[BioC] Low-level analysis of custom microarrays

Martin Morgan mtmorgan at fhcrc.org
Fri Jan 4 15:59:07 CET 2008

Hi Teresa --

"Teresa Colombo" <teresa.colombo at gmail.com> writes:

> Hi Dear BioC list,
> I am a newbie and apologize in advance if this is just a stupid
> question. But I've been trapped by this problem since 2 weeks now and
> dunno how to move on from here without a little help...
> my TASK: Perform background correction on (miRNA) microarray data from
> a custom chip, taking into account slide spatial info (no simple
> subtraction of background intensities).

Others on the list can speak more knowledgeably than me about these
things, so please take my input lightly.

I guess these are two-color Agilent arrays. The data you present below
is not from the 'gpr' files required to perform 'background
correction', but from some later point in the analysis that you must
determine (because the data has likely already had some kinds of data
transformation applied).

Background correction usually involves transformations of individual
spot foreground and background intensities, perhaps taking into
account some properties of all spots in the array but not usually
spatial location. Your data do not include foreground and background
information for each channel (probably some background correction
method has already been applied), so background correction cannot be

Spatial effects might typically be accommodated by within-array
normalization. These methods attempt to make the difference in
(background-corrected) channel intensities ('M' values)
statistically independent of the average intensities ('A' values) of
each spot. The usual methods implicitly incorporate spatial variation
(as a factor contributing to variation in 'A').

An important assumption is that expression of the majority of spots
does not differ between channels. This may not be the case for your
miRNA arrays. miRNAs also likely exhibit significant dye effects, and
these need to be accommodated.

A starting point for two-color analyses is the limma package and its
comprehensive user guide. limma would take you from gpr files through
background correction, normalization, and assessment of differential
expression. Though again miRNAs require special consideration.

Hope that helps.


> my INPUT DATA FORMAT: For each slide, a tab delimited text file
> carrying the following info:
> "Probe_ID"      "Row"   "Column"        "Density_mean_{A}"
> "Density_st.dev._ {A}"
> For example, the following are the first 5 lines for one of the slides:
> "empty" 1       1       174,2   8,57
> "hsa-let-7a"    1       2       49522,89        343,1
> "hsa-miR-150"   1       3       40738,46        677,54
> "hsa-miR-204"   1       4       209,61  15,48
> "hsa-miR-32"    1       5       223,07  15,24
> There are 7 replicates for each experimental probe + many internal
> control probes (row.names are not unique).
> Is there any R package/function available to perform background
> correction taking into account the slide design/spatial info (amenable
> to be used with this kind of raw input data - e.g., neither .CEL nor
> Illumina input data)?
> my R version - attached packages:
>> sessionInfo()
> R version 2.4.0 Patched (2006-11-25 r39997)
> i486-pc-linux-gnu
> locale:
> attached base packages:
> [1] "tools"     "stats"     "graphics"  "grDevices" "utils"     "datasets"
> [7] "methods"   "base"
> other attached packages:
>     affy   affyio  Biobase
> "1.12.2"  "1.2.0" "1.12.2"
> Thank you in advance for your help and time!
> Best,
> teresa
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Martin Morgan
Computational Biology / Fred Hutchinson Cancer Research Center
1100 Fairview Ave. N.
PO Box 19024 Seattle, WA 98109

Location: Arnold Building M2 B169
Phone: (206) 667-2793

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