Dimension Reduction for the Integrative Analysis of Multilevel Omics Data

Description

Biological systems are increasingly studied at multiple omics levels simultaneously. Sequential analysis of the individual omics levels does not take the interconnected nature of the data into account. Integrative analysis of the multilevel data using dimension reduction techniques facilitate the identification of a larger number and more relevant features than sequential methods. We apply principal component-,
correspondence- and multiple co-inertia analysis to transcriptome and proteome data from 57 cancer cell lines and show that MCIA allows identification of a more complete set of relevant features and more accurate classification of the cancer types.

Period	5 May 2018
Event title	SIAM Conference on Applied Linear Algebra
Event type	Conference
Location	Hong Kong, Hong KongShow on map
Degree of Recognition	International