![]() genes, proteins, metabolites) is much larger than the number of samples (patients, cells, mice). Multivariate methods are well suited to large ‘omics data sets where the number of variables (e.g. mixOmics offers a wide range of multivariate methods for the exploration and integration of biological datasets with a particular focus on variable selection ![]() Our mixOmics R package proposes a whole range of multivariate methods that we developed and validated on many biological studies to gain more insight into ‘omics biological studies. It is generally admitted that single ‘omics analysis does not provide enough information to give a deep understanding of a biological system, but we can obtain a more holistic view of a system by combining multiple ‘omics analyses. The core team includes Kim-Anh Lê Cao (University of Melbourne), Florian Rohart (Brisbane) and Sébastien Déjean (Toulouse). We also have key contributors, past (Benoît Gautier, François Bartolo) and present (Al Abadi, University of Melbourne) and several collaborators including Amrit Singh (University of British Columbia), Olivier Chapleur (INRA, Paris) – it could be you too if you wish to be involved: we host many visitors with computational, statistical and biological backgrounds! Why multivariate methods? ![]() Is collaborative project between Australia (Melbourne), France (Toulouse), and Canada (Vancouver). ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |