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. 2012;7(12):e52127.
doi: 10.1371/journal.pone.0052127. Epub 2012 Dec 20.

Pathway correlation profile of gene-gene co-expression for identifying pathway perturbation

Allison N Tegge  1 , Charles W Caldwell, Dong Xu
Affiliations

Pathway correlation profile of gene-gene co-expression for identifying pathway perturbation

Allison N Tegge et al. PLoS One. 2012.

Abstract

Identifying perturbed or dysregulated pathways is critical to understanding the biological processes that change within an experiment. Previous methods identified important pathways that are significantly enriched among differentially expressed genes; however, these methods cannot account for small, coordinated changes in gene expression that amass across a whole pathway. In order to overcome this limitation, we use microarray gene expression data to identify pathway perturbation based on pathway correlation profiles. By identifying the distribution of gene-gene pair correlations within a pathway, we can rank the pathways based on the level of perturbation and dysregulation. We have shown this successfully for differences between two experimental conditions in Escherichia coli and changes within time series data in Saccharomyces cerevisiae, as well as two estrogen receptor response classes of breast cancer. Overall, our method made significant predictions as to the pathway perturbations that are involved in the experimental conditions.

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Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Figure 1
Figure 1. Pathway correlation profiles for Biotin Metabolism Pathway (ecj00780) in E. coli.
(a) Pathway correlation profile kernel density smoothed graphs before fisher transformation of the Biotin Metabolism Pathway. (b) Pathway correlation profile kernel density smoothed graphs after fisher transformation of the Biotin Metabolism Pathway. (pH 8.7: blue, pH 7: black, and pH 5: red).
Figure 2
Figure 2. Pathway correlation profiles for Ribosome Pathway (sce03010) in S. cerevisiae.
(a) Gene expression level plots of the Ribosome Pathway (b) Pathway correlation profile kernel density smoothed graphs before fisher transformation of the Ribosome Pathway. (c) Pathway correlation profile kernel density smoothed graphs after fisher transformation of the Ribosome Pathway. (Control: black, 0 minutes: red, 15 minutes: blue, 45 minutes: green, 90 minutes: yellow, and 360 minutes: magenta).
Figure 3
Figure 3. Heatmap of pathway correlation profiles for Ribosome Pathway (sce03010) in S. cerevisiae under control conditions.
Heatmap and clustering of genes are based on their gene-gene pair correlations. Rows and columns represent genes. (Yellow: positive correlation; red: negative correlation).
Figure 4
Figure 4. Flowchart describing pathway correlation perturbation method for analyzing gene expression data on a pathway level.
Initially, gene expression data is processed and normalized. Expression profiles are then created for the set of genes involved in each pathway. Using these expression profiles, pathway correlation profiles are created in each condition for each pathway. These results are then combined to determine the pathway’s mean difference in gene-gene pair correlations, and then ranked based on their significance of perturbation.
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