Dear QIIME2 developer,
Thanks a lot for developing QIIME2 for us!
I would like to predict the sample metadata use the microbial data based on the random forest algorithm. Therefore, may I ask several questions about the q2-sample-classifier plugin?
Could I use the relative abundance (percentage) or the CLR transformed abundance (with plus and minus values) as the input?
In addition to 16S data, I also have the metagenomic data (genes, pathways) and metabolomic data (metabolites). Could I combine these datasets and use the combined file as the input for q2-sample-classifier? If so, do I need to perform any normalization or scale for all these datasets before running the q2-sample-classifier (because each dataset has specific data characteristics)? What do you suggest?
Could alpha-diversity indices (Shannon, observed features) also be used to predict the sample metadata through the q2-sample-classifier?
Could the shifts of certain taxon units (e.g. difference of an ASV between pre-treatment and after-treatment) be the input to predict the sample metadata through the q2-sample-classifier?
Many thanks for your time and guidance! I sincerely appreciate your help!