Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2005 Sep 12:6:225.
doi: 10.1186/1471-2105-6-225.

Pathway level analysis of gene expression using singular value decomposition

John Tomfohr  1 , Jun Lu, Thomas B Kepler
Affiliations

Pathway level analysis of gene expression using singular value decomposition

John Tomfohr et al. BMC Bioinformatics. .

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.

PubMed Disclaimer

Figures

Figure 1
Figure 1
Outline of pathway level analysis of gene expression.
Figure 2
Figure 2
Pathway activity levels. Schematic illustration of our approach to quantifying the activity level of a pathway. (A) A colormap of the expression levels for the genes in a hypothetical pathway after standardizing the expression levels to have zero mean and unit variance over samples. This represents the matrix Y described in the text. (B) The main component of the variation in the expression matrix depicted in (A). This representation is determined by the activity levels c and weights w (see Methods) associated with the first metagene in the singular value decomposition (SVD) of Y . The activity level in a sample (one column of the expression matrix) can be thought of as specifying a location in the range of expression profiles shown in (C). Positive activity levels here indicate relatively high (low) expression for genes with positive (negative) weight. For example, the expression profile (column) furthest to the left in the expression matrix is in the high positive region of the range of expression profiles. The colormaps in (A) and (B) show the samples divided into two hypothetical groups (e.g., case samples and control samples). We note, however, that the matrix Y contains expression values for all samples: the activity levels are determined by performing SVD using expression data from all samples without regard to how the samples are classified.
Figure 3
Figure 3
Negative correlation between oxidative phosphorylation and blood glucose levels after OGTT. Scatter plot of blood glucose levels 2 hours after OGTT vs. oxidative phosphorylation activity levels. The three subject groups – type 2 diabetic (DM2), normal glucose tolerance (NGT), and impaired glucose tolerance (IGT) – are distinguished by color; solid lines show the first principal component for each group independent of the others. Group means are shown in black squares. The inset shows the 95% confidence intervals for the linear correlation coefficients for each group. Negative correlation between glucose levels and oxidative phosphorylation reaches statistical significance only within DM2 subjects.
Figure 4
Figure 4
Expression profiles in airway epithelia of current (C), former (F), and never (N) smokers. Top: colormap of pathway activity levels for the highest ranking pathways in the comparison between current smokers and never smokers. Bottom: colormap for genes in the KEGG glutathione metabolism pathway. Glutathione is an important anti-oxidant known to be increased in the lungs of smokers. The genes with the highest weights in this pathway, GCLM and GCLC, encode the subunits of glutamate cysteine ligase (GCL), the rate-limiting enzyme in the synthesis of glutathione [24].
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Tusher VG, Tibshirani R, Chu G. Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci USA. 2001;98:5116–21. doi: 10.1073/pnas.091062498. - DOI - PMC - PubMed
    1. Efron B, Tibshirani R, Storey JD, Tusher V. Empirical Bayes analysis of a microarray experiment. J Amer Statist Assoc. 2001;96:1151–1160. doi: 10.1198/016214501753382129. - DOI
    1. Baldi P, Long AD. A Bayesian framework for the analysis of microarray expression data: regularized t-test and statistical inferences of gene changes. Bioinformatics. 2001;17:509–19. doi: 10.1093/bioinformatics/17.6.509. - DOI - PubMed
    1. Kepler TB, Crosby L, Morgan KT. Normalization and analysis of DNA microarray data by self-consistency and local regression. Genome Biol. 2002;3:research0037. doi: 10.1186/gb-2002-3-7-research0037. - DOI - PMC - PubMed
    1. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, Harris MA, Hill DP, Issel-Tarver L, Kasarskis A, Lewis S, Matese JC, Richardson JE, Ringwald M, Rubin GM, Sherlock G. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet. 2000;25:25–9. doi: 10.1038/75556. - DOI - PMC - PubMed

Publication types

MeSH terms