when using affy arrays several normalization tools are available, but quality analysis tools are limited,.....especially compared to cdna arrays does anyone have some code to do some quality analysis next to the packages affy and simpleaffy? best regards, marinus TNO-Quality of Life Dept. Physiological Sciences Marinus C. Dansen Bioinformatician dansen@voeding.tno.nl +31 (0)306944610 This e-mail and its contents are subject to the DISCLAIMER at http://www.tno.nl/disclaimer/email.html [[alternative HTML version deleted]]

Hi Marinus - The procedure we follow when doing QC on Affymetrix arrays is the following with comments (#): # Loads the affy library, at the same time we set the working directory > library(affy) # Load the data from the working directory > AffyData = ReadAffy() # Load the Probe Level Model library to look for problematic areas on the chip > library(affyPLM) # Run and fit the Probe Level Model on the data > Pset.AffyData = fitPLM(junhao) # Set the image export to a PDF file or you can use png() to have individual images exported > pdf() # Export the images of each Probe Level Model chip to see where the problem areas (patches, scratches, dust spots, etc.) are so that they can be masked out with GCOS > image(Pset.AffyData) # Make sure all the image files have been written out > dev.off() # Run the RNA degradation function on each chip to see if there is consistent degradation of the RNA from 5' -> 3' or if there is aberrant degradation to know which chips to ignore from the analysis. > AffyData.deg = AffyRNAdeg(AffyData) # Run the plot function for the RNA degradation to see visually how the degradation is from 5' -> 3' across the chips > plotAffyRNAdeg(AffyData.deg) # Set the image to exported out as a png file > png() # Export out the image to a png format > plotAffyRNAdeg(AffyData.deg) # Run the histogram function on the original loaded data to see if there are any problem of saturation on the high end and if there the distribution of intensities is not too tight or too much shifted to the low end. > hist(AffyData) # Run the boxplot to see if the distribution of the chip as a comparison between the chips in order to identify chip which might skew the normalization. > boxplot(AffyData) # Finishes the saving of images to chips > dev.off() # Prints out the chip/sample name with its corresponding number/order that is was loaded. This is useful for identifying the slope and p-value in the details of the RNA degradation results which will be printed afterwards. > pData(AffyData) # Prints out the slope and p-value (and a few other statistics) regarding the RNA degradation analysis for each chip/sample. These come in the order they were loaded which is listed by the above command. > AffyData.deg # This sets up the output to be printed to a file > sink(file="pdata_deg.txt") # This can also be implemented by print(pData(AffyData)) > pData(AffyData) # This can also be implemented by print(AffyData.deg) > AffyData.deg # Finishes writing the file out and will continue printing outputs to the console. > sink() Just as a mention we've implemented the above procedure to be automatically processed via an easy drag-and-drop visual analysis pipeline for any Harvard researchers and users. Below is a link of a screenshot: http://www.people.fas.harvard.edu/~grosu/images/r_qc.jpg Hope it helps, Paul -----Original Message----- From: Dansen, Ing. M.C. [mailto:DANSEN@voeding.tno.nl] Sent: Friday, April 15, 2005 8:06 AM To: bioconductor@stat.math.ethz.ch Subject: [BioC] affy quality analysis when using affy arrays several normalization tools are available, but quality analysis tools are limited,.....especially compared to cdna arrays does anyone have some code to do some quality analysis next to the packages affy and simpleaffy? best regards, marinus TNO-Quality of Life Dept. Physiological Sciences Marinus C. Dansen Bioinformatician dansen@voeding.tno.nl +31 (0)306944610 This e-mail and its contents are subject to the DISCLAIMER at http://www.tno.nl/disclaimer/email.html [[alternative HTML version deleted]] _______________________________________________ Bioconductor mailing list Bioconductor@stat.math.ethz.ch https://stat.ethz.ch/mailman/listinfo/bioconductor