[BioC] EdgeR: coefficients for GLM for multiple groups
James W. MacDonald
jmacdon at uw.edu
Thu Mar 21 19:45:44 CET 2013
Hi Capricy,
Just like limma, edgeR has a very comprehensive user's guide that you
can consult.
library(edgeR)
edgeRUsersGuide()
Best,
Jim
On 3/21/2013 2:41 PM, capricy gao wrote:
> I am looking at my fit results from GLM of edgeR for my data:
>
> and assume that $coefficients are related to the expression levels. Just wondering how I can get the p values for those coefficients since I remember in limma, each coefficient will be accompanied by the corresponding t value and p value.
>
>
> Thanks a lot for the help.
>
> capricy
>
> ===================
>
>> design=model.matrix(~0+regroup)
>> y<- estimateGLMCommonDisp(y,design)
>> y<- estimateGLMTagwiseDisp(y,design)
>> fit<- glmFit(y,design)
>> fit
> An object of class "DGEGLM"
> $coefficients
> regroupFR regroupFA regroupFM regroupFP regroupFW regroupMA
> GS_14929 -9.543565 -9.267612 -9.310823 -9.167299 -10.62094 -9.260763
> GS_09776 -10.304430 -10.985146 -10.644154 -10.640469 -10.59615 -10.798889
> GS_18434 -11.327664 -11.786421 -11.643292 -11.902728 -12.50470 -11.654008
> GS_08334 -10.789181 -10.271089 -10.511480 -10.375642 -10.39865 -10.836647
> GS_09550 -10.564167 -10.152571 -10.410428 -10.098825 -10.36302 -9.722892
> regroupMM regroupMP regroupMW
> GS_14929 -8.516256 -8.222225 -9.495394
> GS_09776 -10.337434 -10.284703 -10.588544
> GS_18434 -11.755905 -11.662276 -11.583950
> GS_08334 -10.797529 -10.643409 -10.783104
> GS_09550 -10.650495 -10.766462 -9.880359
> 15457 more rows ...
>
> $fitted.values
> MP FA FR FW MA FW.1 MM
> GS_14929 596.53153 205.75696 84.61834 31.844025 169.27178 82.62258 513.34590
> GS_09776 75.81638 36.91137 39.53083 32.643715 36.33823 84.69746 83.04882
> GS_18434 19.09812 16.54813 14.19827 4.825192 15.43876 12.51946 20.07922
> GS_08334 52.95485 75.41272 24.33895 39.775089 34.99088 103.20054 52.41066
> GS_09550 46.82022 84.90529 30.48442 41.218519 106.62251 106.94567 60.71721
> FP MA.1 MW FM MM.1 MW.1 MP.1
> GS_14929 78.226172 123.52247 193.95899 122.61523 173.571103 194.05252 568.23926
> GS_09776 17.921685 26.51705 64.98434 32.30797 28.080239 65.01567 72.22056
> GS_18434 5.064548 11.26610 23.99487 11.88420 6.789129 24.00644 18.19234
> GS_08334 23.358730 25.53385 53.48880 36.89427 17.720948 53.51460 50.44331
> GS_09550 30.811447 77.80550 131.97315 40.81919 20.529537 132.03679 44.59963
> FM.1 FP.1 FA.1 FR.1
> GS_14929 259.44483 324.86875 256.36216 198.47462
> GS_09776 68.36129 74.42772 45.98959 92.72063
> GS_18434 25.14609 21.03277 20.61808 33.30242
> GS_08334 78.06556 97.00745 93.96021 57.08766
> GS_09550 86.37041 127.95815 105.78744 71.50204
> 15457 more rows ...
>
> $counts
> MP FA FR FW MA FW.1 MM FP MA.1 MW FM MM.1 MW.1 MP.1 FM.1 FP.1
> GS_14929 221 284 170 23 267 105 209 106 52 218 158 277 170 926 185 211
> GS_09776 75 32 17 28 41 96 86 15 23 65 23 27 65 73 87 85
> GS_18434 36 22 19 8 21 6 22 7 7 27 13 6 21 2 23 15
> GS_08334 44 77 23 50 41 78 78 14 21 61 29 8 46 59 94 132
> GS_09550 82 92 45 54 105 75 95 18 79 153 41 8 111 11 86 178
> FA.1 FR.1
> GS_14929 159 0
> GS_09776 52 143
> GS_18434 14 23
> GS_08334 92 60
> GS_09550 97 39
> 15457 more rows ...
>
> $deviance
> GS_14929 GS_09776 GS_18434 GS_08334 GS_09550
> 19.059229 3.629342 9.509356 4.268291 8.809585
> 15457 more elements ...
>
> $df.residual
> [1] 9 9 9 9 9
> 15457 more elements ...
>
> $abundance
> [1] -9.081235 -10.552328 -11.717230 -10.579804 -10.233947
> 15457 more elements ...
>
> $design
> regroupFR regroupFA regroupFM regroupFP regroupFW regroupMA regroupMM
> 1 0 0 0 0 0 0 0
> 2 0 1 0 0 0 0 0
> 3 1 0 0 0 0 0 0
> 4 0 0 0 0 1 0 0
> 5 0 0 0 0 0 1 0
> regroupMP regroupMW
> 1 1 0
> 2 0 0
> 3 0 0
> 4 0 0
> 5 0 0
> 13 more rows ...
>
> $offset
> [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
> [1,] 14.61341 14.59445 13.9819 14.08233 14.39241 15.03576 14.75727 13.52703
> [2,] 14.61341 14.59445 13.9819 14.08233 14.39241 15.03576 14.75727 13.52703
> [3,] 14.61341 14.59445 13.9819 14.08233 14.39241 15.03576 14.75727 13.52703
> [4,] 14.61341 14.59445 13.9819 14.08233 14.39241 15.03576 14.75727 13.52703
> [5,] 14.61341 14.59445 13.9819 14.08233 14.39241 15.03576 14.75727 13.52703
> [,9] [,10] [,11] [,12] [,13] [,14] [,15] [,16]
> [1,] 14.07733 14.76322 14.12002 13.67291 14.7637 14.56482 14.86951 14.95085
> [2,] 14.07733 14.76322 14.12002 13.67291 14.7637 14.56482 14.86951 14.95085
> [3,] 14.07733 14.76322 14.12002 13.67291 14.7637 14.56482 14.86951 14.95085
> [4,] 14.07733 14.76322 14.12002 13.67291 14.7637 14.56482 14.86951 14.95085
> [5,] 14.07733 14.76322 14.12002 13.67291 14.7637 14.56482 14.86951 14.95085
> [,17] [,18]
> [1,] 14.81434 14.83441
> [2,] 14.81434 14.83441
> [3,] 14.81434 14.83441
> [4,] 14.81434 14.83441
> [5,] 14.81434 14.83441
> 15457 more rows ...
>
> $dispersion
> [1] 0.7464955 0.2734189 0.4034924 0.2832934 0.3819788
> 15457 more elements ...
>
> $method
> [1] "oneway"
>
> $samples
> group lib.size norm.factors
> MP MP 2220863 1
> FA FA 2179157 1
> FR FR 1181036 1
> FW FW 1305802 1
> MA MA 1780507 1
> 13 more rows ...
> [[alternative HTML version deleted]]
>
>
>
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--
James W. MacDonald, M.S.
Biostatistician
University of Washington
Environmental and Occupational Health Sciences
4225 Roosevelt Way NE, # 100
Seattle WA 98105-6099
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