[adegenet-forum] scatterplot and hybridization

Tue Sep 6 14:15:21 CEST 2011

Dear Thibaut
I am trying to simulate hybridization; I am considering the clusters K=2 
and K=3 and I guess there are hybrid individuals in one of the clusters 
when K=3 (because other programs also given this notion) ; so say 
clust1, 2, 3 where 3 is mostly derived from  1 and 2 by means of 
hybridization (my guess). when I try to simulate taking 1 and 2to make 
hybrids and compare those with clust 3 ...
the code went wrong!....

 > x1<-which (grp$grp==1)
 > x2<-which (grp$grp==2)
 > which (grp$grp==1)
....
 >x1
.......
hyb <- hybridize(x1, x2, n=40)

Error in hybridize(x1, x2, n = 40) : x1 is not a valid genind object

actually my data is through read.table-> obj -> data.frame -> dat-> 
df2genind etc.
so i guess wildly it can not recognize x1 etc.

one more query, whenever in find.clusters to choose no. of clusters; 
when K=2; it is not showing any scatterplot rather showing up two 
discriminant functions; I did it for some time but all in vain, no clue 
whether doing any wrong step though following same steps as in K=3
hope the problems are understandable
sorry for providing all my mails but this may give you some lost reference!

Thanks in advance
cheers

AVIK

On 8/30/2011 3:58 PM, Jombart, Thibaut wrote:
> Hello,
>
> I am afraid the interpretation of the pattern you observe will be up to you, since I don't know a thing about the organism under scrutiny.
>
> As I said, the structure would be compatible with IBD - even with decent genetic structure, STRUCTURE would probably largely underestimate the most likely number of demes. This is the first explanation, but I guess other scenarios would be possible - different processes can lead to the same pattern.
>
> Cheers
>
> Thibaut.
>
> --
> ######################################
> Dr Thibaut JOMBART
> MRC Centre for Outbreak Analysis and Modelling
> Department of Infectious Disease Epidemiology
> Imperial College - Faculty of Medicine
> St Mary’s Campus
> Norfolk Place
> London W2 1PG
> United Kingdom
> Tel. : 0044 (0)20 7594 3658
> t.jombart at imperial.ac.uk
> http://sites.google.com/site/thibautjombart/
> http://adegenet.r-forge.r-project.org/
>
> ________________________________________
> From: AVIK RAY [avik.ray.kol at gmail.com]
> Sent: 29 August 2011 08:06
> To: Jombart, Thibaut; adegenet-forum at r-forge.wu-wien.ac.at
> Subject: Re: [adegenet-forum] PCA query?
>
> Dear Thibaut and all
> resuming the earlier discussion (mails below for reference) :
> I want to narrow it down a little bit; what could be the causal factor/s for this pattern ..as you already mentioned that this is mostly visible in IBD (see your mail below), where it fails to find any clusters or would it be possible for high gene flow among populations, so all of them are quite mixed up and showing up no signature of clusters; since both scenarios are true at least to some extent with my data;
> so to summarize, what would I consider?
> thanks in advance
> cheers
> AVIK
>
>
>
> On 7/5/2011 2:37 PM, Jombart, Thibaut wrote:
> Hello,
>
> actually I doubt there is ever a true K in real biological data, if only for the fact that there is no clear definition of 'genetic clusters'. What we consider as "clusters" are models of reality, and so false by definition.
>
> Anyway. In your case I would stick to BIC-based choice of K. The reason for this is that DAPC scatterplots show you only a few dimensions, while k-means+BIC takes much more (if not all, depending on how many PCs retained) of the genetic information into account.
>
> Cheers
>
> Thibaut
> ________________________________
> From: AVIK RAY [avik.ray.kol at gmail.com<mailto:avik.ray.kol at gmail.com>]
> Sent: 05 July 2011 07:33
> To: Jombart, Thibaut
> Subject: Re: [adegenet-forum] PCA query?
>
> Dear Thibaut
> It is quite unlikely that there is no true K !
> if so, then how can I account for the quite divergent clusters obtained in DAPC analysis, refer to the images attached; say in the image DAPC clust 6 - clusters 2, cluster 3 and cluster 4,5,1  are quite divergent genetic groups it seems, even 6 is well separated  from 2 and 3; similarly in the image DAPC cluster 8- clusters 3,4,7 and 3,8 and 2,6 are widely divergent (however, if you compare both these it appears both very similar except some clusters are breaking into sub clusters which is quite reasonable)
> I think it (in my case) may be wise to optimize number of clusters by looking at BIC curve as well as cluster diagram considering highly divergent clusters
> what do you think?
>
> cheers
>
> AVIK
>
>
> On 6/22/2011 2:49 PM, Jombart, Thibaut wrote:
>
> Dear Avik,
>
> the BIC plot you sent resembles what we usually get under IBD models. In this case, it is not surprising that STRUCTURE identifies less clusters than DAPC (see the paper, STRUCTURE basically failed to identify clusters under the IBD model).
>
> There is probably no "true k", but just a choice of a number of groups useful to summarize the data. You may want to have a look at the section "how many clusters..." in the DAPC vignette, online in "Documents" on the website.
>
> Cheers
>
> Thibaut
>
> ________________________________________
> From: AVIK RAY [avik.ray.kol at gmail.com<mailto:avik.ray.kol at gmail.com>]
> Sent: 21 June 2011 19:08
> To: Jombart, Thibaut; adegenet-forum at r-forge.wu-wien.ac.at<mailto:adegenet-forum at r-forge.wu-wien.ac.at>
> Subject: Re: [adegenet-forum] PCA query?
>
> Dear Thibaut
> Thanks for very effective reply; it seems DAPC is more suitable for my
> dataset and for the question I'm looking at!
> I did few mock runs to see the very initial results, and the BIC curve
> shows gradual leveling off after K=9 it seems, however from STRUCTURE
> (Bayesian) and FLOCK (Max Likelihood) number of putative clusters
> appears to be 2/3; so wondering what made this difference? or I am
> wrongly interpreting it ! ....anyways my dataset contains lot of missing
> data, does that matter much, shall I remove those and then try!
> I am attaching BIC and retained PC curves for reference
> Thanks
> cheers
>
> AVIK
>
>
> On 6/20/2011 6:58 PM, Jombart, Thibaut wrote:
>
>
> Hello,
>
> in none, as far as PCoA / MDS are concerned, they do the same as PCA, but just allow for using fancier Euclidean distances. Loosing information in terms of total variance does not necessarily imply loosing information in terms of group discrimination. But if you're looking for clusters, you don't necessarily need to reduce the dimensionality of the data - most clustering algorithm don't.
>
> Please have a look at the DAPC paper which is really on these topics. You may also be interested in the DAPC vignette for the next release of adegenet.
> DAPC paper is here:
> http://www.biomedcentral.com/1471-2156/11/94
>
> DAPC vignette is there:
> http://adegenet.r-forge.r-project.org/files/adegenet-dapc.pdf
>
> Cheers
>
> Thibaut
>
> ________________________________________
> From: adegenet-forum-bounces at r-forge.wu-wien.ac.at<mailto:adegenet-forum-bounces at r-forge.wu-wien.ac.at>  [adegenet-forum-bounces at r-forge.wu-wien.ac.at<mailto:adegenet-forum-bounces at r-forge.wu-wien.ac.at>] on behalf of AVIK RAY [avik.ray.kol at gmail.com<mailto:avik.ray.kol at gmail.com>]
> Sent: 20 June 2011 13:12
> To: adegenet-forum at r-forge.wu-wien.ac.at<mailto:adegenet-forum at r-forge.wu-wien.ac.at>
> Subject: [adegenet-forum] PCA query?
>
> Hi all
> bit of confusion with PCA in general, I did PCA in adegenet and it has
> shown some plot with multiple clusters. My data is tetraploid
> microsatellite data and I need to find out potential clusters i.e. some
> individuals are more similar than others with allele data. But If not
> mistaken PCA converts allele information into some synthetic variable
> and does clustering where we tend to loose out lot of information since
> it will select most but not all alleles; so in that sense does PCoA/
> Multidimentional scaling or simply clustering analysis (e.g. K means or
> hierarchical clustering) make more sense?
> Thanks in advance for reply
>
> AVIK
>
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>
>
> --
>
> AVIK RAY
> Visiting Fellow
> National Center for Biological Sciences
> Tata Institute of Fundamental Research
> GKVK Campus
> Bellary Road
> Bangalore-560065
> India
> Ph 91-80-23666340
> Fax 91-80-2363 6662
>
>
>
>
> --
> AVIK RAY
> Visiting Fellow
> National Center for Biological Sciences
> Tata Institute of Fundamental Research
> GKVK Campus
> Bellary Road
> Bangalore-560065
> India
> Ph 91-80-23666340
> Fax 91-80-2363 6662
>
>

-- 
AVIK RAY
Visiting Fellow
National Center for Biological Sciences
Tata Institute of Fundamental Research
GKVK Campus
Bellary Road
Bangalore-560065
India
Ph 91-80-23666340
Fax 91-80-2363 6662

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