Hi, I have a question about quality assessment of microarray chips. Can somebody suggest me a quantitative metric to evaluate the chips and in particular the plot about the quality of the chip (for example boxplot of intensity)? Thank you Simona [[alternative HTML version deleted]]

On Tue, Jul 15, 2008 at 1:35 PM, simona dalle carbonare <simona.dallecarbonare at="" gmail.com=""> wrote: > Hi, > I have a question about quality assessment of microarray chips. Can somebody > suggest me a quantitative metric to evaluate the chips and in particular the > plot about the quality of the chip (for example boxplot of intensity)? Hello, Simona. You could begin by looking at the packages on the QualityControl list here: http://bioconductor.org/packages/release/QualityControl.html If you have more specific questions, feel free to write back with more details. There is not a one-size-fits-all quality metric for arrays, though. Sean

On Jul 15, 2008, at 1:35 PM, simona dalle carbonare wrote: > Hi, > I have a question about quality assessment of microarray chips. Can > somebody > suggest me a quantitative metric to evaluate the chips and in > particular the > plot about the quality of the chip (for example boxplot of intensity)? "Quality control through Data Exploration" section of the `affy` vignette in the `affy` library: http://bioconductor.org/packages/2.2/bioc/vignettes/affy/inst/doc/affy .pdf and the `QualityAsses` vignette in the `affyPLM` library: http://bioconductor.org/packages/2.2/bioc/vignettes/affyPLM/inst/doc/Q ualityAssess.pdf address QA issues a bit and include several visualizations. Is that what you had in mind? -steve -- Steve Lianoglou Graduate Student: Physiology, Biophysics and Systems Biology Weill Cornell Medical College http://cbio.mskcc.org/~lianos

Affymetrix provide several different metrics which can be utilised to see if your arrays are of good enough quality. However, the actual cut-off used is always subjective and can only be used as a guide. Generally speaking, you spend quite a good proportion of your microarray analysis doing "Quality control through Data Exploration". As such, it's quite a subjective thing, and you need to produce and explore lots of different graphs etc in order to get a good picture about the quality of your arrays. Again, in general, you shouldn't base your opinion about the quality of an array on a single metric, but use several to inform you about the quality. Some specifics about the plots which I use routinely... 1) I use affyPLM to plot pseudo-images of the arrays with the "weights". This will help you to visualise if any arrays are odd-ones out and have poor hybridisation due to bubbles on the chip etc. See http://plmimagegallery.bmbolstad.com/ for examples of really bad chips. 2) I use the "border elements plot" of the AffyQCReport (or a version I've altered) - again, helps to visualise how consistent hybridisation is around the edges of the arrays 3) The RNA degradation plot AffyRNAdeg() from the affy package 4) The Affymetrix quality control plot from qc() of the simpleaffy package 5) The spike-in control probes table produced by spikeInProbes() from the simpleaffy package 6) An Eisen plot produced by the made4 package. 7) A PCA plot produced by plotPCA() and a scree plot from the affycoretools package 8) A NUSE plot and RLE plot produced by the affyPLM package 9) A MAD plot produced by affyQAReport() of the affyQCReport package 10) A plot of the SD against the ranked mean intensity of probes using meanSdPlot() from the vsn package 11) A density plot of the PM probes using plotDensity.AffyBatch() of the affy package 12) Boxplots of the PM probes using boxplot() I do all the above for raw data and then I do the normalisation and repeat plots 2, 6, 7, 9, 11 and 12. Then I calculate the gene expression summaries and use limma to get differentially expressed genes. I use heatplot() from the made4 package to create heat plots of the Differentially expressed genes. So you can see I do a lot of diagnostic/QC/QA plots to explore the data and to help inform me as to whether any of the arrays should be thrown out. Be careful not to throw out data just because it doesn't sit well with your expectations, you need to be able to justify why any array is discarded, and simply saying that it's an outlier in just one metric is not usually good enough. Here's a useful link: http://bioconductor.org/packages/2.2/bioc/vignettes/simpleaffy/inst/do c/ QCandSimpleaffy.pdf Hope this helps, Nathan -----Original Message----- From: bioconductor-bounces@stat.math.ethz.ch [mailto:bioconductor-bounces at stat.math.ethz.ch] On Behalf Of simona dalle carbonare Sent: Wednesday, 16 July 2008 3:35 AM To: bioconductor mailing list Subject: [BioC] How to objectively evaluate chip quality? Hi, I have a question about quality assessment of microarray chips. Can somebody suggest me a quantitative metric to evaluate the chips and in particular the plot about the quality of the chip (for example boxplot of intensity)? Thank you Simona [[alternative HTML version deleted]] _______________________________________________ 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 Mark, Unfortunately, I have no experience of the Exon ST arrays, so can't give any specific help...sorry. Nathan -----Original Message----- From: Mark Cowley [mailto:m.cowley0@gmail.com] Sent: Monday, 21 July 2008 8:44 AM To: Watson-Haigh, Nathan (LI, Rock. Rendel) Cc: bioconductor list Subject: Re: [BioC] How to objectively evaluate chip quality? Thanks for your post Nathan, I was hoping you could comment on the applicability of these QC metrics for the new Gene/Exon ST arrays? In my experience, a number of the 3' IVT QC metrics can no longer can be calculated, at least not 'out of the box'. cheers, Mark ----------------------------------------------------- Mark Cowley, BSc (Bioinformatics)(Hons) Peter Wills Bioinformatics Centre Garvan Institute of Medical Research, Sydney, Australia ----------------------------------------------------- On 16/07/2008, at 9:20 AM, <nathan.watson-haigh at="" csiro.au=""> <nathan.watson-haigh at="" csiro.au=""> wrote: > Affymetrix provide several different metrics which can be utilised to > see if your arrays are of good enough quality. However, the actual > cut-off used is always subjective and can only be used as a guide. > Generally speaking, you spend quite a good proportion of your > microarray > analysis doing "Quality control through Data Exploration". As such, > it's > quite a subjective thing, and you need to produce and explore lots of > different graphs etc in order to get a good picture about the > quality of > your arrays. Again, in general, you shouldn't base your opinion about > the quality of an array on a single metric, but use several to inform > you about the quality. > > Some specifics about the plots which I use routinely... > > 1) I use affyPLM to plot pseudo-images of the arrays with the > "weights". > This will help you to visualise if any arrays are odd-ones out and > have > poor hybridisation due to bubbles on the chip etc. See > http://plmimagegallery.bmbolstad.com/ for examples of really bad > chips. > 2) I use the "border elements plot" of the AffyQCReport (or a version > I've altered) - again, helps to visualise how consistent hybridisation > is around the edges of the arrays > 3) The RNA degradation plot AffyRNAdeg() from the affy package > 4) The Affymetrix quality control plot from qc() of the simpleaffy > package > 5) The spike-in control probes table produced by spikeInProbes() from > the simpleaffy package > 6) An Eisen plot produced by the made4 package. > 7) A PCA plot produced by plotPCA() and a scree plot from the > affycoretools package > 8) A NUSE plot and RLE plot produced by the affyPLM package > 9) A MAD plot produced by affyQAReport() of the affyQCReport package > 10) A plot of the SD against the ranked mean intensity of probes using > meanSdPlot() from the vsn package > 11) A density plot of the PM probes using plotDensity.AffyBatch() of > the > affy package > 12) Boxplots of the PM probes using boxplot() > > I do all the above for raw data and then I do the normalisation and > repeat plots 2, 6, 7, 9, 11 and 12. Then I calculate the gene > expression > summaries and use limma to get differentially expressed genes. I use > heatplot() from the made4 package to create heat plots of the > Differentially expressed genes. > > So you can see I do a lot of diagnostic/QC/QA plots to explore the > data > and to help inform me as to whether any of the arrays should be thrown > out. Be careful not to throw out data just because it doesn't sit well > with your expectations, you need to be able to justify why any array > is > discarded, and simply saying that it's an outlier in just one metric > is > not usually good enough. > > Here's a useful link: > http://bioconductor.org/packages/2.2/bioc/vignettes/simpleaffy/inst/do c/ > QCandSimpleaffy.pdf > > Hope this helps, > Nathan > > > -----Original Message----- > From: bioconductor-bounces at stat.math.ethz.ch > [mailto:bioconductor-bounces at stat.math.ethz.ch] On Behalf Of simona > dalle carbonare > Sent: Wednesday, 16 July 2008 3:35 AM > To: bioconductor mailing list > Subject: [BioC] How to objectively evaluate chip quality? > > Hi, > I have a question about quality assessment of microarray chips. Can > somebody > suggest me a quantitative metric to evaluate the chips and in > particular > the > plot about the quality of the chip (for example boxplot of intensity)? > Thank you > Simona > > [[alternative HTML version deleted]] > > _______________________________________________ > 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