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Thread: Initial Upper Palaeolithic Homo sapiens from Bacho Kiro Cave, Bulgaria[

  1. #481
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    Quote Originally Posted by K33 View Post
    OK so it looks like convertf is part of eigensoft...

    I downloaded eigensoft and after unzipping there is a CONVERTF folder inside... do you run this program out of the R terminal? (I'm using Windows 10, 64 bit)



    My R program is installed on my C:/ drive... the eigensoft package is under D:/EIG-master, Zlaty Kun dataset is under D:/zlaty_kun, my existing (ancestrymap) dataset I want to merge zlaty kun with is D:/Dataset2.

    So I just go into R and type:

    Plink --bfile "D:/zlaty_kun" --bmerge "D:/Dataset2" --indiv-sort f "a combined .fam file" --make-bed --out "yourmergedfile"


    > R says "Error: unexpected string constant in "Plink "D:/zlaty_kun"

    ... what am I missing...
    If you have admixtools2 installed you can use the following commands to generate plink files.

    packedancestrymap_to_plink( inpref, outpref)

    You can select specific pops or individuals using the full command. Its pretty convenient.

    packedancestrymap_to_plink( inpref, outpref, inds = NULL, pops = NULL, verbose = TRUE )
    Last edited by Mnemonics; 04-19-2021 at 06:37 AM.

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    Last edited by epoch; 04-20-2021 at 06:58 AM.

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    Quote Originally Posted by epoch View Post
    What was the worst Z-score?
    Worst F3 residual was I think 1.7 or so. Overfit between Ust-Ishim and ZlatyKun.
    Collection of 14,000 d-stats: Hidden Content Part 2: Hidden Content Part 3: Hidden Content PM me for d-stats, qpadm, qpgraph, or f3-outgroup nmonte models.

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    Quote Originally Posted by Kale View Post
    Worst F3 residual was I think 1.7 or so. Overfit between Ust-Ishim and ZlatyKun.
    When you do the manual qpGraph thru R, it gives you an overall "score" right, in addition to the worst z-score?

    I'm curious what the scores and worst-z were like for the qpGraphs you've posted here w/Zlaty Kun compared to the runs I did trying to show back-migration into Africa?

    Before Zlaty came out I was also able to get good scores using Ust Ishim/Bacho as the back-migrating pop: https://anthrogenica.com/showthread....l=1#post763322

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    This is an interesting turnout. The apparent North African in Kostenki and BK-1653 look legit. I put in those edges because the f2 between ShumLaka and them was near 2 and the worst two left.

    BK1.pngBK1a.pngBK1b.png

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    f2 output for this graph

    fst: fitted estim diff std. err Z score
    Alt Sun f2: 0.858154 0.858661 0.000507 0.003821 0.133
    Alt Kos f2: 1.035594 1.038843 0.003250 0.003257 0.998
    Alt Bac f2: 0.688439 0.688514 0.000075 0.004313 0.017
    Alt Shu f2: 0.484319 0.484894 0.000576 0.002311 0.249
    Alt Ust f2: 0.812537 0.813441 0.000904 0.004365 0.207
    Alt Tia f2: 1.031070 1.032716 0.001646 0.003322 0.495
    Alt BK_ f2: 1.044841 1.045298 0.000457 0.003382 0.135
    Alt Zla f2: 1.021791 1.021230 -0.000560 0.003920 -0.143
    Alt Goy f2: 1.040566 1.041317 0.000751 0.003421 0.220
    Alt Chi f2: 0.594956 0.595399 0.000444 0.002853 0.155
    Sun Kos f2: 0.624312 0.624764 0.000452 0.004376 0.103
    Sun Bac f2: 0.389789 0.389501 -0.000288 0.003764 -0.076
    Sun Shu f2: 0.416114 0.415988 -0.000125 0.002986 -0.042
    Sun Ust f2: 0.483212 0.485444 0.002232 0.004424 0.504
    Sun Tia f2: 0.701714 0.701126 -0.000588 0.004157 -0.141
    Sun BK_ f2: 0.667500 0.666067 -0.001433 0.004277 -0.335
    Sun Zla f2: 0.715637 0.717355 0.001718 0.004200 0.409
    Sun Goy f2: 0.648789 0.656138 0.007349 0.004528 1.623
    Sun Chi f2: 0.915429 0.915984 0.000555 0.003350 0.166
    Kos Bac f2: 0.572507 0.574884 0.002377 0.003208 0.741
    Kos Shu f2: 0.590294 0.590101 -0.000193 0.002010 -0.096
    Kos Ust f2: 0.666361 0.665347 -0.001014 0.003892 -0.260
    Kos Tia f2: 0.884864 0.889061 0.004197 0.003697 1.135
    Kos BK_ f2: 0.852073 0.844791 -0.007282 0.004198 -1.735
    Kos Zla f2: 0.898125 0.895817 -0.002309 0.004000 -0.577
    Kos Goy f2: 0.833719 0.835183 0.001464 0.004512 0.325
    Kos Chi f2: 1.092580 1.093394 0.000814 0.002655 0.307
    Bac Shu f2: 0.260604 0.260690 0.000086 0.002494 0.035
    Bac Ust f2: 0.344172 0.343377 -0.000795 0.003754 -0.212
    Bac Tia f2: 0.550976 0.552092 0.001116 0.003471 0.322
    Bac BK_ f2: 0.582281 0.582902 0.000621 0.003244 0.192
    Bac Zla f2: 0.565430 0.565941 0.000510 0.003603 0.142
    Bac Goy f2: 0.565929 0.565251 -0.000678 0.003373 -0.201
    Bac Chi f2: 0.751239 0.750857 -0.000382 0.003409 -0.112
    Shu Ust f2: 0.370497 0.371630 0.001133 0.002826 0.401
    Shu Tia f2: 0.589030 0.588155 -0.000875 0.002055 -0.426
    Shu BK_ f2: 0.606003 0.605310 -0.000693 0.002068 -0.335
    Shu Zla f2: 0.579750 0.581078 0.001327 0.001982 0.670
    Shu Goy f2: 0.598526 0.598058 -0.000468 0.001991 -0.235
    Shu Chi f2: 0.531472 0.531230 -0.000242 0.002004 -0.121
    Ust Tia f2: 0.659562 0.663360 0.003798 0.004091 0.928
    Ust BK_ f2: 0.684173 0.687851 0.003678 0.003988 0.922
    Ust Zla f2: 0.670020 0.674981 0.004961 0.004231 1.173
    Ust Goy f2: 0.667461 0.663873 -0.003588 0.004227 -0.849
    Ust Chi f2: 0.869812 0.870334 0.000522 0.003498 0.149
    Tia BK_ f2: 0.897792 0.902546 0.004755 0.003595 1.322
    Tia Zla f2: 0.888553 0.890849 0.002296 0.003913 0.587
    Tia Goy f2: 0.880922 0.877941 -0.002981 0.003887 -0.767
    Tia Chi f2: 1.088345 1.087607 -0.000739 0.002727 -0.271
    BK_ Zla f2: 0.914098 0.916904 0.002806 0.003948 0.711
    BK_ Goy f2: 0.838755 0.838965 0.000210 0.004194 0.050
    BK_ Chi f2: 1.104120 1.104444 0.000324 0.002709 0.120
    Zla Goy f2: 0.898049 0.895938 -0.002111 0.003992 -0.529
    Zla Chi f2: 1.079065 1.076159 -0.002906 0.002875 -1.011
    Goy Chi f2: 1.097841 1.102848 0.005007 0.002627 1.906

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    Chad, your graph indicates that Tianyuan and Goyet aren't directly descended from Bacho Kiro specimens but from a population (E2) that is closer to the latter than to Ust-Ishim, Kostenki, etc. The NA component in Kostenki is new.

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    It's just a Bacho Kiro pop without the extra Neanderthal, as the f2 supports. Also, no need for Bacho Kiro in Ust Ishim.

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    I'll pop up the one with Yana and Native Americans tonight.

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    Quote Originally Posted by Chad Rohlfsen View Post
    f2 output for this graph

    fst: fitted estim diff std. err Z score
    Alt Sun f2: 0.858154 0.858661 0.000507 0.003821 0.133
    Alt Kos f2: 1.035594 1.038843 0.003250 0.003257 0.998
    Alt Bac f2: 0.688439 0.688514 0.000075 0.004313 0.017
    Alt Shu f2: 0.484319 0.484894 0.000576 0.002311 0.249
    Alt Ust f2: 0.812537 0.813441 0.000904 0.004365 0.207
    Alt Tia f2: 1.031070 1.032716 0.001646 0.003322 0.495
    Alt BK_ f2: 1.044841 1.045298 0.000457 0.003382 0.135
    Alt Zla f2: 1.021791 1.021230 -0.000560 0.003920 -0.143
    Alt Goy f2: 1.040566 1.041317 0.000751 0.003421 0.220
    Alt Chi f2: 0.594956 0.595399 0.000444 0.002853 0.155
    Sun Kos f2: 0.624312 0.624764 0.000452 0.004376 0.103
    Sun Bac f2: 0.389789 0.389501 -0.000288 0.003764 -0.076
    Sun Shu f2: 0.416114 0.415988 -0.000125 0.002986 -0.042
    Sun Ust f2: 0.483212 0.485444 0.002232 0.004424 0.504
    Sun Tia f2: 0.701714 0.701126 -0.000588 0.004157 -0.141
    Sun BK_ f2: 0.667500 0.666067 -0.001433 0.004277 -0.335
    Sun Zla f2: 0.715637 0.717355 0.001718 0.004200 0.409
    Sun Goy f2: 0.648789 0.656138 0.007349 0.004528 1.623
    Sun Chi f2: 0.915429 0.915984 0.000555 0.003350 0.166
    Kos Bac f2: 0.572507 0.574884 0.002377 0.003208 0.741
    Kos Shu f2: 0.590294 0.590101 -0.000193 0.002010 -0.096
    Kos Ust f2: 0.666361 0.665347 -0.001014 0.003892 -0.260
    Kos Tia f2: 0.884864 0.889061 0.004197 0.003697 1.135
    Kos BK_ f2: 0.852073 0.844791 -0.007282 0.004198 -1.735
    Kos Zla f2: 0.898125 0.895817 -0.002309 0.004000 -0.577
    Kos Goy f2: 0.833719 0.835183 0.001464 0.004512 0.325
    Kos Chi f2: 1.092580 1.093394 0.000814 0.002655 0.307
    Bac Shu f2: 0.260604 0.260690 0.000086 0.002494 0.035
    Bac Ust f2: 0.344172 0.343377 -0.000795 0.003754 -0.212
    Bac Tia f2: 0.550976 0.552092 0.001116 0.003471 0.322
    Bac BK_ f2: 0.582281 0.582902 0.000621 0.003244 0.192
    Bac Zla f2: 0.565430 0.565941 0.000510 0.003603 0.142
    Bac Goy f2: 0.565929 0.565251 -0.000678 0.003373 -0.201
    Bac Chi f2: 0.751239 0.750857 -0.000382 0.003409 -0.112
    Shu Ust f2: 0.370497 0.371630 0.001133 0.002826 0.401
    Shu Tia f2: 0.589030 0.588155 -0.000875 0.002055 -0.426
    Shu BK_ f2: 0.606003 0.605310 -0.000693 0.002068 -0.335
    Shu Zla f2: 0.579750 0.581078 0.001327 0.001982 0.670
    Shu Goy f2: 0.598526 0.598058 -0.000468 0.001991 -0.235
    Shu Chi f2: 0.531472 0.531230 -0.000242 0.002004 -0.121
    Ust Tia f2: 0.659562 0.663360 0.003798 0.004091 0.928
    Ust BK_ f2: 0.684173 0.687851 0.003678 0.003988 0.922
    Ust Zla f2: 0.670020 0.674981 0.004961 0.004231 1.173
    Ust Goy f2: 0.667461 0.663873 -0.003588 0.004227 -0.849
    Ust Chi f2: 0.869812 0.870334 0.000522 0.003498 0.149
    Tia BK_ f2: 0.897792 0.902546 0.004755 0.003595 1.322
    Tia Zla f2: 0.888553 0.890849 0.002296 0.003913 0.587
    Tia Goy f2: 0.880922 0.877941 -0.002981 0.003887 -0.767
    Tia Chi f2: 1.088345 1.087607 -0.000739 0.002727 -0.271
    BK_ Zla f2: 0.914098 0.916904 0.002806 0.003948 0.711
    BK_ Goy f2: 0.838755 0.838965 0.000210 0.004194 0.050
    BK_ Chi f2: 1.104120 1.104444 0.000324 0.002709 0.120
    Zla Goy f2: 0.898049 0.895938 -0.002111 0.003992 -0.529
    Zla Chi f2: 1.079065 1.076159 -0.002906 0.002875 -1.011
    Goy Chi f2: 1.097841 1.102848 0.005007 0.002627 1.906
    I made it into a heatmap which only shows the estim column multiplied by hundred:



    What are the standard errors or the other columns useful for?

    Code:
    library(pheatmap)
    library(vegan) # for reorder.hclust
    library(colorspace) # for hex
    
    t=read.table(text="Altai_Neanderthal Sunghir 0.858154 0.858661 0.000507 0.003821 0.133
    Altai_Neanderthal Kostenki 1.035594 1.038843 0.003250 0.003257 0.998
    Altai_Neanderthal Bacho_Kiro 0.688439 0.688514 0.000075 0.004313 0.017
    Altai_Neanderthal Shum_Laka 0.484319 0.484894 0.000576 0.002311 0.249
    Altai_Neanderthal Ust'-Ishim 0.812537 0.813441 0.000904 0.004365 0.207
    Altai_Neanderthal Tianyuan 1.031070 1.032716 0.001646 0.003322 0.495
    Altai_Neanderthal BK_1653 1.044841 1.045298 0.000457 0.003382 0.135
    Altai_Neanderthal Zlaty_Kun 1.021791 1.021230 -0.000560 0.003920 -0.143
    Altai_Neanderthal GoyetQ116_1 1.040566 1.041317 0.000751 0.003421 0.220
    Altai_Neanderthal Chimp 0.594956 0.595399 0.000444 0.002853 0.155
    Sunghir Kostenki 0.624312 0.624764 0.000452 0.004376 0.103
    Sunghir Bacho_Kiro 0.389789 0.389501 -0.000288 0.003764 -0.076
    Sunghir Shum_Laka 0.416114 0.415988 -0.000125 0.002986 -0.042
    Sunghir Ust'-Ishim 0.483212 0.485444 0.002232 0.004424 0.504
    Sunghir Tianyuan 0.701714 0.701126 -0.000588 0.004157 -0.141
    Sunghir BK_1653 0.667500 0.666067 -0.001433 0.004277 -0.335
    Sunghir Zlaty_Kun 0.715637 0.717355 0.001718 0.004200 0.409
    Sunghir GoyetQ116_1 0.648789 0.656138 0.007349 0.004528 1.623
    Sunghir Chimp 0.915429 0.915984 0.000555 0.003350 0.166
    Kostenki Bacho_Kiro 0.572507 0.574884 0.002377 0.003208 0.741
    Kostenki Shum_Laka 0.590294 0.590101 -0.000193 0.002010 -0.096
    Kostenki Ust'-Ishim 0.666361 0.665347 -0.001014 0.003892 -0.260
    Kostenki Tianyuan 0.884864 0.889061 0.004197 0.003697 1.135
    Kostenki BK_1653 0.852073 0.844791 -0.007282 0.004198 -1.735
    Kostenki Zlaty_Kun 0.898125 0.895817 -0.002309 0.004000 -0.577
    Kostenki GoyetQ116_1 0.833719 0.835183 0.001464 0.004512 0.325
    Kostenki Chimp 1.092580 1.093394 0.000814 0.002655 0.307
    Bacho_Kiro Shum_Laka 0.260604 0.260690 0.000086 0.002494 0.035
    Bacho_Kiro Ust'-Ishim 0.344172 0.343377 -0.000795 0.003754 -0.212
    Bacho_Kiro Tianyuan 0.550976 0.552092 0.001116 0.003471 0.322
    Bacho_Kiro BK_1653 0.582281 0.582902 0.000621 0.003244 0.192
    Bacho_Kiro Zlaty_Kun 0.565430 0.565941 0.000510 0.003603 0.142
    Bacho_Kiro GoyetQ116_1 0.565929 0.565251 -0.000678 0.003373 -0.201
    Bacho_Kiro Chimp 0.751239 0.750857 -0.000382 0.003409 -0.112
    Shum_Laka Ust'-Ishim 0.370497 0.371630 0.001133 0.002826 0.401
    Shum_Laka Tianyuan 0.589030 0.588155 -0.000875 0.002055 -0.426
    Shum_Laka BK_1653 0.606003 0.605310 -0.000693 0.002068 -0.335
    Shum_Laka Zlaty_Kun 0.579750 0.581078 0.001327 0.001982 0.670
    Shum_Laka GoyetQ116_1 0.598526 0.598058 -0.000468 0.001991 -0.235
    Shum_Laka Chimp 0.531472 0.531230 -0.000242 0.002004 -0.121
    Ust'-Ishim Tianyuan 0.659562 0.663360 0.003798 0.004091 0.928
    Ust'-Ishim BK_1653 0.684173 0.687851 0.003678 0.003988 0.922
    Ust'-Ishim Zlaty_Kun 0.670020 0.674981 0.004961 0.004231 1.173
    Ust'-Ishim GoyetQ116_1 0.667461 0.663873 -0.003588 0.004227 -0.849
    Ust'-Ishim Chimp 0.869812 0.870334 0.000522 0.003498 0.149
    Tianyuan BK_1653 0.897792 0.902546 0.004755 0.003595 1.322
    Tianyuan Zlaty_Kun 0.888553 0.890849 0.002296 0.003913 0.587
    Tianyuan GoyetQ116_1 0.880922 0.877941 -0.002981 0.003887 -0.767
    Tianyuan Chimp 1.088345 1.087607 -0.000739 0.002727 -0.271
    BK_1653 Zlaty_Kun 0.914098 0.916904 0.002806 0.003948 0.711
    BK_1653 GoyetQ116_1 0.838755 0.838965 0.000210 0.004194 0.050
    BK_1653 Chimp 1.104120 1.104444 0.000324 0.002709 0.120
    Zlaty_Kun GoyetQ116_1 0.898049 0.895938 -0.002111 0.003992 -0.529
    Zlaty_Kun Chimp 1.079065 1.076159 -0.002906 0.002875 -1.011
    GoyetQ116_1 Chimp 1.097841 1.102848 0.005007 0.002627 1.906")
    
    t2=t[,c(1,2,4)]
    t3=rbind(t2,setNames(t2[,c(2,1,3)],names(t2)))
    t4=xtabs(t3[,3]~t3[,2]+t3[,1])
    diag(t4)=NA
    
    pheatmap(
      100*t4,
      filename="a.png",
      clustering_callback=function(...){reorder(hclust(as.dist(t4^2)),wts=rowMeans(t4))},
      legend=F,
      cellwidth=16,
      cellheight=16,
      fontsize=8,
      border_color=NA,
      display_numbers=T,
      number_format="%.0f",
      fontsize_number=7,
      number_color="black",
      colorRampPalette(hex(HSV(c(210,170,120,60,40,20,0),.5,1)))(256)
    )

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