions and are frequently co-regulated (as demonstrated by other evidence such as shared promoter sequences and experimental verification). ![]() , cluster analysis was used to identify genes that show similar expression patterns over a wide range of experimental conditions in yeast. Clustering is a useful exploratory technique for gene-expression data as it groups similar objects together and allows the biologist to identify potentially meaningful relationships between the objects (either genes or experiments or both). The two most frequently performed analyses on gene-expression data are the inference of differentially expressed genes and clustering. ![]()
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