I need to solve a multiomic challenge. I have access (N=160) to a precious collection of cross-sectional samples from two body sites (stools and blood) for which I want to generate as much information as possible. This implies applying several omic tools to create six data layers from the stool microbiome and blood from study participants, resulting in thousands of variables per sample.
I have underlying causality assumptions. The central one is that microbiome-host interactions drive a unique clinical phenotype. More specifically, some bacterial genes (level 1) are translated into bacterial proteins (level 2) that generate key metabolites (level 3). These proteins and metabolites interact with some host immune cells (level 4), inducing gene expression (level 5) that is translated into human proteins (level 6) and metabolites (level 7), driving the phenotype of interest.
The sample size limits the use of unsupervised approaches. I was thinking of using dimension reduction techniques at each level for feature selection to fit then structural equations based on the path assumptions. I wonder if DIABLO would be a better tool to solve this analytical challenge.
Any thoughts will be appreciated.