[BioC] Limma: background correction. Use or ignore?

[Naomi Altman]

I have investigated this (somewhat) experimentally.  Background 
correction increases the variability of low-expression genes and 
reduces it for high expression.  This corresponds to the RMA noise 
model since background correction would double the additive variance 
but not affect the multiplicative variance (which is the dominant 
source of variance for highly expressing genes.)

--Naomi


At 12:43 PM 3/31/2006, James W. MacDonald wrote:
>Hi Jose,
>
>J.delasHeras at ed.ac.uk wrote:
> > I have been using LimmaGUI for a while to analyse my cDNA microarrays.
> > I have always used "substract" as a method for background correction.
> > Why? Not sure. Intuitively it made sense, and I didn't observe any
> > obvious problems.
> > Once I played with the different methods for background correction
> > available in LimmaGUI, and when looking at the MA plots I decided I
> > preferred to substract.
> >
> > However, I have recently had problems with the statistics being quite
> > poor in my analises (see my post a week ago or so about low B
> > values)... and whilst checking the data, I noticed that at least in my
> > current experiments, if I do no background correction at all the stats
> > look a lot better, the MA plots look better, and everything looks
> > better in general. The actual list of genes doesn't change a lot, but
> > the values seem a lot tighter.
> >
> > This makes me question whether we should background correct at all. My
> > slides are pretty clean, low background. Am I not adding more noise to
> > the data by removing background?
>
>I have never been a big fan of subtracting background, especially if the
>background of the slide is low and relatively consistent. I have two
>main reasons for this.
>
>First, the portion of the slide used to estimate background doesn't have
>any cDNA bound, so you are estimating the background binding of the spot
>by using a portion of the slide that might not be very similar. When we
>were doing more spotted arrays, we would always spot unrelated cDNA on
>the slides as well (e.g., A.thaliana and salmon sperm DNA). These spots
>almost always had a negative intensity if you subtracted the local
>background, which indicates to me that cDNA does a better job of
>blocking the slide than BSA or other blocking agents.
>
>Second, you *are* adding more noise to the data. When you subtract, the
>variances are additive. However, if you don't subtract then you take the
>chance that you are biasing your expression values, especially if the
>background from chip to chip isn't relatively consistent. So the
>tradeoff is higher variance vs possible bias. If the background was
>consistent I usually took a chance on the bias in order to reduce the
>variance. As you note, the data usually look 'cleaner' if you don't
>adjust the background.
>
>Note that these points are directed towards simple subtraction of a
>local background estimate. Other more sophisticated methods may help
>address these shortcomings.
>
>As for references, have you looked at the references that Gordon gives
>on the man page for backgroundCorrect()? That would probably be a good
>place to start.
>
>Best,
>
>Jim
>
>
> >
> > Can anybody point me to a good reference to learn about the effects of
> > background correction, pros and cons? I'm just a molecular biologist,
> > not a statistician, but I need to understand a bit better these issues
> > or there'll be no molecular biology to work on from my experiments!
> >
> > Jose
> >
> >
>
>
>--
>James W. MacDonald, M.S.
>Biostatistician
>Affymetrix and cDNA Microarray Core
>University of Michigan Cancer Center
>1500 E. Medical Center Drive
>7410 CCGC
>Ann Arbor MI 48109
>734-647-5623
>
>
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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