[BioC] maanova background correction

[James MacDonald]

This is one of many options, and maybe it is a good idea. My main worry
about background is that you are assuming that the non-specific binding
of cDNA to the area just outside a spot is equal to the non-specific
binding within the spot.

All of the spotted arrays we use in our core have negative controls
(salmon sperm cDNA, cot-1, A. thaliana, etc). On the odd occasion that a
slide has a huge smear of background fluorescence going across the
slide, it is invariably true that the negative controls are 'black
holes' in the middle of the background. This implies to me that the
non-specific binding within a spot of cDNA is quite different than to
the remainder of the slide.

Because of this observation, I am reluctant to assume that the current
method of estimating background gives an unbiased estimate.

It might be interesting to do a background estimate like the one used
in rma, where the background is estimated from those spots with no
apparent binding. However, this would require a relatively large array.

Jim



James W. MacDonald
UMCCC Microarray Core Facility
1500 E. Medical Center Drive
7410 CCGC
Ann Arbor MI 48109
734-647-5623

>>> <kfbargad at lg.ehu.es> 06/25/03 05:04AM >>>
I agree that subtracting background can add variability to your 
dataset, but I think that if you don t subtract it you risk having 
spots with a signal value composed of its real signal value plus a 
high background signal value. What do you think about prefiltering for

those spots with an ubnormal high background value and then doing your

analysis? Could this be an option?

David

> I think you have come across a relatively contentious issue, and I 
doubt
> you will be able to get a consensus about background subtraction.
> Additionally, each software/scanner uses a different method of
> estimating background, so the usefulness of the background is
largely
> dependent on how it was estimated.
> 
> Personally, I look at background subtraction the same way I look at 
the
> MM probes on an Affy chip. I am sure there is a reasonable way to
use
> these data, but I am not too sure that simply subtracting background
> from foreground is a good idea. For instance, background is usually
> estimated from portions of the slide that are blocked with something
> other than cDNA. Anybody that has ever looked at a slide with 
negative
> control cDNA spots can tell you that the intensity of the negative
> control is almost always much smaller than background. In my
opinion,
> this indicates that the estimated background almost always 
overestimates
> true background.
> 
> In addition, variability is additive, so if you subtract background
> from foreground, you are adding the variability of your background
> estimate to your new foreground estimate. Considering the inherent
> variability of microarray data, this cannot be considered a good 
thing.
> 
> On the other hand, if you don't subtract background (or some ad hoc
> estimate thereof), your data will be (possibly) upwardly biased.
> 
> So here is what I do; I simply use the raw signal and accept that
the
> data may be biased. This is certainly not the ideal situation, but I
> think it is a reasonable trade off of bias for (hopefully) better
> precision.
> 
> HTH,
> 
> Jim
> 
> 
> 
> James W. MacDonald
> UMCCC Microarray Core Facility
> 1500 E. Medical Center Drive
> 7410 CCGC
> Ann Arbor MI 48109
> 734-647-5623
> 
> >>> "Brendan M. Heavey" <bmheavey at buffalo.edu> 06/24/03 02:56PM >>>
> Hello-
> 
> I am using MAANOVA to analyze cDNA chips.  Does anybody know how to 
> deal with background spot intensity?
> 
> Right now, I have about 4000 genes on an array, each spotted 3 
times. 
> 
> I can input the raw signal strength for each of the 12,000 spots and

> run analysis on those.
> 
> I would like to subtract background intensity from each of the 
spots, 
> but this leads to negative values in some spots (that haven't 
> hybridized).  Maanova seems to not like negatives or missing values,

> which means I have to eliminate all 3 spots for each gene that 
produces
> 
> a single negative...which reduces my dataset to a pitiful number of 
> genes.
> 
> I've considered:
> 1). Replacing the missing/negative value with a number very close to

> zero
> 2). Replacing the missing/negative value with the average of the 
other
> 
> two spots
> 3). Forgetting about background intensity completely and just using 
raw
> 
> signal strength
> 
> ...but none of them seem like the right thing to do
> 
> any ideas?
> 
> thanks in advance
> 
> Brendan Heavey
> Analyst Programmer
> Center for Research in Cardiovascular Medicine
> University at Buffalo
> 
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