Abstract
BackgroundDiagnosis 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.ResultsApplying 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.ConclusionsThe 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.
Highlights
Diagnosis and treatment of patients in the clinical setting is often driven by known symptomatic factors that distinguish one particular condition from another
We identify a novel latent physiological factor for cystic fibrosis that serves as a promising lead for further investigation into diagnostic or prognostic biomarkers for Cystic Fibrosis (CF)
We have developed a novel approach that incorporates independent component analysis of patient laboratory biomarkers represented as vectors in a clinarray
Summary
Diagnosis and treatment of patients in the clinical setting is often driven by known symptomatic factors that distinguish one particular condition from another. 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. PCA can only impose statistical independence of components up to the second order; it can only identify directions that are uncorrelated and orthogonal to each other. ICA is capable of exploiting higher-order statistics to relax the orthogonality assumption and identify components that are mutually statistically independent from each other, which is a stronger condition than lack of correlation [6]. It is likely that this higher-order model is more reflective of biological phenomenon, and offers an explanation for the many successful applications of ICA in the biomedical domain
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