Dear mixOmics group,
good afternoon and I hope my message finds you well !! I would like to ask your opinion and feedback regarding one “upcoming” multi-omics dataset we will produce, and weather any methodology implemented in the mixOmics framework, would be more suitable to implement in our biological scenarios:
Briefly, we sought to create multi-omics layers (i.e. transcriptome, genome, epigenetics) of ~ 50 to 100 patients. The main interest would be the presence of two samples per each patient: one prior therapy, and one after administration of therapy; Thus, we would have actually 2 timepoints/samples per patient (which of course would be “confounded” with biological condition: T1 is always before therapy, T2 is always after).
In addition, we will have further distinct measurements such as histology features and drug screens. Our main question would be to study the heterogeneity of these cancer patients (belonging to the same cancer entity), and identify these molecular circuits that differentiate or significantly perturbed “before vs after therapy”. Of course, intra-patient variation would be present, but we sought to unravel these biological sources of variation that might categorize these patients into at least these “two” distinct groups.
On this premise, and based on the experimental design, you would suggest DIABLO for a direct “supervised” implementation? And for example treat the specimens belonging to the same patient but in different timepoints as “distinct samples”? In addition, if my notion is correct, you would create a “binary categorical variable”, denoting two levels? such as “Before_Treat” & “After_Treat”?
Or due to the fact that these patients will have “paired” samples, supervised models like this could not model appropriately time course measurements from the same sample? As also, each patient has paired samples (nested) and complexes the analysis?
Alternatively, is there also another approach (even unsupervised) that would be also beneficial? Towards the direction of dissentagling “before vs after treatment” most important biological features and most important molecular sources of variation?
Any suggestion, feedback or idea would be grateful