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Causal analysis approaches in Ingenuity Pathway Analysis.

Krämer A, Green J, Pollard J, Tugendreich S - Bioinformatics (2013)

Bottom Line: Prior biological knowledge greatly facilitates the meaningful interpretation of gene-expression data.Causal networks constructed from individual relationships curated from the literature are particularly suited for this task, since they create mechanistic hypotheses that explain the expression changes observed in datasets.We present and discuss a suite of algorithms and tools for inferring and scoring regulator networks upstream of gene-expression data based on a large-scale causal network derived from the Ingenuity Knowledge Base.

View Article: PubMed Central - PubMed

Affiliation: Ingenuity Systems, 1700 Seaport Boulevard, Redwood City, CA and Translational and Experimental Medicine-Bioinformatics, Sanofi-Aventis, 270 Albany Street, Cambridge, MA, USA.

ABSTRACT

Motivation: Prior biological knowledge greatly facilitates the meaningful interpretation of gene-expression data. Causal networks constructed from individual relationships curated from the literature are particularly suited for this task, since they create mechanistic hypotheses that explain the expression changes observed in datasets.

Results: We present and discuss a suite of algorithms and tools for inferring and scoring regulator networks upstream of gene-expression data based on a large-scale causal network derived from the Ingenuity Knowledge Base. We extend the method to predict downstream effects on biological functions and diseases and demonstrate the validity of our approach by applying it to example datasets.

Availability: The causal analytics tools 'Upstream Regulator Analysis', 'Mechanistic Networks', 'Causal Network Analysis' and 'Downstream Effects Analysis' are implemented and available within Ingenuity Pathway Analysis (IPA, http://www.ingenuity.com).

Supplementary information: Supplementary material is available at Bioinformatics online.

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Enrichment of ‘causal transitive triangles’ (A) indicates causal dependency of upstream regulators A and B [compare (B) versus (C); see text]
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btt703-F2: Enrichment of ‘causal transitive triangles’ (A) indicates causal dependency of upstream regulators A and B [compare (B) versus (C); see text]

Mentions: The algorithm is based on the following idea: if the causal effect of r1 on some data set molecule is transmitted through the intermediate regulator r2, we expect an elevated occurrence of cases where all three edges, are present in the network, and the edge is explained by the path . We therefore look for statistical enrichment of these ‘causal transitive triangles’ (Fig. 2A). This enrichment is given by the intersection of the overlaps of the regulated gene sets in the dataset (Fig. 2B and C), i.e. we compute the FET P-value with serving as the universe. These FET P-values are calculated for every edge in G for which the regulators r1 and r2 meet pre-defined cut-offs with respect to their overlap P-value p(r) and activation Z-score z(r). For every upstream regulator r, the MN algorithm then constructs downstream networks with predefined ‘breadth’ N and ‘depth’ K from significant causal edges that connect r to dataset genes through several links by using the following recursive algorithm.


Causal analysis approaches in Ingenuity Pathway Analysis.

Krämer A, Green J, Pollard J, Tugendreich S - Bioinformatics (2013)

Enrichment of ‘causal transitive triangles’ (A) indicates causal dependency of upstream regulators A and B [compare (B) versus (C); see text]
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3928520&req=5

btt703-F2: Enrichment of ‘causal transitive triangles’ (A) indicates causal dependency of upstream regulators A and B [compare (B) versus (C); see text]
Mentions: The algorithm is based on the following idea: if the causal effect of r1 on some data set molecule is transmitted through the intermediate regulator r2, we expect an elevated occurrence of cases where all three edges, are present in the network, and the edge is explained by the path . We therefore look for statistical enrichment of these ‘causal transitive triangles’ (Fig. 2A). This enrichment is given by the intersection of the overlaps of the regulated gene sets in the dataset (Fig. 2B and C), i.e. we compute the FET P-value with serving as the universe. These FET P-values are calculated for every edge in G for which the regulators r1 and r2 meet pre-defined cut-offs with respect to their overlap P-value p(r) and activation Z-score z(r). For every upstream regulator r, the MN algorithm then constructs downstream networks with predefined ‘breadth’ N and ‘depth’ K from significant causal edges that connect r to dataset genes through several links by using the following recursive algorithm.

Bottom Line: Prior biological knowledge greatly facilitates the meaningful interpretation of gene-expression data.Causal networks constructed from individual relationships curated from the literature are particularly suited for this task, since they create mechanistic hypotheses that explain the expression changes observed in datasets.We present and discuss a suite of algorithms and tools for inferring and scoring regulator networks upstream of gene-expression data based on a large-scale causal network derived from the Ingenuity Knowledge Base.

View Article: PubMed Central - PubMed

Affiliation: Ingenuity Systems, 1700 Seaport Boulevard, Redwood City, CA and Translational and Experimental Medicine-Bioinformatics, Sanofi-Aventis, 270 Albany Street, Cambridge, MA, USA.

ABSTRACT

Motivation: Prior biological knowledge greatly facilitates the meaningful interpretation of gene-expression data. Causal networks constructed from individual relationships curated from the literature are particularly suited for this task, since they create mechanistic hypotheses that explain the expression changes observed in datasets.

Results: We present and discuss a suite of algorithms and tools for inferring and scoring regulator networks upstream of gene-expression data based on a large-scale causal network derived from the Ingenuity Knowledge Base. We extend the method to predict downstream effects on biological functions and diseases and demonstrate the validity of our approach by applying it to example datasets.

Availability: The causal analytics tools 'Upstream Regulator Analysis', 'Mechanistic Networks', 'Causal Network Analysis' and 'Downstream Effects Analysis' are implemented and available within Ingenuity Pathway Analysis (IPA, http://www.ingenuity.com).

Supplementary information: Supplementary material is available at Bioinformatics online.

Show MeSH