Dear Sir/Madam,

Would you mind me disturbing you for a few minutes?

I know edgeR can do comparison among multiple libraries. I did have 4
libraries of miRNA, where two of them are normal (library1 and library2),
the other two are abnormal (library3 and library4). And I'm trying to get
the miRNA which expressed differently between the two groups: normal and
abnormal. I utilized common dispersion, and the results are displayed at the
end. When using edgeR, I have some questions as follows,

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*).  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.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*.

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.
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?

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?

I'm very sorry to disturb you with so many naive questions. But I really
confused about the unsatisfied results after using edgeR, and I'm not sure
if I made any mistakes when using edgeR. Could you please give me a favor?
Would you mind giving me any advice about my questions? Thank you very much!

Best wishes,
Zhenling Peng
University of Alberta

> 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

> summary(decideTestsDGE(de.com, p.value = 0.05))
...............................................line 20
   [,1]
-1    0
0   657
1     1

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