[BioC] enquiry about the limma script

[Gordon K Smyth]

Dear Yong Li,

The formatted table you have asked for is not one produced automatically 
by limma.  The closest is that produced by write.fit().  If you are trying 
to reproduce a previous analysis, you should contact the person who 
produced that the previous analysis.

Best wishes
Gordon

> Date: Sun, 24 Mar 2013 20:25:20 +0800
> From: yong li <yonglinet at gmail.com>
> To: <bioconductor at stat.math.ethz.ch>
> Subject: [BioC] enquiry about the limma script
>
> Dear Sir/Madam,
>
>
>
> I meet some problems when I use the limma package to analysis the Agilent
> single channel microarray. I got 8 samples (with 8*60K array) results as
> following:
>
> Samples: (T1, T2, T3) =Treat1
>
> Samples: (T4, T5, T6)=Treat2
> Samples: (C1, C2)=Control
> my targets.txt contain:
> file_name conditon
> x1.txt T1
> x2.txt T2
> x3.txt T3
> x4.txt T4
> x5.txt T5
> x6.txt T6
> x7.txt C1
> x8.txt C8
>
>
> I want to get the different expressed genes under 3 conditions: Treat1 vs
> Control, Treat2 vs Control, Treat2 vs Treat1 with the following format:
>
> *GROUP1*
>
> *GROUP2*
>
> *CONTROL*
>
> *GROUP1-vs-CONTROL*
>
> *GROUP2-vs-CONTROL*
>
> *GROUP1-vs-GROUP2*
>
> *Probeid*
>
> *Accession*
>
> *T1*
>
> *T2*
>
> *T3*
>
> *T4*
>
> *T5*
>
> *T6*
>
> *C1*
>
> *C2*
>
> *avg-group1*
>
> *avg-group2*
>
> *avg-control*
>
> *FC*
>
> *UP/DOWN*
>
> *P value*
>
> *FC*
>
> *UP/DOWN*
>
> *P value*
>
> *FC*
>
> *UP/DOWN*
>
> *P value*
>
> CUST_PI5
>
> AB91
>
> 3
>
> 1
>
> 1
>
> 1
>
> 2
>
> 4
>
> 6
>
> 6
>
> 2
>
> 2.5
>
> 6
>
> 6
>
> DOWN
>
> 0.01
>
> 2
>
> DOWN
>
> 0.02
>
> 1
>
> DOWN
>
> 0.2
>
>
>
> My R script as follow:
>
> library(limma)
>
> targets <- readTargets("targets.txt")
>
> x <- read.maimages(targets[,"file_name"], source="agilent",green.only=TRUE)
>
> y <- backgroundCorrect(x, method="normexp")
>
> y <- normalizeBetweenArrays(y, method="quantile")
>
> y.ave <- avereps(y, ID=y$genes$ProbeName)
>
> f <- factor(targets$condition, levels = unique(targets$condition))
>
> design <- model.matrix(~0 + f)
>
> colnames(design) <- levels(f)
>
>
>
> fit <- lmFit(y.ave, design)
>
> contrast.matrix <- makeContrasts("Treat1-C",
> "Treat2-C","Treat2-Treat1",levels=design)
>
> fit2 <- contrasts.fit(fit, contrast.matrix)
>
> fit2 <- eBayes(fit2)
>
> write.table(topTable(fit2,adjust="BH",number="44000"),
> file="out_file.txt",sep="\t")
>
>
>
> I got the results as following:
>
> *ProbeName*
>
> *SystematicName*
>
> *Treat1.C*
>
> *Treat2.C*
>
> *Treat1.Treat2*
>
> *AveExpr*
>
> *F*
>
> *P.Value*
>
> *adj.P.Val*
>
> CUST_248
>
> EV478
>
> -0.54306
>
> -8.90559
>
> 8.362525
>
> 14.51936
>
> 2562.081
>
> 1.97E-10
>
> 6.12E-06
>
>
> The problem is that I can’t get the normalized data, every group average
> data, FC with up/down label in a file just like the previous format. I
> write you for help and look forward to receiving your reply.
>
> Thanks a lot.
>
>
>
> Yong Li
>
> Institute of Plant Physiology and Ecology, SIBS, CAS
>

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