I have two datasets: one with samples (rows) taken at different months of the year and abundances or counts of different types of particles I found (columns), and the other with samples (rows) and the probability of sourcing from a particular area (columns) in counts or percentages (this is based on particle backtracking statistics). I have standarized the counts of particle types (type count/sum of type counts) before computing the CCA analysis in R due to uneven counts across samples.
I want to find whether the composition of particle types per sample are related to a particular source. It seems that Canonical correspondence analysis or Redundancy analysis may give me what I'm looking for. It also resembles data sets of ecological data (i.e. instead of species abundance, the response variables are particle type abundance; instead of environmental factors, the explanatory variables are proportion/counts of sources). However, I've never used any of these on this type of data and I would appreciate some suggestions.