[adegenet-forum] DAPC group choice and MANOVA significance testing

[J. Richardson]

Hello again Dr. Jombart and Adegenet users,



I have a follow-up question related to the grouping of individuals not
using k-means.



We would like to test whether the group assignment (assigned by us) is
significantly related to the location of individuals in the discriminant
function (DF) space. To do this we have taken the following approach:



1. Perform a MANOVA on the individual DF coordinates with group class as
the predictor variable. The idea here is that (A) the Wilks lamba test
provides a metric of separation among the groups and (B) accounts for
correlation among variables (DFs). The test code is:



model <- manova(dapcobject$ind.coord~genindobject$pop)

summary(model, test=”Wilks”)



2. However, we are worried that the significance value obtained by MANOVA
(which was remarkably small) might be anti-conservative (i.e. high Type-I
error) because DAPC has already maximized among group variation and
uncovered structure that might be evident even in random datasets.



Therefore, we came up with a randomization test.  We first create a null DF
distribution by randomizing the rows/individuals in the “genind” data
object so that the number of individuals per group remains the same, but
the individuals contained in each group are now randomized. We do this 1000
times and perform the DAPC and MANOVA operations on all 1000 sets to obtain
the randomized distribution. Lastly, we compare our empirical Wilks lambda
value with the randomized distribution to determine if our Wilks is larger
than expected based on random chance.



Does this seem reasonable? Our hesitation is related to some initial
results from our dataset. When we run the empirical dataset with 3 defined
groups, the DAPC produces 3 clear clusters with some small overlap (i.e.
the 3 a priori groups segregate very nicely in DF space). However, when we
randomized the alleles and genotypes, the resulting DAPC with the same
group sizes also results in 3 clear clusters, but that have noticeably more
ellipse overlap than the empirical data. So we are wondering whether the a
priori group designation (related to a substantial habitat and phenotypic
difference in our case) will mandate some level of clustering – but with
DAPC also looking to optimize grouping segregation in DF space the patterns
become clearer and maybe somewhat spurious (at least in our case)?



Any insight you can provide would be greatly appreciated. Thank you in
advance.



Jon



On Thu, Feb 23, 2012 at 9:08 AM, Jombart, Thibaut
<t.jombart at imperial.ac.uk>wrote:

>
> Hello,
>
> so I think the in the DAPC vignette, the example based on H3N2 data
> (section 3.4) uses the year of sampling as group factor in DAPC. Also, in
> the same document, the microbov example (p25-34) uses the cattle breeds as
> group factor in DAPC. The H3N2 example was also presented in the original
> paper.
>
> So yes, it does make sense. DAPC provides the best achievable reduced
> space representation of between-group diversity (in the sense of a F
> statistic, var between / var within). It is comparable to STRUCTURE or any
> other similar method when the same groups are used, to the extent that the
> methods give comparable outputs - in this case, the only common thing is
> group membership probabilities.
>
> Cheers
>
> Thibaut
>
>
> ________________________________________
> From: adegenet-forum-bounces at r-forge.wu-wien.ac.at [
> adegenet-forum-bounces at r-forge.wu-wien.ac.at] on behalf of J. Richardson [
> jrichardson4 at gmail.com]
> Sent: 22 February 2012 22:30
> To: adegenet-forum at r-forge.wu-wien.ac.at
> Subject: [adegenet-forum] DAPC group choice
>
> Hi Dr. Jombart and Adegenet users,
>
> I have a question related to DAPC that I have not found in the manual,
> tutorials or forum archive.
>
> I am wondering what the DAPC operation is doing (i.e. how it is
> configuring clusters relative to each other) when you
> do not use the groups created in "find.clusters" (i.e. grp$grp output),
> but rather use the population of origin as the
> group designation (i.e. dataset$pop)?
>
> I ran "find.clusters" and performed the DAPC with these created groups. I
> also performed a DAPC with the groups set
> as the sampling sites (populations of origin) using the number of clusters
> derived from k-means. Interestingly, the DAPC using the k-means
> groupings don't make a lot of intuitive sense. However, the DAPC results
> using the sampling sites/populations of origin for the group
> designation make sense and correspond closely to the output from STRUCTURE
> using their location prior.
>
> So I am wondering if using the sampling site/population designation as the
> group designation is (A) analogous to the
> STRUCTURE operation using the location prior or "population flags", and
> (B) if this is valid if you have good a priori information on
> your population delineations (e.g. a species breeding in discrete,
> contained habitats)?
>
> Thank you so much in advance for any insight you can provide.
>
> Jon
>
>
>
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