Dear Sir/Madam,

I'm very sorry to disturb you again.

Your answers helped me a lot! Thank you very much! But I'm still a little
confused about some small questions, could you please give me a bit more
explanation for those questions?

1) according to the results got from *summary(decideTestsDGE(de.com, p.value
>> = 0.05))*, it seems that there is only 1 miRNA: RNA_de is expressed
>> differently if I take 0.05 as the cut-off of the *adjust.p.val* (please see
>> *line 20* and *line 12*).
>>
>
> This of course may simply be correct.  There may be only a few
> differentially expressed miRs in your data.  The common dispersion estimate
> appears to be reasonably small.
>

Is there any relationship between the number of differentially expressed
miRs and the value of the common dispersion. In general, is tagwise
dispersion estimation better than common dispersion estimation? Is there any
criteria to help us choose dispersion estimation strategy?


> There is no reason why you need to restrict to a FDR (adjusted p-value) of
> less than 0.05.  You could easily examine the top three miRs.
>

Here, I just tried to confirm which p-value is used to make analysis in
edgeR. :)


>
> There is something wrong with your topTags() output.  This function does
> not print column headings in the format that you have presented.  You must
> be using other R code that you haven't shown.
>

I'm not sure if I used some other R codes to help display the column
headings. Because I'm not familiar with R, I only followed the Case study 8
of edgeR manual, where topTags() prints column headings.

 While *line 16* indicates that there is a constriction on the counts in
>> libraries. In fact, the counts in library 1 and 2 are not consistently
>> greater than those in library 3 and 4. So I wondered how the package edgeR
>> deals with this kind of inconsistent data. Is it still reliable to determine
>> that the RNA_de is differently expressed between two groups? When the edgeR
>> makes a comparison among multiple libraries, does the edgeR merge the
>> libraries within the same group together before the comparison or not? if it
>> does, how does it merge these libraries together?
>>
>> I tried to find the answer in the manual and the two papers to be
>> implemented into edgeR, but I failed finally as my limited knowledge on
>> statistics.
>>
>
> No, it doesn't simply merge libraries together.  The whole point of having
> biological replicate libraries is to measure the variation between them.
> This gives an estimate of the underlying biological variation, against which
> significance is assessed.
>
> Think of it like a two-sample t-test, where you assess the separation of
> two groups relative to variation within the two groups.  You do not require
> observations to all be the same, and you don't merge them together.  With
> common dispersion, edgeR is analogous to two-sample t-tests where the same
> standard deviation estimate is used for every miR. With tagwise dispersion,
> edgeR is analogous to two-sample t-tests with different standard deviations
> estimated for each miR.
>
>  So could you please give me a favor to explain those questions for me?2)
>> according to the norm.factor for each library, and the value of the
>> common.dispersion (*line 1-4*,* line10* and *11*), I think it's OK to use
>> common dispersion for our data. What do you think about it? Actually, I'm
>> not sure *when we can use * *common.dispersion*.
>>
>
> I generally recommend tagwise, if you have any reasonable number of
> replicates.  The choice has nothing to do with the norm.factors.
>

I compared the results from the common dispersion and tagwise dispersion
again. The miRNA "RNA_de"  with contradicted counts (line 16) is not
differently expressed any more, according to the results based on tagwise
dispersion. In fact, the FDR of RNA_de is 0.2005 now.


>
>  And I tried tagwise dispersion with prion.n = 10 (20/(4-2)) also, but the
>> results are very similar to the results got from common.dispersion. In
>> addition, I made the pair-wise comparison between two libraries (one is
>> normal, the other is abnormal), but the the value of the
>> *common.dispersion*approximates to 0 (1.0E-16). So I doubt the reliability
>> of the results when using common dispersion to make pair-wise comparison
>> between libraries.
>>
>
> Well, if you try to do a comparison without replicates, it is impossible to
> estimated biological variability, so edgeR puts it to zero.  You are correct
> that this is not reliable, but the problem is with the lack of replicates,
> not with the use of common dispersion.
>
>  In general, if we have multiple libraries and we only want to find the
>> differently expressed miRNA/genes among groups, is their any other
>> comparison except pair-wise comparison between groups? Would you mind giving
>> me some suggestions?
>>
>
> I can't see what is wrong with the pair-wise comparison.  You only have two
> groups, so there is nothing else sensible that could be done.
>
>  3) From *line 19*, we can see that the miRNA---RNA_ad shows consistent and
>> significant difference between groups, this is consistent with the P-value
>> for this miRNA in *line 15*.  While the ajust.p.val is equal to 1 (*line
>> 15*, this ajust.p.val is based on the method "BH"). So I wondered whether we
>> can use the P-value to analyze differential expression or not. If not, do we
>> have to use ajust.p.val to make analysis? Is it always more reliable to use
>> ajust.p.val instead of P-value to make analysis? Could you please give me
>> some advice?
>>
>
> Well, if you simply use unadusted p-values, you would find about 5% of the
> miRs to be signficantly different even from random data.  You will be
> guaranteed to find statistical significant differences even when there are
> none.  This would not be considered defensible in a scientific journal.
>

Do you mean it's more reliable to use adjust.p.val to make analysis?  But
I'm really confused about the statistic result from edgeR. For example,

 miRNA logConc logFC P.Value adj.P.Val library1 library2 library3 library4
miR-127 -3.27733 -0.52769 0.042147 1 293963 218700 508874 573151  miR-124
-5.8367 -0.45822 0.07751 1 43728 44432 97614 80855
 (no matter what dispersion estimation is used, the adj.P.val are both
equivalent to 1 for those two miRNA.)

In these two miRNA, we can determine that they are differently expressed
between normal and abnormal, based on the facts that the counts in library3
and library4 are nearly twice higher than those in library1 and library2.
But both of the adj.p.val equal to 1. Actually, this case is existed in
about 30% miRNAs of my data. I'm not sure whether this case is related to
the sizes of the libraries (Please see line 1-4). Would you mind giving me
some suggestions for this case? Thank you very much!

Best wishes,
Zhenling
University of Alberta

results:
> dim(d)
[1] 658   4
> d
An object of class "DGEList"
$samples
               files         group      lib.size         norm.factors
library1  library1    normal    3184257      1.0025762
........................................................line 1
library2  library2    normal    2825126      0.9873665
........................................................line 2
library3  library3  abnormal  3955120      1.0476795
........................................................line 3
library4  library4  abnormal  4692333      0.9642192
........................................................line 4

$counts
                  library1   library2   library3   library4
RNA_aa    302440   274619   479102   403961
........................................................line 5
RNA_ab    298597   283090   206178   331258
........................................................line 6
RNA_ac    293963   218700   508874   573151
........................................................line 7
RNA_bd    199355   168652   180300   262947
........................................................line 8
RNA_ae    149868   131546   177037   201992
........................................................line 9
653 more rows ...

> d <- estimateCommonDisp(d)
> d$common.dispersion
[1] 0.03584351
         .........................................................line 10
> sqrt(d$common.dispersion)
[1] 0.1893238
         .........................................................line 11

> de.com <- exactTest(d)
Comparison of groups:  normal - abnormal
> topTags(de.com, n=5)
Comparison of groups: normal-abnormal
                   logConc        logFC                P.Value
     adjust.p.val

RNA_de    -15.851627    1.7031386        5.969173e-07    0.0003927716
         .......................line 12
RNA_ea    -16.861371    1.4352602        2.465201e-04    0.0554942582
         .......................line 13
RNA_cf     -34.679446    30.6732173      2.833211e-03     0.3225116431
           .......................line 14
RNA_ad    -3.690956      0.6652757        1.554169e-02     1.0000000000
          .......................line 15

> detags.com <- rownames(topTags(de.com, n=5)$table)
> d$counts[detags.com, ]
                library1    library2     library3      library4
RNA_de   153        * 35 *             *62  *           18
          ........................................................line 16
RNA_ea   49           34              28             16
        ........................................................line 17
RNA_cf    5             4                0                0
            ........................................................line 18
RNA_ad   293963   218700      508874      573151
........................................................line 19

	[[alternative HTML version deleted]]

