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David Garfield
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30
@david-garfield-3316
Last seen 10.2 years ago
Hi all,
I am new to micro-array processing and to Bioconductor. The
documentation provided by the authors of packages like limma and
marray have made it very easy to get a toehold on how to pre-process,
normalize, and analyze microarrays.
The Agilent Feature Extraction Software Manuals, alas, are not as
helpful, and I would be very appreciative of some guidance on two
issues.
First, The Data: I have inherited an Agilent two-color microarray
dataset. This dataset consists of eight microarrays. To each array
was hybridized both a common reference sample and a tissue specific
sample. In 6 of the arrays, the reference is Cy3. In the other 2,
the reference is Cy5 labeled. Included in our probeset are a set of
Agilent designed spike-in and negative controls, along with ~600
probes we have designed that we hope act as negative controls for
this species (sea urchin).
The questions:
1) Loess normalization when many genes are expected to be
differentially expressed: The first pre-processing step I plan to
take is a within array global normalization using loess. However, I
am concerned that because the data consist of comparisons between
different tissues, even with a common reference, there may be a large
number of genes that really are differentially expressed. Can anyone
provide insight as to the limits of loess normalization in the face
of an expectation that many genes will be differentially regulated?
Can anyone suggest alternatives for array normalization based on our
experiment?
2) Spatial Detrending of the Background Signals: Agilent's Feature
Extraction manual discusses something called spatial de-trending.
The goal of the algorithm is to apply an offset to the spot
intensities that reflect spatial specific variation on the array.
Unlike other normalization modules I've seen in bioconductor, this
algorithm is applied only to the negative controls or low intensity
spots and is applied to each channel separately (though they still
refer to the process as using 2D loess normalization). I have not
seen similar steps references in the literature. Can anyone speak to
the importance of this step or bioconductor packages that facilitate
doing this. I'm not against programming my own, if this step is
needed, by why re-invent the wheel.
3) The best ways of incorporating our dye-swaps: The most commonly
referenced between array normalization strategy I have seen is
quantile-normalization. However, given our spoke-and-hub sort of
design (with dye-swaps), it seems that this method would lose some
potential information from the common reference and the dye-swaps.
Can one suggest a (ideally already implemented) normalization
strategy in this case?
I've asked many questions. If you could provide any insight into any
of them, that would be appreciated. Hopefully I will be able to
contribute information from this experience to the list at a future
date.
Cheers,
David
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