A new approach for multi-omic data integration

Author

Yiming Zuo ; Guoqiang Yu ; Chi Zhang ; Ressom, Habtom W.

Author_Institution

Lombardi Comprehensive Cancer Center, Georgetown Univ., Washington, DC, USA

fYear

2014

fDate

2-5 Nov. 2014

Firstpage

214

Lastpage

217

Abstract

Recent technological advances have enabled the generation of various omic data (e.g., genomics, proteomics, metabolomics and glycomics) in a high-throughput manner. The integration of multi-omic data sets is desirable to unravel the complexity of a biological system. In this paper, we propose a new approach to investigate both inter and intra relationships for multi-omic data sets by using regularized canonical correlation analysis and graphical lasso. The application of this novel approach on real multi-omic data sets helps identify hub proteins and their neighbors that may be missed by typical statistical analysis to serve as biomarker candidates. Also, the integration of data from various cellular components (i.e., proteins, metabolites and glycans) offers the potential to discover more reliable biomarker candidates for complex disease.

Keywords

bioinformatics; cellular biophysics; data integration; diseases; genomics; proteins; proteomics; biological system complexity; biomarker; cellular components; complex disease; genomics; glycans; glycomics; graphical lasso; metabolites; metabolomics; multi-omic data integration; proteins; proteomics; regularized canonical correlation analysis; statistical analysis; Bioinformatics; Correlation; Covariance matrices; Data integration; Metabolomics; Proteins; Proteomics; Multi-omic data integration; graphical lasso; regularized canonical correlation analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Bioinformatics and Biomedicine (BIBM), 2014 IEEE International Conference on

Conference_Location

Belfast

Type

conf

DOI

10.1109/BIBM.2014.6999157

Filename

6999157