Causal analysis approaches in Ingenuity Pathway Analysis

By Andreas Krämer, Jeff Green, Jack Pollard, S. Tugendreich. Bioinform., 2013

5,147 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/3d764a187aa61b74b6365efca84100b55f430892

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Citations in the corpus

Big data for cyber physical systems in industry 4.0: a survey (2019, neutral): “Besides handling random noise, another direction is related to causal analysis (Krämer et al. 2013).”
Elevated Calprotectin and Abnormal Myeloid Cell Subsets Discriminate Severe from Mild COVID-19 (2020, neutral): “05 FlowJo https://www.flowjo.com/ Cytoscape Shannon et al., 2003 https://cytoscape.org/ GraphPad Prism 7 GraphPad Software https://www.graphpad.com/ Ingenuity Pathway Analysis Krämer et al., 2014 https://digitalinsights.qiagen.com/ products-overview/discovery-insightsportfolio/content-exploration-a”
Network-based inference of protein activity helps functionalize the genetic landscape of cancer (2016, methodology): “…providing accurate, tissue-specific assessments of protein regulons should be equally effective, we found that ARACNe outperformed competing algorithms that derive regulons from genome-wide ChIP databases, including ChEA 25 and ENCODE 26 , and literature curated Ingenuity networks 27 (see below).”
Systems Biology and Multi-Omics Integration: Viewpoints from the Metabolomics Research Community (2019, methodology): “- Metabolic pathway analysis - Network visualization - Data integration - Upstream regulator analysis - Mechanistic networks - Causal network analysis - Downstream effects analysis Commercial [121]”
Proteomic and Metabolomic Characterization of COVID-19 Patient Sera (2020, methodology): “…Zhou et al., 2019 https://metascape.org/gp/index.html#/main/step1 ClueGO 2.5.6 Bindea et al., 2009 https://cytoscape.org/ Ingenuine pathway analysis Krämer et al., 2014 https://www.qiagen.com/cn/ GeNet Li et al., 2018a http://apps.broadinstitute.org/genets# Other SOLAm Thermo Fisher Scientific…”
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Tumor cell biodiversity drives microenvironmental reprogramming in liver cancer (2019, methodology): “Upstream analysis of stromal cells—IPA (Krämer et al., 2013) was used to search for upstream regulators of stromal cells based on the variable genes of CAFs (k = 204 genes), TAMs (k = 204 genes), TECs (k = 180 genes), and T cells (k = 77 genes), which were derived based on the average expression (be”
High-throughput proteomics: a methodological mini-review (2022, methodology): “Ingenuity Pathway Analysis (IPA) was a web-based software application for causal analysis using expression datasets 104 .”