Decision tree-based method for integrating gene expression, demographic, and clinical data to determine disease endotypes

BMC Systems Biology

Stephen W Edwards³  Jane E Gallagher⁴  Edward E Hudgens⁴  Pierre R Bushel⁶  Elaine Cohen Hubal⁵  David M Reif²  ClarLynda R Williams-DeVane¹ 

[1] Present address: Julius L. Chambers Biomedical/Biotechnology Research Institute, Biology Department-North Carolina Central University, Durham, NC 27707, USA;Present address: Biological Sciences Department, North Carolina State University, Raleigh, NC 27695, USA;National Health and Environmental Effects Research Laboratory – Integrated Systems Toxicology Division, U.S. Environmental Protection Agency, Research Triangle Park, Durham, NC 27711, USA;National Health and Environmental Effects Research Laboratory – Environmental Public Health Division, U.S. Environmental Protection Agency, Research Triangle Park, Durham, NC 27711, USA;National Center for Computational Toxicology, U.S. Environmental Protection Agency, Research Triangle Park, Durham, NC 27711, USA;Biostatistics Branch, National Institute of Environmental Health Sciences, Research Triangle Park, Durham, North Carolina 27709, USA

关键词: Integrated analysis;    Gene Expression;    Endotypes;    Asthma;   

Others  :  1141820
DOI  :  10.1186/1752-0509-7-119

 received in 2012-09-18, accepted in 2013-10-18,  发布年份 2013

【 摘 要 】

Background

Complex diseases are often difficult to diagnose, treat and study due to the multi-factorial nature of the underlying etiology. Large data sets are now widely available that can be used to define novel, mechanistically distinct disease subtypes (endotypes) in a completely data-driven manner. However, significant challenges exist with regard to how to segregate individuals into suitable subtypes of the disease and understand the distinct biological mechanisms of each when the goal is to maximize the discovery potential of these data sets.

Results

A multi-step decision tree-based method is described for defining endotypes based on gene expression, clinical covariates, and disease indicators using childhood asthma as a case study. We attempted to use alternative approaches such as the Student’s t-test, single data domain clustering and the Modk-prototypes algorithm, which incorporates multiple data domains into a single analysis and none performed as well as the novel multi-step decision tree method. This new method gave the best segregation of asthmatics and non-asthmatics, and it provides easy access to all genes and clinical covariates that distinguish the groups.

Conclusions

The multi-step decision tree method described here will lead to better understanding of complex disease in general by allowing purely data-driven disease endotypes to facilitate the discovery of new mechanisms underlying these diseases. This application should be considered a complement to ongoing efforts to better define and diagnose known endotypes. When coupled with existing methods developed to determine the genetics of gene expression, these methods provide a mechanism for linking genetics and exposomics data and thereby accounting for both major determinants of disease.

【 授权许可】

   
2013 Williams-DeVane et al.; licensee BioMed Central Ltd.

【 图 表 】

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