Mixed graphical models for integrative causal analysis with application to chronic lung disease diagnosis and prognosis
2019
Sedgewick, Andrew J | Wren, Jonathan | Buschur, Kristina | Shi, Ivy | Ramsey, Joseph D | Raghu, Vineet K | Manatakis, Dimitris V | Zhang, Yingze | Bon, Jessica | Chandra, Divay | Karoleski, Chad | Sciurba, Frank C | Spirtes, Peter | Glymour, Clark | Benos, Panayiotis V
Integration of data from different modalities is a necessary step for multi-scale data analysis in many fields, including biomedical research and systems biology. Directed graphical models offer an attractive tool for this problem because they can represent both the complex, multivariate probability distributions and the causal pathways influencing the system. Graphical models learned from biomedical data can be used for classification, biomarker selection and functional analysis, while revealing the underlying network structure and thus allowing for arbitrary likelihood queries over the data. In this paper, we present and test new methods for finding directed graphs over mixed data types (continuous and discrete variables). We used this new algorithm, CausalMGM, to identify variables directly linked to disease diagnosis and progression in various multi-modal datasets, including clinical datasets from chronic obstructive pulmonary disease (COPD). COPD is the third leading cause of death and a major cause of disability and thus determining the factors that cause longitudinal lung function decline is very important. Applied on a COPD dataset, mixed graphical models were able to confirm and extend previously described causal effects and provide new insights on the factors that potentially affect the longitudinal lung function decline of COPD patients. The CausalMGM package is available on http://www.causalmgm.org. Supplementary data are available at Bioinformatics online.
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