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

[Gordon Smyth]

Dear Jose,

For some brief but relevant comments see See Section 6.1: Background 
Correction in the Limma User's Guide, and Section 3 of 
http://www.statsci.org/smyth/pubs/mareview.pdf

Whether background subtraction is a good idea depends entirely on the 
background estimation used. You do not mention what image analysis 
program you used or which background estimation method was chosen, 
but everything depends on this.

Firstly, can you get away with ignoring the background entirely? I 
agree with Jim and Naomi's general remarks, and I agree with Jim that 
not background correcting can lead to cleaner results for some data 
sets, especially for good quality arrays with low background. The 
UCSF microarray center has made the same argument for their own 
arrays. But in my lab, we always background correct. There are a lot 
of reasons for this. For one thing, foreground-background plots 
almost always show that background correcting does remove some 
systematic bias. The most critical reason though is to achieve 
comparability between experimental conditions. Not background 
correction is a lot like adding an offset to your data (see the 
backgroundCorrect function in limma), and the size of the offset 
depends on the level of the background. In my lab we see data from 
lots of different labs, platforms, image analysis programs, species 
etc, and the background levels can vary wildly. For example, I 
analysed one important experiment when the scanner changed from Axon 
to Agilent halfway through, and the overall background levels 
increased 10-fold. I prefer to background correct and to add the 
offset explicitly, rather than to allow it to vary with the data in 
an uncontrolled way. Had I not background corrected the Axon-Agilent 
experiment, the results would have been far more damped in the second 
half of the experiment and not comparable to the first.

But background correcting doesn't mean that we simply *subtract* the 
background. We subtract if we have
1. morph background from SPOT
2. morphological opening background from GenePix 6, or
3. background from AgilentFE
and in no other cases. In most other cases, subtracting is so bad 
that you would indeed probably be better off ignoring the background entirely.

In most other cases we currently use 'normexp' background correction 
with an offset. This is an adaptive background method which is a 
modification of the background correction method used by the RMA 
algorithm for affy data. It is adaptive in that it adapts to the 
overall level of background on each array. It avoids the negative 
intensities which so often arise from naive background subtraction.

The Bioconductor book has an example of a data set which was analysed 
both with no background correction (Chapter 4) and with normexp 
background correction (Chapter 23).

Best wishes
Gordon

>Message: 23
>Date: Fri, 31 Mar 2006 16:01:24 -0500
>From: Naomi Altman <naomi at stat.psu.edu>
>Subject: Re: [BioC] Limma: background correction. Use or ignore?
>To: "James W. MacDonald" <jmacdon at med.umich.edu>,
>         J.delasHeras at ed.ac.uk
>Cc: Bioconductor Newsgroup <bioconductor at stat.math.ethz.ch>
>
>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
> >
>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