[BioC] Spatial Correction

Sat Oct 14 21:55:32 CEST 2006

heh. we're often on the same wavelength it seems. i'm already doing this x-y 
slope across the whole 'field' thing a bit in my linear modeling work, 
inspired by borevitz's occosional inclusion of probe set id #'s as null- 
components when checking stats/models, instead i input them as x and y's, 
split up as terms in a model for calling the chip. i have to say: the 
coeffiecients sure don't look like they matter much? i think i'm going to 
leave it in as an engineering/q.a. thing, though.

e.g. coeffiecients for a model that slams everything down to 
snp/sfp-calls/copy-number (sorry no anova, sigs, or f-test output on these 
runs):

statcutoffs tcut: 12.236 high: 21.2475972466423 low: 16.5098342910872
built model of 42317 points, spread as transects across the chip.
constant        2.06606598216225
---
progenypm       0.0813258560574814
progenymm       -0.00294383026705664
highpm1         -0.0486924459388655
highpm2         -0.0365650706946507
highpm3         -0.046313242027037
lowpm1          0.0181522513255337
lowpm2          0.0161600597386146
lowpm3          0.0160355165311527
highmm1         -0.0110724545083531
highmm2         -0.00663396018469053
highmm3         -0.0116972092671024
lowmm1          0.00326259465264771
lowmm2          0.00567155215296054
lowmm3          0.0013044918026059
-- gc content:
gcp  (sign gets flipped)        -0.00101239058730091
-- nearest neighbor temperature:
tmp  (sign gets flipped)        0.00153804487609968
-- number of G's in probe (the c's get labeled):
ngp  (sign gets flipped)        0.00554060155127831
---
xpos (sign gets flipped)        -2.14773325615418e-05 <-- x 10 to the minus 
5, that's not-so-much?
ypos (sign gets flipped)        6.94183011922954e-06
---
as far as abberant spot correction (real spatial correction by-zones) i'm 
not doing this and am simply re-running the bad experiements - for some 
reason a few of our chips have smudges that look like nike-swooths on them 
where the hybridization is killed significantly.
---
as far as labeling for dna is concerned i would die to see someone compare 
end-transferase and random label inclusion (perhaps in the same hyb mix as 
different fluorophore colors on a multi-channel scanner?). is there any work 
/ pubs out there that do this?

Thank You,

Matthew Lyon        UC  Riverside                    lab (951) 827-4736
Ph.D. Student        B O T A N Y                new c.p. (951) 941-5554
                   Citrus Genomics                    apt (951) 328-9930
http: // int - citrusgenomics . org /           messengers: ptrifoliata
mattlyon at mattlyon.com ptrifoliata at hotmail.com mlyon003 at student.ucr.edu

>From: "Justin Borevitz" <borevitz at uchicago.edu>
>To: "'Michael Gore'" <mag87 at cornell.edu>
>CC: Ben Bolstad <bolstad at stat.berkeley.edu>,        "'Bioconductor'" 
><bioconductor at stat.math.ethz.ch>
>Subject: Re: [BioC] Spatial Correction
>Date: Sat, 14 Oct 2006 09:53:52 -0500
>
>It is adhoc and I’ve only thought a little about how to do regional
>correction more systematically (the way field studies correct for wet/soil
>spots locally effecting growth).  Something like the xy pos of the feature
>is in the model for differential expression.  This would be quite
>computational and normalization wouldn’t come in the standard way either.
>
>So bg.correct from RMA is not spatial correction. It doesn’t consider the 
>XY
>pos.  It does a global per array correction for each array, but assumes
>normal noise and exponential RNA signal.  Ben Bolstad was going to model
>normal noise and normal signal for DNA, but I don’t think he has done it
>yet, no pressure Ben...  We might be in lower demand however SNP arrays
>might benefit tremendously from normal/normal. SNP arrays do have their own
>correction method with the oligo package though and I’m not sure if anyone
>tired this for RNA or if it makes sense even.
>
>Yes quantile normalization is the next step and good luck with the maize
>SFPs, if you use whole genome random labeling I guess it will be noisily 
>but
>possible with enough replicates.  The deletions sure should be.  Otherwise 
>a
>complexity reduction eg AFPL etc might help, see our Barley paper
>http://genomebiology.com/2005/6/6/r54
>
>
>-----
>Justin Borevitz
>http://naturalsystems.org/lab
>________________________________________
>From: Michael Gore [mailto:mag87 at cornell.edu]
>Sent: Friday, October 13, 2006 6:08 PM
>To: 'Justin Borevitz'
>Subject: Spatial Correction
>
>Hi Justin,
>
>I have a quick question for you.  How does one determine the appropriate
>array size and filter size for spatial correction?
>
>Is spatial correction comparable to RMA, followed by Quantiles?
>
>Right now, we are doing SFP with the Affy Maize GeneChip.
>
>Thanks,
>
>Mike
>
>
>library(affy)
>read.cel <- function(cel.file, cel.image = F, spatial.correct=T, median=F,
>                                   array.size = 712, filter.size = 51,
>jpeg.save=T){
>
>
>_______________________________________________
>Bioconductor mailing list
>Bioconductor at stat.math.ethz.ch
>https://stat.ethz.ch/mailman/listinfo/bioconductor
>Search the archives: 
>http://news.gmane.org/gmane.science.biology.informatics.conductor