Thank you, Jim.

One more question. The eBayes step adjusts the variance across genes.
Since there is already a good estimate for the variance due to the
large number of samples does the eBayes step shrink the variance even
further?

Thank you,

Giovanni



On Tue, Apr 29, 2014 at 9:20 AM, James W. MacDonald <jmacdon@uw.edu> wrote:

> Hi Giovanni,
>
>
> On 4/28/2014 8:54 PM, Giovanni Bucci wrote:
>
>> Hello everybody,
>>
>> I have 32 samples, 4 factors with 2 levels each. Each level has 2
>> replicates.
>>
>>  str(gxexprs)
>>>
>>   num [1:15584, 1:32] 7.94 6.67 9.93 9.62 12.19 ...
>>
>>  Group
>>>
>>   [1] R52VQ R52VQ R52VE R52VE R52EQ R52EQ R52EE R52EE R95VQ R95VQ R95VQ
>> R95VE
>> [13] R95VE R95VE R95EQ R95EQ R95EQ R95EE R95EE R95EE R97VQ R97VQ R97VQ
>> R97VE
>> [25] R97VE R97VE R97EQ R97EQ R97EQ R97EE R97EE R97EE
>> 16 Levels: R52VQ R52VE R52EQ R52EE R95VQ R95VE R95EQ R95EE R97VQ ... R97EE
>>
>>
>> design <- model.matrix(~0+Group)
>> fit <- lmFit(gxexprs, design)
>>
>> contrast.matrix <- makeContrasts(contrasts="R52VQ - R52VE",levels=design)
>> fit2 <- contrasts.fit(fit, contrast.matrix)
>> fit2 <- eBayes(fit2)
>>
>> TTable = topTable(fit2)
>>
>> global_p_val = TTable$P.Val
>>
>>
>> gxexprs = gxexprs[, 1:4]
>>
>>
>> ## same code as above but the expression matrix has only the first 4
>> columns which represent the contrast tested above
>>
>> design <- model.matrix(~0+Group)
>> fit <- lmFit(gxexprs, design)
>>
>> contrast.matrix <- makeContrasts(contrasts="R52VQ - R52VE",levels=design)
>> fit2 <- contrasts.fit(fit, contrast.matrix)
>> fit2 <- eBayes(fit2)
>>
>> TTable = topTable(fit2)
>>
>> local_p_val = TTable$P.Val
>>
>> local_p_val has much greater values than global_p_val even though they
>> represent the same comparison.
>>
>> What is the explanation for this?
>>
>
> The denominator of your t-statistic is based on the mean square error of
> the model (which is based on the intra-group variance of all groups). When
> you have all the other groups in the model, the number of observations used
> to estimate variance is larger, so you get more degrees of freedom for you
> test (and the variance estimate is more accurate), so you get smaller
> p-values in general.
>
> Best,
>
> Jim
>
>
>
>> Can you point to some diagnostic functions that will show the difference?
>>
>> Thank you,
>>
>> Giovanni
>>
>>         [[alternative HTML version deleted]]
>>
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>>
>
> --
> James W. MacDonald, M.S.
> Biostatistician
> University of Washington
> Environmental and Occupational Health Sciences
> 4225 Roosevelt Way NE, # 100
> Seattle WA 98105-6099
>
>

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