Linked Life Data

15737073

Source:http://linkedlifedata.com/resource/pubmed/id/15737073

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1
pubmed:dateCreated
2005-3-1
pubmed:abstractText
In this article, we propose a method for clustering that produces tight and stable clusters without forcing all points into clusters. The methodology is general but was initially motivated from cluster analysis of microarray experiments. Most current algorithms aim to assign all genes into clusters. For many biological studies, however, we are mainly interested in identifying the most informative, tight, and stable clusters of sizes, say, 20-60 genes for further investigation. We want to avoid the contamination of tightly regulated expression patterns of biologically relevant genes due to other genes whose expressions are only loosely compatible with these patterns. "Tight clustering" has been developed specifically to address this problem. It applies K-means clustering as an intermediate clustering engine. Early truncation of a hierarchical clustering tree is used to overcome the local minimum problem in K-means clustering. The tightest and most stable clusters are identified in a sequential manner through an analysis of the tendency of genes to be grouped together under repeated resampling. We validated this method in a simulated example and applied it to analyze a set of expression profiles in the study of embryonic stem cells.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Mar
pubmed:issn
0006-341X
pubmed:author
pubmed:issnType
Print
pubmed:volume
61
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
10-6
pubmed:dateRevised
2006-11-15
pubmed:meshHeading
pubmed:year
2005
pubmed:articleTitle
Tight clustering: a resampling-based approach for identifying stable and tight patterns in data.
pubmed:affiliation
Department of Biostatistics and Department of Human Genetics, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, USA. ctseng@pitt.edu
pubmed:publicationType
Journal Article, Research Support, U.S. Gov't, Non-P.H.S.