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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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Overlap P-value (A) and activation Z-score (B) calculation (see text). In (B) the pointed arrowheads represent activating relationships, and the blunt arrowheads represent inhibitory relationships
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btt703-F1: Overlap P-value (A) and activation Z-score (B) calculation (see text). In (B) the pointed arrowheads represent activating relationships, and the blunt arrowheads represent inhibitory relationships

Mentions: For a particular regulator r the overlap P-value p(r) measures enrichment of r-regulated genes in the dataset D without taking into account the regulation direction, i.e. independent of the edge weight or sign (Fig. 1A). Protein–DNA-binding edges with sign equal to zero are included. The calculation is based on the one-sided FET which assumes a random dataset with a constant number of genes as the model, and where the P-value is given bywhere is the size of the ‘universe’ Vrg, the ‘overlap’ is given by and Fig. 1.


Causal analysis approaches in Ingenuity Pathway Analysis.

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

Overlap P-value (A) and activation Z-score (B) calculation (see text). In (B) the pointed arrowheads represent activating relationships, and the blunt arrowheads represent inhibitory relationships
© Copyright Policy - creative-commons
Related In: Results  -  Collection

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

btt703-F1: Overlap P-value (A) and activation Z-score (B) calculation (see text). In (B) the pointed arrowheads represent activating relationships, and the blunt arrowheads represent inhibitory relationships
Mentions: For a particular regulator r the overlap P-value p(r) measures enrichment of r-regulated genes in the dataset D without taking into account the regulation direction, i.e. independent of the edge weight or sign (Fig. 1A). Protein–DNA-binding edges with sign equal to zero are included. The calculation is based on the one-sided FET which assumes a random dataset with a constant number of genes as the model, and where the P-value is given bywhere is the size of the ‘universe’ Vrg, the ‘overlap’ is given by and Fig. 1.

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