I was successful in using the MixOmics platform to integrate and analyze my omic datasets:
- Sphingolipids (30 sphingolipids),
- Microbiome (60 taxa)),
- Inflammation (8 cytokines)
I used the TSS method to normalize my microbiome data and filtered out low abundance taxa.
For my sphingolipid and cytokine datasets I performed a median normalization and autoscaling.
Finally I used the multi-block sPLS-DA method as outlined in the mixOmics tutorial.
The results were quite neat and informative, but I have a few more datasets that I’d like to integrate. The datasets are from earlier collection timepoints.
2 sphingolipid datasets (corresponding to sampling at 12 and 21 months)
4 microbiome datasets (corresponding to sampling at 0, 6, and 16 and 21 months)
2 inflammation datasets (corresponding to sampling at 12 and 21 months)
From reading the paper, I noticed that having mismatching timepoints shouldn’t be problem for timeOmics, however, I only have 2 datasets for my inflammatory and sphingolipid data. Would it still be fruitful to analyze these data longitudinally using the timeOmics package? Any advice would be helpful.