Hi Gordon, Thanks for the quick response, and sorry I didn't get back to you yesterday. I'll try to address each of your questions clearly. 1) >> Finally, would you be willing to share some of your date with us offline so that we can >> trouble-shoot? As I said, we haven't seen this behaviour. We'd be happy to share the anonymized data with you offline. Giving you the DGElist, or the dataframe would be best from our perspective. What would be the best way of getting it to you? 2) >> Can you try updating to edgeR 2.3.52 (the current devel version) to see it makes a >> difference? (There are many changes, but one is that exactTest() is much faster now.) I have downloaded v2.3.52, but I'm having some problems building it. R CMD INSTALL edgeR_2.3.52.tar.gz * installing to library ‘/homes/nschurch/myRlibrary/x86_64’ * installing *source* package ‘edgeR’ ... ** R ** data ** inst ** preparing package for lazy loading Error : class "MDS" is not exported by 'namespace:limma' ERROR: lazy loading failed for package ‘edgeR’ * removing ‘/homes/nschurch/myRlibrary/x86_64/edgeR’ I have limma (v3.4.4) installed and it seems to be working fine so I'm not quite sure what is going on here. Do I need a more up to date version of limma for the development build of edgeR? 3) >> Given your experimental layout with multiple groups but no covariates, you could use >> the "classic" edgeR functions: >> >> estimateCommonDisp(y) >> exactTest() >> topTags() >> >> rather than the glm code. This would allow you to compare any two of your groups. >> What results does this give? This seems to give sensible results; I don't get any NaN fold-changes. But it seems to present some new questions. A quick check confirms that the fold-changes calculated by each method are tightly correlated (for those that aren't 'NaN'), but a plot of the p-values calculated by each method is more complicated. Attached are two .pngs of this plot, one for the full range of p-values, and one for a zoomed in region of the plot. In these figures, the blue points are genes with non-NaN fold-changes in both calculations and the red points are those with NaN fold-changes in the glm calculation. The solid red line marks the 1:1 line. While the p-values are correlated, there is considerable structure to the plot. There seems to be at least 3 distinct regions to the plot that are al removed from the 1:1 line. The zoomed plot also shows clear discretization of the p-values from the exactTest for the red points (and maybe also for the glm p-values), that isn't clearly seen in the blue points. This makes me think that while the exactTest is calculating a reasonable-looking fold change, that they are not to be trusted and that something funny is going on. We are also getting strange results when comparing edgeR p-values (from either method) with those calculated by DESeq for the same data (I can send you these plots if you're interested). I really think there is something funny going on here. Have you seen anything like these structures when comparing the p-values calculated by each method? 4) >> Thanks for including output from your objects, but I find that I can't get much information >> from the str(y) output. Could you please use the show method for these objects, e.g., >> show(y)? > # create the design matrix > model.groups<-groups > model.factors<-as.factor(model.groups) > model<-model.matrix(~model.factors) > > # build DGElist and calculate normalization factors > x=as.data.frame(data) > rownames(x)=genenames > y=DGEList(x,group=groups) > y=calcNormFactors(y) > show(y) An object of class "DGEList" $samples group lib.size norm.factors cond07.rep0020 cond07 1866963 0.9916452 cond07.rep0021 cond07 2364994 0.9783033 cond07.rep0022 cond07 1712838 1.0023115 cond08.rep0023 cond08 2782920 1.0357778 cond08.rep0025 cond08 2780054 0.9724019 12 more rows ... $counts cond07.rep0020 cond07.rep0021 cond07.rep0022 cond08.rep0023 38536 7 6 7 11 38537 187 169 146 265 38538 44 117 78 78 38539 0 23 22 40 38540 98 144 97 215 cond08.rep0025 cond08.rep0024 cond07.rep0019 cond07.rep0018 38536 7 9 5 9 38537 186 279 237 132 38538 160 109 7 73 38539 14 23 11 13 38540 175 247 153 87 cond00.rep0029 cond00.rep0028 cond06.rep0015 cond06.rep0016 38536 0 10 5 18 38537 277 153 150 240 38538 46 52 62 84 38539 23 21 13 15 38540 141 119 79 211 cond06.rep0017 cond08.rep0026 cond10.rep0030 cond10.rep0031 38536 0 8 5 14 38537 93 282 211 258 38538 21 120 100 83 38539 9 30 34 42 38540 46 233 208 223 cond00.rep0027 38536 7 38537 238 38538 47 38539 21 38540 231 6346 more rows ... $all.zeros 38536 38537 38538 38539 38540 FALSE FALSE FALSE FALSE FALSE 6346 more elements ... > # estimate dispersion and fit models > z=estimateGLMCommonDisp(y, design)) > show(z) An object of class "DGEList" $samples group lib.size norm.factors cond07.rep0020 cond07 1866963 0.9916452 cond07.rep0021 cond07 2364994 0.9783033 cond07.rep0022 cond07 1712838 1.0023115 cond08.rep0023 cond08 2782920 1.0357778 cond08.rep0025 cond08 2780054 0.9724019 12 more rows ... $counts cond07.rep0020 cond07.rep0021 cond07.rep0022 cond08.rep0023 38536 7 6 7 11 38537 187 169 146 265 38538 44 117 78 78 38539 0 23 22 40 38540 98 144 97 215 cond08.rep0025 cond08.rep0024 cond07.rep0019 cond07.rep0018 38536 7 9 5 9 38537 186 279 237 132 38538 160 109 7 73 38539 14 23 11 13 38540 175 247 153 87 cond00.rep0029 cond00.rep0028 cond06.rep0015 cond06.rep0016 38536 0 10 5 18 38537 277 153 150 240 38538 46 52 62 84 38539 23 21 13 15 38540 141 119 79 211 cond06.rep0017 cond08.rep0026 cond10.rep0030 cond10.rep0031 38536 0 8 5 14 38537 93 282 211 258 38538 21 120 100 83 38539 9 30 34 42 38540 46 233 208 223 cond00.rep0027 38536 7 38537 238 38538 47 38539 21 38540 231 6346 more rows ... $all.zeros 38536 38537 38538 38539 38540 FALSE FALSE FALSE FALSE FALSE 6346 more elements ... $common.dispersion [1] 0.1012043 > fit<-glmFit(z,model,dispersion=z$common.dispersion) > show(fit) An object of class "DGEGLM" $coefficients (Intercept) model.factorscond06 model.factorscond07 model.factorscond08 38536 -12.885005 0.48913051 0.2967643 0.1212677129 38537 -9.219056 -0.01086056 -0.1339230 -0.1877871229 38538 -10.724813 0.38699166 0.3731979 0.5624151466 38539 -11.535562 -0.22950901 -0.3546394 -0.1163631383 38540 -9.555217 -0.13261443 -0.2158204 -0.0002525462 model.factorscond10 38536 0.1592107 38537 -0.2828476 38538 0.2987787 38539 0.2134461 38540 -0.0250628 6346 more rows ... $df.residual [1] 12 12 12 12 12 6346 more elements ... $deviance 38536 38537 38538 38539 38540 23.297974 3.448158 30.568649 25.886776 3.073930 6346 more elements ... $design (Intercept) model.factorscond06 model.factorscond07 model.factorscond08 1 1 0 1 0 2 1 0 1 0 3 1 0 1 0 4 1 0 0 1 5 1 0 0 1 model.factorscond10 1 0 2 0 3 0 4 0 5 0 12 more rows ... $offset [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [1,] 14.43143 14.65435 14.35597 14.87416 14.80999 14.96341 14.60526 14.49537 [2,] 14.43143 14.65435 14.35597 14.87416 14.80999 14.96341 14.60526 14.49537 [3,] 14.43143 14.65435 14.35597 14.87416 14.80999 14.96341 14.60526 14.49537 [4,] 14.43143 14.65435 14.35597 14.87416 14.80999 14.96341 14.60526 14.49537 [5,] 14.43143 14.65435 14.35597 14.87416 14.80999 14.96341 14.60526 14.49537 [,9] [,10] [,11] [,12] [,13] [,14] [,15] [,16] [1,] 14.56309 14.43248 14.25811 14.76053 13.69444 15.0837 14.85685 15.05181 [2,] 14.56309 14.43248 14.25811 14.76053 13.69444 15.0837 14.85685 15.05181 [3,] 14.56309 14.43248 14.25811 14.76053 13.69444 15.0837 14.85685 15.05181 [4,] 14.56309 14.43248 14.25811 14.76053 13.69444 15.0837 14.85685 15.05181 [5,] 14.56309 14.43248 14.25811 14.76053 13.69444 15.0837 14.85685 15.05181 [,17] [1,] 14.85989 [2,] 14.85989 [3,] 14.85989 [4,] 14.85989 [5,] 14.85989 6346 more rows ... $samples group lib.size norm.factors cond07.rep0020 cond07 1866963 0.9916452 cond07.rep0021 cond07 2364994 0.9783033 cond07.rep0022 cond07 1712838 1.0023115 cond08.rep0023 cond08 2782920 1.0357778 cond08.rep0025 cond08 2780054 0.9724019 12 more rows ... $genes NULL $dispersion [1] 0.1012043 $lib.size [1] 1851365.0 2313681.4 1716797.3 2882486.8 2703329.9 3151566.4 2202855.3 [8] 1973607.7 2111876.1 1853304.7 1556748.9 2572866.3 885968.3 3554417.9 [15] 2833000.8 3442857.6 2841627.6 $weights NULL $fitted.values cond07.rep0020 cond07.rep0021 cond07.rep0022 cond08.rep0023 38536 6.316675 7.894054 5.857544 8.251761 38537 160.525760 200.611698 148.857840 236.824760 38538 59.134748 73.901673 54.836500 111.248471 38539 12.695304 15.865531 11.772537 25.084212 38540 105.677922 132.067448 97.996653 204.117136 cond08.rep0025 cond08.rep0024 cond07.rep0019 cond07.rep0018 38536 7.738885 9.022061 7.515926 6.733756 38537 222.105256 258.932306 191.002330 171.125028 38538 104.333981 121.633494 70.361758 63.039324 38539 23.525139 27.425819 15.105567 13.533555 38540 191.430528 223.171432 125.741373 112.655672 cond00.rep0029 cond00.rep0028 cond06.rep0015 cond06.rep0016 38536 5.355276 4.699593 6.438114 10.64038 38537 209.354902 183.722151 152.656946 252.29883 38538 46.445257 40.758647 50.415007 83.32177 38539 20.646073 18.118233 12.097986 19.99456 38540 149.585764 131.270957 96.570986 159.60457 cond06.rep0017 cond08.rep0026 cond10.rep0030 cond10.rep0031 38536 3.664024 10.17531 8.42373 10.23710 38537 86.879281 292.03054 211.65193 257.21400 38538 28.691911 137.18139 83.99967 102.08218 38539 6.885139 30.93155 34.28579 41.66645 38540 54.959948 251.69850 195.69687 237.82431 cond00.rep0027 38536 7.205773 38537 281.696762 38538 62.494255 38539 27.780252 38540 201.274606 6346 more rows ... $abundance 38536 38537 38538 38539 38540 -12.665286 -9.334396 -10.362213 -11.655346 -9.639765 6346 more elements ... > # liklihood ratio statistics > results=glmLRT(z, fit, coef = 4) > show(results) An object of class "DGELRT" $samples group lib.size norm.factors cond07.rep0020 cond07 1866963 0.9916452 cond07.rep0021 cond07 2364994 0.9783033 cond07.rep0022 cond07 1712838 1.0023115 cond08.rep0023 cond08 2782920 1.0357778 cond08.rep0025 cond08 2780054 0.9724019 12 more rows ... $all.zeros 38536 38537 38538 38539 38540 FALSE FALSE FALSE FALSE FALSE 6346 more elements ... $common.dispersion [1] 0.1012043 $table logConc logFC LR.statistic p.value 38536 -12.665286 0.70566616 1.386712521 0.2389611 38537 -9.334396 -0.01566848 0.001650326 0.9675955 38538 -10.362213 0.55831094 1.833689742 0.1756924 38539 -11.655346 -0.33111151 0.467320591 0.4942224 38540 -9.639765 -0.19132218 0.240419682 0.6239032 6346 more rows ... $coefficients.full (Intercept) model.factorscond06 model.factorscond07 model.factorscond08 38536 -12.885005 0.48913051 0.2967643 0.1212677129 38537 -9.219056 -0.01086056 -0.1339230 -0.1877871229 38538 -10.724813 0.38699166 0.3731979 0.5624151466 38539 -11.535562 -0.22950901 -0.3546394 -0.1163631383 38540 -9.555217 -0.13261443 -0.2158204 -0.0002525462 model.factorscond10 38536 0.1592107 38537 -0.2828476 38538 0.2987787 38539 0.2134461 38540 -0.0250628 6346 more rows ... $coefficients.null (Intercept) model.factorscond07 model.factorscond08 model.factorscond10 38536 -12.627557 0.03931612 -0.13618044 -0.09823747 38537 -9.224403 -0.12857630 -0.18244047 -0.27750097 38538 -10.518433 0.16681834 0.35603561 0.09239918 38539 -11.634560 -0.25564159 -0.01736528 0.31244392 38540 -9.618361 -0.15267688 0.06289095 0.03808070 6346 more rows ... $design.full (Intercept) model.factorscond06 model.factorscond07 model.factorscond08 1 1 0 1 0 2 1 0 1 0 3 1 0 1 0 4 1 0 0 1 5 1 0 0 1 model.factorscond10 1 0 2 0 3 0 4 0 5 0 12 more rows ... $design.null (Intercept) model.factorscond07 model.factorscond08 model.factorscond10 1 1 1 0 0 2 1 1 0 0 3 1 1 0 0 4 1 0 1 0 5 1 0 1 0 12 more rows ... $dispersion.used [1] 0.1012043 $comparison [1] "model.factorscond06" I hope this helps! Nick On 8 October 2011 02:02, Gordon K Smyth <smyth@wehi.edu.au> wrote: > Hi Nick, > > We haven't seen anything like this. Here are some suggestions. > > Given your experimental layout with multiple groups but no covariates, you > could use the "classic" edgeR functions: > > estimateCommonDisp(y) > exactTest() > topTags() > > rather than the glm code. This would allow you to compare any two of your > groups. What results does this give? > > Can you try updating to edgeR 2.3.52 (the current devel version) to see it > makes a difference? (There are many changes, but one is that exactTest() is > much faster now.) > > Thanks for including output from your objects, but I find that I can't get > much information from the str(y) output. Could you please use the show > method for these objects, e.g., show(y)? > > Finally, would you be willing to share some of your date with us offline so > that we can trouble-shoot? As I said, we haven't seen this behaviour. > > Best wishes > Gordon > > Date: Fri, 7 Oct 2011 10:10:21 +0100 >> From: Nick Schurch <n.schurch@dundee.ac.uk> >> To: bioconductor@r-project.org >> Subject: [BioC] Problems with edgeR for differential expression. >> >> Dear Bioconductors, >> >> I'm trying to use edgeR to compute the differential expression from some >> RNA-Seq data, but it seems to be generating some odd results. >> >> I'm running edgeR on data on about 6000 genes, across 5 experimental >> conditions. When I compute the differential expression for any two of these >> conditions edgeR it is returning a 'nan' fold-change for about 1500 out of >> the 6000 genes being tested. Amazingly, it is also returning p-values for >> these fold-changes, and the p-values cover a range of values from total >> insignificant (>0.5) to very significant (<1E-4)! At first I thought if >> might be because there is sometimes no signal for a gene in a given >> condition, but 1) other cases of this nature produce '-inf' or 'inf' >> fold-changes, not 'nan' fold-hanges, and 2) in some cases edgeR is >> calculating a 'nan' fold-change for something that has signal in every >> replicate of both conditions! I've checked everything I can think of... I >> don't get any errors or warnings, the Norm Factors are sensible, the raw >> signal is sensible, all the objects look well-formed (i.e. like they contain >> all the bits they should contain).... its very confusing and frustrating. >> >> So, am I doing something wrong? Or is there a deeper problem with this >> package? >> >> I'm using R v 2.13.1, edgeR 2.2.5 and the commands I'm using are: >> >> # create the design matrix >>> model.groups<-groups >>> model.factors<-as.factor(**model.groups) >>> model<-model.matrix(~model.**factors) >>> >>> # build DGElist and calculate normalization factors >>> x=as.data.frame(data) >>> rownames(x)=genenames >>> y=DGEList(x,group=groups) >>> y=calcNormFactors(y) >>> str(y) >>> >> >> Formal class 'DGEList' [package "edgeR"] with 1 slots >> ..@ .Data:List of 3 >> .. ..$ :'data.frame': 17 obs. of 3 variables: >> .. .. ..$ group : Factor w/ 5 levels "cond00","cond06",..: 3 3 3 4 >> 4 >> 4 3 3 1 1 ... >> .. .. ..$ lib.size : num [1:17] 1866963 2364994 1712838 2782920 >> 2780054 >> ... >> .. .. ..$ norm.factors: num [1:17] 0.992 0.978 1.002 1.036 0.972 ... >> .. ..$ : num [1:6351, 1:17] 7 187 44 0 98 ... >> .. .. ..- attr(*, "dimnames")=List of 2 >> .. .. .. ..$ : chr [1:6351] "38536" "38537" "38538" "38539" ... >> .. .. .. ..$ : chr [1:17] "cond07.rep0020" "cond07.rep0021" >> "cond07.rep0022" "cond08.rep0023" ... >> .. ..$ : Named logi [1:6351] FALSE FALSE FALSE FALSE FALSE FALSE ... >> .. .. ..- attr(*, "names")= chr [1:6351] "38536" "38537" "38538" "38539" >> ... >> >> >>> # estimate dispersion and fit models >>> z=estimateGLMCommonDisp(y, design)) >>> str(z) >>> >> >> Formal class 'DGEList' [package "edgeR"] with 1 slots >> ..@ .Data:List of 4 >> .. ..$ :'data.frame': 17 obs. of 3 variables: >> .. .. ..$ group : Factor w/ 5 levels "cond00","cond06",..: 3 3 3 4 >> 4 >> 4 3 3 1 1 ... >> .. .. ..$ lib.size : num [1:17] 1866963 2364994 1712838 2782920 >> 2780054 >> ... >> .. .. ..$ norm.factors: num [1:17] 0.992 0.978 1.002 1.036 0.972 ... >> .. ..$ : num [1:6351, 1:17] 7 187 44 0 98 ... >> .. .. ..- attr(*, "dimnames")=List of 2 >> .. .. .. ..$ : chr [1:6351] "38536" "38537" "38538" "38539" ... >> .. .. .. ..$ : chr [1:17] "cond07.rep0020" "cond07.rep0021" >> "cond07.rep0022" "cond08.rep0023" ... >> .. ..$ : Named logi [1:6351] FALSE FALSE FALSE FALSE FALSE FALSE ... >> .. .. ..- attr(*, "names")= chr [1:6351] "38536" "38537" "38538" "38539" >> ... >> .. ..$ : num 0.101 >> >> >>> fit<-glmFit(z,model,**dispersion=z$common.**dispersion) >>> str(fit) >>> >> >> Formal class 'DGEGLM' [package "edgeR"] with 1 slots >> ..@ .Data:List of 12 >> .. ..$ : num [1:6351, 1:5] -12.89 -9.22 -10.72 -11.54 -9.56 ... >> .. .. ..- attr(*, "dimnames")=List of 2 >> .. .. .. ..$ : chr [1:6351] "38536" "38537" "38538" "38539" ... >> .. .. .. ..$ : chr [1:5] "(Intercept)" "model.factorscond06" >> "model.factorscond07" "model.factorscond08" ... >> .. ..$ : int [1:6351] 12 12 12 12 12 12 12 12 12 12 ... >> .. ..$ : Named num [1:6351] 23.3 3.45 30.57 25.89 3.07 ... >> .. .. ..- attr(*, "names")= chr [1:6351] "38536" "38537" "38538" "38539" >> ... >> .. ..$ : num [1:17, 1:5] 1 1 1 1 1 1 1 1 1 1 ... >> .. .. ..- attr(*, "dimnames")=List of 2 >> .. .. .. ..$ : chr [1:17] "1" "2" "3" "4" ... >> .. .. .. ..$ : chr [1:5] "(Intercept)" "model.factorscond06" >> "model.factorscond07" "model.factorscond08" ... >> .. .. ..- attr(*, "assign")= int [1:5] 0 1 1 1 1 >> .. .. ..- attr(*, "contrasts")=List of 1 >> .. .. .. ..$ model.factors: chr "contr.treatment" >> .. ..$ : num [1:6351, 1:17] 14.4 14.4 14.4 14.4 14.4 ... >> .. ..$ :'data.frame': 17 obs. of 3 variables: >> .. .. ..$ group : Factor w/ 5 levels "cond00","cond06",..: 3 3 3 4 >> 4 >> 4 3 3 1 1 ... >> .. .. ..$ lib.size : num [1:17] 1866963 2364994 1712838 2782920 >> 2780054 >> ... >> .. .. ..$ norm.factors: num [1:17] 0.992 0.978 1.002 1.036 0.972 ... >> .. ..$ : NULL >> .. ..$ : num 0.101 >> .. ..$ : num [1:17] 1851365 2313681 1716797 2882487 2703330 ... >> .. ..$ : NULL >> .. ..$ : num [1:6351, 1:17] 6.32 160.53 59.13 12.7 105.68 ... >> .. .. ..- attr(*, "dimnames")=List of 2 >> .. .. .. ..$ : chr [1:6351] "38536" "38537" "38538" "38539" ... >> .. .. .. ..$ : chr [1:17] "cond07.rep0020" "cond07.rep0021" >> "cond07.rep0022" "cond08.rep0023" ... >> .. ..$ : Named num [1:6351] -12.67 -9.33 -10.36 -11.66 -9.64 ... >> .. .. ..- attr(*, "names")= chr [1:6351] "38536" "38537" "38538" "38539" >> ... >> >> >>> # liklihood ratio statistics >>> results=glmLRT(z, fit, coef = 4) >>> str(results) >>> >> >> Formal class 'DGELRT' [package "edgeR"] with 1 slots >> ..@ .Data:List of 10 >> .. ..$ :'data.frame': 17 obs. of 3 variables: >> .. .. ..$ group : Factor w/ 5 levels "cond00","cond06",..: 3 3 3 4 >> 4 >> 4 3 3 1 1 ... >> .. .. ..$ lib.size : num [1:17] 1866963 2364994 1712838 2782920 >> 2780054 >> ... >> .. .. ..$ norm.factors: num [1:17] 0.992 0.978 1.002 1.036 0.972 ... >> .. ..$ : Named logi [1:6351] FALSE FALSE FALSE FALSE FALSE FALSE ... >> .. .. ..- attr(*, "names")= chr [1:6351] "38536" "38537" "38538" "38539" >> ... >> .. ..$ : num 0.101 >> .. ..$ :'data.frame': 6351 obs. of 4 variables: >> .. .. ..$ logConc : num [1:6351] -12.67 -9.33 -10.36 -11.66 -9.64 ... >> .. .. ..$ logFC : num [1:6351] 0.7057 -0.0157 0.5583 -0.3311 >> -0.1913 >> ... >> .. .. ..$ LR.statistic: num [1:6351] 1.38671 0.00165 1.83369 0.46732 >> 0.24042 ... >> .. .. ..$ p.value : num [1:6351] 0.239 0.968 0.176 0.494 0.624 ... >> .. ..$ : num [1:6351, 1:5] -12.89 -9.22 -10.72 -11.54 -9.56 ... >> .. .. ..- attr(*, "dimnames")=List of 2 >> .. .. .. ..$ : chr [1:6351] "38536" "38537" "38538" "38539" ... >> .. .. .. ..$ : chr [1:5] "(Intercept)" "model.factorscond06" >> "model.factorscond07" "model.factorscond08" ... >> .. ..$ : num [1:6351, 1:4] -12.63 -9.22 -10.52 -11.63 -9.62 ... >> .. .. ..- attr(*, "dimnames")=List of 2 >> .. .. .. ..$ : chr [1:6351] "38536" "38537" "38538" "38539" ... >> .. .. .. ..$ : chr [1:4] "(Intercept)" "model.factorscond07" >> "model.factorscond08" "model.factorscond10" >> .. ..$ : num [1:17, 1:5] 1 1 1 1 1 1 1 1 1 1 ... >> .. .. ..- attr(*, "dimnames")=List of 2 >> .. .. .. ..$ : chr [1:17] "1" "2" "3" "4" ... >> .. .. .. ..$ : chr [1:5] "(Intercept)" "model.factorscond06" >> "model.factorscond07" "model.factorscond08" ... >> .. .. ..- attr(*, "assign")= int [1:5] 0 1 1 1 1 >> .. .. ..- attr(*, "contrasts")=List of 1 >> .. .. .. ..$ model.factors: chr "contr.treatment" >> .. ..$ : num [1:17, 1:4] 1 1 1 1 1 1 1 1 1 1 ... >> .. .. ..- attr(*, "dimnames")=List of 2 >> .. .. .. ..$ : chr [1:17] "1" "2" "3" "4" ... >> .. .. .. ..$ : chr [1:4] "(Intercept)" "model.factorscond07" >> "model.factorscond08" "model.factorscond10" >> .. ..$ : num 0.101 >> .. ..$ : chr "model.factorscond06" >> >> These are some examples of the results: >> >> -------------------- >> edgeR out: >> >> LR.statistic: 0.975075487675 >> logConc: -13.3931239073 >> p.value: 0.323417620577 >> logFC: NaN >> geneID: 38956 >> >> Raw data: >> >> Condition 1: >> replicate 1: 2,372,532 good reads, female: 8.0 >> replicate 2: 3,966,968 good reads, female: no signal >> replicate 3: 1,389,571 good reads, male: no signal >> >> Condition 2: >> replicate 1: 3,102,608 good reads, male: 5.0 >> replicate 2: 3,451,983 good reads, male: no signal >> replicate 3: 2,892,192 good reads, male: no signal >> replicate 4: 3,620,124 good reads, male: 5.0 >> replicate 5: 2,640,968 good reads, female: no signal >> -------------------- >> >> -------------------- >> edgeR out: >> >> LR.statistic: 3.57045101322 >> logConc: -13.6684814046 >> p.value: 0.0588163345523 >> logFC: NaN >> geneID: 38959 >> >> Raw data: >> >> Condition 1: >> replicate 1: 2,372,532 good reads, female: 5.0 >> replicate 2: 3,966,968 good reads, female: 5.0 >> replicate 3: 1,389,571 good reads, male: no signal >> >> Condition 2: >> >> replicate 1: 3,102,608 good reads, male: no signal >> replicate 2: 3,451,983 good reads, male: no signal >> replicate 3: 2,892,192 good reads, male: 7.0 >> replicate 4: 3,620,124 good reads, male: no signal >> replicate 5: 2,640,968 good reads, female: no signal >> -------------------- >> >> -------------------- >> edgeR out: >> >> >> LR.statistic: 1.81602638091 >> logConc: -11.265720531 >> p.value: 0.177786996458 >> logFC: NaN >> geneID: 38965 >> >> Raw data: >> >> Condition 1: >> >> replicate 1: 2,372,532 good reads, female: 22.0 >> replicate 2: 3,966,968 good reads, female: 27.0 >> replicate 3: 1,389,571 good reads, male: 5.0 >> >> Condition 2: >> >> replicate 1: 3,102,608 good reads, male: 26.0 >> replicate 2: 3,451,983 good reads, male: 26.0 >> replicate 3: 2,892,192 good reads, male: 9.0 >> replicate 4: 3,620,124 good reads, male: 36.0 >> replicate 5: 2,640,968 good reads, female: 55.0 >> -------------------- >> >> >> -- >> Cheers, >> >> Nick Schurch >> >> Data Analysis Group (The Barton Group), >> School of Life Sciences, >> University of Dundee, >> Dow St, >> Dundee, >> DD1 5EH, >> Scotland, >> UK >> >> Tel: +44 1382 388707 >> Fax: +44 1382 345 893 >> > > ______________________________**______________________________**____ ______ > The information in this email is confidential and intended solely for the > addressee. > You must not disclose, forward, print or use it without the permission of > the sender. > ______________________________**______________________________**____ ______ > > -- > Cheers, > > Nick Schurch > > Data Analysis Group (The Barton Group), > School of Life Sciences, > University of Dundee, > Dow St, > Dundee, > DD1 5EH, > Scotland, > UK > > Tel: +44 1382 388707 > Fax: +44 1382 345 893 > > -- Cheers, Nick Schurch Data Analysis Group (The Barton Group), School of Life Sciences, University of Dundee, Dow St, Dundee, DD1 5EH, Scotland, UK Tel: +44 1382 388707 Fax: +44 1382 345 893 [[alternative HTML version deleted]]