One thought I had was that I would be careful using KMeans on tSNE transformed data. From my understanding, while tSNE preserves local relationships between data point, it (heavily) distorts long-distance relationships. What this means is that (if I get this right), you have almost arbitrary clustering results if your k is chosen such that it is not equal to the number of local clusters (a thing you don't know in advance). So clustering on distances between data points in tSNE coordinates seems to be a risky way to draw conclusions. I am not saying this is wrong, but if what I am saying is correct (please point out if I got something wrong), then your analysis may perhaps work better if you did KMeans on say the final 5-dimensional PCA space or something like that, and leave tsne to visualize the data (and perhaps label the data points by the clustering on PCAs?). Do you agree / does that make sense?