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Thread: P37 STR UMAP

  1. #1
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    P37 STR UMAP

    I am very pleased with Y-DNA STR37 UMAP plot of all FTDNA users.
    I seems to me that such plot shows relative time distance and kinship between various clades without even knowing the SNP results.


    In case of I-P37 plot, M26, L233 and M423 are generally clearly splitted into separate clusters.
    And inside M423 internal structure is clearly visible. L161 is separated from L621 and they both are internally structured.
    We can see that Disles and Y18331 are more distanced from the main L621 cluster which is made of a bulk of I2a-Din.
    I2a-Din is again splitted into two main clusters: Din-N and Din-S.
    It can bee seen the affinity of BY128 to Y18331 and this can be because of S17250 bigger similarity to Y18331 than Z17855 or Y4460.




    I-P37 STR37 UMAP







    I-M423 STR37 UMAP


     
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    Have you tried using a bigger `min_dist` setting so there's less empty space between the clusters? If you're using the `umap` R package, it can be changed like this:

    def=umap.defaults
    def$min_dist=.5
    umap(mat,config=def)

    Or how does it compare to PCA? I didn't find that much use for UMAP, because it mostly seems to be useful for visualizing clustering between points by means of the spatial position of the points. But even with PCA, you can visualize clustering by for example drawing convex hulls around the points based on the result of hclust+cutree or kmeans, so there isn't really a need to additionally visualize clustering based on the spatial position of the points.

  3. The Following User Says Thank You to Nganasankhan For This Useful Post:

     ph2ter (11-25-2021)

  4. #3
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    Quote Originally Posted by Nganasankhan View Post
    Have you tried using a bigger `min_dist` setting so there's less empty space between the clusters? If you're using the `umap` R package, it can be changed like this:

    def=umap.defaults
    def$min_dist=.5
    umap(mat,config=def)

    Or how does it compare to PCA? I didn't find that much use for UMAP, because it mostly seems to be useful for visualizing clustering between points by means of the spatial position of the points. But even with PCA, you can visualize clustering by for example drawing convex hulls around the points based on the result of hclust+cutree or kmeans, so there isn't really a need to additionally visualize clustering based on the spatial position of the points.
    No, I haven't tried to play with min_dist, but I will.

    PCA is not as good as UMAP in reconstruction of the genetic kinship.
     
    Some simple calculations, maps and plots I make for free, but for more complicated maps, plots and calculations I ask for a donation of $2 or 1.7 per map/plot via Hidden Content PayPalHidden Content account.
    Every fourth map is free. Admix decomposition is $7 or 6.

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