A comparison study of canonical methods: Application to -Omics data

Integrative analysis for better understanding of complex biological systems gains more attention.Observing subjects from various perspectives and conducting integrative analysis of those multiple datasets enables a deeper understanding of the subject.In this paper, we compared two methods that simultaneously consider two datasets gathered from the same objects, canonical correlation analysis (CCA) and co-inertia analysis (CIA).Since CCA cannot handle the case when the data exhibit high-dimensionality, two strategies were considered instead: Utilization of a ridge constant (CCA-ridge) and substitution of covariance matrices of each data to identity matrix and then applying penalized singular value decomposition (CCA-PMD).To illustrate CIA and CCA, both extensions of CCA and CIA were applied to NCI60 cell line data.It is shown that both methods yield biologically meaningful and significant results by identifying important genes that enhance our comprehension of the data.Their results shows some dissimilarities arisen from the different criteria used to measure the relationship between two sets of data in each method.Additionally, CIA exhibits variations dependent on the weight matrices employed.