Pathway level analysis of gene expression using singular value decomposition

By John K. Tomfohr, Jun Lu, Thomas B. Kepler. BMC Bioinformatics, 2005

395 citations recorded by the source corpus.

BackgroundA 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.ResultsWe 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.ConclusionOur 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.duke.edu/pathways.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/7f23fd4ae73aa61e154ba91da43a37b1a26a2cae

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Citations in the corpus

Identification of diagnostic subnetwork markers for cancer in human protein-protein interaction network (2010, methodology): “Tomfohr et al. [22] analyzed the expression levels of genes in a pathway using singular value decomposition (SVD), and they used the eigenvector with the largest eigenvalue as the pathway activity.”
KOBAS-i: intelligent prioritization and exploratory visualization of biological functions for gene enrichment analysis (2021, methodology): “It integrates seven widely used FCS methods: GSEA (14), GSA (16), PADOG (17), PLAGE (18), GAGE (19), GLOBALTEST (20) and SAFE (21) and two prominent PT methods: GANPA (23), and CEPA (24), into a single ensemble score (R score).”
Gene-set approach for expression pattern analysis (2008, methodology): “[10], PLAGE [20], the multivariate approach of Kong et al .”
A framework to identify physiological responses in microarray-based gene expression studies: selection and interpretation of biologically relevant genes. (2008, methodology): “A commonly used approach is to import a list of genes that meets the threshold criteria into a pathway program, such as the freely available ErmineJ (24), GeneMapp, David/EASE, SAFE (3), or PLAGE (45) or commercially available programs like Metacore (16) or Ingenuity.”
Analyzing gene expression data in terms of gene sets: methodological issues (2007, neutral): “Contact: j.j.goeman@lumc.nl”