Different dendograms for correaltion heatamp and distance heatmaps for RNAseq samples

[chudar]

Hi,

I am new to analyze the RNAseq data. I have 6 samples (3 Controls and 3 treated) for which I would like to find how similar/dissimilar are they.
I found the similarity/dissimilarity between samples using Eucledian distance as well as Correlation matrix but the resulting dendogram are different between this two methods. I cant understand why this is happening. Kindly guide me.

Code for heatmap based on Euclidean Distance

vsd <- varianceStabilizingTransformation(dds, blind=TRUE)
heatmap(as.matrix(dist(t(assay(vsd)))))

Code for Correlation heatmap

heatmap(cor(assay(vsd)))

[dpryan]

Given that the correlation and euclidean distance are different, I wonder why you expect them to produce identical dendrograms.

[chudar]

Thanks for your reply. So in order to find which samples are close to each other, which is better to use. Correlation matrix or distance matrix? Kindly guide me

[dpryan]

Neither is better than the other. You simply need to choose which one you prefer. I generally use euclidean distance, but one of my colleagues uses cor(). Just make sure to indicate which, though you tend to get similar results either way.

[chudar]

Thanks for your guidance. . Now I understood the logic.
Is my approach for using cor() on assay(vsd) with following code is right?

vsd=varianceStabilizingTransformation(dds, blind=TRUE)

heatmap(cor(assay(vsd)))

[dpryan]

It looks OK by eye-ball, but I should warn you that I rarely use the heatmap() function manually, so I might easily miss something obvious there.