[BioC] limma - background correct method=none

[Helen Cattan]

Hi,

The bit of code I listed is using the default background correction
which is subtraction - which I did on my manually altered files so B635
and B532 were zero. A bit further down the email I (hope I) explained
that I included in the code RG=backgroundCorrect(RG, method="none")
before the normalizations which should change the default background
correction and I used my original .gpr files for this. I provided the
top 5 for both of these examples. The first top table I gave were my
results for my altered files with default background correction and the
second top table was for unaltered files with background correct method
= none. The results are not the same. Sorry if my last email was unclear
- I hope this now makes sense.

Helen

-----Original Message-----
From: Gordon Smyth [mailto:smyth at wehi.edu.au] 
Sent: 18 April 2004 23:28
To: Helen Cattan
Cc: bioconductor at stat.math.ethz.ch
Subject: Re: [BioC] limma - background correct method=none


At 06:50 AM 19/04/2004, Helen Cattan wrote:
>  Hi,
>
>I have been looking at backgroundCorrect, method=none in limma and 
>compared this to files I have manually altered so that the background 
>values were zero, without using the backgroundCorrect method. I thought

>that these would produce the same results but they were very different 
>- could anyone explain why to me please?

They do produce the same results.

>  Code is below.

The code example you give below uses the default background correction 
which is subtraction, so you have not given an example of what you claim
above.

Gordon

>Thanks,
>Helen
>
> > library(limma)
> > files=dir(pattern="*\\.gpr")
> > RG=read.maimages(files, columns=list(Rf="F635 Median", Gf="F532
>Median", Rb="B635
> > names(RG)
> > RG$genes=readGAL()
> > RG$printer=getLayout(RG$genes) 
> > samples=read.table("sampleinformationa.txt", header=TRUE, sep="\t",
>as.is=TRUE)
> > samples
> > spottypes=readSpotTypes() RG$genes$Status=controlStatus(spottypes, 
> > RG)
> > MA1=normalizeWithinArrays(RG)
> > MA2=normalizeBetweenArrays(MA1)
> > design=c(1,-1)
> > cor=dupcor.series(MA2$M, design, ndups=2, spacing=1) cor$cor
> > fit=gls.series(MA2$M,design,ndups=2,correlation=0.7628949)
> > eb=ebayes(fit)
> > genenames=uniquegenelist(RG$genes, ndups=2)
> > ord=order(eb$lods, decreasing=TRUE)
> > toptable(number=30,genelist=genenames,fit=fit,eb=eb,adjust="fdr")
>      Block Row Column         ID
>Name
>2986    10  24     19     209274
>H63351:Hs.203509::::3:211600
>5981    20  24      9     No_seq                    :Data not
>found:::::212310
>8083    27  24     13     No_seq                    :Data not
>found:::::211255
>2013     7  18     17    3846240     BE617901:In multiple
>clusters::::3:152735
>7492    25  25      7     No_seq                    :Data not
>found:::::223083
>      Status        M        t     P.Value        B
>2986   cDNA 2.032293 21.15504 0.001026994 8.082774
>5981   cDNA 1.946782 17.10056 0.001115381 6.944303
>8083   cDNA 1.957218 16.81269 0.001115381 6.847414
>2013   cDNA 1.659813 15.65550 0.001115381 6.431781
>7492   cDNA 1.739500 15.31887 0.001115381 6.302453
> > top30=ord[1:30]
> > plot(fit$coef,eb$lods,xlab="Log2 Fold Change", ylab="Log
>Odds",pch=16,cex=0.1)
>
>  and then included RG=backgroundCorrect(RG, method="none") without 
>manually altering the files, before the normalizations and got the 
>following top table. The MA plots were also very different between the 
>two tests.
>
>Block Row Column         ID
>Name
>4062    14  14     11     130050             R11620:Data not
>found::::0:130050
>1033     4  12      1     124297             R02202:Data not
>found::::0:124297
>1551     6   5      5    5195930           BI754638:Data not
>found::::10:39204
>6900    23  25     23     214572       H73225:In multiple
>clusters::::0:214572
>5131    18   3     13        N/A           BQ025821:Data not
>found::::10:33091
>      Status        M         t   P.Value           B
>4062   cDNA 2.421487 11.601562 0.1968434 -0.08869236
>1033   cDNA 1.681192  8.298373 0.1968434 -0.58215799
>1551   cDNA 1.904751  8.293908 0.1968434 -0.58312702
>6900   cDNA 2.094721  8.171346 0.1968434 -0.61016473
>5131   cDNA 2.314669  7.761475 0.1968434 -0.70707369
>
>
>
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>
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