Time-course multi-omics integration

I presented this talk for a group of statisticians at the Australian National University in Canberra. The abstract is below.

Topics covered: linear mixed model splines, multi-omics integration (PLS multiblock), correlation circle plot interpretation, timeOmics.

Longitudinal experiments are becoming increasingly popular in omics studies to monitor molecular changes following treatment or during disease progression. Integrating these data sets can give us some mechanistic insights into the different types of omics layers.

However, longitudinal omics data present numerous challenges including a small number of time points that may be unevenly spaced and unmatched between different data types, a small number of individuals, and a high individual variability. While current approaches have focused on differential expression across time or time profile clustering, the modelling of omics time profiles in a multivariate manner is critically lacking to understand longitudinal biological interactions.

I will present a statistical framework, timeOmics, to identify correlated profiles over time and between omics (transcriptomics, metabolomics, microbiome) to give insights into the molecular dynamics of biological systems and discuss future avenues of research in this expanding area.

Some key references