[BioC] QA of two-color array data

Naomi Altman naomi at stat.psu.edu
Tue Oct 27 16:38:34 CET 2009

The weird spots are probably the Agilent quality control 
spots.  Remove them and redo the plot.


At 05:53 AM 10/27/2009, Robert Castelo wrote:
>dear list,
>i have very limited experience in the QA of microarray data and i'd like
>to know the opinion from people with more experience with this job if
>there are issues with the QA of the data i'm analizing, and if could
>pre-process these data differently in order to try to correct for the
>possible QA problems.
>i'm re-analizing a series of 12 two-color microarray experiments
>deposited in GEO (acc. GSE13943). these are custom 4x44K Agilent arrays
>with probes targeting exons and splice junctions in Drosophila
>Melanogaster. the experiments correspond to RNAi knock-downs of 4
>RNA-binding proteins -hrp36, hrp38, hrp40 and hrp48- (red channel)
>against a non-specific RNAi control (green channel) in three independent
>replicates for each KO experiment.
>after reading the raw data files into an RGlist object called 'RG' i've
>performed background correction, within- and between-normalization as
>RGneMLE <- backgroundCorrect(RG, method="normexp", normexp.method="mle",
>MA <- normalizeWithinArrays(RGneMLE[RGneMLE$genes$ControlType!=-1,],
>                             method="loess", bc.method="none")
>MA <- normalizeBetweenArrays(MA, method="scale")
>i have produced the corresponding MA-plots of the latter pre-processed
>MA data object for each of the 12 arrays which i've put on the web so
>that you can take a look at them:
>when i look to these plots i see the following two unexpected features:
>-in the replicates of hrp36, replicate 1 of hrp38, replicate 1 of hrp40
>and replicate 2 of hrp48 there are some small intensity dependent biases
>affecting to the low average values A.
>-through all replicates i see two clusters of probes with low M values
>(i.e., higher green signal).
>if i look to the image plots (generated with 'imageplot3by2(RG)'):
>i see some line crossing from the top to the bottom, but i don't know if
>this is related to the issues raised before.
>i've run the array quality metrics package thorugh these data with the
>following command:
>arrayQualityMetrics(expressionset=RG, outdir="aqm", force=TRUE)
>and put the output here:
>according the this report there are no outlier arrays and so i'm
>wondering whether maybe in fact there are no QA problems and simply i'm
>not using the appropriate pre-processing algorithms for this kind of
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Naomi S. Altman                                814-865-3791 (voice)
Associate Professor
Dept. of Statistics                              814-863-7114 (fax)
Penn State University                         814-865-1348 (Statistics)
University Park, PA 16802-2111

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