Using the fifteen principal components shown before, I tried to use MClust to cluster the 573 individuals.
This time, I ran NNclean first to find out the outliers. NNClean pointed to the following as outliers:
HGDP00104 HGDP00100 HGDP00119 HGDP00112 HGDP00118 HGDP00279 HGDP00060 HGDP00029 HGDP00076 HGDP00041 HGDP00146 HGDP00163 HGDP00234 HGDP00412 HGDP00090 HGDP00148 HGDP00165 HGDP00068 HGDP00134 HGDP00149 HGDP00052 HGDP00074 HGDP00098 HGDP00153 HGDP00173 HGDP00376 HGDP00143 HGDP00158 HGDP00145 HGDP00161 HGDP00151 HGDP00243 HGDP00139 HGDP00140 HGDP00177 HGDP00224 GSM536497 GSM536806 GSM536807 GSM536808 I16 I3 I5 SS231506 HRP0001
As you can see, I am included in this list.
Then I used this list of outliers to initialize "noise" in the MClust procedure. The final list of outliers is as fllows:
HGDP00279 HGDP00029 HGDP00134 HGDP00151 GSM536806 GSM536807 GSM536808
These are 1 Kalash, 1 Brahui, 2 Makranis, and 3 Paniyas.
There are a bunch of interesting things in the results. For example, Pathans and Punjabis were mostly indistinguishable by this technique. But let me leave you with a caution: Some of these clusters are nice, tight ones and others are loose, long ones, so don't overread the results.
1 Comments.