Dear Gordon,

thanks very much for the very informative and helpful message. Indeed, for
large number of locations that show non-statistically significant
difference,
I was thinking to use a Z-test, and indeed, by Z-test there is a
statistical significance difference. However, if my understanding is
correct, a Z-test assumes a normal distribution, while edgeR assumes a NB
distribution for tag counts. From a statistical perspective, hoping that my
question is not poorly reformulated :
could these approaches be reconciled ?, as they do use different
assumptions about underlying distributions. Of course another option would
be to use a non-parametric test, other than the Z-test ....

thanks again, and best regards,

Bogdan


On Tue, Nov 22, 2011 at 5:12 PM, Gordon K Smyth <smyth@wehi.edu.au> wrote:

> Dear Bogdan,
>
> What I suggest is biological replicates!
>
> The edgeR analysis without replicates (with robust=TRUE) is just our
> attempt to offer something better than nothing in a bad situation.  We
> haven't published this methodology in a refereed journal and we don't use
> it in our own biological research.  It hasn't been tested extensively, so
> you should use at your own risk.  How it will go on different technologies
> remains to be seen.
>
> Regarding your question about low count tags, I can tell you for certain
> that no valid statistical test will ever be able to find a statistically
> significant difference for counts of 1 in one group vs 3 in another, or 4
> in one group vs 6 in another.  Theoretically, the smallest possible count
> difference that could possibly yield a two-sided p-value less than 0.05 is
> 0 vs 6, even if the dispersion was zero and even if no adjustment is made
> for multiple testing.  This is because the variability in the data can
> never be less than the Poisson variability inherence in the sequencing
> technology.  When the inter-library dispersion is nonzero, as it always is,
> much larger differences may be required.  When adjustment is made for
> genome-wide testing, much larger differences are required.
>
> If you have a large number of genome locations that consistently show a
> small but non statistically significant difference, then you might obtain a
> statistically significant result by pooling locations, or else by doing a
> gene set test using this locations.  (For the gene set test possibility,
> see the last case study of the limma package.)  I don't understand enough
> about your scientific problem to know whether these approaches would be
> appropriate for your particular study.
>
> Best wishes
> Gordon
>
>
> On Tue, 22 Nov 2011, Bogdan Tanasa wrote:
>
>  Dear Gordon,
>>
>> thanks a lot, it would really help us. Just a tiny additional question:
>> edgeR analysis on samples with no replicates shall have the same
>> statistical validity on either CHIP-seq, GRO-seq, or RNA-seq data ?
>>
>> And, if we deal for instance with 20 000 genome locations in the genome
>> (eg
>> ALU repeats) that have very small number of tags, or very small difference
>> between tag counts in various samples (let's say 1 tag in "-" sample and
>> 2-3 tags in "+" sample, or 5-6 tags in "-" sample and 4-5 tags in "+"
>> sample), is there any appropriate statistical model we can use for
>> assessing the differential expression. What would you suggest ?
>>
>> thanks,
>>
>> Bogdan
>>
>>
>> On Tue, Nov 22, 2011 at 4:20 PM, Gordon K Smyth <smyth@wehi.edu.au>
>> wrote:
>>
>>  Dear Bogdan,
>>>
>>> The advice in the edgeR User's Guide about using estimateGLMCommonDisp()
>>> with method="deviance",robust="****TRUE",subset=NULL is intended to be
>>> used
>>>
>>> without a design matrix, or in conjunction with the immediately preceding
>>> advice about removing terms from the design matrix.
>>>
>>> I have now rewritten the advice in the User's Guide a little to make this
>>> more explicit.
>>>
>>> Best wishes
>>> Gordon
>>>
>>>  Message: 2
>>>
>>>> Date: Mon, 21 Nov 2011 12:41:05 -0800
>>>> From: Bogdan Tanasa <tanasa@gmail.com>
>>>> To: Mark Robinson <mark.robinson@imls.uzh.ch>
>>>> Cc: bioconductor@stat.math.ethz.ch
>>>> Subject: Re: [BioC] edgeR
>>>>
>>>> Hi Mark,
>>>>
>>>> I have two samples "Y" and "S" (with no replicates), and following edgeR
>>>> manual on "what to do if you do not have replicates", I am getting the
>>>> following results below. The end message is "No residual df: cannot
>>>> estimate dispersion". Please could you let me know what is going wrong
>>>> ..
>>>> Thanks a lot !
>>>>
>>>>  raw.data <- read.delim("file")
>>>>
>>>>> d <- raw.data[,2:3]
>>>>> rownames(d) <- raw.data[,1]
>>>>>
>>>>>
>>>>  group <- factor(c("Y","S"))
>>>>
>>>>> design <- model.matrix(~group)
>>>>> d <- DGEList(counts = d, group=group)
>>>>>
>>>>>  Calculating library sizes from column totals.
>>>>
>>>>  dim(d)
>>>>>
>>>>>  [1] 4013    2
>>>>
>>>>  d <- calcNormFactors(d)
>>>>> d
>>>>>
>>>>>  An object of class "DGEList"
>>>> $samples
>>>>  group lib.size norm.factors
>>>> Y     Y   628062     1.049011
>>>> S     S   422542     0.953279
>>>>
>>>> $counts
>>>>                       Y   S
>>>> chr8:53670099-53691880 132 109
>>>> chr8:71033673-71122126 221 107
>>>> chr8:74069636-74074658   7   1
>>>> chr6:72172478-72187779 203 114
>>>> chr6:72096548-72115900  36  15
>>>> 4008 more rows ...
>>>>
>>>> $all.zeros
>>>> chr8:53670099-53691880 chr8:71033673-71122126 chr8:74069636-74074658
>>>>               FALSE                  FALSE                  FALSE
>>>> chr6:72172478-72187779 chr6:72096548-72115900
>>>>               FALSE                  FALSE
>>>> 4008 more elements ...
>>>>
>>>>
>>>>   d<-estimateGLMCommonDisp(d,****design,method="deviance",**
>>>>>
>>>> robust="TRUE",subset=NULL)
>>>> Warning message:
>>>> In estimateGLMCommonDisp.default(****y = y$counts, design = design,  :
>>>>
>>>>  No residual df: cannot estimate dispersion
>>>>
>>>> For Common Dispersion (no GLM modeling):
>>>>
>>>>  d<-estimateCommonDisp(d,****design)
>>>>
>>>>
>>>>>  Warning message:
>>>> In estimateCommonDisp(d, design) :
>>>>  There is no replication.  Setting common dispersion to 0.
>>>>
>>>>  d
>>>>>
>>>>>  An object of class "DGEList"
>>>> $samples
>>>>  group lib.size norm.factors
>>>> Y     Y   628062     1.049011
>>>> S     S   422542     0.953279
>>>>
>>>> $counts
>>>>                       Y   S
>>>> chr8:53670099-53691880 132 109
>>>> chr8:71033673-71122126 221 107
>>>> chr8:74069636-74074658   7   1
>>>> chr6:72172478-72187779 203 114
>>>> chr6:72096548-72115900  36  15
>>>> 4008 more rows ...
>>>>
>>>> $all.zeros
>>>> chr8:53670099-53691880 chr8:71033673-71122126 chr8:74069636-74074658
>>>>               FALSE                  FALSE                  FALSE
>>>> chr6:72172478-72187779 chr6:72096548-72115900
>>>>               FALSE                  FALSE
>>>> 4008 more elements ...
>>>>
>>>> $common.dispersion
>>>> [1] 1e-16
>>>>
>>>> $pseudo.alt
>>>>                              Y         S
>>>> chr8:53670099-53691880 103.192083 139.42506
>>>> chr8:71033673-71122126 172.781586 136.86720
>>>> chr8:74069636-74074658   5.453794   1.30187
>>>> chr6:72172478-72187779 158.707305 145.81970
>>>> chr6:72096548-72115900  28.129216  19.20588
>>>> 4008 more rows ...
>>>>
>>>> $conc
>>>> $conc.common
>>>> chr8:53670099-53691880 chr8:71033673-71122126 chr8:74069636-74074658
>>>>        2.270073e-04           3.089534e-04           7.535477e-06
>>>> chr6:72172478-72187779 chr6:72096548-72115900
>>>>        2.985930e-04           4.803864e-05
>>>> 4008 more elements ...
>>>>
>>>> $conc.group
>>>>                                S            Y
>>>> chr8:53670099-53691880 2.706055e-04 2.003510e-04
>>>> chr8:71033673-71122126 2.656403e-04 3.354361e-04
>>>> chr8:74069636-74074658 2.482619e-06 1.062467e-05
>>>> chr6:72172478-72187779 2.830186e-04 3.081155e-04
>>>> chr6:72096548-72115900 3.723929e-05 5.464117e-05
>>>> 4008 more rows ...
>>>>
>>>>
>>>> $common.lib.size
>>>> $pseudo
>>>>                              Y         S
>>>> chr8:53670099-53691880 103.192083 139.42506
>>>> chr8:71033673-71122126 172.781586 136.86720
>>>> chr8:74069636-74074658   5.453794   1.30187
>>>> chr6:72172478-72187779 158.707305 145.81970
>>>> chr6:72096548-72115900  28.129216  19.20588
>>>> 4008 more rows ...
>>>>
>>>> $conc
>>>> $conc.common
>>>> chr8:53670099-53691880 chr8:71033673-71122126 chr8:74069636-74074658
>>>>        2.270073e-04           3.089534e-04           7.535477e-06
>>>> chr6:72172478-72187779 chr6:72096548-72115900
>>>>        2.985930e-04           4.803864e-05
>>>> 4008 more elements ...
>>>>
>>>> $conc.group
>>>>                                S            Y
>>>> chr8:53670099-53691880 2.706055e-04 2.003510e-04
>>>> chr8:71033673-71122126 2.656403e-04 3.354361e-04
>>>> chr8:74069636-74074658 2.482619e-06 1.062467e-05
>>>> chr6:72172478-72187779 2.830186e-04 3.081155e-04
>>>> chr6:72096548-72115900 3.723929e-05 5.464117e-05
>>>> 4008 more rows ...
>>>>
>>>>
>>>> $N
>>>> [1] 515153
>>>>
>>>>
>>>> On Fri, Nov 18, 2011 at 12:39 PM, Mark Robinson
>>>> <mark.robinson@imls.uzh.ch>****wrote:
>>>>
>>>>
>>>>
>>>>
>>>>> On 18.11.2011, at 21:27, Bogdan Tanasa wrote:
>>>>>
>>>>>  Dear all,
>>>>>
>>>>>>
>>>>>> are the functions "estimateGLMCommonDisp" and "estimateGLMTagwiseDisp"
>>>>>> available in edgeR on any platform ? I am using it on Linux/Ubuntu and
>>>>>> apparently, these functiosn are not available.
>>>>>>
>>>>>>
>>>>>
>>>>> What version of R/edgeR are you using?  Perhaps you have an old
>>>>> version?
>>>>>  These functions have been around for awhile.  What does sessionInfo()
>>>>> give?
>>>>>
>>>>> Mark
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>  thanks,
>>>>>>
>>>>>> Bogdan
>>>>>>
>>>>>
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