Biomarker Discovery by Sparse Canonical Correlation Analysis of Complex Clinical Phenotypes of Tuberculosis and Malaria
| Autor: | Daniel D. Agranoff, Delmiro Fernandez-Reyes, Olugbemiro Sodeinde, Juho Rousu, John Shawe-Taylor |
|---|---|
| Přispěvatelé: | Aalto-yliopisto, Aalto University |
| Rok vydání: | 2013 |
| Předmět: |
Bacterial Diseases
Proteomics Multivariate statistics Global Health computer.software_genre Correlation 0302 clinical medicine ta518 Biomarker discovery lcsh:QH301-705.5 ta515 0303 health sciences ta213 Ecology Systems Biology Hematology Genomics 3. Good health Infectious Diseases Phenotype Computational Theory and Mathematics Modeling and Simulation Medicine Data mining Canonical correlation Algorithms Research Article Computational biology Biology Mycobacterium 03 medical and health sciences Cellular and Molecular Neuroscience Diagnostic Medicine Parasitic Diseases Genetics Tuberculosis Humans Molecular Biology Ecology Evolution Behavior and Systematics 030304 developmental biology ta113 ta112 Univariate Computational Biology Reproducibility of Results Omics Malaria lcsh:Biology (General) Computer Science ta5141 computer Biomarkers 030217 neurology & neurosurgery |
| Zdroj: | PLoS Computational Biology, Vol 9, Iss 4, p e1003018 (2013) PLoS Computational Biology |
| ISSN: | 1553-7358 |
| DOI: | 10.1371/journal.pcbi.1003018 |
| Popis: | Biomarker discovery aims to find small subsets of relevant variables in ‘omics data that correlate with the clinical syndromes of interest. Despite the fact that clinical phenotypes are usually characterized by a complex set of clinical parameters, current computational approaches assume univariate targets, e.g. diagnostic classes, against which associations are sought for. We propose an approach based on asymmetrical sparse canonical correlation analysis (SCCA) that finds multivariate correlations between the ‘omics measurements and the complex clinical phenotypes. We correlated plasma proteomics data to multivariate overlapping complex clinical phenotypes from tuberculosis and malaria datasets. We discovered relevant ‘omic biomarkers that have a high correlation to profiles of clinical measurements and are remarkably sparse, containing 1.5–3% of all ‘omic variables. We show that using clinical view projections we obtain remarkable improvements in diagnostic class prediction, up to 11% in tuberculosis and up to 5% in malaria. Our approach finds proteomic-biomarkers that correlate with complex combinations of clinical-biomarkers. Using the clinical-biomarkers improves the accuracy of diagnostic class prediction while not requiring the measurement plasma proteomic profiles of each subject. Our approach makes it feasible to use omics' data to build accurate diagnostic algorithms that can be deployed to community health centres lacking the expensive ‘omics measurement capabilities. Author Summary Many infectious diseases such as tuberculosis and malaria are challenging both for scientists trying to understand the biochemical basis of the diseases and for medical doctors making diagnosis. The challenges arise both from the dependence of the diseases on sets of proteins and from the complexity of the symptoms. Biomarkers denote small sets of measurements that correlate with the phenotype of interest. They have potential use both in advancing the basic biomedical research of infectious diseases and in facilitating predictive diagnostic tools. We propose a new method for biomarker discovery that works by finding canonical correlations between two sets of data, the plasma proteomic profiles and clinical profiles of the subjects. We show that the method is able to find candidate proteomic biomarkers that correlate with combinations of clinical variables, called the clinical biomarkers. Using the clinical biomarkers improves the accuracy of diagnostic class prediction while not requiring the expensive plasma proteomic profiles to be measured for each subject. |
| Databáze: | OpenAIRE |
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