arrayQualityMetrics: expression or probe-level data?
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@efthimios-motakis-4986
Last seen 7.1 years ago
Hello all, I have a question regarding "arrayQualityMetrics" and I could not find relevant information at the posted Q/A. I have a dataset of 80 U133A Affymetrix .cel files coming from an ovarian cancer study (patients' samples). When I run "arrayQualityMetrics" to the background corrected (RMA) and normalized (quantiles) probe-level data (thus processing the non-summarized AffyBatch object) I get a PCA showing two distinct groups (not a random separation; the first 40 cells belong to one group and the other 40 to the other). I also get 2 outliers (based on 5 out of the 6 different plots provided). When I run "arrayQualityMetrics" to the RMA or gcRMA expression data the two groups effects on PCA and the outliers disappear completely. Can summarization have such a strong influence to my data? Should I rely on the .cel files or the expression results for my analysis Thank you, Makis
Cancer gcrma Cancer gcrma • 615 views
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@wolfgang-huber-3550
Last seen 11 weeks ago
EMBL European Molecular Biology Laborat…
Dear Efthimios On 12/2/11 5:29 AM, Efthimios MOTAKIS wrote: > Hello all, > > I have a question regarding "arrayQualityMetrics" and I could not find > relevant information at the posted Q/A. > > I have a dataset of 80 U133A Affymetrix .cel files coming from an > ovarian cancer study (patients' samples). When I run > "arrayQualityMetrics" to the background corrected (RMA) and normalized > (quantiles) probe-level data (thus processing the non-summarized > AffyBatch object) I get a PCA showing two distinct groups (not a random > separation; the first 40 cells belong to one group and the other 40 to > the other). I also get 2 outliers (based on 5 out of the 6 different > plots provided). > > When I run "arrayQualityMetrics" to the RMA or gcRMA expression data the > two groups effects on PCA and the outliers disappear completely. > > Can summarization have such a strong influence to my data? Are your non-summarized logarithm transformed? If not, then it is plausible that the PCA is driven by the behaviour of a few genes with large values, and that that effect goes away when you logarithm transform. > Should I rely > on the .cel files or the expression results for my analysis Rely on whatever you use subsequently for your biological analysis, thus presumably the expression values. Best wishes Wolfgang > > Thank you, > Makis > > _______________________________________________ > Bioconductor mailing list > Bioconductor at r-project.org > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: > http://news.gmane.org/gmane.science.biology.informatics.conductor -- Best wishes Wolfgang Wolfgang Huber EMBL http://www.embl.de/research/units/genome_biology/huber
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