From j.ovenden at uq.edu.au  Thu Jan 24 07:10:57 2019
From: j.ovenden at uq.edu.au (Jennifer Ovenden)
Date: Thu, 24 Jan 2019 06:10:57 +0000
Subject: [adegenet-forum] Exporting images from DAPC web server
Message-ID: <792BE513-6A4D-4055-A4E8-4206FE1C46F9@uq.edu.au>

Hi Forum members

Does anyone know how to export images such as scatter plots, loading plots etc from the DAPC web server?

Thanks, Jenny

Associate Professor Jennifer R Ovenden

School of Biomedical Sciences, Faculty of Medicine
The University of Queensland
Brisbane Qld 4072 Australia

T +61 7 3346 0806 M +61 415 949 410
E j.ovenden at uq.edu.au<mailto:j.ovenden at uq.edu.au>
W molecularfisherieslaboratory.com.au<http://molecularfisherieslaboratory.com.au>

Read Jenny?s essay about working in science ?..
https://academic.oup.com/icesjms/advance-article/doi/10.1093/icesjms/fsy132/5146511

CRICOS code: 00025B

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From him21024 at um.es  Thu Jan 24 12:58:03 2019
From: him21024 at um.es (HELENA IBANEZ MARTINEZ)
Date: Thu, 24 Jan 2019 12:58:03 +0100
Subject: [adegenet-forum] Doubt with DAPC without prior K (find.clusters)
In-Reply-To: <20190124084409.Horde.ktIxSFAGw008SqkmzbyQrg1@webmail.um.es>
References: <20190123113342.Horde.KnKInFY_Qn6NzTv6Nken1g3@webmail.um.es>
 <20190124084409.Horde.ktIxSFAGw008SqkmzbyQrg1@webmail.um.es>
Message-ID: <20190124125803.Horde.qNuJJ_Isk90YdT1hJQqhTw5@webmail.um.es>

  HELENA IBANEZ MARTINEZ <him21024 at um.es> escribi?:

> HELENA IBANEZ MARTINEZ <him21024 at um.es> escribi?:
>
>> HELLO, MY NAME IS HELENA,
>>
>> I HAVE A QUESTION ABOUT MY DAPC ANALAYSES.
>>
>> I'M FOLLOWING THE TUTORIAL STEP BY STEP AND I GET THE DAPC GRAPH OF MY
>> DATA VERY WELL, BUT MY PROBLEM IS WHEN I REPEAT THE PROCESS MY GRAPHIC
>> RESULTS COME OUT COMPLETELY DIFFERENT EVERY TIME...
>>
>> MY QUESTION IS, WHY DO SUCH DIFFERENT RESULTS COME OUT IF I'M REPEATING
>> EXACTLY THE SAME (SAME DATA AND SAME PARAMETERS)? IT HAPPENS TO ME
>> EVERY TIME I DO IT. WHAT CAN I BE DOING WRONG?
>>
>> HERE I LEAVE THE EXAMPLE OF MY CODE: 135 INDIVIDUALS WITH 8 LOCI
>> SAMPLED IN 6 DIFFERENT LOCATIONS
>>
>> PERCA <-READ.STRUCTURE("PERCAPOPNAME.STR",
>> ????????????????????????????????????
ONEROWPERIND=TRUE,
>> ????????????????????? ? ? ? ? ? ? ??
N.IND=135,
>> ????????????????????? ? ? ? ? ? ? ??
N.LOC=8,
>> ????????????????????????????????????
COL.LAB=1,
>> ????????????????????????????????????
COL.POP=2,
>> ????????????????????????????????????
ROW.MARKNAMES=1,
>> ????????????????????????????????????
ASK=FALSE)
>>
>> PERCA_GRP6 <-FIND.CLUSTERS(PERCA, MAX.N.CLUST = 10)
>> 200
>> 6
>>
>> DAPC6 <-DAPC(PERCA, PERCA_GRP6$GRP)
>> 200
>> 5
>>
>> SCATTER(DAPC6)
>>
>> THANK YOU.
>>
>> Hi, my name is Helena,
>>
>> I have a question about my DAPC analayses.
>>
>> I'm following the tutorial step by step and well, I get the DAPC graph
>> of my data, but the problem is that when I repeat this my graphic
>> results come out completely different, therefore they are not
>> reliable...
>> My question is, why do such different results come out if I'm repeating
>> exactly the same? It happens to me every time I do it. What can I be
>> doing wrong?
>>
>> Here my code:
>>
>> perca <-read.structure("PercaPopName.str",
>>
?????????????????????????????????????
onerowperind=TRUE,
>>
?????????????????????????????????????
n.ind=135,
>>
?????????????????????????????????????
n.loc=8,
>>
?????????????????????????????????????
col.lab=1,
>>
?????????????????????????????????????
col.pop=2,
>>
?????????????????????????????????????
row.marknames=1,
>>
?????????????????????????????????????
ask=FALSE)
>>
>> dapc1 <-dapc(perca, perca_grp$grp)
>>
>> 200
>> 6
>>
>> scatter(dapc1)
>>
>> HELLO AGAIN, I`M HELENA, SORRY BUT I INTRODUCED MY CODE WRONG YESTERDAY:
>>
>> THIS IS MY CODE:
>>
>> PERCA <-READ.STRUCTURE("PERCAPOPNAME.STR",
>> ????????????????????????????????????
ONEROWPERIND=TRUE,
>> ????????????????????? ? ? ? ? ? ? ??
N.IND=135,
>> ????????????????????? ? ? ? ? ? ? ??
N.LOC=8,
>> ????????????????????????????????????
COL.LAB=1,
>> ????????????????????????????????????
COL.POP=2,
>> ????????????????????????????????????
ROW.MARKNAMES=1,
>> ????????????????????????????????????
ASK=FALSE)
>>
>> PERCA_GRP6 <-FIND.CLUSTERS(PERCA, MAX.N.CLUST = 10)
>> 200
>> 6
>>
>> DAPC6 <-DAPC(PERCA, PERCA_GRP6$GRP)
>> 200
>> 5
>>
>> SCATTER(DAPC6)
>>
>> THANK YOU
>> ?
>
> ?
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From cathy.bouffartigue at inra.fr  Thu Jan 24 13:43:57 2019
From: cathy.bouffartigue at inra.fr (Cathy Bouffartigue)
Date: Thu, 24 Jan 2019 12:43:57 +0000
Subject: [adegenet-forum] Doubt with DAPC without prior K (find.clusters)
In-Reply-To: <20190124125803.Horde.qNuJJ_Isk90YdT1hJQqhTw5@webmail.um.es>
References: <20190123113342.Horde.KnKInFY_Qn6NzTv6Nken1g3@webmail.um.es>
 <20190124084409.Horde.ktIxSFAGw008SqkmzbyQrg1@webmail.um.es>
 <20190124125803.Horde.qNuJJ_Isk90YdT1hJQqhTw5@webmail.um.es>
Message-ID: <34ce4d4860c344f4be877e71bfea595d@idfdcpripexmu02.inra.local>

Hi Helena,

This is due to the k-means algorithm used in the find.cluster function which use a different seed for its initialization each time you?re running your code. You have to set the seed before running your find.cluster function to ensure the reproducibility of your results.

set.seeds(24012019)
perca_grp6 <-find.clusters(perca, max.n.clust = 10)


Good luck with your analyses!

Cathy

[Description?: cid:43BA2359-25C8-49EA-9D28-BCFD56FD115B at toulouse.inra.fr]
_____________________________

Cathy Bouffartigue
Doctorante/ PhD student
cathy.bouffartigue at inra.fr<mailto:cathy.bouffartigue at inra.fr>

Centre Inra Occitanie-Toulouse
Fixe : +33 (0)5 61 28 57 74
Mobile : +33 (0)6 14 57 34 55

_____________________________

Chemin de Borde Rouge CS 52627
31326 Castanet Tolosan cedex

www.toulouse.inra.fr<http://www.toulouse.inra.fr/>


De : adegenet-forum <adegenet-forum-bounces at lists.r-forge.r-project.org> De la part de HELENA IBANEZ MARTINEZ
Envoy? : jeudi 24 janvier 2019 12:58
? : adegenet-forum at lists.r-forge.r-project.org
Objet : Re: [adegenet-forum] Doubt with DAPC without prior K (find.clusters)


HELENA IBANEZ MARTINEZ <him21024 at um.es<mailto:him21024 at um.es>> escribi?:

HELENA IBANEZ MARTINEZ <him21024 at um.es<mailto:him21024 at um.es>> escribi?:
Hello, my name is Helena,

I have a question about my DAPC analayses.

I'm following the tutorial step by step and I get the DAPC graph of my data very well, but my problem is when I repeat the process my graphic results come out completely different every time...

My question is, why do such different results come out if I'm repeating exactly the same (same data and same parameters)? It happens to me every time I do it. What can I be doing wrong?

Here I leave the example of my code: 135 individuals with 8 loci sampled in 6 different locations

perca <-read.structure("PercaPopName.str",
                                     onerowperind=TRUE,
                                     n.ind=135,
                                     n.loc=8,
                                     col.lab=1,
                                     col.pop=2,
                                     row.marknames=1,
                                     ask=FALSE)

perca_grp6 <-find.clusters(perca, max.n.clust = 10)
200
6

dapc6 <-dapc(perca, perca_grp6$grp)
200
5

scatter(dapc6)

Thank you.





Hi, my name is Helena,

I have a question about my DAPC analayses.

I'm following the tutorial step by step and well, I get the DAPC graph of my data, but the problem is that when I repeat this my graphic results come out completely different, therefore they are not reliable...
My question is, why do such different results come out if I'm repeating exactly the same? It happens to me every time I do it. What can I be doing wrong?

Here my code:

perca <-read.structure("PercaPopName.str",
                                      onerowperind=TRUE,
                                      n.ind=135,
                                      n.loc=8,
                                      col.lab=1,
                                      col.pop=2,
                                      row.marknames=1,
                                      ask=FALSE)

dapc1 <-dapc(perca, perca_grp$grp)

200
6

scatter(dapc1)


Hello again, I`m Helena, sorry but I introduced my code wrong yesterday:

This is my code:

perca <-read.structure("PercaPopName.str",
                                     onerowperind=TRUE,
                                     n.ind=135,
                                     n.loc=8,
                                     col.lab=1,
                                     col.pop=2,
                                     row.marknames=1,
                                     ask=FALSE)

perca_grp6 <-find.clusters(perca, max.n.clust = 10)
200
6

dapc6 <-dapc(perca, perca_grp6$grp)
200
5

scatter(dapc6)

Thank you





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From thibautjombart at gmail.com  Thu Jan 24 18:31:37 2019
From: thibautjombart at gmail.com (Thibaut Jombart)
Date: Thu, 24 Jan 2019 17:31:37 +0000
Subject: [adegenet-forum] Doubt with DAPC without prior K (find.clusters)
In-Reply-To: <34ce4d4860c344f4be877e71bfea595d@idfdcpripexmu02.inra.local>
References: <20190123113342.Horde.KnKInFY_Qn6NzTv6Nken1g3@webmail.um.es>
 <20190124084409.Horde.ktIxSFAGw008SqkmzbyQrg1@webmail.um.es>
 <20190124125803.Horde.qNuJJ_Isk90YdT1hJQqhTw5@webmail.um.es>
 <34ce4d4860c344f4be877e71bfea595d@idfdcpripexmu02.inra.local>
Message-ID: <CANPRA+oO-EBeaPNmYHKxjWyUyq3LiP0Htm+RfgTy6xYum6shjA@mail.gmail.com>

Hi there,

yep. To add to this: you can increase the number of random starting points
e.g. n.start = 50 to smooth the results.

Best
Thibaut

--
Dr Thibaut Jombart
Associate Professor in Outbreak Analytics, London School of Hygiene and
Tropical Medicine
Senior Lecturer in Genetic Analysis, Imperial College London
President of RECON: repidemicsconsortium.org
https://thibautjombart.netlify.com
Twitter: @TeebzR


On Thu, 24 Jan 2019 at 12:44, Cathy Bouffartigue <cathy.bouffartigue at inra.fr>
wrote:

> Hi Helena,
>
>
>
> This is due to the k-means algorithm used in the find.cluster function
> which use a different seed for its initialization each time you?re running
> your code. You have to set the seed before running your find.cluster
> function to ensure the reproducibility of your results.
>
>
>
> set.seeds(24012019)
>
> *perca_grp6 <-find.clusters(perca, max.n.clust = 10)*
>
>
>
>
>
> *Good luck with your analyses!*
>
>
>
> *Cathy*
>
>
>
> [image: Description :
> cid:43BA2359-25C8-49EA-9D28-BCFD56FD115B at toulouse.inra.fr]
>
>
> *_____________________________ **Cathy Bouffartigue*
> Doctorante/ PhD student
>
> cathy.bouffartigue at inra.fr
>
>
>
> *Centre Inra Occitanie-Toulouse*
>
> Fixe : +33 (0)5 61 28 57 74
>
> Mobile : +33 (0)6 14 57 34 55
>
>
>
>
> *_____________________________ *
> Chemin de Borde Rouge CS 52627
> 31326 Castanet Tolosan cedex
>
>
> *www.toulouse.inra.fr* <http://www.toulouse.inra.fr/>
>
>
>
>
>
> *De :* adegenet-forum <adegenet-forum-bounces at lists.r-forge.r-project.org>
> *De la part de* HELENA IBANEZ MARTINEZ
> *Envoy? :* jeudi 24 janvier 2019 12:58
> *? :* adegenet-forum at lists.r-forge.r-project.org
> *Objet :* Re: [adegenet-forum] Doubt with DAPC without prior K
> (find.clusters)
>
>
>
> HELENA IBANEZ MARTINEZ <him21024 at um.es> escribi?:
>
> HELENA IBANEZ MARTINEZ <him21024 at um.es> escribi?:
>
> *Hello, my name is Helena,*
>
>
>
>
>
>
>
> * I have a question about my DAPC analayses. I'm following the tutorial
> step by step and I get the DAPC graph of my data very well, but my problem
> is when I repeat the process my graphic results come out completely
> different every time... My question is, why do such different results come
> out if I'm repeating exactly the same (same data and same parameters)? It
> happens to me every time I do it. What can I be doing wrong? Here I leave
> the example of my code: 135 individuals with 8 loci sampled in 6 different
> locations*
>
> *perca <-read.structure("PercaPopName.str",*
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
> *                                      onerowperind=TRUE,
>                                      n.ind=135,
>          n.loc=8,                                      col.lab=1,
>                                      col.pop=2,
>                                      row.marknames=1,
>                                      ask=FALSE) perca_grp6
> <-find.clusters(perca, max.n.clust = 10) 200 6 dapc6 <-dapc(perca,
> perca_grp6$grp) 200 5 scatter(dapc6)*
>
> *Thank you.*
>
>
>
>
>
> Hi, my name is Helena,
>
> I have a question about my DAPC analayses.
>
> I'm following the tutorial step by step and well, I get the DAPC graph of
> my data, but the problem is that when I repeat this my graphic results come
> out completely different, therefore they are not reliable...
> My question is, why do such different results come out if I'm repeating
> exactly the same? It happens to me every time I do it. What can I be doing
> wrong?
>
> Here my code:
>
> perca <-read.structure("PercaPopName.str",
>                                       onerowperind=TRUE,
>                                       n.ind=135,
>                                       n.loc=8,
>                                       col.lab=1,
>                                       col.pop=2,
>                                       row.marknames=1,
>                                       ask=FALSE)
>
> dapc1 <-dapc(perca, perca_grp$grp)
>
> 200
> 6
>
> scatter(dapc1)
>
>
> *Hello again, I`m Helena, sorry but I introduced my code wrong yesterday:*
>
> * This is my code: *
>
> *perca <-read.structure("PercaPopName.str",*
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
> *                                      onerowperind=TRUE,
>                                      n.ind=135,
>          n.loc=8,                                      col.lab=1,
>                                      col.pop=2,
>                                      row.marknames=1,
>                                      ask=FALSE) perca_grp6
> <-find.clusters(perca, max.n.clust = 10) 200 6 dapc6 <-dapc(perca,
> perca_grp6$grp) 200 5 scatter(dapc6) Thank you *
>
>
>
>
>
>
>
> _______________________________________________
> adegenet-forum mailing list
> adegenet-forum at lists.r-forge.r-project.org
> https://lists.r-forge.r-project.org/cgi-bin/mailman/listinfo/adegenet-forum
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From bowlese at gmail.com  Fri Jan 25 17:08:12 2019
From: bowlese at gmail.com (Ella Bowles)
Date: Fri, 25 Jan 2019 11:08:12 -0500
Subject: [adegenet-forum] yes/no answer. are xval results only relevant for
 the dapc part of dapc analysis and not find.cluster
Message-ID: <CAHpKFdAgiTqsbh50i8ngyGo6Pr=LzdSUKT_57bO0GYE40_-SjA@mail.gmail.com>

Hello,

I am writing to follow up on a couple of questions that were not answered
on the forum. It should just be a quick yes/no answer.

1). I'm wondering if xval only needs to be used for the dapc part and not
find.cluster?
2) Can I verify that for xval anaysis, n.pca.max should be set to n/3?

I have 186 individuals spread over 11 sites for which I am trying to sort
out the population structure.

In case this thread can be correlated with my last on the forum, the
previous question had the title "
are xval results only relevant for the dapc part of dapc analysis and not
find.cluster"
With thanks,
Ella

-- 
Ella Bowles, PhD
Mitacs Elevate Postdoctoral Fellow,
Department of Biology, Concordia University
Niskamoon Corporation

Website: https://ellabowlesphd.wordpress.com/
Email: bowlese at gmail.com
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From thibautjombart at gmail.com  Fri Jan 25 17:13:35 2019
From: thibautjombart at gmail.com (Thibaut Jombart)
Date: Fri, 25 Jan 2019 16:13:35 +0000
Subject: [adegenet-forum] yes/no answer. are xval results only relevant
 for the dapc part of dapc analysis and not find.cluster
In-Reply-To: <CAHpKFdAgiTqsbh50i8ngyGo6Pr=LzdSUKT_57bO0GYE40_-SjA@mail.gmail.com>
References: <CAHpKFdAgiTqsbh50i8ngyGo6Pr=LzdSUKT_57bO0GYE40_-SjA@mail.gmail.com>
Message-ID: <CANPRA+rOEhsLx0qU8B45kDdJuLCaYtxcEmjNd1KJ16q+vFyc7Q@mail.gmail.com>

Hi Ella,

1) Yes, only for DAPC, to prevent over-discrimination of the groups when
there are many variables

2) n.pca.max: there's no real rule, this is to avoid useless computational
times; the idea is to avoid i) retaining too few PCA axes, in which case
your DAPC will be sub-optimal in terms of discrimination, and ii) retaining
too many PCA axes, in which case discrimination will be artefactually good
on your data (but bad for cross validation, hence the use of it)

Hope this helps
Best
Thibaut

--
Dr Thibaut Jombart
Associate Professor in Outbreak Analytics, London School of Hygiene and
Tropical Medicine
Senior Lecturer in Genetic Analysis, Imperial College London
President of RECON: repidemicsconsortium.org
https://thibautjombart.netlify.com
Twitter: @TeebzR


On Fri, 25 Jan 2019 at 16:09, Ella Bowles <bowlese at gmail.com> wrote:

> Hello,
>
> I am writing to follow up on a couple of questions that were not answered
> on the forum. It should just be a quick yes/no answer.
>
> 1). I'm wondering if xval only needs to be used for the dapc part and not
> find.cluster?
> 2) Can I verify that for xval anaysis, n.pca.max should be set to n/3?
>
> I have 186 individuals spread over 11 sites for which I am trying to sort
> out the population structure.
>
> In case this thread can be correlated with my last on the forum, the
> previous question had the title "
> are xval results only relevant for the dapc part of dapc analysis and not
> find.cluster"
> With thanks,
> Ella
>
> --
> Ella Bowles, PhD
> Mitacs Elevate Postdoctoral Fellow,
> Department of Biology, Concordia University
> Niskamoon Corporation
>
> Website: https://ellabowlesphd.wordpress.com/
> Email: bowlese at gmail.com
> _______________________________________________
> adegenet-forum mailing list
> adegenet-forum at lists.r-forge.r-project.org
> https://lists.r-forge.r-project.org/cgi-bin/mailman/listinfo/adegenet-forum
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