Agi4x44Preprocess/Limma and number of significant p-values
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Paulo Nuin ▴ 200
@paulo-nuin-3012
Last seen 6.9 years ago
Canada
> Hi everyone I have used Agi4x44Preprocess (script below) to analyze three mouse (mgug4122a) Agilent microarray sets and I want to check if the methods I'm using make sense, as the number of significant p-values I'm getting seems quite high. I'm using Limma for the significance analysis. the lowest value I'm getting is 1.09667003222346e-12, while the adjusted p-value 2.43208513046197e-08. If I consider significant p-value below 0.01, I get 9721 significant ones, roughly half of the gene list. This was for one of the datasets, with 2 (four result sets) micorarrays, 1 for each treatment. For another similar dataset, 4 microarrays with 2 samples for each treatment, results are very similar. I also tried the approach in this page http://matticklab.com/index.php ?title=Single_channel_analysis_of_Agilent_microarray_data_with_Limma and the results seem better (I mean less p-values below 0.01). I was wondering what would be the suggested approach and if there's any reason that I'm getting so many values below 0.01, with some extreme values at 10-12 range. Is there a bug on Agi4x44Preprocess? It seism to get the same columns as the approach on the MAttick's lab webpage. Thanks in advance for any help Paulo library(Agi4x44PreProcess) library(mgug4122a.db) targets=read.targets(infile="targets.txt") dd=read.AgilentFE(targets, makePLOT=FALSE) CV.rep.probes(dd, "mgug4122a.db", foreground = "MeanSignal", raw.data = TRUE, writeR = T, targets) genes.rpt.agi(dd, "mgug4122a.db", raw.data = TRUE, WRITE.html = T, REPORT = T) ddNORM = BGandNorm(dd, BGmethod = "half", NORMmethod = "quantile", foreground = "MeanSignal", background = "BGMedianSignal", offset = 50, makePLOTpre = FALSE, makePLOTpost = F) ddFILT = filter.probes(ddNORM, control = TRUE, wellaboveBG = TRUE, isfound = TRUE, wellaboveNEG = TRUE, sat = TRUE, PopnOL = TRUE, NonUnifOL = T, nas = TRUE, limWellAbove = 75, limISF = 75, limNEG = 75, limSAT = 75, limPopnOL = 75, limNonUnifOL = 75, limNAS = 100, makePLOT = F, annotation.package = "mgug4122a.db", flag.counts = T, targets) ddPROC = summarize.probe(ddFILT, makePLOT = F, targets) esetPROC = build.eset(ddPROC, targets, makePLOT = F, annotation.package = "mgug4122a.db") write.eset(esetPROC,ddPROC,"mgug4122a.db",targets) levels.treatment = levels(factor(targets$Treatment)) treatment = factor(targets$Treatment, levels = levels.treatment) design = model.matrix(~0 + treatment) print(design) colnames(design) = c("EV50", "SHGR19") fit = lmFit(esetPROC, design) CM = makeContrasts(EV50vsSHGR19=EV50-SHGR19, levels=design ) fit2 = contrasts.fit(fit, CM) fit2 = eBayes(fit2) my.lfc <- 0 output <- topTable(fit2, coef=1, adjust.method="BH", lfc=my.lfc, number = 20000) write.table(output, file="output.txt", sep="\t", quote=FALSE)
Microarray Annotation limma Agi4x44PreProcess Microarray Annotation limma Agi4x44PreProcess • 588 views
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Wei Xu ▴ 30
@wei-xu-4939
Last seen 7.2 years ago
Did you try other methods for background subtraction, such as normexp? Wei On 11/2/2011 2:47 PM, Paulo Nuin wrote: > Hi everyone > > I have used Agi4x44Preprocess (script below) to analyze three mouse (mgug4122a) Agilent microarray sets and I want to check if the methods I'm using make sense, as the number of significant p-values I'm getting seems quite high. I'm using Limma for the significance analysis. the lowest value I'm getting is 1.09667003222346e-12, while the adjusted p-value 2.43208513046197e-08. If I consider significant p-value below 0.01, I get 9721 significant ones, roughly half of the gene list. This was for one of the datasets, with 2 (four result sets) micorarrays, 1 for each treatment. For another similar dataset, 4 microarrays with 2 samples for each treatment, results are very similar. > > I also tried the approach in this page http://matticklab.com/index.p hp?title=Single_channel_analysis_of_Agilent_microarray_data_with_Limma and the results seem better (I mean less p-values below 0.01). > > I was wondering what would be the suggested approach and if there's any reason that I'm getting so many values below 0.01, with some extreme values at 10-12 range. Is there a bug on Agi4x44Preprocess? It seism to get the same columns as the approach on the MAttick's lab webpage. > > Thanks in advance for any help > > Paulo > > > library(Agi4x44PreProcess) > library(mgug4122a.db) > targets=read.targets(infile="targets.txt") > dd=read.AgilentFE(targets, makePLOT=FALSE) > > > CV.rep.probes(dd, "mgug4122a.db", foreground = "MeanSignal", raw.data = TRUE, writeR = T, targets) > genes.rpt.agi(dd, "mgug4122a.db", raw.data = TRUE, WRITE.html = T, REPORT = T) > ddNORM = BGandNorm(dd, BGmethod = "half", NORMmethod = "quantile", foreground = "MeanSignal", background = "BGMedianSignal", offset = 50, makePLOTpre = FALSE, makePLOTpost = F) > > ddFILT = filter.probes(ddNORM, control = TRUE, wellaboveBG = TRUE, > isfound = TRUE, wellaboveNEG = TRUE, sat = TRUE, PopnOL = TRUE, > NonUnifOL = T, nas = TRUE, limWellAbove = 75, limISF = 75, > limNEG = 75, limSAT = 75, limPopnOL = 75, limNonUnifOL = 75, > limNAS = 100, makePLOT = F, annotation.package = "mgug4122a.db", > flag.counts = T, targets) > ddPROC = summarize.probe(ddFILT, makePLOT = F, targets) > esetPROC = build.eset(ddPROC, targets, makePLOT = F, annotation.package = "mgug4122a.db") > write.eset(esetPROC,ddPROC,"mgug4122a.db",targets) > > > levels.treatment = levels(factor(targets$Treatment)) > treatment = factor(targets$Treatment, levels = levels.treatment) > design = model.matrix(~0 + treatment) > print(design) > colnames(design) = c("EV50", "SHGR19") > fit = lmFit(esetPROC, design) > CM = makeContrasts(EV50vsSHGR19=EV50-SHGR19, levels=design ) > > fit2 = contrasts.fit(fit, CM) > fit2 = eBayes(fit2) > my.lfc<- 0 > output<- topTable(fit2, coef=1, adjust.method="BH", lfc=my.lfc, number = 20000) > write.table(output, file="output.txt", sep="\t", quote=FALSE) > > _______________________________________________ > Bioconductor mailing list > Bioconductor at r-project.org > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor -- Wei Xu, Ph.D. Wound Healing and Regenerative Medicine Laboratory Department of Surgery / Plastic Surgery Division Northwestern University, Feinberg School of Medicine 303 East Chicago Ave, Tarry 4-720 Chicago, IL 60611 Phone: (312) 908 0566 Fax: (312) 908 4013
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