Linked Life Data

14960463

Source:http://linkedlifedata.com/resource/pubmed/id/14960463

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
3
pubmed:dateCreated
2004-2-12
pubmed:abstractText
MOTIVATION: We present a statistical framework for the analysis of high-dimensional microarray data, where the goal is to compare intensities among several groups based on as few as a single sample from each group. In this setting, it is of interest to compare gene expression among several phenotypes to define candidate genes that simultaneously characterize several criteria, simultaneously, among the comparison groups. We motivate the approach by a comparative microarray experiment in which clones of a cell were singly exposed to several distinct but related conditions. The experiment was conducted to elucidate genes involved in pathways leading to T cell clonal anergy. RESULTS: By integrating inference principles within a bioinformatics setting, we introduce a two-stage approach to select candidate genes that characterize several criteria. The method is unified in its non-parametric approach to inference and description. For inference, we construct a testable hypothesis based on the criteria of interest in a high-dimensional space, while preserving the dependence among genes. Upon rejecting the null, we estimate the cardinality of a set of individual candidate genes (or gene pairs) that depict the events of interest. With this estimate, we then select individual genes (or gene pairs) based upon a two-dimensional ranking that examines relations within and between genes, among comparison groups, using singular value decomposition in combination with inner product concepts.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Feb
pubmed:issn
1367-4803
pubmed:author
pubmed:issnType
Print
pubmed:day
12
pubmed:volume
20
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
364-73
pubmed:dateRevised
2007-11-14
pubmed:meshHeading
pubmed:year
2004
pubmed:articleTitle
Non-parametric, hypothesis-based analysis of microarrays for comparison of several phenotypes.
pubmed:affiliation
Department of Oncology, Johns Hopkins University, Baltimore, MD 21205, USA. jkowals1@jhmi.edu
pubmed:publicationType
Journal Article, Comparative Study, Research Support, U.S. Gov't, P.H.S., Research Support, Non-U.S. Gov't, Evaluation Studies, Validation Studies