Hello, I am looking at this heatmap and I do not understand why some of the tumours are grouped with  the controls, seems as if the heatmap is 'moved to the left':
Top of the heatmap:
![Top of the heatmap][1]

(it is a long heatmap so i upload just a section)

Bottom of the heatmap:
![Bottom of the heatmap][2]

I tried to check on the phenotype of the samples but I found no correlations of the phenotype with this grouping.

There seem to be two tumour subgroups: one that is the biggest, and the small group of tumors that seem to be grouped with the controls. I did a t-test on the mean beta values for all the cpgs between these two groups, and it turns out there is significance among the means of these two tumour groups. 
I am afraid when we publish a heatmap similar to this one, we will have trouble explaining this phenomenon. Any ideas or any opinions on this? Thank you!

  [1]: https://i.imgur.com/v3oAPra.png
  [2]: https://i.imgur.com/NgTGrB3.png
There can be many reasons, some not really relating to bioinformatics:

 1. these tumours genuinely exhibit the 'normal-like' methylation
    profile over these probes
 2. these tumours have normal cell contamination

Some informatics reasons:

 - your coding is incorrect and you have incorrectly assigned a normal
   sample as a tumour
 - you have scaled your data incorrectly
 - you should consider the distance and linkage metric that you're using

By the way, for methylation, you could probably also perform the Wilcoxon Signed Rank test on the matched T-N pairs. If just a regular t-test, I would at least use the Mann Whitney (non-parametric) test.

You must also have an extra checkpoint: you should obtain the difference in mean β value between tumour and normal, i.e.:

    difference in mean = mean β (tumour) - mean β (normal)

Then, use that as an extra cut-off in addition to the p-value.

Kevin