Not sure if this is best placed here but I will have a go.
I am working with clinical data in order to stratify patients using different biomarkers. I have log transformed and MinMax normalised all variables for the 1200 patients.
After plotting a heatmap, it would seem some individuals do cluster together, and biologically the phenotypes identified are meaningful.
Despite this PCA does not show any such clustering and instead shows a large agglomeration of individuals. K-means identifies the best number of clusters (if any) to be 3 (based on elbow method)
A silhouette plot also indicates that the the patients are very close to/on the neighbouring decision boundaries with an average coefficient of 0.21 with 3 clusters.
My question is - is it ok to separate patients on the basis of these statistics? We would like to separat them as biologically/clinically they behave different, and it seems there may be some difference here too. Equally, it is of course important the the plot is correct and I do not "force" the patients into different groups out of wont.
Any input is much appreciated!