Detecting Stable Clusters Using Principal Component Analysis View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2003-03-13

AUTHORS

Michael J. Brownstein , Arkady Khodursky , Asa Ben-Hur , Isabelle Guyon

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. More... »

PAGES

159-182

Book

TITLE

Functional Genomics

ISBN

1-59259-364-X

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1385/1-59259-364-x:159

DOI

http://dx.doi.org/10.1385/1-59259-364-x:159

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1032495735


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