[BioC] LIMMA vs. dChip

[Naomi Altman]

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.

--Naomi

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
>that
>used Tukey's biweight within array to combine probes into gene
>expression,
>instead of median polish.  We then applied 2-sample t-tests and SAM to
>both
>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
>between
>the expression values, array by array, can be very close to 100%.  This
>has
>been found many times.  The recent thread "RMA vs gcRMA" sheds some
>light
>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 "absent".
>
>--Naomi
>
>At 02:46 PM 3/7/2005, Adaikalavan Ramasamy wrote:
> >Your question is bit vague and you provide little information. I do not
> >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. fold
> >change, t-test, wilcoxon) did you use in dChip ?
> >
> >3) How did you select the differentially expressed genes ? (e.g. via p-
> >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
>than
> >1 in which case you get 100% agreement between the two methods.
> >
> >And yet another, you may have made a coding/programming error
>somewhere.
> >
> >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
>into
> > 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
>expressed
> > > genes of the two have only less than 50% in common.
> > >
> > > Why the number of the overlapped genes of the two results is so low?
>Is
> > 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
>
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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