I’m writing to congratulate you on the brilliant paper.
I would be grateful if you could help me to clarify some aspects about the application of N-integration (DIABLO) that are still not so clear for me after reading the paper.
Could N-integration be applied to smaller datasets as well (datasets with small number of predictors)?
I ask this question because in mixOmics paper its described that " Here we applied our multivariate frameworks to transcriptomics, proteomics and miRNA data. However, other types of biological data can be analysed, as well as data beyond the realm of ‘omics as long as they are expressed as continuous values. "
I’m working in my master degree trying to combine markers measured from different methodologies in the same samples such as transcriptome (9997 predictors), plasma markers such as cytokines (about 19 predictors) and heart damage molecules (4 predictors) measured by Luminex and ELISA, respectively, antibody reactivity from a panel of 15 different antigens specific for T. cruzi (parasite responsible for Chagas disease) and clinical data (7 variables).
Our aim is to find if a combination of markers from these different methodologies is more efficient than a single methodology, such as clinical data, to discriminate the Chagas disease groups of patients, indeterminate form and cardiac form, with and without severe left ventricle dysfunction.
I’m trying to implement you methodology from DIABLO with these data, do you think I can do such evaluation with the package? Comparing prediction performance by cross-validation of each dataset with PLS-DA or sPLS-DA with the prediction performance of N-integration with the combination of all or some of these datasets?
Thank you in advance for your attention. I look forward to hearing from you.
If you need further information, please contact me.