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. 2011 May 4:12:133.
doi: 10.1186/1471-2105-12-133.

The PathOlogist: an automated tool for pathway-centric analysis

Sharon I Greenblum  1 , Sol Efroni, Carl F Schaefer, Ken H Buetow
Affiliations

The PathOlogist: an automated tool for pathway-centric analysis

Sharon I Greenblum et al. BMC Bioinformatics. .

Abstract

Background: The PathOlogist is a new tool designed to transform large sets of gene expression data into quantitative descriptors of pathway-level behavior. The tool aims to provide a robust alternative to the search for single-gene-to-phenotype associations by accounting for the complexity of molecular interactions.

Results: Molecular abundance data is used to calculate two metrics--'activity' and 'consistency'--for each pathway in a set of more than 500 canonical molecular pathways (source: Pathway Interaction Database, http://pid.nci.nih.gov). The tool then allows a detailed exploration of these metrics through integrated visualization of pathway components and structure, hierarchical clustering of pathways and samples, and statistical analyses designed to detect associations between pathway behavior and clinical features.

Conclusions: The PathOlogist provides a straightforward means to identify the functional processes, rather than individual molecules, that are altered in disease. The statistical power and biologic significance of this approach are made easily accessible to laboratory researchers and informatics analysts alike. Here we show as an example, how the PathOlogist can be used to establish pathway signatures that robustly differentiate breast cancer cell lines based on response to treatment.

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Figures

Figure 1
Figure 1
Activity and consistency scores are calculated for each interaction within a pathway.
Figure 2
Figure 2
Bi-dimensionally clustered heatmap of pathway metrics, with separate rows for activity and consistency scores, for a) 28 cancer cell lines, and b) 368 GBM and 10 normal samples. In b), all 10 normal samples cluster together, as indicated by the colored bars to the right.
Figure 3
Figure 3
Scatterplots depicting relationship between GI50 and pathway metrics for two highly correlated pathways.
Figure 4
Figure 4
Heatmap of activity and consistency scores for the four pathways most highly correlated with sensitivity to treatment. Samples were divided into four equal groups and labeled very sensitive, sensitive, resistant, or very resistant based on their response.
Figure 5
Figure 5
Network structure of the Toll-like Receptor Signaling' pathway, for two sensitive (a, b) and two resistant (c, d) cell lines. Molecules with copy number alterations are outlined in yellow.
Figure 6
Figure 6
Portion of the 'Toll-like Receptor Signaling' pathway for two representative cell lines. Rectangles represent molecules, circles represent interactions. Molecular expression is represented by shading of molecule nodes: white = lowest expression, bright turquoise = highest expression. Molecules with copy number alterations are outlined in yellow. Interaction activity is represented by circle size: large circle = high activity, small circle = low activity. Interaction consistency is represented by arrow color: bright blue: high consistency, dark green: low consistency.
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