[adegenet-forum] Data type/format and admixed individuals using DAPC

[Hugo Gante]

Dear Thibaut,
Thanks for the clarification!
Best,
Hugo


On Mon, Apr 18, 2011 at 9:53 PM, Jombart, Thibaut
<t.jombart at imperial.ac.uk>wrote:

>  Yes, MDS is fine, but you'll lose variable contributions. I don't think
> admixture plays a role here.
> Cheers
> Thibaut
>  ------------------------------
> *From:* Hugo Gante [hugo.gante at gmail.com]
> *Sent:* 18 April 2011 20:15
> *To:* Jombart, Thibaut
> *Cc:* adegenet-forum at r-forge.wu-wien.ac.at
> *Subject:* Re: [adegenet-forum] Data type/format and admixed individuals
> using DAPC
>
>  Dear Thibaut,
> Thanks for the detailed reply!
> Along the same lines, would non-metric multidimensional scaling be another
> alternative to MCA? Which one (if any) would deal better with admixed
> individuals??
> Best,
> Hugo
>
>
> On Mon, Apr 18, 2011 at 8:29 PM, Jombart, Thibaut <
> t.jombart at imperial.ac.uk> wrote:
>
>>  Hello,
>>
>> DAPC is meant for quantitative data. One workaround is to transform your
>> data first, i.e. using dummy vectors with some centring/scaling. This is
>> done implicitly by the multiple correspondence analysis  (MCA, dudi.acm in
>> ade4), the multivariate analysis dedicated to categorical data. For
>> instance:
>> ####
>> > f1 <- function(){factor(as.vector(replicate(2, sample(letters[1:4],50,
>> p=runif(4), replace=TRUE))))} # generates 100 indiv following two different
>> distributions
>> > f1()
>>   [1] b b b b b c d c d a d c b c d b b b c b c c b b b c b d d b d d d b
>> d d d
>>  [38] c b b b b c d d b d b b c b c d b c c d b d b d c d b a c a b c b b
>> c b b
>>  [75] b a b b b d d b b b b d b a b b d b c b d b d b c d
>> Levels: a b c d
>>
>> > barplot(unlist(lapply(split(x,rep(1:2,each=50)),table))) # show the
>> differences, for one 'loci'
>> > dat <- data.frame(lapply(1:10, function(i) f1()))
>> > names(dat) <- paste("variable",1:10)
>> > mca1 <- dudi.acm(dat,scannf=FALSE, nf=10) # replace "nf " by the nb of
>> factors you want
>> > fac <- factor(rep(1:2, each=50)) # in practice, replace with the groups
>> > s.class(mca1$li, fac=fac) # to see the MCA results
>>
>> ## then in find.clusters and dapc, use mca1$tab as the data, and specify
>> dudi=mca1; e.g.:
>> > grp <- find.clusters(mca1$tab, dudi=mca1, n.iter=1e5, n.start=30,
>> n.pc=10, n.clust=2) # find.clusters
>> > table(grp$grp, fac) # I find about 90% accurate classification
>>
>> > dapc1 <- dapc(mca1$tab, fac, dudi=mca1, n.pca=10, n.da=1) # dapc
>> > scatter(dapc1) # plot results - here there's just one dimension
>> ####
>>
>> To ensure that the "dudi" argument will be correctly taken into account,
>> you will need to use the devel version of adegenet (see download section on
>> the website).
>>
>> Also, be aware that so far uniform weights are used for all variables,
>> meaning that in your analysis factors with more levels will likely be given
>> stronger weight in the analysis.
>>
>> All the best,
>>
>> 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 Hugo Gante [
>> hugo.gante at gmail.com]
>> *Sent:* 18 April 2011 15:24
>> *To:* adegenet-forum at r-forge.wu-wien.ac.at
>> *Subject:* [adegenet-forum] Data type/format and admixed individuals
>> using DAPC
>>
>>    Hi,
>> Perhaps someone could help me out with a basic file format question?
>> To run DAPC can I use qualitative (coded) data or do I have to use
>> quantitative data since it first runs a PCA? I found some information about
>> data file format (matrix vs tabular?) and data type (quantitative vs
>> characters) but some clarification on usage and where to find more detail
>> (examples?) on file formats would be most appreciated.
>>
>>  Also, I was wondering how admixed individuals are treated and if they
>> will be identified by DAPC?
>>
>> Thanks in advance!
>> Best,
>>  Hugo
>>
>>
>
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