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Thread: Exit modern Global25 or unsupervised_modern_Europeans?

  1. #1
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    Exit modern Global25 or unsupervised_modern_Europeans?

    demo.North.png
    demo.South.png

    On 18 aug 2020 Eurogenes announced that he will phase out the modern Global25 samples, because he doesn't trust the labels of the self-reported ethnicity classifications.
    Neither do I, but I think an even greater problem is that the ethnic classification of the Global25 samples is too fine-grained, especially for modern European samples.

    This type of classification, wich uses a prior established set of group-labels is called 'supervised classification'.
    The alternative, where the estimation does not use the pre-established labels but only the quantitative genetic variables, is called 'unsupervised classification' or 'clustering'.
    If we suspect that the labels of the modern G25 samples are unreliable, we should not use these labels and limit ourselves to unsupervised clustering.
    As a demonstration I did a clustering on the modern European samples. However this required some advanced methods.

    IMO there are 5 ways to technically improve the clustering of this type of data:
    1. Removing the populations that are very distant from the bulk of the samples. These far-away populations introduce dimensions that are alien to the bulk, which impedes the recognition of locally important dimensions.
    I think that the choice of these outgroups is similar to the choice of right pops in qpAdm.
    In the case of Modern Europeans, my choice of these distant populations was:
    - the Caucasus populations
    - the far North-East populations (Udmurt, Tatar, Komi, Saami, Bashkir, Russian_Pinega etc)
    - the 5 Sardinian samples; these are anyway to few to build their own cluster.
    2. Reducing the number of dimensions, not necessarily by PCA. Many of the Global25 dimensions are not relevant for modern European populations.
    3. Searching for the optimal number of clusters.
    I found an optimal result with 4 dimensions and 7 clusters.
    4. For each sample, partition 100% membership among the 7 clusters, preferably by fuzzy clustering. This results in an Admixture-like partitioning.
    5. Another improvement is a better balance in the population sampling. In the present project I downsized Dutch, Irish, Ashkenazi and Moldovan.

    In this project I processed the modern European samples from Eurogenes Global_25_PCA (except the outgroups, see #1)
    On the basis of unsupervised clustering I calculated the membership of the 7 clusters for all of the samples.
    Next I averaged over the 112 ethnic groups in Global25, which results in a 7x112 membership matrix.
    So first I estimated the unsupervised scores of all the individual samples and next I calculated the averaged unsupervised membership scores of the Eurogenes-labeled groups.
    The complete results can be be found in the next spoiler box; save them as a textfile or import them in a spreadsheet (separator is comma). I will refer to these results in the spoiler box as the 'crosstab' data.
     

    Group,Number,A,B,C,D,E,F,G
    Albanian,12,0.049,0.086,0.157,0.074,0.067,0.54,0.0 28
    Ashkenazi,25,0.034,0.105,0.587,0.064,0.057,0.138,0 .015
    Austrian,17,0.174,0.067,0.057,0.174,0.199,0.269,0. 061
    Basque,22,0.042,0.674,0.121,0.109,0.022,0.02,0.012
    Belarusian,15,0.027,0.008,0.007,0.012,0.735,0.107, 0.104
    Belgian,27,0.29,0.074,0.053,0.47,0.046,0.049,0.018
    Bosnian,5,0.066,0.031,0.025,0.043,0.154,0.61,0.071
    Bulgarian,5,0.048,0.05,0.046,0.051,0.08,0.696,0.02 9
    Cossack_Kuban,1,0.027,0.008,0.007,0.013,0.076,0.04 5,0.825
    Cossack_Ukrainian,1,0.009,0.003,0.002,0.004,0.879, 0.053,0.049
    Croatian,10,0.09,0.034,0.029,0.049,0.289,0.417,0.0 92
    Cypriot,8,0.032,0.073,0.642,0.051,0.051,0.133,0.01 8
    Czech,13,0.156,0.034,0.03,0.077,0.289,0.278,0.137
    Danish,4,0.535,0.036,0.032,0.267,0.046,0.047,0.038
    Dutch,40,0.522,0.045,0.036,0.268,0.047,0.047,0.035
    English,19,0.579,0.035,0.028,0.266,0.033,0.036,0.0 22
    English_Cornwall,13,0.665,0.028,0.023,0.221,0.023, 0.025,0.015
    Estonian,10,0.051,0.013,0.012,0.024,0.434,0.158,0. 307
    Finnish,15,0.1,0.023,0.02,0.049,0.123,0.115,0.57
    Finnish_East,10,0.047,0.014,0.012,0.024,0.061,0.04 8,0.794
    French_Alsace,40,0.208,0.09,0.068,0.456,0.071,0.08 5,0.023
    French_Auvergne,27,0.077,0.293,0.145,0.388,0.04,0. 045,0.012
    French_Brittany,40,0.463,0.051,0.038,0.36,0.033,0. 038,0.018
    French_Corsica,14,0.052,0.219,0.361,0.218,0.055,0. 082,0.014
    French_Nord,33,0.199,0.064,0.048,0.577,0.045,0.053 ,0.015
    French_Occitanie,36,0.11,0.323,0.118,0.363,0.035,0 .039,0.013
    French_Paris,11,0.221,0.122,0.083,0.451,0.05,0.054 ,0.018
    French_Pas-de-Calais,3,0.224,0.107,0.08,0.44,0.059,0.064,0.026
    French_Provence,17,0.095,0.214,0.188,0.338,0.065,0 .085,0.015
    French_Seine-Maritime,2,0.352,0.062,0.053,0.365,0.065,0.08,0.02 3
    French_South,7,0.039,0.671,0.124,0.115,0.021,0.019 ,0.01
    Gagauz,9,0.049,0.058,0.053,0.056,0.079,0.676,0.029
    German,79,0.32,0.061,0.051,0.274,0.118,0.12,0.056
    German_East,8,0.202,0.043,0.038,0.103,0.24,0.263,0 .111
    Greek_Central_Macedonia,17,0.049,0.086,0.125,0.073 ,0.07,0.567,0.029
    Greek_Crete,11,0.032,0.076,0.523,0.054,0.048,0.249 ,0.018
    Greek_Kos,9,0.023,0.054,0.654,0.042,0.037,0.178,0. 013
    Greek_Peloponnese,3,0.043,0.081,0.353,0.069,0.057, 0.371,0.025
    Greek_Thessaly,3,0.036,0.081,0.233,0.068,0.053,0.5 14,0.015
    Hungarian,14,0.119,0.032,0.025,0.066,0.267,0.413,0 .078
    Icelandic,12,0.589,0.032,0.028,0.23,0.039,0.045,0. 038
    Ingrian,3,0.064,0.018,0.015,0.032,0.137,0.085,0.65 1
    Irish,40,0.624,0.036,0.029,0.219,0.032,0.034,0.026
    Italian_Central,44,0.03,0.083,0.618,0.075,0.046,0. 138,0.01
    Italian_Jew,10,0.032,0.09,0.637,0.062,0.054,0.11,0 .014
    Italian_North,78,0.064,0.173,0.323,0.225,0.064,0.1 37,0.014
    Italian_South,72,0.02,0.049,0.698,0.041,0.032,0.15 2,0.008
    Karelian,33,0.035,0.011,0.009,0.017,0.073,0.046,0. 808
    Latvian,5,0.031,0.009,0.008,0.014,0.709,0.133,0.09 6
    Lithuanian,56,0.028,0.008,0.007,0.013,0.734,0.124, 0.086
    Macedonian,5,0.062,0.056,0.057,0.055,0.116,0.59,0. 064
    Maltese,8,0.031,0.096,0.657,0.068,0.046,0.09,0.012
    Moldovan,20,0.074,0.047,0.041,0.055,0.198,0.502,0. 082
    Moldovan_o,5,0.062,0.023,0.017,0.033,0.333,0.292,0 .24
    Montenegrin,5,0.074,0.053,0.044,0.062,0.135,0.582, 0.051
    Mordovian,45,0.041,0.013,0.01,0.02,0.135,0.076,0.7 05
    Norwegian,7,0.584,0.034,0.027,0.24,0.04,0.041,0.03 5
    Orcadian,10,0.612,0.029,0.024,0.256,0.028,0.032,0. 02
    Polish,41,0.059,0.017,0.015,0.029,0.525,0.194,0.16 2
    Portuguese,25,0.035,0.686,0.105,0.125,0.021,0.02,0 .008
    Romanian,10,0.049,0.047,0.042,0.051,0.09,0.694,0.0 27
    Romaniote_Jew,7,0.031,0.078,0.661,0.054,0.051,0.11 ,0.015
    Russian_Kostroma,6,0.033,0.01,0.008,0.015,0.123,0. 064,0.747
    Russian_Kursk,4,0.031,0.009,0.007,0.014,0.556,0.10 2,0.28
    Russian_Orel,7,0.034,0.01,0.008,0.016,0.581,0.148, 0.204
    Russian_Smolensk,8,0.037,0.011,0.009,0.017,0.64,0. 138,0.149
    Russian_Tver,4,0.034,0.009,0.008,0.015,0.495,0.143 ,0.297
    Russian_Voronez,4,0.037,0.011,0.009,0.016,0.548,0. 14,0.24
    Scottish,28,0.623,0.031,0.025,0.239,0.029,0.031,0. 022
    Serbian,5,0.075,0.057,0.048,0.062,0.143,0.555,0.06 1
    Shetlandic,3,0.556,0.035,0.027,0.283,0.036,0.033,0 .03
    Sicilian,6,0.031,0.102,0.607,0.079,0.046,0.124,0.0 11
    Slovakian,5,0.064,0.019,0.015,0.032,0.386,0.341,0. 143
    Slovenian,5,0.083,0.03,0.025,0.046,0.272,0.45,0.09 2
    Sorb_Niederlausitz,1,0.029,0.008,0.007,0.013,0.697 ,0.156,0.09
    Spanish_Alacant,6,0.032,0.673,0.11,0.14,0.02,0.019 ,0.007
    Spanish_Andalucia,19,0.028,0.735,0.093,0.106,0.017 ,0.015,0.007
    Spanish_Aragon,4,0.046,0.633,0.118,0.144,0.025,0.0 25,0.01
    Spanish_Asturias,1,0.025,0.79,0.072,0.082,0.013,0. 012,0.006
    Spanish_Baleares,3,0.038,0.474,0.201,0.223,0.027,0 .029,0.008
    Spanish_Barcelones,5,0.043,0.591,0.136,0.174,0.023 ,0.022,0.009
    Spanish_Camp_de_Tarragona,8,0.041,0.607,0.12,0.177 ,0.024,0.022,0.008
    Spanish_Canarias,19,0.048,0.623,0.129,0.132,0.03,0 .024,0.013
    Spanish_Cantabria,3,0.019,0.818,0.061,0.079,0.011, 0.01,0.004
    Spanish_Castello,7,0.033,0.672,0.11,0.14,0.02,0.01 8,0.007
    Spanish_Castilla_La_Mancha,4,0.042,0.617,0.131,0.1 51,0.025,0.023,0.01
    Spanish_Castilla_Y_Leon,3,0.025,0.775,0.077,0.092, 0.013,0.012,0.006
    Spanish_Cataluna,3,0.022,0.796,0.075,0.079,0.012,0 .011,0.005
    Spanish_Catalunya_Central,9,0.046,0.566,0.135,0.18 9,0.027,0.027,0.009
    Spanish_Eivissa,13,0.038,0.58,0.144,0.183,0.024,0. 024,0.007
    Spanish_Extremadura,3,0.018,0.828,0.061,0.07,0.011 ,0.009,0.004
    Spanish_Galicia,18,0.043,0.63,0.115,0.156,0.024,0. 022,0.01
    Spanish_Girona,10,0.04,0.57,0.137,0.198,0.024,0.02 4,0.007
    Spanish_La_Rioja,1,0.034,0.678,0.107,0.135,0.02,0. 017,0.009
    Spanish_Lleida,9,0.039,0.629,0.107,0.174,0.022,0.0 22,0.007
    Spanish_Mallorca,9,0.042,0.472,0.184,0.236,0.028,0 .03,0.008
    Spanish_Menorca,3,0.043,0.56,0.161,0.17,0.029,0.02 7,0.01
    Spanish_Murcia,4,0.035,0.657,0.118,0.138,0.023,0.0 2,0.009
    Spanish_Navarra,3,0.069,0.506,0.15,0.185,0.038,0.0 36,0.015
    Spanish_Penedes,11,0.047,0.533,0.154,0.199,0.029,0 .029,0.009
    Spanish_Peri-Barcelona,10,0.046,0.585,0.123,0.184,0.027,0.027,0 .009
    Spanish_Pirineu,6,0.045,0.584,0.141,0.166,0.027,0. 025,0.012
    Spanish_Soria,2,0.019,0.812,0.064,0.08,0.011,0.01, 0.004
    Spanish_Terres_de_l'Ebre,5,0.035,0.664,0.116,0.136 ,0.021,0.02,0.008
    Spanish_Valencia,12,0.033,0.704,0.101,0.12,0.019,0 .017,0.008
    Swedish,21,0.496,0.035,0.031,0.214,0.071,0.083,0.0 69
    Swiss_French,8,0.088,0.246,0.167,0.372,0.053,0.06, 0.015
    Swiss_German,8,0.173,0.125,0.1,0.409,0.078,0.093,0 .023
    Swiss_Italian,3,0.038,0.145,0.511,0.111,0.056,0.12 7,0.011
    Ukrainian,37,0.038,0.011,0.009,0.017,0.546,0.171,0 .208
    Vepsian,18,0.028,0.009,0.007,0.013,0.063,0.038,0.8 42
    Welsh,20,0.651,0.024,0.019,0.248,0.021,0.024,0.013


    Don't expect the results to be accurate up to a percent. For a complex dataset as the modern European samples, "true" results do not exist. The results will always be dependent on the model.
    The advantage of the chosen model is that all estimates can be compared, because they are all estimated with the same methodologically sound workflow.

    The relations between the European populations are too complex to visualize in a single plot.
    Therefore I split the crosstab results into a number of views.
    In this post I presented two views for respectively the Northern populations and the Southern populations.

    The first view shows the North Europeans from West to East:
    In the upper row the average of the 19 English samples is partitioned into the memberships of the 7 clusters:
    cluster A: 0.579
    cluster B: 0.035
    cluster C: 0.028
    cluster D: 0.266
    cluster E: 0.033
    cluster F: 0.036
    cluster G: 0.022
    sum: 1.000
    The membership of 'English' is mostly of cluster A.
    Lower on the plot are some East-European populations where the dominant cluster is E.
    And on the final rows are some populations from North-East Russia, where G is dominant.

    As I had been hoping for, the 7 clusters A to G attract specific populations, albeit in a more global grouping.
    From inspection of the crosstab in the spoiler box, I inferred the next scheme:
    cluster A consists of North-West populations, especially the British, Norwegian and Icelandic samples.
    cluster B consists of Iberian and the South of French.
    cluster C consists of Central- and South-Central Italian, the Greek Islands and three Jewish populations.
    cluster D consists of French (except the South) and Belgian.
    cluster E consists of Russian, Belarusian, Baltic, Polish.
    cluster F consists of Balkan populations
    cluster G consists of Finnish and populations from North East Russia

    In my next post I will show some more views of the crosstab table.

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  3. #2
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    Interesting. Can you do this for users?
     
    All simple calculations, maps and plots I make for free, but for more complicated maps and calculations I ask for a donation via Hidden Content PayPalHidden Content account

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  5. #3
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    Quote Originally Posted by ph2ter View Post
    Interesting. Can you do this for users?
    The plots are averaged over the ethnic groups, for instance English is based on 17 samples.
    For single users the calculation of the memberships must be based on a single sample.So I expect it will be unreliable.
    Also there is a technical problem. After estimating a model it is often possible to simply project new data on the model.
    In this workflow this is not possible. I have to add a new sample to the dataset and start from scratch.
    If an English member asks me to process his sample, the result may be that he is 70% North West which he knew already. The information value will not match the effort.
    But if somebody has a very good reason to ask, I might relent.

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    In my previous post I demonstrated admixture-like views of modern European Global25 data; I started with the Northern populations and the Southern populations.
    Here I add several more of these views: the North-West, the French, the Iberian and the Eastern populations.

    NothWest:
    demo.NW.png

    French:
    demo.French.png

    Iberian:
    demo.Iberian.png

    Eastern:
    demo.East.png

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    Cool idea. You might also try this with West Asian populations, which could be especially useful for groups like Syrians who show a lot of heterogeneity.
    Ελευθερία ή θάνατος.

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    Could this be done on mixed europeans like myself? Would it even make sense?
    Hidden Content

    Eurogenes K13 Mixed Mode Population Sharing:
    1. 63.1% Swedish + 36.9% Spanish_Andalucia @ 2.93

    Dodecad K12b
    1. 87,89% Szolad3 + 12,11% CHV001_Chalmny_Varre_18th-19th @ 1,131
    7. 66.4% Norwegian + 33.6% TSI30 (Metspalu) @ 3.24


     

    Target: Nino
    Distance: 1.5423% / 0.01542331
    55.0 ISL_Viking_Age_Norse
    36.6 ITA_Rome_Latini_IA
    8.4 FIN_Levanluhta_IA2


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    Quote Originally Posted by Nino90 View Post
    Could this be done on mixed europeans like myself? Would it even make sense?
    I don't know, I have little experience with this workflow myself. The problem is that the algorithm might be fooled into believing that halfway between Swedish and Spanish means French.
    On the other hand the North West cluster and the Iberian cluster are strong attractors and the French cluster has a weaker pull.
    My feeling is that you better trust your paper trail.

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    I remember I've done similar analysis some time ago.
    I've tried many methods of decomposition including factor analysis and indepdendent component analysis -> this was not giving models with coefficients summing to 1 if I remember correctly.
    Also I've tried various mixture models.
    Last but not least I've tried various clustering methods combined with fitting logistic regression against the results of the clustering.

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    In posts #1 and #4 I presented an advanced unsupervised clustering of the modern European Global25.
    IMO the trends in in the several views appear convincing.
    For instance, as a Dutchman, I am especially content with the similarity of Dutch, Danish and Swedish in the plot of Northern Europe.
    Now an obvious question is whether this workflow is also productive with the single samples of AG members.
    As a demonstration I plotted 10 samples of Dutch AG-members:
    demo.Dutch.png
    Most of the Dutch member samples have a rather high membership on the NW cluster A.
    My own score is even the highest of the pack, while I am solidly South-Dutch.
    So I am confirmed in my pessismism about estimating individual modern European samples from G25.
    The maternal line of Kellebel is even more French than the average French_Nord. Actually, of the individual Frech samples in G25, only two are higher on cluster D.

  17. The Following 5 Users Say Thank You to Huijbregts For This Useful Post:

     JMcB (09-24-2020),  Kellebel (09-25-2020),  miremont (09-26-2020),  Radboud (09-25-2020),  sheepslayer (09-24-2020)

  18. #10
    Registered Users
    Posts
    1,019
    Sex
    Location
    Netherlands
    Ethnicity
    South-Dutch
    Nationality
    Dutch
    Y-DNA (P)
    I2a2a1b2-CTS1977
    mtDNA (M)
    H13a1a1

    Netherlands Belgium
    I felt really bad about my last post.
    Kellebel and her mother and grandmother got a very low membership on the North-West cluster.
    And their membership on the French cluster was even greater than the French membership of nearly all of the French samples.
    Something seemed terribly wrong.

    In hindsight it is so obvious, but I didn't see it.
    I can quote my late countryman Johan Cruijff "You don't see it until you've got it".
    To understand what is happening, I followed my workflow, but at the last step I did not perform a clustering, but an nMonte (without penalizing).
    Here is the result:

    Kel_mom

    Dutch,17
    French_Occitanie,15.4
    Basque_French,15
    German,13

    Of course, that's it.
    My clustering algoritm has created an artificial French ethnicity by merging the Dutch and German with two southern French populations and interpreting the mixture as French.
    In the same way as a mixture of English and Belarusian can be interpreted as German.

  19. The Following 7 Users Say Thank You to Huijbregts For This Useful Post:

     Alexander87 (09-24-2020),  Garimund (09-24-2020),  JMcB (09-25-2020),  Kellebel (09-25-2020),  Radboud (09-25-2020),  sheepslayer (09-24-2020),  timberwolf (09-24-2020)

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