[BioC] Normalizing single-channel data [was: is my normalization right?]

xpzhang xpzhang at genetics.ac.cn
Wed Jun 2 04:52:01 CEST 2004

Dear Gordon, 

Thank you very much!

I will try the method that you teach me as soon as possible. 

On Wed, 02 Jun 2004 09:44:25 +1000
Gordon Smyth <smyth at wehi.edu.au> wrote:
> Firstly, let me point out that your text file doesn't contain the "raw 
> data" from Genepix since it doesn't contain background intensities. Have 
> you already subtracted the background or have you just ignored it? What did 
> you do with the Genepix flags?
  Here I talk something about my data which will be used for
normalization. The scanner is Axon's 4000B.I chose the raw data from
*.gpr file, and I only chose two columns, one is F532 Median, the other
is B532 median. Then I subtracted the backgroud from F532 Median by
Excel.At last, I got a *.text file just as I described last time. 

And there is another question about the sbutraction.After I substracted
the backgroud, I got some data negative.I think it is really
unreasonable.And what will be if I did not substract the backgroud? Is
there more errors in the data with background subtraction than that without
background substraction?

> 1. Given a text file like you describe, you can read into R using the basic 
> function read.table()
> Data <- read.table("myfile.txt",sep="\t")  # I assume your file is 
> tab-delimited
> y <- as.matrix(Data[,-1])
> rownames(y) <- as.character(Data[,1])
> Now you have two major normalization choices, quantile or vsn normalization.
> library(limma)
> y2 <- normalizeBetweenArrays(log2(y), method="quantile")
> or
> y2 <- normalizeBetweenArrays(y, method="vsn")
> Now you are ready to go straight into analysis differential expression 
> using limma like
> fit <- lmFit(y2, design)
> If you use quantile normalization, you must make sure that all your 
> intensities are positive before normalizing, for example by
> y <- pmax(1, y)
> 2. You never did need to extract the intensity data from the Genepix gpr 
> files in the first place. You could have proceeded in limma as
> targets <- readTargets()  # Always good practice to make a targets file
> RG <- read.maimages(targets$FileName, source="genepix", 
> columns=list(Rf="F532 Mean",Gf="F532 Mean",Rb="B532 Median",Gb="B532 Median"))
> y2 <- normalizeBetweenArrays(RG$G, method="quantile")
> Or you might choose to apply backgroundCorrect() before 
> normalizeBetweenArrays()
> Gordon
> >xpzhang xpzhang at genetics.ac.cn
> >Sat May 29 09:21:55 CEST 2004
> >
> >
> >Thank you for your answer!
> >
> >My raw-data was from GenePix. Because I used only Cy3 in my whole
> >microarray experiment, I only extract data by the software,and try to
> >normalize the data by Bioconductor.
> >
> >I made a .txt file for the raw data, it was just like this:
> >
> >Gene Name  Contrl(intensity)   Treat1(intensity)   Treat2(intensity) 
> >Treat3(intensity)
> >1
> >2
> >3
> >4
> >5
> >...
> >
> >I want to use mutiple slides normalization with intensity dependent, is
> >it appropriate? And could you tell me howto? I am trying to find out
> >ways by reading Bioconductor's document and help files,but I feel really
> >difficult.
> >
> >Thank you very much!

Xiaopeng ZHANG<xpzhang at genetics.ac.cn>

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