MultiPhen 0.3 is available on CRAN

Dear All,

a new version of MultiPhen (0.3) is available on CRAN, and will be available to a mirror near you soon. 

*Please upgrade* because this release is a bug fix release. A new version, with improvements in the output
and more useful error messages is basically ready, but I will wait to release it until next week to add other
possible improvements.

For all asking for the related paper, Paul should be ready with the final draft 'soon'.

Cheers

Federico

--
Federico C. F. Calboli
Neuroepidemiology and Ageing Research
Imperial College, St. Mary's Campus
Norfolk Place, London W2 1PG

Tel +44 (0)20 75941602   Fax +44 (0)20 75943193

f.calboli [.a.t] imperial.ac.uk
f.calboli [.a.t] gmail.com

visualising clusters when not separated geographically

Dear List members,
I have a question regarding the DAPC scatter.
I have 8 sampled populations (from 8 geographic locations), and the DAPC 
analysis (as well as Structure), found 6 clusters. Now, these clusters 
are not separate geographically, which means that each sampled location 
has a proportion of each cluster. I wanted to see this on a scatter, to 
see how different clusters were, hence I plotted the $ind.coord (I used 
points()), and simply coloured the points by clustering this time (as 
per DAPC analysis) and not by geographic location. I was expecting to 
get same-colour points clustered close together (whereas in the first 
DAPC, where colours were based on geog location, they were all 
overlapping). Instead, colours remained all overlapping. I do not 
understand how this explains the existence of 6 clusters, if they're not 
there on the graph.
Maybe my assumption that re-colouring the points by cluster (and not 
location) would present distinct groups was wrong?
I hope what I say makes sense...
I should say that I re-analysed from the start the same dataset too, 
this time grouping individuals by cluster assignment and not by 
geographical location, and I do get nicely separated clusters on the 
scatter. I thought this was wrong in principle though, as I was basing 
it on a new analysis.
Cheers
Ilaria

--

-- 
Dr Ilaria Coscia
Institute of Biological, Environmental and Rural Science (IBERS)
Edward Llwyd Building
Aberystwyth University

package mvtnorm is obsolete

> library(genetics)
Loading required package: combinat

Attaching package: ‘combinat’

The following object(s) are masked from ‘package:utils’:

    combn

Loading required package: gdata
gdata: read.xls support for 'XLS' (Excel 97-2004) files ENABLED.

gdata: read.xls support for 'XLSX' (Excel 2007+) files ENABLED.

Attaching package: ‘gdata’

The following object(s) are masked from ‘package:Hmisc’:

    combine

The following object(s) are masked from ‘package:stats’:

    nobs

The following object(s) are masked from ‘package:utils’:

    object.size

Loading required package: gtools
Loading required package: mvtnorm

package MultiPhen

Dear All,

the new package MultiPhen is up. The latest version is 0.2, which should be available in a couple of days from
a CRAN mirror near to you. MultiPhen performs genetic association tests between SNPs (one-at-a-time)
and multiple phenotypes (separately or in joint model). So far it deals with SNPs, called or imputed, CNV
and raw calls will be added in future releases.

bw

Federico

--
Federico C. F. Calboli
Department of Epidemiology and Biostatistics
Imperial College, St. Mary's Campus
Norfolk Place, London W2 1PG

Tel +44 (0)20 75941602   Fax +44 (0)20 75943193

f.calboli [.a.t] imperial.ac.uk
f.calboli [.a.t] gmail.com

Microsatellite analysis - Genetic Structure

Hi,

I would like to calculate genetic polymorphism for each population (mean number
of alleles per locus (A), the percentage of polymorphic loci (P), the mean
observed heterozygosity (HO) and the mean expected heterozygosity
under Hardy-Weinberg equilibrium (HE)), and population differentiation (analyzed
for polymorphic loci by F-statistics (Wright 1978)).

Please, could you point me out any paper or packages that do that?

Thank you very much!

Marcelo

Pairwise Fst and G'st in R

Is anyone aware of functions(s) packages in R: thant caclulate pairwise 
Fst and or G'st between all populations in a dataset?

cheers

Nevil Amos

r-genetics and galaxy

Hi Brad

Work on the rgenetics tools in R has more or less come to a halt - I'm
not aware of anyone working on them and I don't think many people are
using them although we tried hard! I think David Clayton's package
snpMatrix can handle large datasets but it's something of a struggle
to fit billions of genotypes efficiently into the R memory model.

For me personally, a bigger problem is enabling the Faculty and
biologists I support to work reproducibly without having to worry
about the technical problems of hundreds of GB of data. Galaxy really
helps with those goals so I've shifted my effort to that framework.
The Galaxy tools do use R and BioC under the hood where they're the
best solution - but for SNP QC, Plink seems more appropriate because
it has the specialized reporting we needed.

On Sat, Sep 11, 2010 at 6:00 AM,  <r-sig-genetics-request@...> wrote:

> Message: 2
> Date: Fri, 10 Sep 2010 16:54:46 -0700
> From: Brad McNeney <mcneney@...>
> To: r-sig-genetics@...
> Subject: Re: [R-sig-genetics] R-sig-genetics Digest, Vol 8, Issue 2
> Message-ID: <4C8AC546.6080503@...>
> Content-Type: text/plain; charset=ISO-8859-1; format=flowed
>
> Hi Ross,
>
> Thanks for this.  I take it then that the QC tools you have in Galaxy
> are not in any of the Bioconductor Genetics{Base,Design,Ped} packages?

Re: R-sig-genetics Digest, Vol 8, Issue 2

Hi, Fernando,

I think there are some QC tools in David Clayton's snpMatrix package,
but there's no single R package to do all the reports you need AFAIK.
For comprehensive reporting, if you don't mind not using R, one option
is to try the SNP/WGA tools in Galaxy - they do use R for graphics but
you don't need to install anything as it all works through an ordinary
web browser.

Essentially, if you have your genotype and pedigree data in Plink
style linkage format (separate map and ped files), the steps are
something like this:

1. make yourself a new user account at the main Galaxy server
(http://usegalaxy.org) so your histories are preserved between logins

2. From the analysis window, left (tool) pane, click the Get Data tool
group header to expand the group, then click the 'upload file' tool.
A form will appear in the center pane of your browser.

3. Change the file format (first field on the form) from Auto to
"lped" format as autodetect won't work for these multi-part datatypes
4. Make the 'ped' and 'map' file upload fields point to the right map
and ped files on your local machine, set the 'build' to hg18 and
change the name to reflect something informative about your data then
click execute.

5. After the data are uploaded (should only take a minute or two for a
small file) to your history, you can select the SNP/WGA QC LD Plots
tool submenu in the tool pane and then click the QC tool. Another form

Available R packages to QC/summarize large SNP datasets?

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Gloria Rocio Bautista Mendoza

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