Detecting Stable Clusters Using Principal Component Analysis

by: Asa Ben-Hur, Isabelle Guyon

Functional Genomics (2003), pp. 159-182.

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Abstract

Clustering is one of the most commonly used tools in the analysis of gene expression data (1,2). The usage in grouping genes is based on the premise that coexpression is a result of coregulation. It is often used as a preliminary step in extracting gene networks and inference of gene function (3,4). Clustering of experiments can be used to discover novel phenotypic aspects of cells and tissues (3,5,6), including sensitivity to drugs (7), and can also detect artifacts of experimental conditions (8). Clustering and its applications in biology are presented in greater detail in Chapter 13 (see also ref. 9). While we focus on gene expression data in this chapter, the methodology presented here is applicable for other types of data as well.

@incollection{citeulike:2861514, abstract = {Clustering is one of the most commonly used tools in the analysis of gene expression data (1,2). The usage in grouping genes is based on the premise that coexpression is a result of coregulation. It is often used as a preliminary step in extracting gene networks and inference of gene function (3,4). Clustering of experiments can be used to discover novel phenotypic aspects of cells and tissues (3,5,6), including sensitivity to drugs (7), and can also detect artifacts of experimental conditions (8). Clustering and its applications in biology are presented in greater detail in Chapter 13 (see also ref. 9). While we focus on gene expression data in this chapter, the methodology presented here is applicable for other types of data as well.}, author = {Ben-Hur, Asa and Guyon, Isabelle }, citeulike-article-id = {2861514}, doi = {http://dx.doi.org/10.1385/1-59259-364-X:159}, journal = {Functional Genomics}, keywords = {analysis, clustering, component, dimensionality, pca, principal, reduction, selection, unsupervised, variable}, pages = {159--182}, posted-at = {2008-06-04 16:43:30}, priority = {2}, title = {Detecting Stable Clusters Using Principal Component Analysis}, url = {http://dx.doi.org/10.1385/1-59259-364-X:159}, year = {2003} }

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TY - CHAP ID - citeulike:2861514 L3 - citeulike-article-id:2861514 TI - Detecting Stable Clusters Using Principal Component Analysis JF - Functional Genomics SP - 159 EP - 182 N2 - Clustering is one of the most commonly used tools in the analysis of gene expression data (1,2). The usage in grouping genes is based on the premise that coexpression is a result of coregulation. It is often used as a preliminary step in extracting gene networks and inference of gene function (3,4). Clustering of experiments can be used to discover novel phenotypic aspects of cells and tissues (3,5,6), including sensitivity to drugs (7), and can also detect artifacts of experimental conditions (8). Clustering and its applications in biology are presented in greater detail in Chapter 13 (see also ref. 9). While we focus on gene expression data in this chapter, the methodology presented here is applicable for other types of data as well. KW - analysis KW - clustering KW - component KW - dimensionality KW - pca KW - principal KW - reduction KW - selection KW - unsupervised KW - variable AU - Ben-Hur, Asa AU - Guyon, Isabelle PY - 2003/// UR - http://dx.doi.org/10.1385/1-59259-364-X:159 ER -

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