Causal analysis approaches in Ingenuity Pathway Analysis

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ID: 289478
2013
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Abstract
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. 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.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 material is available at Bioinformatics online.
Reference Key
openalex_W2162143298 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors A. Krämer, Jeff Green, Jack Pollard, Stuart Tugendreich
Journal BMC Bioinformatics
Year 2013
DOI
10.1093/bioinformatics/btt703
URL
https://academic.oup.com/bioinformatics/article-pdf/30/4/523/17343942/btt703.pdf
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