Multi-timepoint integration analysis


It’s my first post, but I’d like to start by thanking the mixomics group for all of their fascinating work!

I have a question about multi-omics multiple-time-point integration analysis. I have a fairly large data-set consisting of transcriptomics, proteomics and metabolomics data, sampled over a week.

It includes:

  • transcriptomics (~50,000 genes, over 13 time-points, with 1 control and 3 conditions, and 3-4 replicates each)
  • proteomics (~8000 proteins, over 9 time-points, with 1 control and 2 conditions, and 3-4 replicates each)
  • metabolomics (~250 metabolites, over 20 time-points, with 3 conditions, and 2 replicates in 2/3 of time-points)

I have read, with interest, the recent Biorxiv paper (, and I would like to ask whether it would be feasible to apply this method for my dataset?


Dear @fxf,

Looks like you have a great longitudinal study! Yes, you can apply the timeOmics framework, now published here and available through Bioconductor. However, you will not really be able to do a differential analysis between conditions (you can do separate analysis and interpret the different clusters of molecules). I would also recommend using the lmms package (which is also part of timeOmics) beforehand.