I take out the controls before doing any differential expression analysis.

The assumption in limma is that the variances 
come from a chi-squared population.  But the 
controls do not have any biological 
variation.  Hence they come from a different 
population, with a different variance.  Besides, 
I do not see any need to adjust for testing genes in which I have no interest.

Regarding a choice of FDR cut-off, people seem to 
use very arbitrary numbers like 0.05 or 0.01, or 
to decide after they have seen the data.  I 
always use the estimate of pi0 to get a guess at 
the number of genes that differentially express 
before I pick a cut-off.  (You always have a 
false negative rate, so there is no hope in 
trying to get all of the differentially expressed 
genes.)  If pi0 is very small, the false positive 
rate will be small, so I pick a p-value 
(unadjusted) and report the FDR associated with 
that p-value.  If pi0 is big, I use FDR = 0.05 or 
0.01, since most of the genes with small p-values will be false positives.

I hope this helps.

--Naomi

At 10:11 PM 4/20/2006, Lisa Luo wrote:
>Thank you, Naomi.
>Yes. They are biological replicates.
>In the case of small variance within group, how 
>do you decide the fdr-adjusted p-value cutoff?
>Lisa,
>
>Naomi Altman <naomi@stat.psu.edu> wrote:
>I never use the p-values for the control probes 
>for anything.  I would still use some type of 
>FDR adjustment to pick statistically significant 
>genes.  I don't know what others would suggest.
>
>One reason you might have a highly significant 
>effect for the probes is very small variance within group.
>
>Are all of  your replicates biological replicates?
>
>As far as I know, nonparametric ANOVA means that 
>the errors are not normally distributed.  It 
>does not handle a change in variance between the 
>2 populations.  You might be able to use the 
>weight matrix to do weighted least 
>squares.  That would entail using weight 1 for 
>all of the normals, and weight 
>var(normal)/var(tumor) for all the tumor 
>samples.  I think we need Gordon to give an opinion here.
>
>--Naomi
>
>At 06:33 PM 4/20/2006, Lisa Luo wrote:
>>Thank you, Naomi.
>>I did the pair plots and found the variance 
>>among each of the two tumor samples is bigger 
>>that the variance among each of the three 
>>normal samples.  I guess I should only use the 
>>normal sample groups to see the difference 
>>between them using limma, while using 
>>non-parametric ANOVA for comparison between tumor and normal.
>>
>>When I compare the normal against normal 
>>samples (different cell origin), one or two 
>>affy control probes are significantly 
>>differentially expressed (among top30).  These 
>>probes should not be differentially expressed, 
>>right?  Should I use that p-value as cutoff?
>>
>>Thanks,
>>Lisa
>>
>>Naomi Altman <naomi@stat.psu.edu> wrote:
>>Generally, the power is so low with only 3 replicates that you are
>>better off using all the samples. What I do is plot all the
>>log(expression) values verse each other using the "pairs" command in
>>R. You should see a diagonal line near y=x on each plot, at least
>>within treatment. The spread in the orthogonal direction is the
>>important one for ANOVA. It should be about the same for each group.
>>I have never used the affy control probes. Perhaps someone else can
>>address this part of the question.
>>At 11:02 AM 4/20/2006, Lisa Luo wrote:
>> >Thanks, Naomi.
>> >
>> > Now I understand where the difference comes from. But how to
>> > measure the difference between groups to decide if to include them
>> > for analysis together? In the five sample groups I mentioned,
>> > there are tumor samples as well as normal tissue (cell) samples. I
>> > would like to know to which tissue (cell) the tumor is more
>> > similar. Should I analyze them together or separately?
>> >
>> > Another question is about the p-value. I used the p-value from
>> > affy control probes to select the p-value cutoff. In the case of
>> > using 15 samples, the p-value for affy probes is 1e-7. Does this
>> > indicate problems in the analysis?
>> > Thank you,
>> > Lisa
>> >
>> >Naomi Altman wrote:
>> > The difference between your analyses comes from the denominator of
>> >the test. In both cases, the numerator is the differences in
>> >means. But in the first case, all of the samples are used to compute
>> >the within sums of squares, and all of these sums of squares are used
>> >in the limma ebayes adjustment. In the second case, only the 6
>> >samples were used to compute the within sums of squares.
>> >
>> >Assuming that the groups have the about the same variance, the method
>> >using all 15 samples is more powerful (has a smaller error rate) and
>> >is preferable. If the 2 groups of interest have VERY difference
>> >variances, then you might we better off using just the 2 groups.
>> >
>> >If you did gcrma first using all the data and then using only the 6
>> >samples, that would also contribute to the differences. Unless the
>> >groups are very different, my choice would be to use all the samples.
>> >
>> >--Naomi
>> >
>> >
>> >At 09:27 AM 4/20/2006, Lisa Luo wrote:
>> > >Dear list,
>> > > I am confused with my problem and hope get some help from you.
>> > > I have 5 groups of sample, each with 3 samples (all AFFY). I
>> > > first read in all the 15 samples and did lmFit. I am interested in
>> > > the difference between group1 and group2, so I made a contrast
>> > > matrix with "group1-group2". Then I only read the 6 samples of
>> > > group1 and group2 and did the same thing. However, the
>> > > differentially expressed gene list are very different.
>> > > I used gcrma to normalize the dataset. Do you think the
>> > > difference is caused by normalization or I did something wrong?
>> > > Thanks,
>> > > Lisa
>> > >
>> > >
>> > >---------------------------------
>> > >
>> > > [[alternative HTML version deleted]]
>> > >
>> > >_______________________________________________
>> > >Bioconductor mailing list
>> > >Bioconductor@stat.math.ethz.ch
>> > > https://stat.ethz.ch/mailman/listinfo/bioconductor
>> > >Search the archives:
>> > > http://news.gmane.org/gmane.science.biology.informatics.conductor
>> >
>> >Naomi S. Altman 814-865-3791 (voice)
>> >Associate Professor
>> >Dept. of Statistics 814-863-7114 (fax)
>> >Penn State University 814-865-1348 (Statistics)
>> >University Park, PA 16802-2111
>> >
>> >
>> >
>> >
>> >---------------------------------
>> >
>> > [[alternative HTML version deleted]]
>> >
>> >_______________________________________________
>> >Bioconductor mailing list
>> >Bioconductor@stat.math.ethz.ch
>> > https://stat.ethz.ch/mailman/listinfo/bioconductor
>> >Search the archives:
>> > http://news.gmane.org/gmane.science.biology.informatics.conductor
>>Naomi S. Altman 814-865-3791 (voice)
>>Associate Professor
>>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
>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
Dept. of Statistics                              814-863-7114 (fax)
Penn State University                         814-865-1348 (Statistics)
University Park, PA 16802-2111

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