[adegenet-forum] are xval results only relevant for the dapc part of dapc analysis and not find.cluster

Tue Jun 5 16:37:49 CEST 2018

Hello,

I'm writing to follow up on my message from last week. Although my email
looks a bit long, I don't think my two questions (which are at the end)
should take very long to answer. I re-attached the work document with the
questions and plots as well.

When trying to find the number of clusters, as is known, I get different
results when I retain different numbers of PCs.

As background, I have samples from 180 individuals over 11 different sites,
and am trying to find the best structure.

In the tutorial, it says that when you run find.clusters there is no reason
for keeping small numbers of principle components here. When I run with
n.pca.max = 60 (so, n/3), using xval I get pretty consistently that the
good number of PCs to retain is 50.

When I run find.cluster using 50 PCs I get anywhere between 7 and 9
clusters, mostly telling the same story for the data. However, when I run
find.cluster with over 100 PCs I consistently get k = 4 or 5, and the plot
is much cleaner. In addition, however, when I look at my variance explained
plots, they don’t really asymptote, either for find.cluster or for dapc.

Both of the variance explained plots look like

Using the scaled dataset

mat <- scaleGen(Stickle8c10NoOdds, NA.method="mean")

I use 120 PCs, and get

Running with 90 PCs

However, if I run find.cluster and choose 50 PCs, I get

> head(NumClust$Kstat, 11)

     K=1      K=2      K=3      K=4      K=5      K=6      K=7      K=8
K=9     K=10     K=11

1492.620 1472.790 1455.980 1448.216 1443.735 1442.909 1440.166 1440.344
1440.867 1441.979 1443.101

Are the xval procedure results (i.e., 50 PCs in my case) meant to be used
only at the dapc1 <- dapc(mat, NumClust$grp) stage? And, do my variance
explained plots concern you at all given that they don’t asymptote?

With many thanks for your time.

Ella

On Fri, Jun 1, 2018 at 11:56 AM, Ella Bowles <bowlese at gmail.com> wrote:

> Hello,
>
> When trying to find the number of clusters, as is known, I get different
> results when I retain different numbers of PCs.
>
> As background, I have samples from 180 individuals over 11 different
> sites, and am trying to find the best structure.
>
> In the tutorial, it says that when you run find.clusters there is no
> reason for keeping small numbers of principle components here. When I run
> with n.pca.max = 60 (so, n/3), using xval I get pretty consistently that
> the good number of PCs to retain is 50.
>
>
>
> When I run find.cluster using 50 PCs I get anywhere between 7 and 9
> clusters, mostly telling the same story for the data. However, when I run
> find.cluster with over 100 PCs I consistently get k = 4 or 5, and the plot
> is much cleaner. In addition, however, when I look at my variance explained
> plots, they don’t really asymptote, either for find.cluster or for dapc.
>
> Both of the variance explained plots look like
>
>
> Using the scaled dataset
>
> mat <- scaleGen(Stickle8c10NoOdds, NA.method="mean")
>
> I use 120 PCs, and get
>
>
> If I run with 90 PCs the number of clusters bumps up to 5,
>
>
>
> However, if I run find.cluster and choose 50 PCs, I get
>
>
> > head(NumClust$Kstat, 11)
>
>      K=1      K=2      K=3      K=4      K=5      K=6      K=7      K=8
> K=9     K=10     K=11
>
> 1492.620 1472.790 1455.980 1448.216 1443.735 1442.909 1440.166 1440.344
> 1440.867 1441.979 1443.101
>
>
>
> Are the xval procedure results (i.e., 50 PCs in my case) meant to be used
> only at the dapc1 <- dapc(mat, NumClust$grp) stage? And, do my variance
> explained plots concern you at all given that they don’t asymptote?
>
>
> I've attached a word document with all the same text and plots as are in
> this message in case they don't show up on your screen.
>
> Thank you for your time,
>
> Ella
> --
> Ella Bowles, PhD
> Postdoctoral Researcher
> Department of Biology
> Concordia University
>
> Website: https://ellabowlesphd.wordpress.com/
> Email: bowlese at gmail.com
>

-- 
Ella Bowles, PhD
Postdoctoral Researcher
Department of Biology
Concordia University

Website: https://ellabowlesphd.wordpress.com/
Email: bowlese at gmail.com
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