Hello everyone: a wish to know if there is one way of doing an anova like test in edgeR, comparing all the possibilities between the groups defined in the experimental design. in the users guide the authors explain one way anova test, but the comparison is done only against the intercept group, is there  a way of doing a general test comparing all the posibilities and not only against the intercept? also i'd like to know what does the following differential expression analysis does because the output is different when i include an intercept, i can't deduce what comparison are being doing.... > grp<-c("D10","D10","D20","D20","D40","D40","D60","D60") >  dge = DGEList(counts=general.m, group=grp) >  dge = calcNormFactors(dge) > design <- model.matrix(~0+grp, data=dge$samples) Warning message: In model.matrix.default(~0 + grp, data = dge$samples) :   variable 'grp' converted to a factor >  design        grpD10 grpD20 grpD40 grpD60 i10re1      1      0      0      0 i10re2      1      0      0      0 i20re1      0      1      0      0 i20re2      0      1      0      0 i40re1      0      0      1      0 i40re2      0      0      1      0 i60re1      0      0      0      1 i60re2      0      0      0      1 attr(,"assign") [1] 1 1 1 1 attr(,"contrasts") attr(,"contrasts")$grp [1] "contr.treatment" > dge <- estimateGLMCommonDisp(dge,design) > dge <- estimateGLMTrendedDisp(dge,design) Loading required package: splines >  dge <- estimateGLMTagwiseDisp(dge,design) >  fit2 <- glmFit(dge, design) > lrt.anova_like <- glmLRT(fit2, coef=2:4) > lrt.anova_like An object of class "DGELRT" $coefficients               grpD10    grpD20    grpD40    grpD60 comp101_c0 -12.55010 -16.12974 -13.27081 -16.12974 comp103_c0 -13.85721 -13.35290 -12.61155 -13.96824 comp120_c0 -12.33347 -13.98877 -16.12974 -16.12974 comp179_c0 -12.55135 -13.98566 -16.12974 -13.96827 comp192_c0 -13.85721 -13.98569 -13.27548 -12.67228 23484 more rows ... $fitted.values               i10re1    i10re2    i20re1    i20re2   i40re1   i40re2 i60re1 comp101_c0 2.0108643 1.9941877 0.0000000 0.0000000 0.841309 1.168177 0.0000000 comp103_c0 0.5020816 0.4979177 1.1140306 0.8859780 1.674705 2.325366 0.5414430 comp120_c0 2.5112747 2.4904480 0.5552870 0.4416145 0.000000 0.000000 0.0000000 comp179_c0 2.0083306 1.9916749 0.5570245 0.4429963 0.000000 0.000000 0.5414282 comp192_c0 0.5020816 0.4979177 0.5570036 0.4429797 0.837345 1.162673 2.1657497               i60re2 comp101_c0 0.0000000 comp103_c0 0.4585719 comp120_c0 0.0000000 comp179_c0 0.4585593 comp192_c0 1.8342685 23484 more rows ... $deviance comp101_c0 comp103_c0 comp120_c0 comp179_c0 comp192_c0   4.386443   3.070288   2.848548   3.917710   2.629182 23484 more elements ... $df.residual [1] 4 4 4 4 4 23484 more elements ... $abundance [1] -13.63381 -13.35715 -13.64240 -13.64482 -13.35716 23484 more elements ... $design        grpD10 grpD20 grpD40 grpD60 i10re1      1      0      0      0 i10re2      1      0      0      0 i20re1      0      1      0      0 i20re2      0      1      0      0 i40re1      0      0      1      0 i40re2      0      0      1      0 i60re1      0      0      0      1 i60re2      0      0      0      1 attr(,"assign") [1] 1 1 1 1 attr(,"contrasts") attr(,"contrasts")$grp [1] "contr.treatment" $offset          [,1]     [,2]     [,3]     [,4]     [,5]     [,6]     [,7] [,8] [1,] 13.27698 13.26865 13.52514 13.29609 13.15729 13.48553 13.47707 13.31095 [2,] 13.27698 13.26865 13.52514 13.29609 13.15729 13.48553 13.47707 13.31095 [3,] 13.27698 13.26865 13.52514 13.29609 13.15729 13.48553 13.47707 13.31095 [4,] 13.27698 13.26865 13.52514 13.29609 13.15729 13.48553 13.47707 13.31095 [5,] 13.27698 13.26865 13.52514 13.29609 13.15729 13.48553 13.47707 13.31095 23484 more rows ... $dispersion [1] 0.2161256308 0.0003318302 0.0634519684 0.0003270568 0.0003318016 23484 more elements ... $method [1] "oneway" $samples        group lib.size norm.factors i10re1   D10   594988    0.9808651 i10re2   D10   590850    0.9795431 i20re1   D20   723182    1.0343009 i20re2   D20   567524    1.0481805 i40re1   D40   452242    1.1449003 i40re2   D40   671656    1.0703965 i60re1   D60   801682    0.8892299 i60re2   D60   685351    0.8809633 $table            logFC.grpD20 logFC.grpD40 logFC.grpD60    logCPM LR comp101_c0    -23.27030    -19.14573    -23.27030 0.2621324 647.0115 comp103_c0    -19.26416    -18.19462    -20.15192 0.6612798 194043.3654 comp120_c0    -20.18153    -23.27030    -23.27030 0.2497509 1999.3276 comp179_c0    -20.17704    -23.27030    -20.15196 0.2462508 196431.3868 comp192_c0    -20.17709    -19.15248    -18.28224 0.6612611 194057.4372                   PValue comp101_c0 6.475824e-140 comp103_c0  0.000000e+00 comp120_c0  0.000000e+00 comp179_c0  0.000000e+00 comp192_c0  0.000000e+00 23484 more rows ... $comparison [1] "grpD20" "grpD40" "grpD60" $df.test [1] 3 3 3 3 3 23484 more elements ... > top<-topTags(lrt.anova_like) > top Coefficient:  grpD20 grpD40 grpD60             logFC.grpD20 logFC.grpD40 logFC.grpD60    logCPM LR PValue comp628_c0      -23.2703    -23.27030    -19.23749 0.2462499 196434.924      0 comp2607_c0     -23.2703    -20.07173    -18.68223 0.4686227 195146.814      0 comp2858_c0     -23.2703    -23.27030    -16.85200 1.1206869 192585.615      0 comp2885_c0     -23.2703    -18.60303    -17.96733 0.6593429 4326.878      0 comp2904_c0     -23.2703    -23.27030    -16.21482 1.8318474 2065.059      0 comp2912_c0     -23.2703    -17.88139    -20.15196 1.1207038 192646.480      0 comp2933_c0     -23.2703    -18.59551    -18.68219 0.4686259 195111.455      0 comp2950_c0     -23.2703    -19.16586    -17.97899 0.4607410 1715.721      0 comp2999_c0     -23.2703    -23.27030    -23.27030 1.2524134 3853.539      0 comp3028_c0     -23.2703    -19.15248    -17.30519 0.9831770 192775.649      0             FDR comp628_c0    0 comp2607_c0   0 comp2858_c0   0 comp2885_c0   0 comp2904_c0   0 comp2912_c0   0 comp2933_c0   0 comp2950_c0   0 comp2999_c0   0 comp3028_c0   0 > lrt.anova_like <- glmLRT(fit2, coef=1:4) Error in mglmLevenberg(y, design = design, dispersion = dispersion, offset = offset,  :   BLAS/LAPACK routine 'DGEMM ' gave error code -13 The DGELRT object says that is comparing $comparison [1] "grpD20" "grpD40" "grpD60" but what exactly this means? Luis [[alternative HTML version deleted]]

Luis, Note that your experimental design is the same whether or not you include an intercept in the design matrix. If you include an intercept, then the intercept represents the level in the D10 group, and the other three coefficients represent the differences between D20/40/60 and D10. So with an intercept, you would specify coef=2:4 to do an ANOVA-like test for any differences among all four groups. If you choose not to use an intercept term, then the design matrix is just a different parametrization of the same design. Instead of representing differences, each coefficient simply represents the expression level in a group. So you can still perform the same test, you simply have to specify the contrasts (differences) explicitly: > anova.contrasts <- makeContrasts(contrasts=c("grpD20-grpD10", "grpD40-grpD10", "grpD60-grpD10"), levels=design) > print(anovalike.contrasts) Contrasts Levels grpD20-grpD10 grpD40-grpD10 grpD60-grpD10 grpD10 -1 -1 -1 grpD20 1 0 0 grpD40 0 1 0 grpD60 0 0 1 > glmLRT(fit2, contrast=anova.contrasts) Hope this helps, -Ryan Thompson On 03/11/2013 05:18 PM, Luis alberto Martinez lopez wrote: > Hello everyone: > > a wish to know if there is one way of doing an anova like test in edgeR, comparing all the possibilities between the groups defined in the experimental design. > > in the users guide the authors explain one way anova test, but the comparison is done only against the intercept group, is there a way of doing a general test comparing all the posibilities and not only against the intercept? > > also i'd like to know what does the following differential expression analysis does because the output is different when i include an intercept, i can't deduce what comparison are being doing.... > >> grp<-c("D10","D10","D20","D20","D40","D40","D60","D60") >> dge = DGEList(counts=general.m, group=grp) >> dge = calcNormFactors(dge) >> design <- model.matrix(~0+grp, data=dge$samples) > Warning message: > In model.matrix.default(~0 + grp, data = dge$samples) : > variable 'grp' converted to a factor >> design > grpD10 grpD20 grpD40 grpD60 > i10re1 1 0 0 0 > i10re2 1 0 0 0 > i20re1 0 1 0 0 > i20re2 0 1 0 0 > i40re1 0 0 1 0 > i40re2 0 0 1 0 > i60re1 0 0 0 1 > i60re2 0 0 0 1 > attr(,"assign") > [1] 1 1 1 1 > attr(,"contrasts") > attr(,"contrasts")$grp > [1] "contr.treatment" > >> dge <- estimateGLMCommonDisp(dge,design) >> dge <- estimateGLMTrendedDisp(dge,design) > Loading required package: splines >> dge <- estimateGLMTagwiseDisp(dge,design) >> fit2 <- glmFit(dge, design) >> lrt.anova_like <- glmLRT(fit2, coef=2:4) >> lrt.anova_like > An object of class "DGELRT" > $coefficients > grpD10 grpD20 grpD40 grpD60 > comp101_c0 -12.55010 -16.12974 -13.27081 -16.12974 > comp103_c0 -13.85721 -13.35290 -12.61155 -13.96824 > comp120_c0 -12.33347 -13.98877 -16.12974 -16.12974 > comp179_c0 -12.55135 -13.98566 -16.12974 -13.96827 > comp192_c0 -13.85721 -13.98569 -13.27548 -12.67228 > 23484 more rows ... > > $fitted.values > i10re1 i10re2 i20re1 i20re2 i40re1 i40re2 i60re1 > comp101_c0 2.0108643 1.9941877 0.0000000 0.0000000 0.841309 1.168177 0.0000000 > comp103_c0 0.5020816 0.4979177 1.1140306 0.8859780 1.674705 2.325366 0.5414430 > comp120_c0 2.5112747 2.4904480 0.5552870 0.4416145 0.000000 0.000000 0.0000000 > comp179_c0 2.0083306 1.9916749 0.5570245 0.4429963 0.000000 0.000000 0.5414282 > comp192_c0 0.5020816 0.4979177 0.5570036 0.4429797 0.837345 1.162673 2.1657497 > i60re2 > comp101_c0 0.0000000 > comp103_c0 0.4585719 > comp120_c0 0.0000000 > comp179_c0 0.4585593 > comp192_c0 1.8342685 > 23484 more rows ... > > $deviance > comp101_c0 comp103_c0 comp120_c0 comp179_c0 comp192_c0 > 4.386443 3.070288 2.848548 3.917710 2.629182 > 23484 more elements ... > > $df.residual > [1] 4 4 4 4 4 > 23484 more elements ... > > $abundance > [1] -13.63381 -13.35715 -13.64240 -13.64482 -13.35716 > 23484 more elements ... > > $design > grpD10 grpD20 grpD40 grpD60 > i10re1 1 0 0 0 > i10re2 1 0 0 0 > i20re1 0 1 0 0 > i20re2 0 1 0 0 > i40re1 0 0 1 0 > i40re2 0 0 1 0 > i60re1 0 0 0 1 > i60re2 0 0 0 1 > attr(,"assign") > [1] 1 1 1 1 > attr(,"contrasts") > attr(,"contrasts")$grp > [1] "contr.treatment" > > > $offset > [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] > [1,] 13.27698 13.26865 13.52514 13.29609 13.15729 13.48553 13.47707 13.31095 > [2,] 13.27698 13.26865 13.52514 13.29609 13.15729 13.48553 13.47707 13.31095 > [3,] 13.27698 13.26865 13.52514 13.29609 13.15729 13.48553 13.47707 13.31095 > [4,] 13.27698 13.26865 13.52514 13.29609 13.15729 13.48553 13.47707 13.31095 > [5,] 13.27698 13.26865 13.52514 13.29609 13.15729 13.48553 13.47707 13.31095 > 23484 more rows ... > > $dispersion > [1] 0.2161256308 0.0003318302 0.0634519684 0.0003270568 0.0003318016 > 23484 more elements ... > > $method > [1] "oneway" > > $samples > group lib.size norm.factors > i10re1 D10 594988 0.9808651 > i10re2 D10 590850 0.9795431 > i20re1 D20 723182 1.0343009 > i20re2 D20 567524 1.0481805 > i40re1 D40 452242 1.1449003 > i40re2 D40 671656 1.0703965 > i60re1 D60 801682 0.8892299 > i60re2 D60 685351 0.8809633 > > $table > logFC.grpD20 logFC.grpD40 logFC.grpD60 logCPM LR > comp101_c0 -23.27030 -19.14573 -23.27030 0.2621324 647.0115 > comp103_c0 -19.26416 -18.19462 -20.15192 0.6612798 194043.3654 > comp120_c0 -20.18153 -23.27030 -23.27030 0.2497509 1999.3276 > comp179_c0 -20.17704 -23.27030 -20.15196 0.2462508 196431.3868 > comp192_c0 -20.17709 -19.15248 -18.28224 0.6612611 194057.4372 > PValue > comp101_c0 6.475824e-140 > comp103_c0 0.000000e+00 > comp120_c0 0.000000e+00 > comp179_c0 0.000000e+00 > comp192_c0 0.000000e+00 > 23484 more rows ... > > $comparison > [1] "grpD20" "grpD40" "grpD60" > > $df.test > [1] 3 3 3 3 3 > 23484 more elements ... > > >> top<-topTags(lrt.anova_like) >> top > Coefficient: grpD20 grpD40 grpD60 > logFC.grpD20 logFC.grpD40 logFC.grpD60 logCPM LR PValue > comp628_c0 -23.2703 -23.27030 -19.23749 0.2462499 196434.924 0 > comp2607_c0 -23.2703 -20.07173 -18.68223 0.4686227 195146.814 0 > comp2858_c0 -23.2703 -23.27030 -16.85200 1.1206869 192585.615 0 > comp2885_c0 -23.2703 -18.60303 -17.96733 0.6593429 4326.878 0 > comp2904_c0 -23.2703 -23.27030 -16.21482 1.8318474 2065.059 0 > comp2912_c0 -23.2703 -17.88139 -20.15196 1.1207038 192646.480 0 > comp2933_c0 -23.2703 -18.59551 -18.68219 0.4686259 195111.455 0 > comp2950_c0 -23.2703 -19.16586 -17.97899 0.4607410 1715.721 0 > comp2999_c0 -23.2703 -23.27030 -23.27030 1.2524134 3853.539 0 > comp3028_c0 -23.2703 -19.15248 -17.30519 0.9831770 192775.649 0 > FDR > comp628_c0 0 > comp2607_c0 0 > comp2858_c0 0 > comp2885_c0 0 > comp2904_c0 0 > comp2912_c0 0 > comp2933_c0 0 > comp2950_c0 0 > comp2999_c0 0 > comp3028_c0 0 >> lrt.anova_like <- glmLRT(fit2, coef=1:4) > Error in mglmLevenberg(y, design = design, dispersion = dispersion, offset = offset, : > BLAS/LAPACK routine 'DGEMM ' gave error code -13 > > > > > > The DGELRT object says that is comparing > > $comparison > [1] "grpD20" "grpD40" "grpD60" > > but what exactly this means? > > Luis > [[alternative HTML version deleted]] > > > > _______________________________________________ > Bioconductor mailing list > Bioconductor@r-project.org > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor [[alternative HTML version deleted]]

Luis, Your design has 4 groups,which means that the inter-group ANOVA-like test has 4 - 1 = 3 degrees of freedom. Hence, the ANOVA-like test is performed by testing 3 coefficients/contrasts equal to zero. The procedures I have described will yield will test where the null hypothesis is all group means being equal, and the alternative hypothesis is that any two or more group means differ significantly from each other. It is true that between 4 groups, there are 6 possible pairwise comparisons to be made. If you want individual significance rankings for each of the 6 possible contrasts, you can do that too, by making 6 separate calls to glmLRT, each with a single coefficient or contrast. Doing so will give you an appropriate rank order of significance for each contrast, but the exact FDR values will probably be too liberal, since there is no correction for the fact that you are performing 6 tests on each gene instead of just 1. Ideally, you would use a post-hoc testing method to assess individual contrasts within genes that are called significant by their FDR in the ANOVA-like test. However, I'm not sure how to perform such tests with the negative binomial distribution. You might consider using the limma-voom method instead of edgeR, since that will perform an actual ANOVA based on the normal distribution, so standard ANOVA post-hoc analysis is possible. If anyone has a take on how to do rigorous post-hoc testing on negative binomial GLMs, I'd be interested in hearing about it. If you just want the logFC values for the missing 3 contrasts, then simply note that they can all be written in terms of the X vs 10 contrasts. For example, the 20 vs 40 contrast would be the 10 vs 40 contrast minus the 10 vs 20 contrast. -Ryan On 03/12/2013 06:42 PM, Luis alberto Martinez lopez wrote: > thank you Ryan for your clear answer: > but in your answer again only considered the comparison versus the 10 > group(the first) > i dont't have a control group so i'm looking for a way of doing an > anova like test in edgeR that compares 10 vs 20 ,20 vs 40, 40 vs 60,10 > vs 40, 10 vs 60,20 vs 60 ie all the posibilities at the same time and > that give me a list with the genes more differential in any condition, > thanks in advance, > Luis > *De:* Ryan C. Thompson <rct@thompsonclan.org> > *Para:* Luis alberto Martinez lopez <lewisluis2013@yahoo.com> > *CC:* "bioconductor@r-project.org" <bioconductor@r-project.org> > *Enviado:* Lunes, 11 de marzo, 2013 22:46:19 > *Asunto:* Re: [BioC] anova like test in edgeR > > Luis, > > Note that your experimental design is the same whether or not you > include an intercept in the design matrix. If you include an > intercept, then the intercept represents the level in the D10 group, > and the other three coefficients represent the differences between > D20/40/60 and D10. So with an intercept, you would specify coef=2:4 to > do an ANOVA-like test for any differences among all four groups. > > If you choose not to use an intercept term, then the design matrix is > just a different parametrization of the same design. Instead of > representing differences, each coefficient simply represents the > expression level in a group. So you can still perform the same test, > you simply have to specify the contrasts (differences) explicitly: > > > anova.contrasts <- makeContrasts(contrasts=c("grpD20-grpD10", > "grpD40-grpD10", "grpD60-grpD10"), levels=design) > > print(anovalike.contrasts) > Contrasts > Levels grpD20-grpD10 grpD40-grpD10 grpD60-grpD10 > grpD10 -1 -1 -1 > grpD20 1 0 0 > grpD40 0 1 0 > grpD60 0 0 1 > > glmLRT(fit2, contrast=anova.contrasts) > > Hope this helps, > > -Ryan Thompson > > > On 03/11/2013 05:18 PM, Luis alberto Martinez lopez wrote: >> Hello everyone: >> >> a wish to know if there is one way of doing an anova like test in edgeR, comparing all the possibilities between the groups defined in the experimental design. >> >> in the users guide the authors explain one way anova test, but the comparison is done only against the intercept group, is there a way of doing a general test comparing all the posibilities and not only against the intercept? >> >> also i'd like to know what does the following differential expression analysis does because the output is different when i include an intercept, i can't deduce what comparison are being doing.... >> >>> grp<-c("D10","D10","D20","D20","D40","D40","D60","D60") >>> dge = DGEList(counts=general.m, group=grp) >>> dge = calcNormFactors(dge) >>> design <- model.matrix(~0+grp, data=dge$samples) >> Warning message: >> In model.matrix.default(~0 + grp, data = dge$samples) : >> variable 'grp' converted to a factor >>> design >> grpD10 grpD20 grpD40 grpD60 >> i10re1 1 0 0 0 >> i10re2 1 0 0 0 >> i20re1 0 1 0 0 >> i20re2 0 1 0 0 >> i40re1 0 0 1 0 >> i40re2 0 0 1 0 >> i60re1 0 0 0 1 >> i60re2 0 0 0 1 >> attr(,"assign") >> [1] 1 1 1 1 >> attr(,"contrasts") >> attr(,"contrasts")$grp >> [1] "contr.treatment" >> >>> dge <- estimateGLMCommonDisp(dge,design) >>> dge <- estimateGLMTrendedDisp(dge,design) >> Loading required package: splines >>> dge <- estimateGLMTagwiseDisp(dge,design) >>> fit2 <- glmFit(dge, design) >>> lrt.anova_like <- glmLRT(fit2, coef=2:4) >>> lrt.anova_like >> An object of class "DGELRT" >> $coefficients >> grpD10 grpD20 grpD40 grpD60 >> comp101_c0 -12.55010 -16.12974 -13.27081 -16.12974 >> comp103_c0 -13.85721 -13.35290 -12.61155 -13.96824 >> comp120_c0 -12.33347 -13.98877 -16.12974 -16.12974 >> comp179_c0 -12.55135 -13.98566 -16.12974 -13.96827 >> comp192_c0 -13.85721 -13.98569 -13.27548 -12.67228 >> 23484 more rows ... >> >> $fitted.values >> i10re1 i10re2 i20re1 i20re2 i40re1 i40re2 i60re1 >> comp101_c0 2.0108643 1.9941877 0.0000000 0.0000000 0.841309 1.168177 0.0000000 >> comp103_c0 0.5020816 0.4979177 1.1140306 0.8859780 1.674705 2.325366 0.5414430 >> comp120_c0 2.5112747 2.4904480 0.5552870 0.4416145 0.000000 0.000000 0.0000000 >> comp179_c0 2.0083306 1.9916749 0.5570245 0.4429963 0.000000 0.000000 0.5414282 >> comp192_c0 0.5020816 0.4979177 0.5570036 0.4429797 0.837345 1.162673 2.1657497 >> i60re2 >> comp101_c0 0.0000000 >> comp103_c0 0.4585719 >> comp120_c0 0.0000000 >> comp179_c0 0.4585593 >> comp192_c0 1.8342685 >> 23484 more rows ... >> >> $deviance >> comp101_c0 comp103_c0 comp120_c0 comp179_c0 comp192_c0 >> 4.386443 3.070288 2.848548 3.917710 2.629182 >> 23484 more elements ... >> >> $df.residual >> [1] 4 4 4 4 4 >> 23484 more elements ... >> >> $abundance >> [1] -13.63381 -13.35715 -13.64240 -13.64482 -13.35716 >> 23484 more elements ... >> >> $design >> grpD10 grpD20 grpD40 grpD60 >> i10re1 1 0 0 0 >> i10re2 1 0 0 0 >> i20re1 0 1 0 0 >> i20re2 0 1 0 0 >> i40re1 0 0 1 0 >> i40re2 0 0 1 0 >> i60re1 0 0 0 1 >> i60re2 0 0 0 1 >> attr(,"assign") >> [1] 1 1 1 1 >> attr(,"contrasts") >> attr(,"contrasts")$grp >> [1] "contr.treatment" >> >> >> $offset >> [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] >> [1,] 13.27698 13.26865 13.52514 13.29609 13.15729 13.48553 13.47707 13.31095 >> [2,] 13.27698 13.26865 13.52514 13.29609 13.15729 13.48553 13.47707 13.31095 >> [3,] 13.27698 13.26865 13.52514 13.29609 13.15729 13.48553 13.47707 13.31095 >> [4,] 13.27698 13.26865 13.52514 13.29609 13.15729 13.48553 13.47707 13.31095 >> [5,] 13.27698 13.26865 13.52514 13.29609 13.15729 13.48553 13.47707 13.31095 >> 23484 more rows ... >> >> $dispersion >> [1] 0.2161256308 0.0003318302 0.0634519684 0.0003270568 0.0003318016 >> 23484 more elements ... >> >> $method >> [1] "oneway" >> >> $samples >> group lib.size norm.factors >> i10re1 D10 594988 0.9808651 >> i10re2 D10 590850 0.9795431 >> i20re1 D20 723182 1.0343009 >> i20re2 D20 567524 1.0481805 >> i40re1 D40 452242 1.1449003 >> i40re2 D40 671656 1.0703965 >> i60re1 D60 801682 0.8892299 >> i60re2 D60 685351 0.8809633 >> >> $table >> logFC.grpD20 logFC.grpD40 logFC.grpD60 logCPM LR >> comp101_c0 -23.27030 -19.14573 -23.27030 0.2621324 647.0115 >> comp103_c0 -19.26416 -18.19462 -20.15192 0.6612798 194043.3654 >> comp120_c0 -20.18153 -23.27030 -23.27030 0.2497509 1999.3276 >> comp179_c0 -20.17704 -23.27030 -20.15196 0.2462508 196431.3868 >> comp192_c0 -20.17709 -19.15248 -18.28224 0.6612611 194057.4372 >> PValue >> comp101_c0 6.475824e-140 >> comp103_c0 0.000000e+00 >> comp120_c0 0.000000e+00 >> comp179_c0 0.000000e+00 >> comp192_c0 0.000000e+00 >> 23484 more rows ... >> >> $comparison >> [1] "grpD20" "grpD40" "grpD60" >> >> $df.test >> [1] 3 3 3 3 3 >> 23484 more elements ... >> >> >>> top<-topTags(lrt.anova_like) >>> top >> Coefficient: grpD20 grpD40 grpD60 >> logFC.grpD20 logFC.grpD40 logFC.grpD60 logCPM LR PValue >> comp628_c0 -23.2703 -23.27030 -19.23749 0.2462499 196434.924 0 >> comp2607_c0 -23.2703 -20.07173 -18.68223 0.4686227 195146.814 0 >> comp2858_c0 -23.2703 -23.27030 -16.85200 1.1206869 192585.615 0 >> comp2885_c0 -23.2703 -18.60303 -17.96733 0.6593429 4326.878 0 >> comp2904_c0 -23.2703 -23.27030 -16.21482 1.8318474 2065.059 0 >> comp2912_c0 -23.2703 -17.88139 -20.15196 1.1207038 192646.480 0 >> comp2933_c0 -23.2703 -18.59551 -18.68219 0.4686259 195111.455 0 >> comp2950_c0 -23.2703 -19.16586 -17.97899 0.4607410 1715.721 0 >> comp2999_c0 -23.2703 -23.27030 -23.27030 1.2524134 3853.539 0 >> comp3028_c0 -23.2703 -19.15248 -17.30519 0.9831770 192775.649 0 >> FDR >> comp628_c0 0 >> comp2607_c0 0 >> comp2858_c0 0 >> comp2885_c0 0 >> comp2904_c0 0 >> comp2912_c0 0 >> comp2933_c0 0 >> comp2950_c0 0 >> comp2999_c0 0 >> comp3028_c0 0 >>> lrt.anova_like <- glmLRT(fit2, coef=1:4) >> Error in mglmLevenberg(y, design = design, dispersion = dispersion, offset = offset, : >> BLAS/LAPACK routine 'DGEMM ' gave error code -13 >> >> >> >> >> >> The DGELRT object says that is comparing >> >> $comparison >> [1] "grpD20" "grpD40" "grpD60" >> >> but what exactly this means? >> >> Luis >> [[alternative HTML version deleted]] >> >> >> >> _______________________________________________ >> Bioconductor mailing list >> Bioconductor@r-project.org <mailto:bioconductor@r-project.org> >> https://stat.ethz.ch/mailman/listinfo/bioconductor >> Search the archives:http://news.gmane.org/gmane.science.biology.inf ormatics.conductor > > > [[alternative HTML version deleted]]

> Date: Mon, 11 Mar 2013 17:18:09 -0700 (PDT) > From: Luis alberto Martinez lopez <lewisluis2013 at="" yahoo.com=""> > To: "bioconductor at r-project.org" <bioconductor at="" r-project.org=""> > Subject: [BioC] anova like test in edgeR > > Hello everyone: > > a wish to know if there is one way of doing an anova like test in edgeR, > comparing all the possibilities between the groups defined in the > experimental design. > > in the users guide the authors explain one way anova test, but the > comparison is done only against the intercept group, is there a way of > doing a general test comparing all the posibilities and not only against > the intercept? The example in the User's Guide is a general test comparing all possibilities. Best wishes Gordon ______________________________________________________________________ The information in this email is confidential and intend...{{dropped:4}}