Classification methods that leverage the strengths of data from multiple sources (multiview data) simultaneously have enormous potential to yield more powerful findings than two-step methods: association followed by classification. We propose two methods, sparse integrative discriminant analysis (SIDA), and SIDA with incorporation of network information (SIDANet), for joint association and classification studies. The methods consider the overall association between multiview data, and the separation within each view in choosing discriminant vectors that are associated and optimally separate subjects into different classes. SIDANet is among the first methods to incorporate prior structural information in joint association and classification studies. It uses the normalized Laplacian of a graph to smooth coefficients of predictor variables, thus encouraging selection of predictors that are connected. We demonstrate the effectiveness of our methods on a set of synthetic datasets and explore their use in identifying potential nontraditional risk factors that discriminate healthy patients at low versus high risk for developing atherosclerosis cardiovascular disease in 10 years. Our findings underscore the benefit of joint association and classification methods if the goal is to correlate multiview data and to perform classification.
|Original language||English (US)|
|Number of pages||12|
|Early online date||Mar 19 2021|
|State||E-pub ahead of print - Mar 19 2021|
Bibliographical noteFunding Information:
We are grateful to the Emory Predictive Health Institute for providing us with the gene expression, metabolomics, and clinical data. This research is partly supported by NIH grants 5KL2TR002492‐03 and T32HL129956. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.
© 2021 The International Biometric Society.
- canonical correlation analysis
- integrative analysis
- joint association and classification
- multiple sources of data
- pathway analysis
PubMed: MeSH publication types
- Journal Article
- Research Support, N.I.H., Extramural