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This article is part of the supplement: Selected Proceedings of the 2010 AMIA Summit on Translational Bioinformatics

Open Access Proceedings

Latent physiological factors of complex human diseases revealed by independent component analysis of clinarrays

David P Chen123, Joel T Dudley123 and Atul J Butte23*

Author Affiliations

1 Program in Biomedical Informatics, Stanford University School of Medicine, Stanford, CA 94305, USA

2 Departments of Pediatrics and Cancer Biology, Stanford University, Stanford, CA 94305, USA

3 Lucile Packard Children's Hospital, 725 Welch Road, Palo Alto, CA 94304, USA

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BMC Bioinformatics 2010, 11(Suppl 9):S4  doi:10.1186/1471-2105-11-S9-S4

Published: 28 October 2010

Abstract

Background

Diagnosis and treatment of patients in the clinical setting is often driven by known symptomatic factors that distinguish one particular condition from another. Treatment based on noticeable symptoms, however, is limited to the types of clinical biomarkers collected, and is prone to overlooking dysfunctions in physiological factors not easily evident to medical practitioners. We used a vector-based representation of patient clinical biomarkers, or clinarrays, to search for latent physiological factors that underlie human diseases directly from clinical laboratory data. Knowledge of these factors could be used to improve assessment of disease severity and help to refine strategies for diagnosis and monitoring disease progression.

Results

Applying Independent Component Analysis on clinarrays built from patient laboratory measurements revealed both known and novel concomitant physiological factors for asthma, types 1 and 2 diabetes, cystic fibrosis, and Duchenne muscular dystrophy. Serum sodium was found to be the most significant factor for both type 1 and type 2 diabetes, and was also significant in asthma. TSH3, a measure of thyroid function, and blood urea nitrogen, indicative of kidney function, were factors unique to type 1 diabetes respective to type 2 diabetes. Platelet count was significant across all the diseases analyzed.

Conclusions

The results demonstrate that large-scale analyses of clinical biomarkers using unsupervised methods can offer novel insights into the pathophysiological basis of human disease, and suggest novel clinical utility of established laboratory measurements.