Dear all, I notice recently that the heatmap3 package uses pearson distance instead of the default euclidean/manhattan.
as.dist(1 - cor(df, use = "pa"))
Is there a benefit of using correlation instead of euclidean when it comes to calculating distance? The reason why I ask is because the heatmap generated ( for gene expression matrix ) actually looks much better and I can actually see a nice pattern for expression between some clusters. I would like to study a few of the groups in the tree to see if there are any trends. However, I usually do this when the distance function was generated with euclidean and I'm not sure if I can do that with this method. Any suggestions?