Pathway level analysis of gene expression using singular value decomposition View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2005-12

AUTHORS

John Tomfohr, Jun Lu, Thomas B Kepler

ABSTRACT

BACKGROUND: A promising direction in the analysis of gene expression focuses on the changes in expression of specific predefined sets of genes that are known in advance to be related (e.g., genes coding for proteins involved in cellular pathways or complexes). Such an analysis can reveal features that are not easily visible from the variations in the individual genes and can lead to a picture of expression that is more biologically transparent and accessible to interpretation. In this article, we present a new method of this kind that operates by quantifying the level of 'activity' of each pathway in different samples. The activity levels, which are derived from singular value decompositions, form the basis for statistical comparisons and other applications. RESULTS: We demonstrate our approach using expression data from a study of type 2 diabetes and another of the influence of cigarette smoke on gene expression in airway epithelia. A number of interesting pathways are identified in comparisons between smokers and non-smokers including ones related to nicotine metabolism, mucus production, and glutathione metabolism. A comparison with results from the related approach, 'gene-set enrichment analysis', is also provided. CONCLUSION: Our method offers a flexible basis for identifying differentially expressed pathways from gene expression data. The results of a pathway-based analysis can be complementary to those obtained from one more focused on individual genes. A web program PLAGE (Pathway Level Analysis of Gene Expression) for performing the kinds of analyses described here is accessible at http://dulci.biostat.edu/pathways. More... »

PAGES

225

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/1471-2105-6-225

DOI

http://dx.doi.org/10.1186/1471-2105-6-225

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1018731931

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/16156896


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