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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