[BioC] LIMMA vs. dChip

Stephen Henderson s.henderson at ucl.ac.uk
Mon Mar 14 14:13:43 CET 2005

True but I think you maybe overstating the problem.

Differences in the tail are not biologically all that interesting. the range
of rma is squashed compared to other methods, and tukey bi-weight has an
unreliable baseline for low expressing values. The top100 is often a small
fraction, and the tail maybe not that extreme.

The interesting point is whether all the data considered significant by one
test is significant by the other and as you say how well correlated the raw
data is.

I think??? I sometimes worry about this too.


-----Original Message-----
From: Naomi Altman
To: Stephen Henderson; 'ramasamy at cancer.org.uk '; 'jun.yan.a at utoronto.ca '
Cc: 'BioConductor mailing list '
Sent: 3/14/05 12:57 PM
Subject: RE: [BioC] LIMMA vs. dChip

We did not do any further analysis, and we currently have no plans to do

any.  To really solve this, a properly designed experiment, possibly WT 
versus a well-understood knockout, should be done.  The data we have at 
hand is not suitable to determine which normalization is best for 
determining differential expression.


At 04:54 AM 3/14/2005, Stephen Henderson wrote:
>What result do you get if you try and estimate how many are changing
and the
>spearman rank correlation for that set?
>This seems a more meaningful metric as up to 50% of genes in some
>experiments maybe changing.
>-----Original Message-----
>From: Naomi Altman
>To: ramasamy at cancer.org.uk; jun.yan.a at utoronto.ca
>Cc: BioConductor mailing list
>Sent: 3/13/05 6:00 PM
>Subject: Re: [BioC] LIMMA vs. dChip
>We normalized the same data set using RMA and a very similar procedure
>used Tukey's biweight within array to combine probes into gene
>instead of median polish.  We then applied 2-sample t-tests and SAM to
>sets of data.  The overlap in the "top 100" and "top 200" sets of
>differentially expressed genes was 50%.
>Normalization makes a huge difference, even though the correlation
>the expression values, array by array, can be very close to 100%.  This
>been found many times.  The recent thread "RMA vs gcRMA" sheds some
>on this problem.  I suspect that much of the difference lies in the low
>expressing genes - but this does not mean that these genes are
>At 02:46 PM 3/7/2005, Adaikalavan Ramasamy wrote:
> >Your question is bit vague and you provide little information. I do
> >think LIMMA has preprocessing capabilities for Affymetrix data.
> >
> >1) How did you preprocess the data ?
> >
> >2) How did you "analyse" your data in dChip ? What technique (e.g.
> >change, t-test, wilcoxon) did you use in dChip ?
> >
> >3) How did you select the differentially expressed genes ? (e.g. via
> >value cutoff or biological significance).
> >
> >
> >One possibility is that you are using very different test statistics.
> >With 5 in each group, it is difficult to draw any conclusions as some
> >methods are more robust than others at small number of arrays.
> >
> >Another is that you choose a threshold that includes a lot of noisy
> >gene. An extreme example is to select all genes with a p-value less
> >1 in which case you get 100% agreement between the two methods.
> >
> >And yet another, you may have made a coding/programming error
> >
> >Regards, Adai
> >
> >
> >
> >On Mon, 2005-03-07 at 14:15 -0500, jun.yan.a at utoronto.ca wrote:
> > > Dear list member,
> > > I have a set of Affymetrix data of 10 arrays, HG_U133A, seperated
> > unpaired
> > > two groups of 5 arrays each. I processed the data using LIMMA and
> > dChip. For
> > > dChip, I used all the default setting. The resulted differential
> > > genes of the two have only less than 50% in common.
> > >
> > > Why the number of the overlapped genes of the two results is so
> > there
> > > any problems? Can anyone help me?
> > >
> > > Thanks in advance,
> > > Jun
> > >
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> >
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>Naomi S. Altman                                814-865-3791 (voice)
>Associate Professor
>Bioinformatics Consulting Center
>Dept. of Statistics                              814-863-7114 (fax)
>Penn State University                         814-865-1348 (Statistics)
>University Park, PA 16802-2111
>Bioconductor mailing list
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>This email and any files transmitted with it are
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Naomi S. Altman                                814-865-3791 (voice)
Associate Professor
Bioinformatics Consulting Center
Dept. of Statistics                              814-863-7114 (fax)
Penn State University                         814-865-1348 (Statistics)
University Park, PA 16802-2111

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