Subtraction of Healthy control average from treatment averages
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@paul-cristina-5211
Last seen 9.6 years ago
16 CEL files 4 treatments (one per chip- citrus chip) x 4 replications of each trt/chip with no subtreatments . Been googling and reading etc but can't find a solution. I would like to subtract the average reading from each probe 30K+ from the averaged reading of each of the 3 other treatments before I compare/contrast the remaining 3 trts for expression. We want to find out if there are differences between all 3 plant pathogens and expression using this chip to predict infectivity as well as which genes are up-regulated and down regulated etc. Code below as far as I've gotten... Is this the best way to proceed? Or other? 6 weeks and still at bottom of Bioconductor and R learning curve.... Using R Studio with R 2.14 all BiocLite packages are up to date. R version 2.14.1 (2011-12-22) Copyright (C) 2011 The R Foundation for Statistical Computing ISBN 3-900051-07-0 Platform: x86_64-pc-mingw32/x64 (64-bit) library("affy") list.celfiles() Gdata <- ReadAffy() Gdata Loading required package: AnnotationDbi AffyBatch object size of arrays=984x984 features (23 kb) cdf=Citrus (30395 affyids) number of samples=16 number of genes=30395 annotation=citrus notes= pm(Gdata)[1:5, ] Gdataeset <- expresso (Gdata, bgcorrect.method="rma", normalize.method="quantiles", pmcorrect.method="mas", summary.method="medianpolish") sampleNames(Gdataeset)=sub("\\.CEL$","",sampleNames(Gdataeset)) exprs(Gdataeset)[1:20,] targets<-readTargets("Gdata.txt", sep="") targets f<-paste(targets$Pathogen, sep="") f<-factor(f) f Gdatadesign<-model.matrix(~0+f) colnames(Gdatadesign)<-levels(f) Gdatadesign Virus bacterial Healthy phytoplasma 1 0 1 0 0 2 0 1 0 0 3 0 1 0 0 4 0 1 0 0 5 1 0 0 0 6 1 0 0 0 7 1 0 0 0 8 1 0 0 0 9 0 0 1 0 10 0 0 1 0 11 0 0 1 0 12 0 0 1 0 13 0 0 0 1 14 0 0 0 1 15 0 0 0 1 16 0 0 0 1 contrast.matrix<-makeContrasts(bacterial-Healthy, phytoplasma-Healthy, virus-Healthy, levels=Gdatadesign) contrast.matrix Levels bacterial- Healthy phytoplasma - Healthy virus - Healthy Virus 0 0 1 Bacteria 1 0 0 Healthy -1 -1 -1 phyto 0 1 0 as far as I've gotten.... But didn't want to put more effort if this isn't the way to proceed..... Thank you in advance.... Tina If you are not part of the solution your are part of the precipitate... Cristina Paul Citrus Quarantine Unit USDA-ARS-BA-PSI-MPPL Mailling address: Bldg 004 Rm 118 Beltsville MD 20705 office: 301-504-7657 FAX: 301-504-5449 Cell: 240-286-6709 Physical Address: Range 1 head house #6 This electronic message contains information generated by the USDA solely for the intended recipients. Any unauthorized interception of this message or the use or disclosure of the information it contains may violate the law and subject the violator to civil or criminal penalties. If you believe you have received this message in error, please notify the sender and delete the email immediately. [[alternative HTML version deleted]]
probe probe • 698 views
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@sean-davis-490
Last seen 3 months ago
United States
On Tue, Apr 24, 2012 at 8:43 AM, Paul, Cristina <cristina.paul@ars.usda.gov>wrote: > 16 CEL files 4 treatments (one per chip- citrus chip) x 4 replications of > each trt/chip with no subtreatments . Been googling and reading etc but > can't find a solution. I would like to subtract the average reading from > each probe 30K+ from the averaged reading of each of the 3 other > treatments before I compare/contrast the remaining 3 trts for expression. > We want to find out if there are differences between all 3 plant pathogens > and expression using this chip to predict infectivity as well as which > genes are up-regulated and down regulated etc. Code below as far as I've > gotten... Is this the best way to proceed? Or other? 6 weeks and still at > bottom of Bioconductor and R learning curve.... Using R Studio with R 2.14 > all BiocLite packages are up to date. > > > R version 2.14.1 (2011-12-22) > > Copyright (C) 2011 The R Foundation for Statistical Computing > > ISBN 3-900051-07-0 > > Platform: x86_64-pc-mingw32/x64 (64-bit) > > library("affy") > list.celfiles() > Gdata <- ReadAffy() > Gdata > > Loading required package: AnnotationDbi > > > > AffyBatch object > > size of arrays=984x984 features (23 kb) > > cdf=Citrus (30395 affyids) > > number of samples=16 > > number of genes=30395 > > annotation=citrus > > notes= > > pm(Gdata)[1:5, ] > Gdataeset <- expresso (Gdata, bgcorrect.method="rma", > normalize.method="quantiles", pmcorrect.method="mas", > summary.method="medianpolish") > sampleNames(Gdataeset)=sub("\\.CEL$","",sampleNames(Gdataeset)) > exprs(Gdataeset)[1:20,] > > > targets<-readTargets("Gdata.txt", sep="") > targets > f<-paste(targets$Pathogen, sep="") > f<-factor(f) > f > > Gdatadesign<-model.matrix(~0+f) > colnames(Gdatadesign)<-levels(f) > Gdatadesign > Virus bacterial Healthy phytoplasma > 1 0 1 0 0 > 2 0 1 0 0 > 3 0 1 0 0 > 4 0 1 0 0 > 5 1 0 0 0 > 6 1 0 0 0 > 7 1 0 0 0 > 8 1 0 0 0 > 9 0 0 1 0 > 10 0 0 1 0 > 11 0 0 1 0 > 12 0 0 1 0 > 13 0 0 0 1 > 14 0 0 0 1 > 15 0 0 0 1 > 16 0 0 0 1 > contrast.matrix<-makeContrasts(bacterial-Healthy, phytoplasma- Healthy, > virus-Healthy, levels=Gdatadesign) > contrast.matrix > > Levels bacterial- Healthy phytoplasma - Healthy virus - Healthy > > Virus 0 0 1 > > Bacteria 1 0 0 > > Healthy -1 -1 -1 > > phyto 0 1 0 > > as far as I've gotten.... But didn't want to put more effort if this isn't > the way to proceed..... > Thank you in advance.... > > Hi, Tina. Looks pretty good at first glance. Keep going with limma. Sean [[alternative HTML version deleted]]
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