[BioC] LIMMA vs. dChip

[Stephen Henderson]

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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