Cel image
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@yogi-sundaravadanam-2312
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@saroj-mohapatra-1446
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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
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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@vbi.vt.edu [mailto:smohapat@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
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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 >
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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 >
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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
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