[BioC] Newbie Problems with Agilent data

elliot harrison e.harrison at epistem.co.uk
Mon Sep 24 11:15:22 CEST 2007


Hi BioC,
I am relatively new to R and array analysis in general.
> sessionInfo()
R version 2.5.1 (2007-06-27) 
i386-pc-mingw32 
 
locale:
LC_COLLATE=English_United Kingdom.1252;LC_CTYPE=English_United
Kingdom.1252;LC_MONETARY=English_United
Kingdom.1252;LC_NUMERIC=C;LC_TIME=English_United Kingdom.1252
 
attached base packages:
[1] "stats"     "graphics"  "grDevices" "utils"     "datasets"
"methods"   "base"     
 
other attached packages:
   limma 
"2.10.5" 


I'm trying to follow the workshops I've found online (Lab 4 -
Differential Expression and Linear Modeling using limma)
but I'm coming unstuck at the first hurdle.
I have 8 files of 2-colour agilent 44k whole human array data.
In Limma I'm I use 

RG <- read.maimages(targets$FileName, source="agilent", quote="") 
That loads fine.
In the workshop the following is then used
 
Now read the CEL file data into an AffyBatch object and normalize using
RMA:
 
 library(affy)
 library(hgu95av2cdf)
 abatch <- ReadAffy(filenames=targets$filename)
 eset <- rma(abatch)
 
Obviously this will not work on my agilent data. What should I be doing
instead?
I've ploughed on and got the designing the matrix for my experiment. 
My arrays fall into 2 groups, pre and post treatment the design matrix
looks as follows

> f
[1] Pre  Pre  Pre  Pre  Post Post Post Post
Levels: Post Pre

> cont.matrix       
Contrasts
Levels PreVPost  
Post       -1  
Pre         1

> design  
  Post Pre
1    0   1
2    0   1
3    0   1
4    0   1
5    1   0
6    1   0
7    1   0
8    1   0
attr(,"assign")
[1] 1 1
attr(,"contrasts")
attr(,"contrasts")$f
[1] "contr.treatment"
 
That seems logical but I wanted to check that was in place as well.
 
Right as if that weren't enough I have a second query.
I had some agilent 1-colour data as well.
I found a post regarding this and tried using
 
http://article.gmane.org/gmane.science.biology.informatics.conductor/128
18/match=agilent
 
 
myFlagFun <- function(x) {
>   #Weight only strongly positive spots 1, everything else 0
>   present <- x$gIsPosAndSignif == 1
>   probe <- x$ControlType == 0
>   manual <- x$IsManualFlag == 0
>   strong <- x$gIsWellAboveBG == 1
>   y <- as.numeric(present & probe & manual & strong)
>
>   #Weight weak spots 0.5
>
>   weak <- strong == FALSE
>   weak <- (present & probe & manual & weak)
>   weak <- grep(TRUE,weak)
>   y[weak] <- 0.5
>
>   #Weight flagged spots 0.5
>
>   sat <- x$gIsSaturated == 0
>   xdr <- x$gIsLowPMTScaledUp == 0
>   featureOL1 <- x$gIsFeatNonUnifOL == 0
>   featureOL2 <- x$gIsFeatPopnOL == 0
>   flagged <- (sat & xdr & featureOL1 & featureOL2)
>   flagged <- grep(FALSE, flagged)
>   good <- grep(TRUE, y==1)
>   flagged <- intersect(flagged, good)
>   y[flagged] <- 0.5
>   y
>   }
>
> G <- read.maimages(targets,
>             columns = list(G = "gMeanSignal", Gb = "gBGUsed", R =
> "gProcessedSignal",
>              Rb = "gBGMedianSignal"),
>             annotation= c("Row", "Col", "FeatureNum", "ProbeUID",
> "ControlType",
>              "ProbeName", "GeneName", "SystematicName"),
>             wt.fun=myFlagFun)

I keep getting the error
 
Error in readGenericHeader(fullname, columns = columns, sep = sep) : 
        Specified column headings not found in file
 
The only difference I make to this proceedure is changing the g column
header to and r as I have red data.
I found an article referring to changing the encoding setting of
readLines() as a fix but I've had no luck with that.
Anyway I hesitate to go hacking with such a little knowledge.
 
Sorry it's such a long post.
Any and all help gratefully received.
Elliott Harrison


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