Naomi Altman naomi at stat.psu.edu
Tue Mar 29 18:50:57 CEST 2005

I used several levels of differential expression including 10% and 80%.


At 11:35 AM 3/29/2005, Wu, Xiwei wrote:

>Thanks a lot, Naomi. Your result is very interesting. I am wondering 
>whether the number of DE genes in your simultion dataset will affect the 
>I tested SAM and Limma using the same dataset (but without the knowledge 
>of what genes should be DE). I know this is not the best way to compare 
>different methods, but I just want to get some idea. At the level of 0.05 
>FDR, SAM finds a lot more DE genes than Limma. However, with some other 
>datasets, SAM and Limma perform similarly. In addition, I also found using 
>median FDR or mean FDR in SAM makes a big difference for some datasets, 
>but not for others.
>The message I got is that there is no common answer to this question, 
>because it depends on the datasets? Any comments?
>In addition, is there a guideline for the minimum number of replicates 
>should be used with SAM? I assume that with small number of replicates, 
>the permutaion does not mean much.
>-----Original Message-----
>From: Naomi Altman [<mailto:naomi at stat.psu.edu>mailto:naomi at stat.psu.edu]
>Sent: Monday, March 28, 2005 8:34 PM
>To: Wu, Xiwei; bioconductor at stat.math.ethz.ch
>Subject: Re: [BioC] SAM vs LIMMA vs EBAM
>I have not tried EBAM, but I did do this experiment with SAM and LIMMA on 
>a data set I simulated from an actual data set.
>On these data, the SAM statistic and LIMMA F-test gave almost identical 
>ordering of the genes.  However, the FDR adjustment was too stringent for 
>SAM (i.e. the true FDR was lower than SAM's estimate) and was too liberal 
>for LIMMA.
>This was not a big study.  I took my gene means and variances from an 
>actual study, and then added either normal or t-4 errors and a couple of 
>levels of differential expression.
>The sample sizes I used were very small - 2 or 4 replicates with 22000 
>genes.  Results were much, much, much better with 4 replicates than with 2.
>At 08:48 PM 3/28/2005, Wu, Xiwei wrote:
> >Hi, BioC Members,
> >
> >I have a general question on identifying DE genes. Since there are many
> >ways to do this, I am wondering whether people has compared methods
> >such as SAM, EBAM, and LIMMA by applying them to the same dataset. Of
> >course, they have different assumptions and different models, but
> >should they always give similar results (assuming the parameter
> >settings are optimized to get similar number of DE genes)? Is it better
> >to get a common list of genes using three different methods? Do I have
> >more confidence on this common list of genes than using a single method?
> >
> >Xiwei
> >
> >
> >"EMF <COH.org>" made the following annotations.
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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

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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