[adegenet-forum] A few basic questions

[Thomas Vignaud]

Hi everyone,

I find Adegenet -DAPC-  to be very usefull -yet I don't fully understand
all the subtilities.

I'll here try to ask a few simple questions with associated screenshots.
I'll mostly use examples to ask my questions as I believe it a very
efficient way to do it.
(I'm working with 17 microsats on animals)

I'm sorry if all this sounds newbie - please feel free to redirect me to
any .pdf I might have miss.

I believe the two main questions I want to answer with DAPC are :

1 - How different my clusters are ?  (I know this depend on a lot of things
and that I can't compare with other species/genes)
  I feel like one way to do it is to check is a few components still finds
a lot of structure.
  Another is, using alpha scores and the whole classic process, to visually
see how assigned to their cluster the individuals are.

2 - Is there any sub-(genetic)clusters in my sample? for example, I have
sampled 50 ids in the same location. But maybe there is two (sub)
population here and I sample 40 of the first one and 10 of the other. I
want to see that (i.e. compoplot), to go back to my data and to check if I
can find patterns related with what the genetic tells me.

Now here is my problem : depending what number of discriminant function I'm
using, I get totally different results with the same sub-dataset.
And, with the same number of discriminant function but with adding another
population (very structured) to my first sub-dataset, then the first
sub-dataset will be different again.

--->  I'm a little lost in what to choose as a number of discriminant
function (I understand the alpha-score, but sometimes it will tell me "21",
when using only "5" will give me the same exact compoplot).
It would not be such a problem if differences would be small, but here it
is : often all my individuals are 100% in one color, but it's never the
same pattern.
One compoplot I'll have ids 1, 2, 5, 6 that are 100% red, and 3, 4, 7 that
are 100% blue.
Then I just redo the analysis changing the number of discriminant function
and I get 1, 3, 7 100% red and 2, 4, 5, 6 100% blue.
See attached screenshots A, B and C from the SAME dataset. (I'm trying to
use small number of DF as I don't like my ids to be 100% in one color, I
feel I miss some information)

---> the same thing happen if I add other populations. The whole pattern
change again. See screenshot D


So is there any guideline that would give me something a little less
absolute that totally different results?

If I want, for example, to note all my outliers (ids that does not belong
the their original geographic cluster) and check for their caracteristic
(size, sex etc...) how am I supposed to do that if outliers change
depending on priors ? especially with more than 700 individuals and 16
geographic clusters.

If I want to account for how much different 3 clusters are, and if using
the opt alpha score gives me three 100% differenciated clusters, but using
a lower one start to create a mix between two of the clusters : can I just
decide to use a lot of different numbers of discriminant function to
explore the dataset ? or is it "wrong" ?



Additional information :
my 'exploring' workflow looks like :

> grp <- find.clusters(obj, max.n.clust = 35)
x (50-150)
x (depend what I want to see)

> dapc1 <- dapc(obj, grp$grp)
x (N/3 or 100 if N is large)
x (either alpha score number or smaller because I have a strong structure)

> compoplot(dapc1, grp$grp)




Any imput or help more than welcome.

Best,

Thomas
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