Causal analysis approaches in Ingenuity Pathway Analysis
Andreas Krämer, Jeff Green, Jack Pollard Jr, Stuart Tugendreich, Andreas Krämer, Jeff Green, Jack Pollard Jr, Stuart Tugendreich
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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References
- Abatangelo L, et al. Comparative study of gene set enrichment methods. BMC Bioinform. 2009;10:275.
- Arteaga MF, et al. The histone demethylase PHF8 governs retinoic acid response in acute promyelocytic leukemia. Cancer cell. 2013;3:376–389.
- Chindelevitch L, et al. Causal reasoning on biological networks: interpreting transcriptional changes. Bioinformatics. 2012a;28:1114–1121.
- Chindelevitch L, et al. Assessing statistical significance in causal graphs. BMC Bioinform. 2012b;13:35.
- Felciano RM, et al. Predictive systems biology approach to broad-spectrum, host-directed drug target discovery in infectious diseases. Pac. Symp. Biocomput. 2013;2013:17–28.
- Go JT, et al. 2009 pandemic H1N1 influenza virus elicits similar clinical course but differential host transcriptional response in mouse, macaque, and swine infection models. BMC Genom. 2012;13:627.
- Hayden A, et al. S-adenosylhomocysteine hydrolase inhibition by 3-deazaneplanocin A analogues induces anti-cancer effects in breast cancer cell lines and synergy with both histone deacetylase and HER2 inhibition. Breast Cancer Res. Treat. 2011;1:109–119.
- Haynes KR, et al. Excessive bone formation in a mouse model of ankylosing spondylitis is associated with decreases in Wnt pathway inhibitors. Arth. Res. Ther. 2012;14:R253.
- Kumar R, et al. Causal reasoning identifies mechanisms of sensitivity for a novel AKT kinase inhibitor, GSK690693. BMC Genom. 2010;11:419.
- Lamb J, et al. The connectivity map: using gene-expression signatures to connect small molecules, genes, and disease. Science. 2006;313:1929–1935.
- Lin CY, et al. Whole-genome cartography of estrogen receptor alpha binding sites. PLoS Genet. 2007;3:e87.
- Martin F, et al. Assessment of network perturbation amplitudes by applying high-throughput data to causal networks. BMC Syst. Biol. 2012;6:54.
- Meier-Abt F, et al. Parity induces differentiation and reduces Wnt/Notch signaling ratio and proliferation potential of basal stem/progenitor cells isolated from mouse mammary epithelium. Breast Cancer Res. 2013;2:R36.
- Muller P, et al. The anti-estrogenic effect of all-trans-retinoic acid on the breast cancer cell line MCF-7 is dependent on HES-1 expression. J.Biol. Chem. 2002;32:28376–28379.
- Ombra MN, et al. Retinoic acid impairs estrogen signaling in breast cancer cells by interfering with activation of LSD1 via PKA. Biochimica et Biophysica Acta. 2013;1829:480–486.
- Pollard J, Jr., et al. A computational model to define the molecular causes of type 2 diabetes mellitus. Diabetes Technol. Ther. 2005;7:323–336.
- Shneiderman B. Tree visualization with tree-maps: 2-d space-filling approach. ACM Transact. Graph. 1992;11:92–99.
- Viemann D, et al. TNF induces distinct gene expression programs in microvascular and macrovascular human endothelial cells. J. Leukoc Biol. 2006;80:174–185.
- Wang Q, et al. 1,25-Dihydroxyvitamin D3 and all-trans-retinoic acid sensitize breast cancer cells to chemotherapy-induced cell death. Cancer Res. 2000;7:2040–2048.
- Zhu WY, et al. Retinoic acid inhibition of cell cycle progression in MCF-7 human breast cancer cells. Exp. Cell Res. 1997;234:293–299.
Source: PubMed