Our recent breakthroughs and advances in culture independent techniques, such as whole genome shotgun metagenomics and 16S rRNA amplicon sequencing have dramatically changed the way we can examine microbial communities. But are many hurdles to tackle before we are able to identify and compare bacteria driving changes in their ecosystem. In addition to the bioinformatics challenges, current statistical methods are limited to make sense of these complex data that are inherently sparse, compositional and multivariate.
I will present our latest methodological developments to identify multivariate multi-omics microbial signatures using dimension reduction methods. Our methods are implemented in our R toolkit mixOmics dedicated to biological (omics) data integration.