Hi Steve, It would seem that most of the columns that you have are also present in an Agilent output file (Except for the "H_C1 A_mock A and B/"-part). You apparently have all the columns necessary to do the analysis: (I'll remove the extra header part to keep a more clear overview, perhaps it's a good idea to remove these parts from your files as well) - r/gMeanSignal (Mean Signal intensity for red/green channel) - r/gBGMeanSignal (Mean Background Signal Intensity for red/green channel) - r/gMedianSignal (Median Signal r/g) - r/gBGMedianSignal (Median Background Signal r/g) - r/gBGUsed (Estimated background using a specific algorithm called spatial detrending. This value is usually lower compared to r/gBGMe(di)an signal, and should be used during the background subtraction (if you intend to use that), in my humble opinion. So what I usually do then is the following: # Read the Target file (experimental description) - See Limma user guide for more information on this targets <- readTargets("description.txt", sep="\t", quote="\"") # Reading the Images Agilent.RG <- read.maimages(targets$FileName, source="agilent", path=datapath, names=targets$Description, columns= list( R = "rMeanSignal", G = "gMeanSignal", Rb = "rBGUsed", Gb = "gBGUsed", Rb.real = "rBGMeanSignal", Gb.real = "gBGMeanSignal"), annotation = c("FeatureNum","Row","Col","ProbeName","ControlType","GeneName", "Description","SystematicName")) This way you can check both background values (estimated (Agilent.RG$Rb, Agilent.RG$Gb) vs really measured (Agilent.RG$Rb.real, Agilent.RG$Gb.real)) during my quality control checks. As Sean mentioned, the normalization used for the r/gProcessedSignals is dependant on the Scanner type and software used for image conversion, but if the original Feature Annotation Software (and Agilent Scanner) has been used, then I would think in the direction of a LOESS algorithm (for within-array normalisation) followed by scaling to a reference value. If I recall correctly (but I keep forgetting the minor details), then the Processed signal (i.e. for red) is calculated using ( rMeanSignal - rBGUsed ) --> corrected through LOESS Normalization --> Scaling --> Processed Value. I think each Feature Extraction Software comes with a built-in manual where these procedures are more clearly explained. I would suggest reading that. The ratio between rProcessedSignal and gProcessedSignal is then calculated and transformed into a log10-scale! (Note: Bioconductor often uses a log2 scale, so don't compare the Agilent LogRatio directly with the Ratio (LogOdds) you will get while using for instance the limma package in R. I hope that this clarifies a bit. -- Stan -----Original Message----- From: bioconductor-bounces@stat.math.ethz.ch [mailto:bioconductor-bounces at stat.math.ethz.ch] On Behalf Of Steve Taylor Sent: 15 August 2007 10:03 To: Sean Davis Cc: Bioconductor Subject: Re: [BioC] Reading Agilent files using read.maimages Hi Sean, >>A typical header for one of these raw files is >> >>CompositeSequence Identifier Database ebi.ac.uk:Database:embl Database ebi.ac.uk:Database:ensembl Database ebi.ac.uk:Database:locus Database ebi.ac.uk:Database:refseq Database >>ebi.ac.uk:Database:tigr_thc Database www.chem.agilent.com:Database:agc Database www.chem.agilent.com:Database:agp Feature coordinates: metaColumn metaRow column row Reporter control >>type Reporter group Reporter identifier Reporter name Reporter sequence type H_C1 A_mock A and B/FEATURES H_C1 A_mock A and B/FeatureNum H_C1 A_mock A and B/gbpri H_C1 A_mock A and >>B/gp H_C1 A_mock A and B/sp H_C1 A_mock A and B/ProbeUID H_C1 A_mock A and B/ControlType H_C1 A_mock A and B/ProbeName H_C1 A_mock A and B/GeneName H_C1 A_mock A and B/SystematicName >>H_C1 A_mock A and B/Description H_C1 A_mock A and B/LogRatio H_C1 A_mock A and B/LogRatioError H_C1 A_mock A and B/PValueLogRatio H_C1 A_mock A and B/gSurrogateUsed H_C1 A_mock A >>and B/rSurrogateUsed H_C1 A_mock A and B/gIsFound H_C1 A_mock A and B/rIsFound H_C1 A_mock A and B/gProcessedSignal H_C1 A_mock A and B/rProcessedSignal H_C1 A_mock A and >>B/gProcessedSigError H_C1 A_mock A and B/rProcessedSigError H_C1 A_mock A and B/gNumPixOLHi H_C1 A_mock A and B/rNumPixOLHi H_C1 A_mock A and B/gNumPixOLLo H_C1 A_mock A and B/rNumPixOLLo H_C1 >>A_mock A and B/gNumPix H_C1 A_mock A and B/rNumPix H_C1 A_mock A and B/gMeanSignal H_C1 A_mock A and B/rMeanSignal H_C1 A_mock A and B/gMedianSignal H_C1 A_mock A and B/rMedianSignal >> H_C1 A_mock A and B/gPixSDev H_C1 A_mock A and B/rPixSDev H_C1 A_mock A and B/gBGNumPix H_C1 A_mock A and B/rBGNumPix H_C1 A_mock A and B/gBGMeanSignal H_C1 A_mock A and >>B/rBGMeanSignal H_C1 A_mock A and B/gBGMedianSignal H_C1 A_mock A and B/rBGMedianSignal H_C1 A_mock A and B/gBGPixSDev H_C1 A_mock A and B/rBGPixSDev H_C1 A_mock A and B/gNumSatPix >>H_C1 A_mock A and B/rNumSatPix H_C1 A_mock A and B/gIsSaturated H_C1 A_mock A and B/rIsSaturated H_C1 A_mock A and B/PixCorrelation H_C1 A_mock A and B/BGPixCorrelation H_C1 >>A_mock A and B/gIsFeatNonUnifOL H_C1 A_mock A and B/rIsFeatNonUnifOL H_C1 A_mock A and B/gIsBGNonUnifOL H_C1 A_mock A and B/rIsBGNonUnifOL H_C1 A_mock A and B/gIsFeatPopnOL H_C1 >>A_mock A and B/rIsFeatPopnOL H_C1 A_mock A and B/gIsBGPopnOL H_C1 A_mock A and B/rIsBGPopnOL H_C1 A_mock A and B/IsManualFlag H_C1 A_mock A and B/gBGSubSignal H_C1 A_mock A and >>B/rBGSubSignal H_C1 A_mock A and B/gBGSubSigError H_C1 A_mock A and B/rBGSubSigError H_C1 A_mock A and B/BGSubSigCorrelation H_C1 A_mock A and B/gIsPosAndSignif H_C1 A_mock A and >>B/rIsPosAndSignif H_C1 A_mock A and B/gPValFeatEqBG H_C1 A_mock A and B/rPValFeatEqBG H_C1 A_mock A and B/gNumBGUsed H_C1 A_mock A and B/rNumBGUsed H_C1 A_mock A and B/gIsWellAboveBG >> H_C1 A_mock A and B/rIsWellAboveBG H_C1 A_mock A and B/IsUsedBGAdjust H_C1 A_mock A and B/gBGUsed H_C1 A_mock A and B/rBGUsed H_C1 A_mock A and B/gBGSDUsed H_C1 A_mock A and >>B/rBGSDUsed H_C1 A_mock A and B/IsNormalization H_C1 A_mock A and B/gDyeNormSignal H_C1 A_mock A and B/rDyeNormSignal H_C1 A_mock A and B/gDyeNormError H_C1 A_mock A and >>B/rDyeNormError H_C1 A_mock A and B/DyeNormCorrelation H_C1 A_mock A and B/ErrorModel > > > This is not an Agilent Raw Data file, I do not think. The column names > are similar, but ArrayExpress has significantly changed the file from > its original format. That said, the columns with "LogRatio", > "rProcessedSignal" and "gProcessedSignal" are the columns of interest > that have already been background corrected and, typically, a > normalization method applied (not sure which one without some more > description of the scanner settings). > > >Ok. Thanks. That's useful information. In the protocols section of AE it says 'Default settings' >(http://www.ebi.ac.uk/aerep/details?class=MAGE.Experiment_protocols&c ri teria=Experiment%>3D921408317&contextClass=MAGE.Protocol&templateName= Pr otocol.vm). If that means it has been normalised I will >have a look at LogRatio, rProcessedSignal and gProcessedSignal, though it would be nice to know how it had been processed... > > >>Does this look correct? How do I get access to the intensities, for example to do a boxplot? > > > I'm not sure if the files loaded correctly, given my comments above. > RG$R and RG$G contain the Red and Green intensities, if it loaded correctly. > That's what I thought. Thanks for the advice, Steve _______________________________________________ Bioconductor mailing list Bioconductor at stat.math.ethz.ch https://stat.ethz.ch/mailman/listinfo/bioconductor Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor

Hi, I removed the prefixes from the file headers and they eventually read in. I did get: Read ./E-MEXP-668-raw-data-921408043.txt Read ./E-MEXP-668-raw-data-921408048.txt Read ./E-MEXP-668-raw-data-921408053.txt Read ./E-MEXP-668-raw-data-921408058.txt Error in RG[[a]][, i] <- obj[, columns[[a]]] : number of items to replace is not a multiple of replacement length which I think is due to inconsistent numbers of columns in some files (and read.maimages doesn't seem to cope with), but I managed to get round this by reording the targets file. I have got the data to normalise now. Thank you all very much for helping me on this problem. Steve > I have download the files and it seems that each file has its own > header. By this I mean that a prefix has been add to each standard column. > For example, for file "E-MEXP-668-raw-data-921408043.txt" every column > start with "H_C1 A_mock A and B/" whereas in file > "E-MEXP-668-raw-data-921408128.txt" it is "H_C52 B_mock A and B/". In > order to make the read.maimages work you should remove those prefixes. I > have changed it for a couple of slides and it worked for me. > > targets <-read.("AE.csv", path=".",sep=",") > RG <- read.maimages(targets$FileName[c(1,16)]),path=".",source="agilent") > Read ./E-MEXP-668-raw-data-921408043.txt > Read ./E-MEXP-668-raw-data-921408118.txt > >>str(RG$G) > > num [1:22153, 1:2] 960.4 73.1 83.3 648.1 132.9 ... > - attr(*, "dimnames")=List of 2 > ..$ : NULL > ..$ : chr [1:2] "E-MEXP-668-raw-data-921408043" > "E-MEXP-668-raw-data-921408118" > > Hope it helps, > Nolwenn > > Scott Reagan Franklin wrote: > >>Steve: >> >>It should be noted that when you are using read.maimages with >>source="agilent" and you are using the actual Agilent Raw Data file, >>you will, by default, have RG$R and RG$G equal to the rMeanSignal and >>gMeanSignal. In order to specify that they be equal to the >>rProcessedSignal and gProcessedSignal you need to add an option to >>read.maimages: >> >>RG<-read.maimages(targets$FileName,path=".",columns=list(R="rProcess edSignal",G="gProcessedSignal)); >> >>You can also specify which background signal is used as well by >>including in Rb and Gb in the list above. By default Rb and Gb are >>chosen as the median background signals. >> >> >> >