Running Gedmatch calculators on the command line with stevenliuyi/admix

**~~Komintasavalta~~** · 10-24-2021, 08:01 PM

AFAIK, DIYDodecad only has Linux and Windows binaries, and I didn't want to install a VM, so I wasn't able to use it before. But I now found this Python-based alternative to it: https://github.com/stevenliuyi/admix.

Code:

$ wget https://reichdata.hms.harvard.edu/pub/datasets/amh_repo/curated_releases/V50/V50.0/SHARE/public.dir/v50.0_HO_public.{anno,ind,snp,geno}
$ f=v50.0_HO_public;convertf -p <(printf %s\\n genotypename:\ $f.geno snpname:\ $f.snp indivname:\ $f.ind outputformat:\ PACKEDPED genotypeoutname:\ $f.bed snpoutname:\ $f.bim indivoutname:\ $f.fam)
$ pip3 install git+https://github.com/stevenliuyi/admix
$ plink --bfile v50.0_HO_public --keep <(grep Chuvash33 v50.0_HO_public.fam) --recode 23
$ admix -f plink.txt -v 23andme -m K12b

Admixture calculation models: K12b

Calcuation is started...

K12b
Gedrosia: 2.06%
Siberian: 20.86%
Northwest African: 1.28%
Southeast Asian: 0.00%
Atlantic Med: 4.46%
North European: 53.12%
South Asian: 0.70%
East African: 0.00%
Southwest Asian: 1.85%
East Asian: 2.89%
Caucasus: 12.78%
Sub Saharan: 0.00%

It comes with files for these calculators: https://github.com/stevenliuyi/admix...ter/admix/data. It doesn't have files for K13, but where can I download them?

I used it to make K12b averages of Siberian samples from the Reich dataset:

Code:

,Gedrosia,Siberian,Northwest_African,Southeast_Asian,Atlantic_Med,North_European,South_Asian,East_African,Southwest_Asian,East_Asian,Caucasus,Sub_Saharan
Chukchi,2.02,57.32,0,1.37,0,7.42,1.85,0.01,0,30.00,0,0.02
Dolgan,0.30,70.24,0,0.16,1.86,4.90,1.00,0.02,0.45,20.23,0.62,0.23
Enets,3.16,66.82,0,0,0.95,17.61,0.15,0,0,11.30,0,0.02
Even,0.92,49.56,0.12,0.31,5.90,19.70,0.63,0.08,1.02,18.40,3.35,0
Itelmen,2.57,55.38,0,1.45,0,6.50,1.22,0,0,32.82,0,0.05
Ket,7.09,56.91,0.01,0.40,0.07,23.48,1.33,0.04,0,10.60,0,0.07
Koryak,1.87,56.27,0,2.48,0,6.19,1.13,0,0,32.03,0,0.04
Mansi,5.75,42.24,0,0.76,2.52,38.74,1.29,0.01,0,7.74,0.96,0
Nganasan,0.13,90.28,0.06,0.40,0.02,1.09,0.15,0.08,0.02,7.63,0,0.13
Selkup,5.90,58.22,0.16,1.30,0.41,26.25,1.02,0,0.01,6.63,0.01,0.08
Todzin,2.53,61.11,0,0.92,0.46,6.92,1.72,0,0.32,26.01,0,0
Tofalar,2.61,61.25,0.04,0.48,0.97,8.12,0.91,0.14,0.56,24.37,0.55,0
Ulchi,0.05,43.23,0.03,1.24,0.02,0.13,0.43,0.06,0,54.67,0.04,0.09

I didn't try to eliminate any outliers or samples that looked mixed, but here's individual samples:

Code:

,Gedrosia,Siberian,Northwest_African,Southeast_Asian,Atlantic_Med,North_European,South_Asian,East_African,Southwest_Asian,East_Asian,Caucasus,Sub_Saharan
Chukchi:ADR00057,0.88,53.31,0,0.13,0,9.44,3.47,0,0,32.78,0,0
Chukchi:ADR00059,2.17,54.93,0,0,0,8.11,1.66,0,0,33.13,0,0
Chukchi:ADR00060,2.46,60.32,0,0.79,0,5.00,0,0,0,31.42,0,0
Chukchi:ADR00061,0.99,59.88,0,2.31,0,7.16,2.77,0,0,26.89,0,0
Chukchi:ADR00064,1.34,55.80,0,1.77,0,8.67,1.60,0,0,30.82,0,0
Chukchi:ADR00065,2.97,57.07,0,1.94,0,5.59,1.93,0,0,30.51,0,0
Chukchi:ADR00066,0.81,57.01,0,0,0,7.01,3.03,0,0,31.81,0,0.34
Chukchi:ADR00068,3.21,56.99,0,0.51,0,7.43,0.87,0,0,30.98,0,0
Chukchi:ADR00074,2.82,58.01,0,1.14,0,5.94,1.11,0,0,30.98,0,0
Chukchi:ADR00079,2.59,56.18,0,4.38,0,7.42,2.18,0,0,27.25,0,0
Chukchi:MC_06,1.97,55.80,0,0,0,8.35,1.06,0,0,32.82,0,0
Chukchi:MC_08,0.76,56.93,0,3.49,0,8.82,1.89,0.19,0,27.92,0,0
Chukchi:MC_14,3.44,56.91,0,3.32,0,8.94,1.24,0,0,26.16,0,0
Chukchi:MC_15,1.47,59.29,0,0,0,5.93,3.18,0,0,30.13,0,0
Chukchi:MC_16,1.14,57.12,0,0.71,0,6.77,3.22,0,0,31.04,0,0
Chukchi:MC_17,2.58,58.81,0,1.63,0,5.99,2.51,0,0,28.49,0,0
Chukchi:MC_18,0.40,61.63,0,0.31,0,9.53,0.31,0,0,27.82,0,0
Chukchi:MC_25,3.13,58.27,0,1.64,0,6.16,1.25,0,0,29.54,0,0
Chukchi:MC_38,1.90,56.27,0,1.08,0,8.95,1.27,0,0,30.53,0,0
Chukchi:MC_40,3.29,55.79,0,2.23,0,7.14,2.48,0,0,29.08,0,0
Dolgan:Dolgan1708,0.01,70.78,0,0.19,0,2.20,1.11,0.07,0,25.63,0,0
Dolgan:Dolgan3185,1.17,63.44,0,0,0.30,3.77,0,0,0.48,30.74,0,0.10
Dolgan:Dolgan3857,0,70.50,0,0.43,4.55,7.69,1.75,0,0,12.38,2.50,0.19
Dolgan:Dolgan3875,0,76.26,0,0,2.59,5.92,1.12,0,1.32,12.16,0,0.63
Enets:Tuebingen02,3.77,69.76,0,0,0,13.71,0,0,0,12.69,0,0.07
Enets:Tuebingen35,4.45,60.42,0,0,1.90,22.28,0,0,0,10.94,0,0
Enets:Tuebingen44,1.25,70.28,0,0,0.94,16.83,0.45,0,0,10.26,0,0
Even:Nlk10,0.24,36.72,0,0,10.77,25.21,0.90,0,1.46,14.22,10.47,0
Even:Nlk14,1.16,41.16,0,0.69,12.27,26.85,0,0,0,13.29,4.58,0
Even:Nlk16,0.38,42.16,0,0.30,6.46,27.39,0.21,0,2.01,15.46,5.63,0
Even:Nlk18,0.23,74.01,0,0,0,0.16,0,0,0.31,24.58,0.71,0
Even:Nlk19,3.73,20.49,0,0.37,13.15,43.42,3.18,0,1.43,11.54,2.68,0
Even:Nlk3,0,72.04,0,0,0.49,0,0.23,0.32,0.33,25.77,0.83,0
Even:Nlk5,1.65,46.38,0.95,0.91,4.09,23.51,0,0.36,1.26,20.58,0.32,0
Even:Nlk6,0,63.48,0,0.19,0,11.09,0.52,0,1.39,21.79,1.55,0
Itelmen:Kor57,2.07,56.04,0,1.59,0,7.54,1.25,0,0,31.50,0,0
Itelmen:Kor60,0.33,56.28,0,4.16,0,7.94,1.48,0,0,29.81,0,0
Itelmen:Kor62,3.28,53.46,0,0.97,0,5.77,2.20,0,0,34.21,0,0.11
Itelmen:Kor72,1.50,55.74,0,0.73,0,7.32,0.15,0,0,34.34,0,0.21
Itelmen:Kor76,3.66,56.18,0,1.02,0,5.48,1.31,0,0,32.35,0,0
Itelmen:Kor78,4.59,54.60,0,0.21,0,4.98,0.90,0,0,34.72,0,0
Ket:Tuebingen101,5.18,60.24,0,0,0,22.73,2.17,0.24,0,9.36,0,0.09
Ket:Tuebingen103,6.11,57.77,0,0.64,0,26.29,0,0,0,9.19,0,0
Ket:Tuebingen104,8.01,54.84,0,0,0,23.12,1.42,0,0,12.60,0,0
Ket:Tuebingen76,6.88,57.50,0,3.24,0,22.97,0,0,0,9.41,0,0
Ket:Tuebingen80,7.87,54.85,0,0,0,23.31,1.85,0,0,12.12,0,0
Ket:Tuebingen81,7.27,56.69,0,0,0,22.56,1.31,0,0,12.16,0,0
Ket:Tuebingen82,6.95,57.37,0,0,0,22.80,0.39,0,0,12.49,0,0
Ket:Tuebingen83,7.12,57.74,0,0,0,20.51,1.47,0,0,13.17,0,0
Ket:Tuebingen87,8.58,60.63,0,1.98,0,20.27,0.84,0,0,7.69,0,0
Ket:Tuebingen90,7.84,55.59,0,0.02,0,21.96,2.59,0,0,12.00,0,0
Ket:Tuebingen94,6.11,56.42,0,0,0,24.92,0,0,0,12.55,0,0
Ket:Tuebingen95,5.30,57.45,0,0,1.39,23.82,1.04,0,0,10.31,0,0.69
Ket:Tuebingen97,8.81,57.53,0,0,0,20.51,2.01,0,0,11.13,0,0
Ket:TuebingenK15,5.85,53.42,0,0,0,28.47,1.63,0.30,0,10.33,0,0
Ket:TuebingenK16,6.15,57.17,0,0,0,22.24,3.66,0.10,0,10.55,0,0.13
Ket:TuebingenK17,9.10,55.32,0,1.80,0,24.76,0.38,0,0,8.37,0,0.27
Ket:TuebingenK22,7.01,53.56,0.21,0,0.02,28.77,2.99,0.03,0,7.25,0,0.15
Ket:TuebingenK29,6.99,57.05,0,0,0,21.45,1.59,0,0,12.93,0,0
Ket:TuebingenK7,7.57,60.09,0,0,0,24.59,0,0,0,7.75,0,0
Koryak:Kor2,1.98,57.07,0,4.71,0,5.20,2.03,0,0,28.65,0,0.35
Koryak:Kor22,2.96,56.18,0,3.85,0,6.26,0.17,0,0,30.58,0,0
Koryak:Kor30,0.97,57.06,0,0.63,0,5.19,2.39,0,0,33.76,0,0
Koryak:Kor35,1.89,56.22,0,3.48,0,5.26,0.88,0,0,32.27,0,0
Koryak:Kor40,4.33,56.45,0,4.13,0,4.21,1.52,0,0,29.36,0,0
Koryak:Kor49,1.22,54.91,0,1.57,0,7.39,1.40,0,0,33.51,0,0
Koryak:Kor54,1.25,54.38,0,1.26,0,8.75,1.16,0,0,33.21,0,0
Koryak:Kor61,1.97,56.49,0,0,0,7.77,0,0,0,33.76,0,0
Koryak:Kor66,0.24,57.64,0,2.67,0,5.65,0.59,0,0,33.20,0,0
Mansi:Mansi43,5.49,35.11,0,0.37,7.92,35.94,0.90,0,0,9.40,4.87,0
Mansi:Mansi48,7.42,39.45,0,0.62,7.35,33.06,2.57,0,0,9.52,0,0
Mansi:Mansi56,7.72,39.63,0,0,2.52,37.88,2.27,0.05,0,8.35,1.58,0
Mansi:Mansi76,9.66,44.98,0,0,0,30.22,3.94,0,0,11.20,0,0
Mansi:Mansi79,0,39.17,0,0,0,60.81,0.03,0,0,0,0,0
Mansi:Mansi84,0.62,47.22,0,1.51,0,39.22,0,0,0,10.16,1.26,0
Mansi:Mansi91,7.83,45.79,0,0,1.31,36.29,0,0,0,8.78,0,0
Mansi:Mansi94,7.24,46.54,0,3.60,1.02,36.46,0.59,0,0,4.55,0,0
Nganasan:ADR00504,0,99.76,0,0.01,0,0,0,0,0,0.01,0,0.24
Nganasan:ADR00507,0.27,88.51,0,0,0,0.57,0,0,0,10.14,0,0.51
Nganasan:ADR00508,0,88.31,0,0.78,0,1.72,0,0.16,0,9.03,0,0
Nganasan:ADR00509,0.34,87.39,0,0,0,2.17,0,0,0,10.11,0,0
Nganasan:ADR00510,0,87.43,0,0,0,0.74,0,0,0,11.29,0,0.54
Nganasan:ADR00511,0,88.67,0.40,0,0.34,0.78,0,0,0,9.81,0,0
Nganasan:ADR00512,0,87.02,0,0,0,0.32,0.33,0.39,0,11.94,0,0
Nganasan:ADR00513,0.87,90.33,0,0,0,0.04,0,0.07,0,8.63,0,0.07
Nganasan:ADR00514,0,85.37,0,0,0,2.40,0,0,0,11.97,0,0.26
Nganasan:ADR00515,1.01,90.72,0,2.01,0,1.12,0,0,0,4.99,0,0.16
Nganasan:Nov_005,0,90.25,0,1.65,0,0,0,0.12,0,7.98,0,0
Nganasan:Tuebingen06,0,90.82,0.70,0,0,1.32,0,0,0.29,6.68,0,0.17
Nganasan:Tuebingen07,0,86.74,0.05,2.84,0,1.24,0,0,0,8.84,0,0.29
Nganasan:Tuebingen106,0,89.52,0,0.72,0,1.99,0.51,0.01,0,7.26,0,0
Nganasan:Tuebingen111,0,87.66,0,0,0,0.93,0,0,0,11.23,0,0.18
Nganasan:Tuebingen112,0.09,93.44,0,0,0,1.09,0.48,0,0,4.90,0,0
Nganasan:Tuebingen114,0,87.01,0,0.68,0,4.63,0.41,0,0,7.28,0,0
Nganasan:Tuebingen116,0,88.29,0.63,0,0,0.78,0.26,0.10,0,9.94,0,0
Nganasan:Tuebingen119,0,88.41,0,0,0,0.74,0,0.38,0,10.45,0,0.02
Nganasan:Tuebingen12,0,92.80,0,0.12,0,0.81,0.17,0,0.45,5.65,0,0
Nganasan:Tuebingen121,0,89.22,0,1.86,0,1.18,0,0,0,7.75,0,0
Nganasan:Tuebingen123,0,89.01,0,0,0,1.45,0.24,0.13,0,8.84,0,0.34
Nganasan:Tuebingen124,0,99.66,0,0.01,0,0.27,0,0.01,0,0.01,0,0.08
Nganasan:Tuebingen126,1.62,89.23,0,1.07,0,0.82,0.05,0,0,7.21,0,0
Nganasan:Tuebingen127,0.03,89.20,0,0,0,1.20,0.99,0,0,8.17,0,0.41
Nganasan:Tuebingen14,0,99.93,0,0,0,0,0,0,0,0,0,0.07
Nganasan:Tuebingen17,0,89.81,0,0.17,0,2.40,0.70,0,0,6.92,0,0
Nganasan:Tuebingen19,0.16,89.21,0,0,0,0.57,0.15,0.23,0,9.68,0,0
Nganasan:Tuebingen21,0,88.64,0,0,0,1.92,0,0,0,9.43,0,0
Nganasan:Tuebingen23,0,87.02,0,1.06,0,2.10,0,0.67,0,8.81,0,0.33
Nganasan:Tuebingen25,0,92.23,0,0,0.31,0.54,0.75,0.21,0,5.78,0.13,0.04
Nganasan:Tuebingen27,0,97.36,0,0,0,0,0.04,0.08,0,2.04,0,0.47
Nganasan:Tuebingen28,0,90.12,0.14,0.16,0,0.24,0,0,0,9.08,0,0.27
Selkup:Selkup105,4.40,35.44,0.89,2.32,9.90,45.74,1.20,0,0.11,0,0,0
Selkup:Selkup121,6.91,57.74,0,3.83,0,21.71,3.34,0,0,6.47,0,0
Selkup:Selkup21,8.01,58.59,0,2.71,0,30.69,0,0,0,0,0,0
Selkup:Selkup220,7.18,53.36,0.55,1.86,0,34.07,0.40,0,0,2.56,0,0
Selkup:Selkup38,4.43,67.41,0,2.61,0,25.22,0.33,0,0,0,0,0
Selkup:Selkup4,7.20,61.96,0,1.44,0,27.08,0,0,0,2.32,0,0
Selkup:Selkup4a,2.85,72.87,0,2.47,0,18.03,0,0,0,3.78,0,0
Selkup:Selkup82,4.43,59.37,0,0,0,26.20,0.89,0,0,9.11,0,0
Selkup:Selkup83,0.75,68.80,0,0.48,0,16.13,0,0,0,13.70,0,0.14
Selkup:Selkup87,1.33,71.21,0,0,0,8.78,2.21,0,0,16.47,0,0
Selkup:Tuebingen50,5.00,59.90,1.08,1.19,0,22.64,1.35,0.07,0,8.77,0,0
Selkup:Tuebingen51,6.91,59.41,0,0.76,0,26.50,0,0,0,6.42,0,0
Selkup:Tuebingen52,0.99,58.23,0,6.39,0,31.91,0,0,0,2.48,0,0
Selkup:Tuebingen53,7.94,54.61,0,0,0,26.44,1.17,0,0,9.84,0,0
Selkup:Tuebingen54,7.32,54.78,0,0.41,0,26.96,1.81,0,0,8.71,0,0
Selkup:Tuebingen58,6.91,58.89,0,0.93,0,24.90,0.87,0,0,7.50,0,0
Selkup:Tuebingen59,6.19,57.77,0,1.10,0,27.27,0.91,0,0,6.76,0,0
Selkup:Tuebingen60,6.22,57.34,0,2.63,0,25.15,1.59,0,0,6.95,0,0.12
Selkup:Tuebingen62,7.11,57.97,0,0,0,26.66,0,0,0,7.96,0,0.31
Selkup:Tuebingen64,8.60,52.84,0,0,0,25.62,1.61,0,0,10.71,0,0.63
Selkup:Tuebingen72,7.85,54.02,0,0.05,0,26.62,3.02,0,0,8.42,0,0
Selkup:Tuebingen74,6.99,51.39,1.36,0,0,32.40,0.49,0,0.22,6.85,0.31,0
Selkup:Tuebingen77,9.34,54.58,0,0,0,27.74,2.00,0,0,5.87,0,0.47
Selkup:Tuebingen79,6.65,58.86,0,0,0,25.43,1.24,0,0,7.48,0,0.34
Todzin:TUV-121,4.10,58.39,0,0.52,0.04,7.21,2.83,0,0,26.91,0,0
Todzin:TUV-195,2.52,65.01,0,2.25,1.33,5.60,1.93,0,0,21.34,0,0
Todzin:TUV-199,0.97,59.93,0,0,0,7.96,0.39,0,0.97,29.78,0,0
Tofalar:Vgut1,5.34,65.16,0,0,0,2.10,1.03,0,1.45,24.93,0,0
Tofalar:Vgut11,0,64.21,0,0.70,0,7.20,0,0.71,0.08,27.10,0,0
Tofalar:Vgut12,1.87,63.29,0,1.91,0,9.22,0,0,0,23.71,0,0
Tofalar:Vgut13,2.81,63.61,0,0,0,7.19,0,0.24,0.12,26.04,0,0
Tofalar:Vgut14,3.99,26.79,0.56,0,11.81,37.91,1.88,0.24,0,10.62,6.19,0
Tofalar:Vgut15,2.78,65.77,0,0,0,4.55,0,0.07,0.15,26.68,0,0
Tofalar:Vgut18,2.45,64.59,0,0.47,0,4.51,2.14,0,0,25.85,0,0
Tofalar:Vgut19,3.07,61.62,0,0.05,0,3.87,2.93,0,1.37,27.09,0,0
Tofalar:Vgut2,2.51,63.70,0,0,0,4.17,1.51,0.55,0.98,26.59,0,0
Tofalar:Vgut4,2.60,64.52,0,0,0,6.36,0,0,0,26.52,0,0
Tofalar:Vgut6,3.69,64.59,0,0.43,0,4.67,1.18,0,0.08,25.36,0,0
Tofalar:Vgut7,1.26,68.68,0,0,0,1.99,1.21,0,3.07,23.78,0,0
Tofalar:Vgut8,1.59,59.68,0,2.66,0.82,11.78,0,0,0,22.56,0.91,0
Ulchi:Ul1,0,33.06,0,1.61,0,0.56,0.81,0.26,0,63.63,0,0.07
Ulchi:Ul10,0,37.22,0,0,0,0,0,0,0,62.78,0,0
Ulchi:Ul16,0,39.95,0,0,0,0.02,0,0,0,59.90,0,0.13
Ulchi:Ul19,0,29.16,0,4.15,0,0,1.82,0,0,64.88,0,0
Ulchi:Ul24,0.12,55.89,0,2.33,0.25,0,0,0,0,41.22,0.12,0.07
Ulchi:Ul25,0,40.19,0.17,1.13,0,0.18,0,0,0,58.33,0,0
Ulchi:Ul31,0,47.48,0,0,0,0,1.04,0,0,51.48,0,0
Ulchi:Ul33,0,48.09,0,1.03,0,0,0,0,0,50.61,0.27,0
Ulchi:Ul36,0,56.02,0,0,0,1.04,0,0,0.02,42.78,0.14,0
Ulchi:Ul39,0,41.97,0,2.40,0,0,0,0,0,55.62,0,0
Ulchi:Ul43,0,48.12,0,3.21,0,0,0,0,0,48.67,0,0
Ulchi:Ul44,0.35,40.82,0,0,0,0,0,0,0,58.60,0.23,0
Ulchi:Ul5,0.72,44.87,0,0.45,0,0,0.83,0,0,53.14,0,0
Ulchi:Ul51,0,40.84,0,0,0,0,2.13,0,0,57.03,0,0
Ulchi:Ul52,0,42.08,0,4.92,0,0.05,0,0,0,52.51,0,0.44
Ulchi:Ul55,0,48.01,0.14,0.63,0,0.41,0,0,0,50.40,0,0.41
Ulchi:Ul56,0,41.96,0,5.50,0,1.03,0,0,0,50.78,0,0.73
Ulchi:Ul59,0,43.57,0,0.21,0,0.06,1.08,0.21,0,54.87,0,0
Ulchi:Ul6,0,33.94,0,0,0,0,2.86,0,0,63.20,0,0
Ulchi:Ul65,0,50.85,0,0,0.28,0,0,0,0,48.86,0,0
Ulchi:Ul69,0,44.28,0.48,2.15,0,0,0,0,0,53.09,0,0
Ulchi:Ul70,0,44.59,0,0,0,0,0.01,0,0,55.00,0,0.39
Ulchi:Ul71,0,42.63,0,0,0,0,0.28,0.11,0,56.98,0,0
Ulchi:Ul72,0.07,43.66,0,0.71,0,0,0,0.90,0,54.53,0.13,0
Ulchi:Ul74,0,41.40,0,0.56,0,0,0,0,0,57.96,0.08,0

PCA and heatmap of the new samples:

I probably did something wrong, so I'm waiting for feedback from Lucas before I post more averages. Does this produce the same results as DIYDodecad, GEDmatch, or Admixture Studio?

Script for making population averages:

Code:

mkdir 23 admix
printf %s\\n Chukchi Dolgan Enets|awk 'NR==FNR{a[$0];next}$3 in a{print$1}' - v50.0_HO_public.ind|while read x;do plink --bfile v50.0_HO_public --allow-no-sex --keep <(awk '$2==x' "x=$x" v50.0_HO_public.fam) --recode 23 --out 23/$x;done
printf %s\\n 23/*.txt|while read l;do b=${l%.txt};admix -f $l -m K12b|grep %>admix/${b##*/};done
# printf %s\\n 23/*.txt|parallel -j10 'admix -f {} -m K12b|grep %>admix/{/.}' # run 10 parallel jobs
for x in admix/*;do sed 's/.* //' $x|tr -d %|paste -sd\  -|sed s/^/${x##*/}\ /;done|awk 'NR==FNR{a[$1]=$3;next}{print a[$1]":"$0}' v50.0_HO_public.ind -|tr \  ,>admix.csv
awk -F, '{n[$1]++;for(i=2;i<=NF;i++){a[$1,i]+=$i}}END{for(i in n){o=i;for(j=2;j<=NF;j++)o=o FS sprintf("%.2f",a[i,j]/n[i]);print o}}' admix.csv>admix.ave

**Lucas** · 10-24-2021, 09:16 PM

Originally Posted by Komintasavalta

AFAIK, DIYDodecad only has Linux and Windows binaries, and I didn't want to install a VM, so I wasn't able to use it before. But I now found this Python-based alternative to it: https://github.com/stevenliuyi/admix.

I probably did something wrong, so I'm waiting for feedback from Lucas before I post more averages. Does this produce the same results as DIYDodecad, GEDmatch, or Admixture Studio?

Why you think is something wrong exactly?

I used this script for massive checking of results until in AdmixtureStudio didn't implement option for checking multiple files at once. Of course massive checking was done using some sort of commands not from stevenliuyi admix but you know it better how to do it.

Generally there are discrepancies about 0.1-0.5% (rather first value is more common) for component between this and Gedmatch/Dodecad.
But sometimes when I checked let's say 100 files in stevenliuyi admix there were errors in one or two random results, some got 90% or more of one component (and they weren't author proxies for that calculator). Repeated test for them usually fixed problem.

It is much better then Dodecad because is much faster, even for high K calcs.

**Lucas** · 10-24-2021, 09:35 PM

Originally Posted by Komintasavalta

It doesn't have files for K13, but where can I download them?

In AdmixtureStudio in app folder is created subfolder Calculators. Here are calc files for every of them, so K13, k15 too.

**~~Komintasavalta~~** · 10-24-2021, 10:03 PM

Originally Posted by Lucas

In AdmixtureStudio in app folder is created subfolder Calculators. Here are calc files for every of them, so K13, k15 too.

It only comes with a .exe installer, and I don't want to install a Windows VM. Can someone upload the calculator files somewhere?

**Lucas** · 10-24-2021, 10:45 PM

Originally Posted by Komintasavalta

It only comes with a .exe installer, and I don't want to install a Windows VM. Can someone upload the calculator files somewhere?

K13 https://drive.google.com/file/d/1X1n...ew?usp=sharing
K15 https://drive.google.com/file/d/1xWD...ew?usp=sharing

**~~Komintasavalta~~** · 10-25-2021, 01:52 PM

Originally Posted by Lucas

K13 https://drive.google.com/file/d/1X1n...ew?usp=sharing
K15 https://drive.google.com/file/d/1xWD...ew?usp=sharing

Ok, adding the calculators was straightforward. I copied the `.F` and `.alleles` files to `/usr/local/lib/python3.9/site-packages/admix/data`. Then I modified `admix_models.py` to add entries named K13 and K15 to the array returned by the `models` function, and I added these lines to the `populations` function:

Code:

elif model == 'K13':
    return [('North_Atlantic','North_Atlantic'),
            ('Baltic','Baltic'),
            ('West_Med','West_Med'),
            ('West_Asian','West_Asian'),
            ('East_Med','East_Med'),
            ('Red_Sea','Red_Sea'),
            ('South_Asian','South_Asian'),
            ('East_Asian','East_Asian'),
            ('Siberian','Siberian'),
            ('Amerindian','Amerindian'),
            ('Oceanian','Oceanian'),
            ('Northeast_African','Northeast_African'),
            ('Sub-Saharan','Sub-Saharan')]
elif model == 'K15':
    return [('North_Sea','North_Sea'),
            ('Atlantic','Atlantic'),
            ('Baltic','Baltic'),
            ('Eastern_Euro','Eastern_Euro'),
            ('West_Med','West_Med'),
            ('West_Asian','West_Asian'),
            ('East_Med','East_Med'),
            ('Red_Sea','Red_Sea'),
            ('South_Asian','South_Asian'),
            ('Southeast_Asian','Southeast_Asian'),
            ('Siberian','Siberian'),
            ('Amerindian','Amerindian'),
            ('Oceanian','Oceanian'),
            ('Northeast_African','Northeast_African'),
            ('Sub-Saharan','Sub-Saharan')]

I made these averages for K13 from the Reich dataset and from Tambets et al. 2018 (Khanty, Saami_Kola, and Saami_Sweden):

Code:

Aleut,15.28,35.26,3.07,4.61,1.58,0.44,1.38,3.08,17.02,17.25,0.52,0.15,0.36
Enets,4.74,15.29,0.28,0.92,0.00,0.37,1.68,1.09,70.16,4.10,0.68,0.00,0.70
Itelmen,0.00,4.39,0.00,0.00,0.00,0.00,1.80,12.25,62.76,17.18,1.07,0.00,0.54
Kalmyk,2.52,5.75,0.80,6.79,1.15,0.37,0.84,30.84,48.41,1.56,0.56,0.06,0.37
Karelian,30.32,50.92,4.11,1.41,0.83,0.69,1.40,0.26,7.57,1.28,0.58,0.49,0.16
Kusunda,0.96,0.00,0.90,3.36,0.00,0.00,31.41,40.72,18.24,0.88,2.46,0.72,0.34
Mansi,8.34,30.51,0.00,4.75,0.00,0.00,4.24,1.92,43.90,5.26,0.81,0.14,0.13
Nasioi,0.08,0.49,0.34,0.00,0.17,0.23,4.44,21.24,0.21,0.23,72.24,0.04,0.29
Newar,1.09,2.39,1.12,9.15,0.30,0.63,37.11,30.97,14.64,0.99,1.01,0.09,0.51
Nganasan,0.00,2.75,0.00,0.00,0.02,0.01,0.44,0.41,94.26,1.61,0.21,0.03,0.27
Nogai_Astrakhan,9.15,14.62,4.35,11.10,4.02,1.05,1.57,19.78,31.69,1.61,0.72,0.20,0.16
Nogai_Karachay_Cherkessia,4.82,15.81,7.60,37.36,5.83,1.28,1.55,8.50,14.96,0.96,0.67,0.50,0.16
Nogai_Stavropol,9.78,13.59,3.45,17.17,4.16,0.77,3.38,17.32,27.75,1.40,0.81,0.13,0.29
Tatar_Mishar,22.02,36.72,6.67,10.78,3.49,0.61,2.65,3.98,11.20,1.27,0.14,0.14,0.34
Tatar_Siberian,11.35,22.67,0.79,12.78,1.01,0.73,3.84,10.60,31.58,3.63,0.42,0.21,0.37
Tatar_Siberian_Zabolotniye,7.98,28.63,0.00,9.09,0.00,0.00,4.28,6.16,39.24,3.75,0.44,0.00,0.43
Thai,0.56,1.94,1.13,0.79,0.60,0.72,15.28,72.41,3.15,0.83,2.22,0.20,0.18
Tharu,0.71,1.42,1.98,7.59,0.09,0.05,37.14,32.52,15.57,0.89,1.76,0.09,0.20
Tlingit,10.16,25.66,0.84,4.18,0.12,0.06,1.74,4.72,24.47,26.92,0.12,0.42,0.60
Todzin,0.00,8.50,0.23,1.52,0.01,0.23,3.28,13.04,69.16,3.39,0.53,0.00,0.12
Tofalar,1.43,9.78,0.55,1.74,0.00,0.33,1.32,12.23,68.16,3.28,0.98,0.11,0.08
Ulchi,0.00,0.06,0.07,0.00,0.00,0.03,0.13,31.83,64.44,2.97,0.32,0.09,0.07
Veps,27.06,52.06,3.86,1.66,0.87,1.20,1.72,0.17,8.94,1.38,0.61,0.13,0.34
Yukagir_Forest,13.18,28.16,3.70,1.11,3.10,0.61,1.46,4.38,40.97,2.04,0.34,0.39,0.56
Yukagir_Tundra,0.00,2.99,0.00,0.12,0.02,0.10,1.06,8.36,78.20,8.07,0.64,0.08,0.38
Khanty,5.65,31.35,0.00,3.55,0.00,0.00,4.34,0.83,47.59,6.01,0.56,0.00,0.10
Saami_Kola,24.77,47.67,2.75,1.05,0.12,0.05,1.53,0.30,17.59,2.79,0.34,0.68,0.37
Saami_Sweden,24.98,43.85,0.00,0.74,0.00,0.00,1.25,1.23,23.50,3.64,0.41,0.06,0.35

K15:

Code:

Aleut,13.39,6.62,19.19,19.15,1.19,2.11,0.57,0.12,1.22,3.14,15.76,16.78,0.43,0.11,0.22
Enets,3.09,1.88,2.28,15.79,0.16,0.00,0.00,0.10,1.21,1.36,69.10,3.80,0.63,0.00,0.60
Itelmen,0.00,0.06,0.10,5.39,0.00,0.00,0.00,0.00,1.55,12.87,61.69,16.93,0.96,0.00,0.44
Kalmyk,1.98,0.85,1.29,6.45,0.49,5.79,0.85,0.35,0.92,31.23,47.33,1.62,0.50,0.06,0.28
Karelian,24.95,14.98,23.62,25.51,1.75,0.26,0.01,0.08,0.68,0.14,6.14,1.07,0.45,0.28,0.10
Kusunda,0.74,0.20,0.08,0.50,1.01,1.61,0.04,0.04,32.46,41.51,17.48,0.95,2.47,0.53,0.38
Mansi,9.97,1.13,5.60,28.96,0.00,0.70,0.00,0.00,3.75,2.09,42.05,4.93,0.68,0.04,0.09
Nasioi,0.04,0.03,0.67,0.00,0.22,0.00,0.02,0.12,3.88,21.80,0.23,0.22,72.52,0.01,0.26
Newar,1.19,1.16,0.98,3.14,0.87,5.35,0.10,0.70,38.66,31.38,13.96,1.02,0.86,0.12,0.49
Nganasan,0.00,0.03,0.18,3.19,0.00,0.00,0.00,0.00,0.24,0.55,93.78,1.51,0.23,0.02,0.26
Nogai_Astrakhan,7.27,4.90,5.78,11.06,2.38,9.26,3.76,0.89,1.71,19.96,30.36,1.67,0.71,0.14,0.15
Nogai_Karachay_Cherkessia,3.66,5.98,9.18,8.25,2.86,39.24,3.13,1.09,2.01,8.01,14.40,0.94,0.56,0.63,0.06
Nogai_Stavropol,8.61,4.51,4.79,11.18,1.71,14.54,3.74,0.76,3.65,17.36,26.68,1.39,0.74,0.09,0.26
Tatar_Mishar,15.09,13.09,19.63,21.62,3.20,7.79,1.82,0.20,2.52,3.71,9.93,0.98,0.07,0.08,0.27
Tatar_Siberian,9.27,4.63,7.33,19.39,0.12,9.36,0.44,0.46,3.97,10.70,30.11,3.39,0.33,0.14,0.36
Tatar_Siberian_Zabolotniye,9.40,1.11,8.05,24.82,0.00,4.26,0.00,0.00,4.32,6.28,37.55,3.46,0.37,0.00,0.38
Thai,0.60,0.43,1.00,0.73,0.90,0.39,0.03,0.77,15.50,73.31,3.00,0.70,2.22,0.16,0.26
Tharu,0.15,0.80,0.77,2.89,1.29,4.95,0.18,0.00,38.30,32.80,15.04,0.88,1.69,0.15,0.10
Tlingit,10.24,2.20,14.10,14.21,0.00,1.74,0.00,0.00,2.10,4.93,23.40,26.38,0.06,0.11,0.54
Todzin,0.00,0.05,0.77,10.12,0.00,0.40,0.00,0.09,2.90,13.46,68.08,3.61,0.46,0.00,0.06
Tofalar,0.96,0.90,1.33,11.14,0.35,0.23,0.00,0.21,1.06,12.71,67.03,3.04,0.90,0.06,0.07
Ulchi,0.00,0.00,0.02,0.12,0.04,0.00,0.00,0.03,0.24,32.50,63.00,3.36,0.39,0.19,0.11
Veps,21.66,14.23,25.49,26.84,0.88,0.17,0.09,0.36,0.80,0.12,7.55,1.11,0.40,0.19,0.12
Yukagir_Forest,10.30,7.61,14.56,14.81,2.26,0.81,0.69,0.36,1.16,4.45,40.01,1.77,0.38,0.26,0.55
Yukagir_Tundra,0.01,0.00,0.46,3.09,0.06,0.08,0.00,0.11,0.77,8.86,77.66,7.92,0.58,0.03,0.37
Khanty,6.97,0.34,5.33,31.74,0.00,0.15,0.00,0.00,3.55,0.84,45.06,5.61,0.35,0.01,0.05
Saami_Kola,21.70,10.22,20.56,26.16,0.89,0.15,0.00,0.00,0.80,0.16,16.08,2.60,0.21,0.26,0.21
Saami_Sweden,23.38,8.25,13.76,27.35,0.00,0.00,0.00,0.00,0.37,1.04,22.05,3.40,0.23,0.00,0.17

K12b:

Code:

Aleut,5.70,19.08,0.07,1.30,10.16,44.66,1.68,0.13,0.16,12.75,4.31,0.00
Enets,3.16,66.82,0.00,0.00,0.95,17.61,0.15,0.00,0.00,11.30,0.00,0.02
Itelmen,2.57,55.38,0.00,1.45,0.00,6.50,1.22,0.00,0.00,32.82,0.00,0.05
Kalmyk,4.21,33.74,0.04,3.94,1.73,7.66,0.86,0.10,0.36,44.01,3.35,0.01
Karelian,3.04,6.84,0.23,0.46,18.06,65.75,0.97,0.06,0.60,0.73,3.21,0.03
Kusunda,9.46,5.84,0.41,14.24,0.25,1.13,26.66,0.30,0.02,41.46,0.17,0.06
Mansi,5.75,42.24,0.00,0.76,2.52,38.74,1.29,0.01,0.00,7.74,0.96,0.00
Nasioi,3.43,4.83,0.54,34.20,1.66,2.03,36.85,3.57,0.51,8.75,0.05,3.58
Newar,16.22,4.66,0.10,10.98,0.87,3.51,30.34,0.04,0.25,31.69,1.33,0.00
Nganasan,0.13,90.28,0.06,0.40,0.02,1.09,0.15,0.08,0.02,7.63,0.00,0.13
Nogai_Astrakhan,7.17,23.65,0.43,2.80,8.09,17.77,1.16,0.02,1.95,27.43,9.50,0.04
Nogai_Karachay_Cherkessia,13.08,10.90,0.23,1.16,3.83,20.54,0.52,0.16,0.72,12.86,35.92,0.07
Nogai_Stavropol,10.77,20.06,0.18,2.25,7.03,17.51,2.10,0.03,1.66,24.76,13.64,0.00
Tatar_Mishar,7.19,10.06,0.01,0.89,16.60,46.04,1.67,0.00,1.33,5.20,10.98,0.03
Tatar_Siberian,9.42,26.59,0.25,1.56,6.42,27.29,1.96,0.07,0.81,17.86,7.77,0.00
Tatar_Siberian_Zabolotniye,10.85,36.40,0.00,1.13,2.99,31.94,1.47,0.24,0.00,12.92,2.01,0.03
Thai,3.49,1.38,0.07,57.25,0.68,0.73,12.36,0.15,0.45,22.17,1.18,0.10
Tharu,15.85,4.64,0.19,10.28,0.34,2.39,31.17,0.00,0.13,34.60,0.41,0.00
Tlingit,7.36,27.62,0.00,1.82,4.48,35.86,1.58,0.00,0.00,19.27,2.00,0.00
Todzin,2.53,61.11,0.00,0.92,0.46,6.92,1.72,0.00,0.32,26.01,0.00,0.00
Tofalar,2.61,61.25,0.04,0.48,0.97,8.12,0.91,0.14,0.56,24.37,0.55,0.00
Ulchi,0.05,43.23,0.03,1.24,0.02,0.13,0.43,0.06,0.00,54.67,0.04,0.09
Veps,2.67,9.17,0.44,0.30,16.67,63.09,1.03,0.12,1.22,1.20,4.04,0.04
Yukagir_Forest,2.01,35.37,0.15,1.02,9.92,31.54,1.50,0.12,0.81,11.98,5.47,0.11
Yukagir_Tundra,0.54,68.96,0.06,0.29,0.02,3.30,0.72,0.01,0.00,26.06,0.00,0.04
Saami_SWE,4.28,23.14,0.03,0.32,11.85,55.53,0.35,0.01,0.00,4.49,0.00,0.00
Khanty,8.11,47.13,0.00,0.14,0.92,33.67,1.38,0.01,0.04,8.57,0.01,0.02
Saami_Kola,3.57,17.22,0.02,0.14,14.72,58.87,1.03,0.00,0.25,2.60,1.56,0.00

K13 heatmap:

Here's how you can make a heatmap where the clustering takes FST into account, and where the branches of the clustering tree are ordered based on the value of PC1 in a PCA of the populations:

Code:

library(pheatmap)
library(colorspace) # for hex
library(vegan) # for reorder.hclust

t=read.csv(r=1,text=",North_Atlantic,Baltic,West_Med,West_Asian,East_Med,Red_Sea,South_Asian,East_Asian,Siberian,Amerindian,Oceanian,Northeast_African,Sub-Saharan
Aleut,15.28,35.26,3.07,4.61,1.58,0.44,1.38,3.08,17.02,17.25,0.52,0.15,0.36
Enets,4.74,15.29,0.28,0.92,0.00,0.37,1.68,1.09,70.16,4.10,0.68,0.00,0.70
Itelmen,0.00,4.39,0.00,0.00,0.00,0.00,1.80,12.25,62.76,17.18,1.07,0.00,0.54
Kalmyk,2.52,5.75,0.80,6.79,1.15,0.37,0.84,30.84,48.41,1.56,0.56,0.06,0.37
Karelian,30.32,50.92,4.11,1.41,0.83,0.69,1.40,0.26,7.57,1.28,0.58,0.49,0.16
Kusunda,0.96,0.00,0.90,3.36,0.00,0.00,31.41,40.72,18.24,0.88,2.46,0.72,0.34
Mansi,8.34,30.51,0.00,4.75,0.00,0.00,4.24,1.92,43.90,5.26,0.81,0.14,0.13
Nasioi,0.08,0.49,0.34,0.00,0.17,0.23,4.44,21.24,0.21,0.23,72.24,0.04,0.29
Newar,1.09,2.39,1.12,9.15,0.30,0.63,37.11,30.97,14.64,0.99,1.01,0.09,0.51
Nganasan,0.00,2.75,0.00,0.00,0.02,0.01,0.44,0.41,94.26,1.61,0.21,0.03,0.27
Nogai_Astrakhan,9.15,14.62,4.35,11.10,4.02,1.05,1.57,19.78,31.69,1.61,0.72,0.20,0.16
Nogai_Karachay_Cherkessia,4.82,15.81,7.60,37.36,5.83,1.28,1.55,8.50,14.96,0.96,0.67,0.50,0.16
Nogai_Stavropol,9.78,13.59,3.45,17.17,4.16,0.77,3.38,17.32,27.75,1.40,0.81,0.13,0.29
Tatar_Mishar,22.02,36.72,6.67,10.78,3.49,0.61,2.65,3.98,11.20,1.27,0.14,0.14,0.34
Tatar_Siberian,11.35,22.67,0.79,12.78,1.01,0.73,3.84,10.60,31.58,3.63,0.42,0.21,0.37
Tatar_Siberian_Zabolotniye,7.98,28.63,0.00,9.09,0.00,0.00,4.28,6.16,39.24,3.75,0.44,0.00,0.43
Thai,0.56,1.94,1.13,0.79,0.60,0.72,15.28,72.41,3.15,0.83,2.22,0.20,0.18
Tharu,0.71,1.42,1.98,7.59,0.09,0.05,37.14,32.52,15.57,0.89,1.76,0.09,0.20
Tlingit,10.16,25.66,0.84,4.18,0.12,0.06,1.74,4.72,24.47,26.92,0.12,0.42,0.60
Todzin,0.00,8.50,0.23,1.52,0.01,0.23,3.28,13.04,69.16,3.39,0.53,0.00,0.12
Tofalar,1.43,9.78,0.55,1.74,0.00,0.33,1.32,12.23,68.16,3.28,0.98,0.11,0.08
Ulchi,0.00,0.06,0.07,0.00,0.00,0.03,0.13,31.83,64.44,2.97,0.32,0.09,0.07
Veps,27.06,52.06,3.86,1.66,0.87,1.20,1.72,0.17,8.94,1.38,0.61,0.13,0.34
Yukagir_Forest,13.18,28.16,3.70,1.11,3.10,0.61,1.46,4.38,40.97,2.04,0.34,0.39,0.56
Yukagir_Tundra,0.00,2.99,0.00,0.12,0.02,0.10,1.06,8.36,78.20,8.07,0.64,0.08,0.38")

fst=as.matrix(as.dist(read.csv(h=F,text=",,,,,,,,,,,,
19,,,,,,,,,,,,
28,36,,,,,,,,,,,
26,32,36,,,,,,,,,,
26,35,28,21,,,,,,,,,
52,62,50,48,39,,,,,,,,
64,65,76,57,60,82,,,,,,,
114,114,122,110,111,127,76,,,,,,
111,111,123,109,112,130,83,56,,,,,
138,137,154,138,144,161,120,113,105,,,,
179,181,187,177,176,191,146,166,177,217,,,
122,127,124,116,108,121,113,145,151,185,203,,
146,150,150,140,135,141,133,164,170,204,220,41,")))

t2=as.matrix(t)%*%cmdscale(fst,ncol(fst)-1)
p=prcomp(t2)$x
hc=hclust(dist(t2))
hc=reorder(hc,p[,1])

pheatmap::pheatmap(
  t[,ncol(t):1],
  filename="1.png",
  clustering_callback=function(...)hc,
  cluster_cols=F,
  legend=F,
  cellwidth=18,
  cellheight=18,
  fontsize=10,
  treeheight_row=100,
  treeheight_col=100,
  border_color=NA,
  display_numbers=T,
  number_format="%.0f",
  fontsize_number=8,
  number_color="black",
  colorRampPalette(hex(HSV(c(210,210,170,135,100,60,30,0),c(0,rep(.4,7)),1)))(256)
)

**vbnetkhio** · 10-25-2021, 02:07 PM

Originally Posted by Komintasavalta

Ok, adding the calculators was straightforward. I copied the `.F` and `.alleles` files to `/usr/local/lib/python3.9/site-packages/admix/data`. Then I modified `admix_models.py` to add entries named K13 and K15 to the array returned by the `models` function, and I added these lines to the `populations` function:

Code:

elif model == 'K13':
    return [('North_Atlantic','North_Atlantic'),
            ('Baltic','Baltic'),
            ('West_Med','West_Med'),
            ('West_Asian','West_Asian'),
            ('East_Med','East_Med'),
            ('Red_Sea','Red_Sea'),
            ('South_Asian','South_Asian'),
            ('East_Asian','East_Asian'),
            ('Siberian','Siberian'),
            ('Amerindian','Amerindian'),
            ('Oceanian','Oceanian'),
            ('Northeast_African','Northeast_African'),
            ('Sub-Saharan','Sub-Saharan')]
elif model == 'K15':
    return [('North_Sea','North_Sea'),
            ('Atlantic','Atlantic'),
            ('Baltic','Baltic'),
            ('Eastern_Euro','Eastern_Euro'),
            ('West_Med','West_Med'),
            ('West_Asian','West_Asian'),
            ('East_Med','East_Med'),
            ('Red_Sea','Red_Sea'),
            ('South_Asian','South_Asian'),
            ('Southeast_Asian','Southeast_Asian'),
            ('Siberian','Siberian'),
            ('Amerindian','Amerindian'),
            ('Oceanian','Oceanian'),
            ('Northeast_African','Northeast_African'),
            ('Sub-Saharan','Sub-Saharan')]

I made these averages for K13 from the Reich dataset and from Tambets et al. 2018 (Khanty, Saami_Kola, and Saami_Sweden):

Code:

Aleut,15.28,35.26,3.07,4.61,1.58,0.44,1.38,3.08,17.02,17.25,0.52,0.15,0.36
Enets,4.74,15.29,0.28,0.92,0.00,0.37,1.68,1.09,70.16,4.10,0.68,0.00,0.70
Itelmen,0.00,4.39,0.00,0.00,0.00,0.00,1.80,12.25,62.76,17.18,1.07,0.00,0.54
Kalmyk,2.52,5.75,0.80,6.79,1.15,0.37,0.84,30.84,48.41,1.56,0.56,0.06,0.37
Karelian,30.32,50.92,4.11,1.41,0.83,0.69,1.40,0.26,7.57,1.28,0.58,0.49,0.16
Kusunda,0.96,0.00,0.90,3.36,0.00,0.00,31.41,40.72,18.24,0.88,2.46,0.72,0.34
Mansi,8.34,30.51,0.00,4.75,0.00,0.00,4.24,1.92,43.90,5.26,0.81,0.14,0.13
Nasioi,0.08,0.49,0.34,0.00,0.17,0.23,4.44,21.24,0.21,0.23,72.24,0.04,0.29
Newar,1.09,2.39,1.12,9.15,0.30,0.63,37.11,30.97,14.64,0.99,1.01,0.09,0.51
Nganasan,0.00,2.75,0.00,0.00,0.02,0.01,0.44,0.41,94.26,1.61,0.21,0.03,0.27
Nogai_Astrakhan,9.15,14.62,4.35,11.10,4.02,1.05,1.57,19.78,31.69,1.61,0.72,0.20,0.16
Nogai_Karachay_Cherkessia,4.82,15.81,7.60,37.36,5.83,1.28,1.55,8.50,14.96,0.96,0.67,0.50,0.16
Nogai_Stavropol,9.78,13.59,3.45,17.17,4.16,0.77,3.38,17.32,27.75,1.40,0.81,0.13,0.29
Tatar_Mishar,22.02,36.72,6.67,10.78,3.49,0.61,2.65,3.98,11.20,1.27,0.14,0.14,0.34
Tatar_Siberian,11.35,22.67,0.79,12.78,1.01,0.73,3.84,10.60,31.58,3.63,0.42,0.21,0.37
Tatar_Siberian_Zabolotniye,7.98,28.63,0.00,9.09,0.00,0.00,4.28,6.16,39.24,3.75,0.44,0.00,0.43
Thai,0.56,1.94,1.13,0.79,0.60,0.72,15.28,72.41,3.15,0.83,2.22,0.20,0.18
Tharu,0.71,1.42,1.98,7.59,0.09,0.05,37.14,32.52,15.57,0.89,1.76,0.09,0.20
Tlingit,10.16,25.66,0.84,4.18,0.12,0.06,1.74,4.72,24.47,26.92,0.12,0.42,0.60
Todzin,0.00,8.50,0.23,1.52,0.01,0.23,3.28,13.04,69.16,3.39,0.53,0.00,0.12
Tofalar,1.43,9.78,0.55,1.74,0.00,0.33,1.32,12.23,68.16,3.28,0.98,0.11,0.08
Ulchi,0.00,0.06,0.07,0.00,0.00,0.03,0.13,31.83,64.44,2.97,0.32,0.09,0.07
Veps,27.06,52.06,3.86,1.66,0.87,1.20,1.72,0.17,8.94,1.38,0.61,0.13,0.34
Yukagir_Forest,13.18,28.16,3.70,1.11,3.10,0.61,1.46,4.38,40.97,2.04,0.34,0.39,0.56
Yukagir_Tundra,0.00,2.99,0.00,0.12,0.02,0.10,1.06,8.36,78.20,8.07,0.64,0.08,0.38
Khanty,5.65,31.35,0.00,3.55,0.00,0.00,4.34,0.83,47.59,6.01,0.56,0.00,0.10
Saami_Kola,24.77,47.67,2.75,1.05,0.12,0.05,1.53,0.30,17.59,2.79,0.34,0.68,0.37
Saami_Sweden,24.98,43.85,0.00,0.74,0.00,0.00,1.25,1.23,23.50,3.64,0.41,0.06,0.35

K15:

Code:

Aleut,13.39,6.62,19.19,19.15,1.19,2.11,0.57,0.12,1.22,3.14,15.76,16.78,0.43,0.11,0.22
Enets,3.09,1.88,2.28,15.79,0.16,0.00,0.00,0.10,1.21,1.36,69.10,3.80,0.63,0.00,0.60
Itelmen,0.00,0.06,0.10,5.39,0.00,0.00,0.00,0.00,1.55,12.87,61.69,16.93,0.96,0.00,0.44
Kalmyk,1.98,0.85,1.29,6.45,0.49,5.79,0.85,0.35,0.92,31.23,47.33,1.62,0.50,0.06,0.28
Karelian,24.95,14.98,23.62,25.51,1.75,0.26,0.01,0.08,0.68,0.14,6.14,1.07,0.45,0.28,0.10
Kusunda,0.74,0.20,0.08,0.50,1.01,1.61,0.04,0.04,32.46,41.51,17.48,0.95,2.47,0.53,0.38
Mansi,9.97,1.13,5.60,28.96,0.00,0.70,0.00,0.00,3.75,2.09,42.05,4.93,0.68,0.04,0.09
Nasioi,0.04,0.03,0.67,0.00,0.22,0.00,0.02,0.12,3.88,21.80,0.23,0.22,72.52,0.01,0.26
Newar,1.19,1.16,0.98,3.14,0.87,5.35,0.10,0.70,38.66,31.38,13.96,1.02,0.86,0.12,0.49
Nganasan,0.00,0.03,0.18,3.19,0.00,0.00,0.00,0.00,0.24,0.55,93.78,1.51,0.23,0.02,0.26
Nogai_Astrakhan,7.27,4.90,5.78,11.06,2.38,9.26,3.76,0.89,1.71,19.96,30.36,1.67,0.71,0.14,0.15
Nogai_Karachay_Cherkessia,3.66,5.98,9.18,8.25,2.86,39.24,3.13,1.09,2.01,8.01,14.40,0.94,0.56,0.63,0.06
Nogai_Stavropol,8.61,4.51,4.79,11.18,1.71,14.54,3.74,0.76,3.65,17.36,26.68,1.39,0.74,0.09,0.26
Tatar_Mishar,15.09,13.09,19.63,21.62,3.20,7.79,1.82,0.20,2.52,3.71,9.93,0.98,0.07,0.08,0.27
Tatar_Siberian,9.27,4.63,7.33,19.39,0.12,9.36,0.44,0.46,3.97,10.70,30.11,3.39,0.33,0.14,0.36
Tatar_Siberian_Zabolotniye,9.40,1.11,8.05,24.82,0.00,4.26,0.00,0.00,4.32,6.28,37.55,3.46,0.37,0.00,0.38
Thai,0.60,0.43,1.00,0.73,0.90,0.39,0.03,0.77,15.50,73.31,3.00,0.70,2.22,0.16,0.26
Tharu,0.15,0.80,0.77,2.89,1.29,4.95,0.18,0.00,38.30,32.80,15.04,0.88,1.69,0.15,0.10
Tlingit,10.24,2.20,14.10,14.21,0.00,1.74,0.00,0.00,2.10,4.93,23.40,26.38,0.06,0.11,0.54
Todzin,0.00,0.05,0.77,10.12,0.00,0.40,0.00,0.09,2.90,13.46,68.08,3.61,0.46,0.00,0.06
Tofalar,0.96,0.90,1.33,11.14,0.35,0.23,0.00,0.21,1.06,12.71,67.03,3.04,0.90,0.06,0.07
Ulchi,0.00,0.00,0.02,0.12,0.04,0.00,0.00,0.03,0.24,32.50,63.00,3.36,0.39,0.19,0.11
Veps,21.66,14.23,25.49,26.84,0.88,0.17,0.09,0.36,0.80,0.12,7.55,1.11,0.40,0.19,0.12
Yukagir_Forest,10.30,7.61,14.56,14.81,2.26,0.81,0.69,0.36,1.16,4.45,40.01,1.77,0.38,0.26,0.55
Yukagir_Tundra,0.01,0.00,0.46,3.09,0.06,0.08,0.00,0.11,0.77,8.86,77.66,7.92,0.58,0.03,0.37
Khanty,6.97,0.34,5.33,31.74,0.00,0.15,0.00,0.00,3.55,0.84,45.06,5.61,0.35,0.01,0.05
Saami_Kola,21.70,10.22,20.56,26.16,0.89,0.15,0.00,0.00,0.80,0.16,16.08,2.60,0.21,0.26,0.21
Saami_Sweden,23.38,8.25,13.76,27.35,0.00,0.00,0.00,0.00,0.37,1.04,22.05,3.40,0.23,0.00,0.17

K12b:

Code:

Aleut,5.70,19.08,0.07,1.30,10.16,44.66,1.68,0.13,0.16,12.75,4.31,0.00
Enets,3.16,66.82,0.00,0.00,0.95,17.61,0.15,0.00,0.00,11.30,0.00,0.02
Itelmen,2.57,55.38,0.00,1.45,0.00,6.50,1.22,0.00,0.00,32.82,0.00,0.05
Kalmyk,4.21,33.74,0.04,3.94,1.73,7.66,0.86,0.10,0.36,44.01,3.35,0.01
Karelian,3.04,6.84,0.23,0.46,18.06,65.75,0.97,0.06,0.60,0.73,3.21,0.03
Kusunda,9.46,5.84,0.41,14.24,0.25,1.13,26.66,0.30,0.02,41.46,0.17,0.06
Mansi,5.75,42.24,0.00,0.76,2.52,38.74,1.29,0.01,0.00,7.74,0.96,0.00
Nasioi,3.43,4.83,0.54,34.20,1.66,2.03,36.85,3.57,0.51,8.75,0.05,3.58
Newar,16.22,4.66,0.10,10.98,0.87,3.51,30.34,0.04,0.25,31.69,1.33,0.00
Nganasan,0.13,90.28,0.06,0.40,0.02,1.09,0.15,0.08,0.02,7.63,0.00,0.13
Nogai_Astrakhan,7.17,23.65,0.43,2.80,8.09,17.77,1.16,0.02,1.95,27.43,9.50,0.04
Nogai_Karachay_Cherkessia,13.08,10.90,0.23,1.16,3.83,20.54,0.52,0.16,0.72,12.86,35.92,0.07
Nogai_Stavropol,10.77,20.06,0.18,2.25,7.03,17.51,2.10,0.03,1.66,24.76,13.64,0.00
Tatar_Mishar,7.19,10.06,0.01,0.89,16.60,46.04,1.67,0.00,1.33,5.20,10.98,0.03
Tatar_Siberian,9.42,26.59,0.25,1.56,6.42,27.29,1.96,0.07,0.81,17.86,7.77,0.00
Tatar_Siberian_Zabolotniye,10.85,36.40,0.00,1.13,2.99,31.94,1.47,0.24,0.00,12.92,2.01,0.03
Thai,3.49,1.38,0.07,57.25,0.68,0.73,12.36,0.15,0.45,22.17,1.18,0.10
Tharu,15.85,4.64,0.19,10.28,0.34,2.39,31.17,0.00,0.13,34.60,0.41,0.00
Tlingit,7.36,27.62,0.00,1.82,4.48,35.86,1.58,0.00,0.00,19.27,2.00,0.00
Todzin,2.53,61.11,0.00,0.92,0.46,6.92,1.72,0.00,0.32,26.01,0.00,0.00
Tofalar,2.61,61.25,0.04,0.48,0.97,8.12,0.91,0.14,0.56,24.37,0.55,0.00
Ulchi,0.05,43.23,0.03,1.24,0.02,0.13,0.43,0.06,0.00,54.67,0.04,0.09
Veps,2.67,9.17,0.44,0.30,16.67,63.09,1.03,0.12,1.22,1.20,4.04,0.04
Yukagir_Forest,2.01,35.37,0.15,1.02,9.92,31.54,1.50,0.12,0.81,11.98,5.47,0.11
Yukagir_Tundra,0.54,68.96,0.06,0.29,0.02,3.30,0.72,0.01,0.00,26.06,0.00,0.04
Khanty,6.97,0.34,5.33,31.74,0.00,0.15,0.00,0.00,3.55,0.84,45.06,5.61,0.35,0.01,0.05
Saami_Kola,21.70,10.22,20.56,26.16,0.89,0.15,0.00,0.00,0.80,0.16,16.08,2.60,0.21,0.26,0.21
Saami_Sweden,23.38,8.25,13.76,27.35,0.00,0.00,0.00,0.00,0.37,1.04,22.05,3.40,0.23,0.00,0.17

K13 heatmap:

Here's how you can make a heatmap where the clustering takes FST into account, and where the branches of the clustering tree are ordered based on the value of PC1 in a PCA of the populations:

Code:

library(pheatmap)
library(colorspace) # for hex
library(vegan) # for reorder.hclust

t=read.csv(r,1=text=",North_Atlantic,Baltic,West_Med,West_Asian,East_Med,Red_Sea,South_Asian,East_Asian,Siberian,Amerindian,Oceanian,Northeast_African,Sub-Saharan
Aleut,15.28,35.26,3.07,4.61,1.58,0.44,1.38,3.08,17.02,17.25,0.52,0.15,0.36
Enets,4.74,15.29,0.28,0.92,0.00,0.37,1.68,1.09,70.16,4.10,0.68,0.00,0.70
Itelmen,0.00,4.39,0.00,0.00,0.00,0.00,1.80,12.25,62.76,17.18,1.07,0.00,0.54
Kalmyk,2.52,5.75,0.80,6.79,1.15,0.37,0.84,30.84,48.41,1.56,0.56,0.06,0.37
Karelian,30.32,50.92,4.11,1.41,0.83,0.69,1.40,0.26,7.57,1.28,0.58,0.49,0.16
Kusunda,0.96,0.00,0.90,3.36,0.00,0.00,31.41,40.72,18.24,0.88,2.46,0.72,0.34
Mansi,8.34,30.51,0.00,4.75,0.00,0.00,4.24,1.92,43.90,5.26,0.81,0.14,0.13
Nasioi,0.08,0.49,0.34,0.00,0.17,0.23,4.44,21.24,0.21,0.23,72.24,0.04,0.29
Newar,1.09,2.39,1.12,9.15,0.30,0.63,37.11,30.97,14.64,0.99,1.01,0.09,0.51
Nganasan,0.00,2.75,0.00,0.00,0.02,0.01,0.44,0.41,94.26,1.61,0.21,0.03,0.27
Nogai_Astrakhan,9.15,14.62,4.35,11.10,4.02,1.05,1.57,19.78,31.69,1.61,0.72,0.20,0.16
Nogai_Karachay_Cherkessia,4.82,15.81,7.60,37.36,5.83,1.28,1.55,8.50,14.96,0.96,0.67,0.50,0.16
Nogai_Stavropol,9.78,13.59,3.45,17.17,4.16,0.77,3.38,17.32,27.75,1.40,0.81,0.13,0.29
Tatar_Mishar,22.02,36.72,6.67,10.78,3.49,0.61,2.65,3.98,11.20,1.27,0.14,0.14,0.34
Tatar_Siberian,11.35,22.67,0.79,12.78,1.01,0.73,3.84,10.60,31.58,3.63,0.42,0.21,0.37
Tatar_Siberian_Zabolotniye,7.98,28.63,0.00,9.09,0.00,0.00,4.28,6.16,39.24,3.75,0.44,0.00,0.43
Thai,0.56,1.94,1.13,0.79,0.60,0.72,15.28,72.41,3.15,0.83,2.22,0.20,0.18
Tharu,0.71,1.42,1.98,7.59,0.09,0.05,37.14,32.52,15.57,0.89,1.76,0.09,0.20
Tlingit,10.16,25.66,0.84,4.18,0.12,0.06,1.74,4.72,24.47,26.92,0.12,0.42,0.60
Todzin,0.00,8.50,0.23,1.52,0.01,0.23,3.28,13.04,69.16,3.39,0.53,0.00,0.12
Tofalar,1.43,9.78,0.55,1.74,0.00,0.33,1.32,12.23,68.16,3.28,0.98,0.11,0.08
Ulchi,0.00,0.06,0.07,0.00,0.00,0.03,0.13,31.83,64.44,2.97,0.32,0.09,0.07
Veps,27.06,52.06,3.86,1.66,0.87,1.20,1.72,0.17,8.94,1.38,0.61,0.13,0.34
Yukagir_Forest,13.18,28.16,3.70,1.11,3.10,0.61,1.46,4.38,40.97,2.04,0.34,0.39,0.56
Yukagir_Tundra,0.00,2.99,0.00,0.12,0.02,0.10,1.06,8.36,78.20,8.07,0.64,0.08,0.38")

fst=as.matrix(as.dist(read.csv(h=F,text=",,,,,,,,,,,,
19,,,,,,,,,,,,
28,36,,,,,,,,,,,
26,32,36,,,,,,,,,,
26,35,28,21,,,,,,,,,
52,62,50,48,39,,,,,,,,
64,65,76,57,60,82,,,,,,,
114,114,122,110,111,127,76,,,,,,
111,111,123,109,112,130,83,56,,,,,
138,137,154,138,144,161,120,113,105,,,,
179,181,187,177,176,191,146,166,177,217,,,
122,127,124,116,108,121,113,145,151,185,203,,
146,150,150,140,135,141,133,164,170,204,220,41,")))

t2=as.matrix(t)%*%cmdscale(fst,ncol(fst)-1)
p=prcomp(t2)$x
hc=hclust(dist(t2))
hc=reorder(hc,p[,1])

pheatmap::pheatmap(
  t[,ncol(t):1],
  filename="1.png",
  clustering_callback=function(...)hc,
  cluster_cols=F,
  legend=F,
  cellwidth=18,
  cellheight=18,
  fontsize=10,
  treeheight_row=100,
  treeheight_col=100,
  border_color=NA,
  display_numbers=T,
  number_format="%.0f",
  fontsize_number=8,
  number_color="black",
  colorRampPalette(hex(HSV(c(210,210,170,135,100,60,30,0),c(0,rep(.4,7)),1)))(256)
)

is this script much faster than the regular DiyDodecad, or you have very high CPU/RAM?

could you run these Lithuanians trough k13? https://figshare.com/articles/datase...P_data/7964159

**Leto** · 10-25-2021, 02:13 PM

I wanna borrow some of those averages for Dodecad

But the Khanty one is broken, please fix it.

**vbnetkhio** · 10-25-2021, 02:15 PM

Originally Posted by Leto

I wanna borrow some of those averages for Dodecad

But the Khanty one is broken, please fix it.

probably calc effect.
edit: it's not calc effect, those are just k13 values instead of k12b

**Leto** · 10-25-2021, 02:26 PM

I see Lukasz already added most of them. But we need to delete the old Yugakir and replace it with the new ones. Also Khanty, Sámi and Thai should be added.