title: A multivariate approach to the integration of multi-omics datasets.
abstract: BACKGROUND
  To leverage the potential of multi-omics studies, exploratory data analysis methods that provide systematic integration and comparison of multiple layers of omics information are required. We describe multiple co-inertia analysis (MCIA), an exploratory data analysis method that identifies co-relationships between multiple high dimensional datasets. Based on a covariance optimization criterion, MCIA simultaneously projects several datasets into the same dimensional space, transforming diverse sets of features onto the same scale, to extract the most variant from each dataset and facilitate biological interpretation and pathway analysis.
  
  
  RESULTS
  We demonstrate integration of multiple layers of information using MCIA, applied to two typical "omics" research scenarios. The integration of transcriptome and proteome profiles of cells in the NCI-60 cancer cell line panel revealed distinct, complementary features, which together increased the coverage and power of pathway analysis. Our analysis highlighted the importance of the leukemia extravasation signaling pathway in leukemia that was not highly ranked in the analysis of any individual dataset. Secondly, we compared transcriptome profiles of high grade serous ovarian tumors that were obtained, on two different microarray platforms and next generation RNA-sequencing, to identify the most informative platform and extract robust biomarkers of molecular subtypes. We discovered that the variance of RNA-sequencing data processed using RPKM had greater variance than that with MapSplice and RSEM. We provided novel markers highly associated to tumor molecular subtype combined from four data platforms. MCIA is implemented and available in the R/Bioconductor "omicade4" package.
  
  
  CONCLUSIONS
  We believe MCIA is an attractive method for data integration and visualization of several datasets of multi-omics features observed on the same set of individuals. The method is not dependent on feature annotation, and thus it can extract important features even when there are not present across all datasets. MCIA provides simple graphical representations for the identification of relationships between large datasets.
authors.forename: Chen
authors.initials: C
authors.lastname: Meng
authors.forename: Bernhard
authors.initials: B
authors.lastname: Kuster
authors.affiliation: Chair of Proteomics and Bioanalytics, Technische Universität München, Freising, Germany. aedin@jimmy.harvard.edu.
authors.forename: Aedín C
authors.initials: AC
authors.lastname: Culhane
authors.forename: Amin Moghaddas
authors.initials: AM
authors.lastname: Gholami
mesh_terms: D014408:Biomarkers, Tumor / Q000032:analysis; D045744:Cell Line, Tumor; D005260:Female; D023281:Genomics / Q000379:methods*; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods*; D006801:Humans; D010051:Ovarian Neoplasms / Q000737:chemistry / Q000235:genetics; D020543:Proteome / Q000235:genetics
chemical_list: D014408:Biomarkers, Tumor; D020543:Proteome