Check also this webpage from Ben Bolstad, http://plmimagegallery.bmbolstad.com/ hth David > > On 9/20/07, Yogi Sundaravadanam <yogi.sundaravadanam at="" agrf.org.au=""> wrote: > > Thanks for the reply... let me rephrase that a bit. I would like to know as to how I should interpret the image. > > > > Cheers > > Yogi > > > > -----Original Message----- > > From: smohapat at vbi.vt.edu [mailto:smohapat at vbi.vt.edu] > > Sent: Friday, 21 September 2007 10:46 AM > > To: Yogi Sundaravadanam > > Cc: bioconductor > > Subject: Re: [BioC] Cel image > > > > Hi Yogi: > > > > > How informative can these images be? In other words, just be looking at > > > the images, would I be able to pick out chips that aren't OK? > > > > > > > I usually run some other checks with simpleaffy and affyPLM before > > considering a chip of poor quality. > > > > -- Saroj > > > > _______________________________________________ > > 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 > > > > _______________________________________________ > 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 Yogi, As mention Henrik there is different way to look at the chip image. Here is a script I wrote to do all the test I need to decide to reject or not a chip, in one step. The script is based on what I learned in this book: Gentleman R, Carey V, Huber W et al. Bioinformatics and Computational Biology Solution Using R and Bioconductor. Berlin, Heidelberg, New York: Springer, 2005. --- # R script for automatic quality control # argument: an affybatch qa <- function(abatch) { require(affy) require(simpleaffy) require(RColorBrewer) require(affyPLM) if (class(abatch)!= 'AffyBatch') { stop("argument must be AffyBatch!") } # colors cols <- brewer.pal(12, "Set3") # Boxplot pdf(file='boxplot.pdf', height=8, width=8) boxplot(abatch, col=cols, main="Unprocessed log scale probe- level data", xlab="If discrepancy, they are not conclusive\n Difference can be reduce by normalization") dev.off() # Histogram pdf(file='histogram.pdf', height=8, width=8) hist(abatch, col=cols, xlab="Log(base2) intensities; Bimodal distribution indicate spatial artifact\n Second mode is the result of array(s) having abnormally high value") legend("topright", sampleNames(abatch), lty=1,col=cols) dev.off() #RNA degradation pdf(file="RNAdeg.pdf", height=8, width=8) RNAdeg <- AffyRNAdeg(abatch) plotAffyRNAdeg(RNAdeg, cols=cols) legend("topleft", sampleNames(abatch), lty=1,col=cols) box() dev.off() # simpleaffy graph abatch.qc <- qc(abatch) pdf(file="QC-simpleaffy.pdf", height=8, width=10) plot(abatch.qc) dev.off() # affyPLM pset <- fitPLM(abatch) # false color image control for (n in 1:length(abatch)) { filename <- paste("QC",as.vector(sampleNames(abatch))[n],".png") png(file=filename, height=900, width=800) img.Test(abatch,pset,n) dev.off() } # RLE plot pdf(file="RLE.pdf", height=8, width= 8) Mbox(pset, col = cols, main ="RLE (Relative Log Expression)", xlab="Assuming that the majority of the gene are not changing\n Ideally these boxes would have small spread and be centered at M=0") dev.off() # NUSE plot pdf(file="NUSE.pdf", height=8, width= 8) boxplot(pset, col=cols, main= "NUSE (Normalized Unscaled Standard Error", xlab="High values of median NUSE are indicative of a problematic array") dev.off() } img.Test <- function(batch,pset,x) { par(mfrow = c(2,2)) affy::image(batch[,x]) affy::image(pset, type = "weights", which = x) affy::image(pset, type = "resids", which = x) affy::image(pset, type = "sign.resids", which = x) } --- Presently there is a package call Harshlight that can be use to remove the faulty zone of the array. Suarez-Farinas M, Pellegrino M, Wittkowski K et al. Harshlight: a "corrective make-up" program for microarray chips, BMC Bioinformatics 2005;6:294. By the way if other people have additional test or improvement to suggest I am open to suggestion. Best, David --- David Ruau Institute for Biomedical Engineering RWTH Aachen On Sep 21, 2007, at 5:04 AM, Henrik Bengtsson wrote: > Hi, > > it all depends. There are at least two ways to generate spatial plots > of an microarray. The first one is to plot the probe intensities, > which you get directly from the CEL file(s). Here you want to pick > some color transform (e.g. sqrt or log) and color map. This might > give some clues/hints on extreme spatial artifacts. If you fit > probe-level models to the units ("probesets"), you can plot the PLM > residuals as well, because that will better show you the spatial > artifacts compare with looking at the probe intensities. > > ( BTW, be careful when you look at spatial intensity plots on the > screen, especially when you zoom in and out, because you will most > likely get rastering effects that are due to the display and that are > not in the array. Some illustrations I found by a quick search: > http://pixelmapping.wikispaces.com/Pixel+mapping+explained ) > > Depending on what chip type and type of analysis you are looking > at/using, a spatial artifact has more or less severe impact on the end > result. For instance, if you run standard gene expression arrays > where each unit has multiple probes, a spatial artifact is less severe > than if you run, say tiling arrays or single-probe copy-number arrays. > In the former case, two things typically save/help you: i) the fact > that Affymetrix randomized the position of the probes such that > spatial artifacts are likely only to affect one or two probes in a > unit, and ii) robust fitting of probe-level models (PLM, e.g. > log-additive modelling via via affyPLM). > However, in the latter case with single-probe units, neither will help > you. Maybe some downstream algorithm has some robustification to it, > that you have to check out. > > So, if you are trying to identify poor hybridizations from spatial > images and decide which ones to filter out, your filtering criteria > really depend on the chip type and what it is going to be used for. > > Cheers > > Henrik > > > On 9/20/07, Yogi Sundaravadanam <yogi.sundaravadanam at="" agrf.org.au=""> > wrote: >> Thanks for the reply... let me rephrase that a bit. I would like >> to know as to how I should interpret the image. >> >> Cheers >> Yogi >> >> -----Original Message----- >> From: smohapat at vbi.vt.edu [mailto:smohapat at vbi.vt.edu] >> Sent: Friday, 21 September 2007 10:46 AM >> To: Yogi Sundaravadanam >> Cc: bioconductor >> Subject: Re: [BioC] Cel image >> >> Hi Yogi: >> >>> How informative can these images be? In other words, just be >>> looking at >>> the images, would I be able to pick out chips that aren't OK? >>> >> >> I usually run some other checks with simpleaffy and affyPLM before >> considering a chip of poor quality. >> >> -- Saroj >> >> _______________________________________________ >> 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 >> > > _______________________________________________ > 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