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 >>>>>> >>>>> > ______________________________**______________________________**____ ______ > The information in this email is confidential and inte...{{dropped:10}}