Hi, I am using arrayQualityMetrics and have a few questions. 1) When creating the NChannelSet I am using Bluefuse data (the AMPCH1 and AMPCH2 columns) which do not have background readings, so I am reading it in like this assayData = with(RG, assayDataNew(R=R, G=G)) This seems to be ok but I was wondering if this has any knock on effects I should be aware of, either with processing or report generation? 2)RNA Integrity Number (RIN) This seems to be required for phenoData, which is required to create the NChannelSet. If I don't have these values what should I do? 3)I like the fact there are descriptions of different QC methods in the report. What would also be helpful is if there were some example reports online to compare what good/bad reports look like. Are there any available? Thanks for any help, Steve ------------------------------------------------------------------ Weatherall Institute of Molecular Medicine/Sir William Dunn School Oxford University

Hi Steve, 1) The absence of background intensity will not have any hidden effect on the report. The only plot of the report that would make use of the background intensity is the spatial distribution, to see spatial effects. If you have R, G, Rb and Gb, each of them will be represented (4 spatial plots per array) and if only R and G are available, only 2 spatial plots will be represented per array. There is no processing within the arrayQualityMetrics function. If you want to subtract background or normalise your data, you need to do it before (using limma for instance). 2) The RIN column is not needed in the phenoData, it was just an example. Another example would be, if you have a factor of interest, like treatment/control you can define a column in your phenoData with this information and then, when you call arrayQualityMetrics you can set the argument intgroup (which stands for interesting group) equal to the name of the column containing the treatment/control information. This will draw a colour side bar to your heatmap. 3) There are some examples here: http://www.microarray-quality.org/quality_metrics.html But they are not especially good/bad reports. It is a good suggestion, I will try to find some to share online. Audrey -- Audrey Kauffmann EMBL - EBI Cambridge UK http://www.ebi.ac.uk/~audrey Steve Taylor wrote: > Hi, > > I am using arrayQualityMetrics and have a few questions. > > 1) When creating the NChannelSet I am using Bluefuse data (the AMPCH1 > and AMPCH2 columns) which do not have background readings, so I am > reading it in like this > > assayData = with(RG, assayDataNew(R=R, G=G)) > > This seems to be ok but I was wondering if this has any knock on > effects I should be aware of, either with processing or report > generation? > > 2)RNA Integrity Number (RIN) > This seems to be required for phenoData, which is required to create > the NChannelSet. If I don't have these values what should I do? > > 3)I like the fact there are descriptions of different QC methods in > the report. What would also be helpful is if there were some example > reports online to compare what good/bad reports look like. Are there > any available? > > Thanks for any help, > > Steve > ------------------------------------------------------------------ > Weatherall Institute of Molecular Medicine/Sir William Dunn School > Oxford University > > _______________________________________________ > 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 -- Audrey Kauffmann EMBL - EBI Cambridge UK http://www.ebi.ac.uk/~audrey

Hi Steve, If we consider an NChannelSet named obj and the pData of obj look like this: Treatment Cy3 Cy5 Replicate Array1 U WT Ref 1 Array2 S WT Ref 2 Array3 U MU Ref 1 Array4 S MU Ref 2 Array5 S MU Ref 1 If in your quality report you are interested in seeing the heatmap with a side colour bar representing the treatment, the right way to call the function is: arrayQualityMetrics(obj, intgroup = "Treatment") If you want to see the heatmap with the colour bar representing the replicates, you need to use: arrayQualityMetrics(obj, intgroup = "Replicate") The default column name of the phenoData that would be used for the colour side bar is "Covariate", but you can use any column name of your phenoData. I do not know what is in your p object but you can use any of the column name of p that makes sense for you to be represented next to the heatmap. This is completely optionnal and you can perfectly run the arrayQualityMetrics function without setting an group of interest. I hope that helps, Audrey > Hi Audrey, > > Thanks for your reply. > >> >> 1) The absence of background intensity will not have any hidden effect >> on >> the report. The only plot of the report that would make use of the >> background intensity is the spatial distribution, to see spatial >> effects. >> If you have R, G, Rb and Gb, each of them will be represented (4 spatial >> plots per array) and if only R and G are available, only 2 spatial plots >> will be represented per array. There is no processing within the >> arrayQualityMetrics function. If you want to subtract background or >> normalise your data, you need to do it before (using limma for >> instance). >> > > ok. > >> 2) The RIN column is not needed in the phenoData, it was just an >> example. >> Another example would be, if you have a factor of interest, like >> treatment/control you can define a column in your phenoData with this >> information and then, when you call arrayQualityMetrics you can set the >> argument intgroup (which stands for interesting group) equal to the name >> of the column containing the treatment/control information. This will >> draw >> a colour side bar to your heatmap. > > > I am currently doing > > > p=read.AnnotatedDataFrame('sinfo.txt') > > varMetadata(p)$channel=factor(c("G", "R", "G", > "R"),levels=c(ls(assayData), "_ALL_")) > > Can you send me an example of what you mean? > > Many thanks, > > Steve >