Hi John,
> affylmGUI ROCKS!!!
Thanks! It's great to get some positive feedback as well as the
many inevitable bug reports!
Hopefully, I will implement gcrma in affylmGUI within the next
few days, but for now, here's a "hacker's method".
After you've loaded your affy data into affylmGUI, click on the
"Evaluate" menu and select "Evaluate R code". Then paste in the
following commands:
library(gcrma)
NormalizedAffyData <- gcrma(RawAffyData)
NormalizedAffyData.Available <- TRUE
NormMethod <- "gcrma"
tkdelete(.affylmGUIglobals$mainTree,"NormalizedAffyData.Status")
tkinsert(.affylmGUIglobals$mainTree,"end",
"NormalizedAffyData","NormalizedAffyData.Status",
text="Available (gcrma)",
font=.affylmGUIglobals$affylmGUIfontTree)
Then from the "Run" menu, select "Show Text Results Only"
[ These commands will be evaluated in the environment,
"affylmGUIenvironment". ]
IF YOU WANT A MUCH SHORTER VERSION, YOU CAN CHEAT AS FOLLOWS:
Just normalize with RMA (even though you really want gcrma),
then just paste in the first two lines of code from above:
library(gcrma)
NormalizedAffyData <- gcrma(RawAffyData)
But then as a penalty for "cheating", affylmGUI will confuse you
by referring to your normalized data as "RMA" data
(in the tree/drill-down diagram) when in fact it is "GCRMA".
You only need to type in "library(gcrma)" once per affylmGUI
session (in fact once per R session) so you could even get away
with just one line of code:
NormalizedAffyData <- gcrma(RawAffyData)
> Venn diagrams in affylmGUI- what is used as the cutoff for
> significance in the Venn Diagrams, and can that cutoff be
> manipulated/changed?
The venn diagrams in affylmGUI use the classifyTestsF function
from limma which uses a default p-value cutoff of 0.01.
Unforunately, I have not yet implemented a way to adjust this
from the GUI via a dialog box. I have put this on my "TO DO"
list! Hopefully next week, you will be able to download a new
version of affylmGUI from
http://bioinf.wehi.edu.au/affylmGUI
or
http://www.bioconductor.org/repository/devel/package/html/affylmGUI.ht
ml
I could try to explain a "hacking" way to do this, but then that
would just delay my implementation of the "nice way", so for
now, I'll at least refer you to the affylmGUI hacker's guide:
http://bioinf.wehi.edu.au/affylmGUI/Doc/extract.pdf
See also the limmaGUI developer's guide (cDNA version):
http://bioinf.wehi.edu.au/limmaGUI/Doc/lgDevel.html
and the limmaGUI (cDNA version) hacker's guide:
http://bioinf.wehi.edu.au/limmaGUI/Doc/extract.pdf
The limma package is loaded automatically whenever you load
affylmGUI. Once the limma package is loaded, you can view
help on classifyTestsF from within R, by typing :
?classifyTestsF
> Final ?- If an obvious defect/scratch is seen on a chip in
> affylmGUI-what is your opinion on the most appropriate method
> to dealing with it within R short of tossing out all the data?
> This is the first time I've actually looked at my chips
> visually, some of the defects are significant, but I don't
> want to toss the baby out with the bathwater.
This is not an easy question to answer in general. If you have
a reasonable number of replicate chips, it may be OK to include
at least one chip with a spatial artifact such as a scratch,
because although the chip by itself may suggest incorrect gene
expression levels/ratios for genes with probes in the scratch
region, hopefully the replicate chips won't have the same
spatial artifacts, so the linear modelling will in a way take
care of this. Because of the inevitable inconsistency between
chips, the linear model will effectively down-weight your
t-statistics etc. to tell you that you shouldn't be too
confident about your expression measurements for those genes in
the scratch-area (i.e. the results may not be reproducible).
But if the other (non-scratched) chips are reasonably
consistent, it's still possible to see those genes
high-up on your list.
That's just one way of looking at spatial artifacts. There are
many other ways of assessing and dealing with chip-quality
issues and spatial variation and I'm really not the best person
to ask about this, so I'd suggest posting to the Bioconductor
list with a subject line, something like: "affy quality weights"
and see what sort of response you get.
It's very likely that someone will suggest a method which is
not yet available within affylmGUI (unless you want to "hack").
I'm certainly interested in adding more array-quality weighting
options into affylmGUI and I'd love to hear some suggestsions
about this from the affy quality experts!
Best regards,
James
----------------------------------------------------------------------
----
James Wettenhall Tel: (+61 3) 9345
2629
Division of Genetics and Bioinformatics Fax: (+61 3) 9347
0852
The Walter & Eliza Hall Institute E-mail:
wettenhall@wehi.edu.au
of Medical Research, Mobile: (+61 / 0 ) 438 527
921
1G Royal Parade,
Parkville, Vic 3050, Australia
http://www.wehi.edu.au
