Hi

I have some data that I am going to analyze, but I have some problems regarding what to choose/do.

The dataset consists of 4 treatments groups: NG, NG+benfo, HG and HG+benfo and it has been used samples from 3 donors in each group: donor 29, 30 and 34. This means that it is 4 groups consisting of 3 samples in each. The platform that has been used is Illumina Human WG6, and the comparisons that are wanted is: NG - NG_benfo, HG - HG_benfo, NG – HG.

The data was read into R using lumiR, quality controlled, normalized, log2 transformed and the expression values was extracted resulting in a data matrix called dataSet_Norm_exp_log2.

First I run a quality control which showed me that the distribution is fine, but the samples were divided into donors not treatment in the PCA plot and hierarchical clustering plot. For me this indicates the differences between the donors are bigger than the differences between the treatments. And because of this I did not expect a “basic” analysis in limma to give me any good results, see ########### Basic ############

Since the donor effect was so visible I thought I should try to block the donor effect, but that actually gave me worse adjusted p-value. Is it wrong to do this, am I using it incorrectly or can the higher p-values being explained?
Example in ############ Block #################

The three donors are in each group and the scientists also want a paired test: Donor 29 NG vs. donor 29 NG_benfo etc. I looked in the limma user guide and found an example of paired t-test, but I am not sure I am doing it correct and how to interpret the results. Example in ####### Pair ###########

In this situation would a paired test or a “group” test be the best? Is there a way to “see” what analysis is the best before one start the job? I do not like to try too many analyses because eventually the data will give the desired answer; I just have to torture it enough. And when can one say that there are no significant findings in the data, when to stop the torture? 

Regards,
Ingrid


> ############################## Basic ##################################
> 
> sampleType <- c('NG','NG','NG','HG','HG','HG','NG_benfo','NG_benfo','NG_benfo','HG_benfo','HG_benfo','HG_benfo')     
>    
> designMa <- model.matrix(~-1+factor(sampleType))
> 
> rownames(designMa) <- sampleType
>       
> colnames(designMa) <- c("HG_s","HG_benfo_s","NG_s","NG_benfo_s")     
> 
> fitDesMa <- lmFit(dataSet_Norm_exp_log2, designMa)
> 
> designMa
         HG_s HG_benfo_s NG_s NG_benfo_s
NG          0          0    1          0
NG          0          0    1          0
NG          0          0    1          0
HG          1          0    0          0
HG          1          0    0          0
HG          1          0    0          0
NG_benfo    0          0    0          1
NG_benfo    0          0    0          1
NG_benfo    0          0    0          1
HG_benfo    0          1    0          0
HG_benfo    0          1    0          0
HG_benfo    0          1    0          0
attr(,"assign")
[1] 1 1 1 1
attr(,"contrasts")
attr(,"contrasts")$`factor(sampleType)`
[1] "contr.treatment"

>                                                                     
> contrast.matrix <- makeContrasts(NG_s - NG_benfo_s,HG_s - HG_benfo_s, NG_s-HG_s, levels = designMa)                               
> 
> fitContr <- contrasts.fit(fitDesMa, contrast.matrix)
> 
> fitContr2 <- eBayes(fitContr)
> 
> top1 <- topTable(fitContr2, coef= 1, number = 20, adjust= "BH", sort.by="p")
> top1
         ID      logFC   AveExpr         t      P.Value adj.P.Val         B
2426   2426 -1.0843868 10.716173 -8.938389 5.482310e-06 0.2675587 1.8529767
17320 17320 -0.6227589  6.281775 -8.116261 1.261991e-05 0.2784645 1.5080171
36670 36670  1.3093614  7.984147  7.600320 2.202967e-05 0.2784645 1.2574042
2752   2752 -0.4334247  7.923580 -7.518626 2.412326e-05 0.2784645 1.2150191
25602 25602  0.6119058  9.056356  7.316350 3.029915e-05 0.2784645 1.1066829
41399 41399  0.5812922  8.270799  7.209620 3.423464e-05 0.2784645 1.0475136
14334 14334  0.5852656  5.977673  6.889422 4.977374e-05 0.3036840 0.8612701
2427   2427 -1.0252133 10.001603 -6.784891 5.639001e-05 0.3036840 0.7975167
7100   7100  0.7322154  7.625884  6.688233 6.336271e-05 0.3036840 0.7372208
47668 47668  0.6990338  8.337938  6.545533 7.542180e-05 0.3036840 0.6457823
2592   2592 -0.9705914  6.310054 -6.527260 7.713743e-05 0.3036840 0.6338612
31680 31680 -1.0568705  6.573984 -6.504740 7.931020e-05 0.3036840 0.6191020
1859   1859  1.0862890  6.535754  6.488754 8.089278e-05 0.3036840 0.6085795
7059   7059  0.6369413  5.951195  6.423283 8.774005e-05 0.3045383 0.5650911
42018 42018 -0.8482179 11.479166 -6.350866 9.605183e-05 0.3045383 0.5162425
41398 41398  0.6482108  8.106491  6.285794 1.042501e-04 0.3045383 0.4716697
35368 35368  1.6933744  9.167829  6.272022 1.060805e-04 0.3045383 0.4621527
4815   4815  0.6222491 11.210936  6.120113 1.287430e-04 0.3322279 0.3552235
3965   3965 -0.8698427  8.916196 -6.116510 1.293404e-04 0.3322279 0.3526427
155     155 -0.8420104  8.091452 -5.983258 1.536784e-04 0.3750060 0.2557538

> top2 <- topTable(fitContr2, coef= 2, number = 20, adjust= "BH", sort.by="p")
> top2
         ID      logFC   AveExpr         t      P.Value adj.P.Val          B
25602 25602  0.6544021  9.056356  7.824463 1.723540e-05 0.5557462 -0.9515403
36670 36670  1.2095773  7.984147  7.021113 4.261105e-05 0.5557462 -1.1474102
2426   2426 -0.8373037 10.716173 -6.901732 4.905181e-05 0.5557462 -1.1804654
43939 43939  1.5657869  9.799353  6.858864 5.161622e-05 0.5557462 -1.1926108
25601 25601  0.6822865  7.984298  6.767874 5.755464e-05 0.5557462 -1.2188870
38431 38431  0.3340007  5.317240  6.626234 6.832385e-05 0.5557462 -1.2611723
35368 35368  1.7394872  9.167829  6.442817 8.563363e-05 0.5970377 -1.3185448
35394 35394  0.4021078  6.140240  6.222546 1.129480e-04 0.6492061 -1.3915750
43459 43459  0.8113102  9.200194  6.057577 1.395481e-04 0.6492061 -1.4494001
35519 35519 -0.3788567  7.472821 -5.964382 1.575082e-04 0.6492061 -1.4833109
3896   3896  0.3074361  5.432543  5.940904 1.624160e-04 0.6492061 -1.4919995
36692 36692  0.8720572  7.116580  5.899135 1.715600e-04 0.6492061 -1.5076044
42124 42124 -0.6213031  7.597989 -5.893084 1.729301e-04 0.6492061 -1.5098808
27084 27084  0.5015841  5.690324  5.752551 2.083097e-04 0.7149992 -1.5638914
5019   5019  0.4185694  5.712520  5.691197 2.261369e-04 0.7149992 -1.5881724
42018 42018 -0.7514740 11.479166 -5.626515 2.467244e-04 0.7149992 -1.6142433
41399 41399  0.4513182  8.270799  5.597586 2.565770e-04 0.7149992 -1.6260629
31680 31680 -0.9018399  6.573984 -5.550571 2.735040e-04 0.7149992 -1.6454852
3019   3019 -0.5514822 11.274571 -5.533892 2.797945e-04 0.7149992 -1.6524395
35615 35615  0.8136739  6.947605  5.475631 3.030174e-04 0.7149992 -1.6769968

> top3 <- topTable(fitContr2, coef= 3, number = 20, adjust= "BH", sort.by="p")
> top3
         ID      logFC  AveExpr         t      P.Value adj.P.Val         B
39854 39854 -0.2387620 5.392370 -4.855615 0.0007294172 0.9999495 -1.982072
24894 24894  0.3724066 6.205436  4.835843 0.0007508215 0.9999495 -1.992056
14122 14122 -0.2238631 5.325398 -4.710873 0.0009025967 0.9999495 -2.056463
32475 32475 -0.2275235 5.393603 -4.687553 0.0009343768 0.9999495 -2.068734
37028 37028 -0.2340294 6.164496 -4.671185 0.0009573938 0.9999495 -2.077396
12902 12902  0.2264364 5.285410  4.604682 0.0010573110 0.9999495 -2.112996
32023 32023 -0.2136657 5.541409 -4.549203 0.0011491544 0.9999495 -2.143203
12908 12908  0.1898669 5.417419  4.518870 0.0012029254 0.9999495 -2.159916
7786   7786 -0.1972366 5.218460 -4.481958 0.0012719856 0.9999495 -2.180443
26520 26520 -0.2213202 5.278165 -4.452604 0.0013299104 0.9999495 -2.196916
4727   4727 -0.2274668 5.314431 -4.436143 0.0013636118 0.9999495 -2.206211
44298 44298  0.2378503 6.214914  4.394183 0.0014536867 0.9999495 -2.230096
18945 18945 -0.2495693 5.582078 -4.347342 0.0015617426 0.9999495 -2.257083
1900   1900 -0.2107123 5.414634 -4.244593 0.0018297003 0.9999495 -2.317484
46220 46220 -0.1773682 5.446607 -4.234698 0.0018579597 0.9999495 -2.323388
29257 29257 -0.2125592 5.599029 -4.229925 0.0018717555 0.9999495 -2.326242
4590   4590  0.1954713 5.340609  4.175245 0.0020377623 0.9999495 -2.359194
19285 19285  0.1960713 5.378438  4.139800 0.0021536291 0.9999495 -2.380809
15867 15867  0.1582910 5.298160  4.127889 0.0021941097 0.9999495 -2.388117
33071 33071 -0.2201014 5.464051 -4.103212 0.0022805505 0.9999495 -2.403331
> ########################################################################
 
> ######################## Block #######################################
> SS <- read.table(file = "GEXSampleSheet.csv",sep = ",",colClasses = "character",header = T,skip = 8)
> SS
   Sample_Name Sample_Well Sample_Plate Sample_Group Sentrix_ID Pool_ID Sentrix_Position X X.1 X.2
1         1_NG                                    NG 4254964042    LD29                A          
2         7_HG                                    HG 4254964042    LD30                B          
3   2_NG_benfo                              NG_benfo 4254964042    LD29                C          
4   8_HG_benfo                              HG_benfo 4254964042    LD30                D          
5         3_HG                                    HG 4254964042    LD29                E          
6         9_NG                                    NG 4254964042    LD34                F          
7   4_HG_benfo                              HG_benfo 4254964032    LD29                A          
8  10_NG_benfo                              NG_benfo 4254964032    LD34                B          
9         5_NG                                    NG 4254964032    LD30                C          
10       11_HG                                    HG 4254964032    LD34                D          
11  6_NG_benfo                              NG_benfo 4254964032    LD30                E          
12 12_HG_benfo                              HG_benfo 4254964032    LD34                F          
> dataSet_Norm_exp_log2_ordnet <- dataSet_Norm_exp_log2[,SS[,1]]
> 
> S_gr <- SS[,4]
> s_gr
Error: object "s_gr" not found
> S_gr_faktor <- as.factor(S_gr)
> S_gr_faktor
 [1] NG       HG       NG_benfo HG_benfo HG       NG       HG_benfo NG_benfo NG       HG       NG_benfo HG_benfo
Levels: HG HG_benfo NG NG_benfo
> 
> designMa <- model.matrix(~-1+S_gr_faktor)
> colnames(designMa) <- levels(S_gr_faktor)
> 
> rownames(designMa) <- S_gr_faktor
> 
> blokk <- SS[,6]
> blokk
 [1] "LD29" "LD30" "LD29" "LD30" "LD29" "LD34" "LD29" "LD34" "LD30" "LD34" "LD30" "LD34"
> corfit <- duplicateCorrelation(dataSet_Norm_exp_log2_ordnet, design = designMa, ndups = 1, block = as.factor(blokk))
> 
> fitDesMa <- lmFit(dataSet_Norm_exp_log2, design = designMa,block = as.factor(blokk),cor = corfit$consensus)
> 
> designMa
         HG HG_benfo NG NG_benfo
NG        0        0  1        0
HG        1        0  0        0
NG_benfo  0        0  0        1
HG_benfo  0        1  0        0
HG        1        0  0        0
NG        0        0  1        0
HG_benfo  0        1  0        0
NG_benfo  0        0  0        1
NG        0        0  1        0
HG        1        0  0        0
NG_benfo  0        0  0        1
HG_benfo  0        1  0        0
attr(,"assign")
[1] 1 1 1 1
attr(,"contrasts")
attr(,"contrasts")$S_gr_faktor
[1] "contr.treatment"

> 
> contrast.matrix <- makeContrasts(NG - NG_benfo,HG - HG_benfo, NG-HG, levels = designMa)                               
> 
> fitContr <- contrasts.fit(fitDesMa, contrast.matrix)
> 
> fitContr2 <- eBayes(fitContr)
> 
> top1 <- topTable(fitContr2, coef= 1, number = 20, adjust= "BH", sort.by="p")
> top1
         ID      logFC  AveExpr         t      P.Value adj.P.Val         B
7611   7611 -0.2527342 5.594157 -5.439053 0.0003198243 0.9999325 -1.521966
32680 32680  0.3197339 5.304647  5.213128 0.0004380912 0.9999325 -1.630612
13175 13175  0.2373210 5.436111  4.912886 0.0006730955 0.9999325 -1.786076
12484 12484 -0.2244960 5.545403 -4.887328 0.0006985769 0.9999325 -1.799920
48359 48359  0.2393309 5.316981  4.863403 0.0007233641 0.9999325 -1.812967
14456 14456  0.2406121 5.618726  4.858584 0.0007284695 0.9999325 -1.815605
21982 21982 -0.2415139 5.354844 -4.799576 0.0007942028 0.9999325 -1.848198
4668   4668  0.2189805 5.294603  4.752691 0.0008509389 0.9999325 -1.874474
24165 24165 -0.2138513 5.438563 -4.723256 0.0008887563 0.9999325 -1.891143
8228   8228 -0.3325391 6.138562 -4.615691 0.0010429202 0.9999325 -1.953203
23640 23640  0.2089381 5.580925  4.596901 0.0010726550 0.9999325 -1.964230
12265 12265  0.1636480 5.471247  4.590664 0.0010827233 0.9999325 -1.967903
16029 16029  0.2862802 6.143381  4.584987 0.0010919753 0.9999325 -1.971252
26564 26564  0.2162782 5.452064  4.572604 0.0011124478 0.9999325 -1.978572
25896 25896  0.2122054 5.509182  4.568471 0.0011193727 0.9999325 -1.981022
12563 12563  0.2087823 5.555015  4.517547 0.0012085463 0.9999325 -2.011420
29041 29041 -0.1691662 5.467616 -4.501427 0.0012383247 0.9999325 -2.021129
8086   8086 -0.2228617 6.070106 -4.489329 0.0012611850 0.9999325 -2.028443
286     286  0.1975515 6.102632  4.480072 0.0012789776 0.9999325 -2.034055
47658 47658  0.2078376 5.293210  4.459124 0.0013202373 0.9999325 -2.046807

> top2 <- topTable(fitContr2, coef= 2, number = 20, adjust= "BH", sort.by="p")
> top2
         ID      logFC  AveExpr         t      P.Value adj.P.Val         B
23091 23091  0.2354301 5.244294  5.224066 0.0004313957   0.99981 -1.625191
32259 32259  0.2518479 5.332262  5.165712 0.0004684309   0.99981 -1.654305
31838 31838 -0.2355327 5.340717 -5.126989 0.0004948792   0.99981 -1.673889
44090 44090 -0.2251488 5.383418 -5.040692 0.0005597503   0.99981 -1.718304
45163 45163  0.2894191 8.376503  4.892174 0.0006936669   0.99981 -1.797287
33415 33415  0.2636604 5.306506  4.855887 0.0007313444   0.99981 -1.817084
43956 43956  0.2483797 5.346141  4.831985 0.0007573518   0.99981 -1.830232
48396 48396  0.1977520 5.400935  4.678863 0.0009492160   0.99981 -1.916536
35709 35709 -0.5484775 9.632759 -4.643015 0.0010012341   0.99981 -1.937266
23341 23341  0.1741108 5.346627  4.563827 0.0011272060   0.99981 -1.983776
41288 41288  0.3055193 5.338962  4.538377 0.0011711893   0.99981 -1.998935
24890 24890 -0.2025487 5.238161 -4.496772 0.0012470673   0.99981 -2.023940
47443 47443  0.1997024 5.298141  4.381631 0.0014855767   0.99981 -2.094596
13408 13408 -0.2320642 5.335356 -4.380533 0.0014880710   0.99981 -2.095280
19419 19419  0.2029474 5.785537  4.364322 0.0015254257   0.99981 -2.105404
33589 33589  0.1680122 5.321035  4.329689 0.0016086005   0.99981 -2.127177
21838 21838  0.2162749 6.009970  4.308605 0.0016615763   0.99981 -2.140528
28720 28720  0.2007788 5.305068  4.288266 0.0017144334   0.99981 -2.153476
32627 32627 -0.1571718 5.386997 -4.279245 0.0017384451   0.99981 -2.159241
36979 36979 -0.1851754 5.272498 -4.249410 0.0018204265   0.99981 -2.178404

> top3 <- topTable(fitContr2, coef= 3, number = 20, adjust= "BH", sort.by="p")
> top3
         ID      logFC  AveExpr         t      P.Value adj.P.Val         B
20549 20549 -0.2562633 5.297783 -5.787760 0.0001995769 0.9998617 -1.367343
46835 46835 -0.2730167 5.579581 -5.741662 0.0002122093 0.9998617 -1.386916
18794 18794  0.2400783 5.352057  5.411186 0.0003323510 0.9998617 -1.534995
15282 15282 -0.3525527 5.574132 -5.284227 0.0003964783 0.9998617 -1.595671
19419 19419 -0.2395820 5.785537 -5.152140 0.0004775244 0.9998617 -1.661145
31093 31093  0.2084498 5.679371  5.033290 0.0005657241 0.9998617 -1.722164
14231 14231  0.1895001 5.504983  4.938160 0.0006488719 0.9998617 -1.772483
13615 13615  0.2638662 5.282304  4.835343 0.0007536390 0.9998617 -1.828380
24296 24296  0.1750666 5.267973  4.828252 0.0007615022 0.9998617 -1.832294
14926 14926 -0.2954851 5.358565 -4.734460 0.0008741543 0.9998617 -1.884782
29229 29229  0.2305711 5.673516  4.731604 0.0008778519 0.9998617 -1.886402
31572 31572 -0.1835065 5.353449 -4.668348 0.0009641692 0.9998617 -1.922596
10319 10319  0.2386456 6.346361  4.649946 0.0009909460 0.9998617 -1.933243
32211 32211  0.2222265 5.483684  4.619019 0.0010377456 0.9998617 -1.951256
20957 20957  0.1950235 5.440708  4.599657 0.0010682380 0.9998617 -1.962610
31581 31581 -0.2053997 5.378321 -4.592595 0.0010795955 0.9998617 -1.966765
31817 31817  0.1761750 5.382717  4.575112 0.0011082689 0.9998617 -1.977088
12484 12484 -0.2067044 5.545403 -4.500002 0.0012409931 0.9998617 -2.021989
29205 29205  0.2347584 5.334616  4.457761 0.0013229690 0.9998617 -2.047639
32048 32048  0.1927972 5.312840  4.436783 0.0013658003 0.9998617 -2.060484
######################################################################## 

> ############################## Pair ####################################
> targets <- readTargets("targets2.txt")
> targets
   FileName Donor Treatment
1     File1    29        NG
2     File2    34        NG
3     File3    30        NG
4     File4    30        HG
5     File5    29        HG
6     File6    34        HG
7     File7    29   NGbenfo
8     File8    34   NGbenfo
9     File9    30   NGbenfo
10   File10    30   HGbenfo
11   File11    29   HGbenfo
12   File12    34   HGbenfo
> 
> Target <- factor(targets$Donor)
> Target
 [1] 29 34 30 30 29 34 29 34 30 30 29 34
Levels: 29 30 34
> Treat <- factor(targets$Treatment, levels=c("NG","HG","NGbenfo","HGbenfo"))
> Treat
 [1] NG      NG      NG      HG      HG      HG      NGbenfo NGbenfo NGbenfo HGbenfo HGbenfo HGbenfo
Levels: NG HG NGbenfo HGbenfo
> design <- model.matrix(~-1+Treat)
> design
   TreatNG TreatHG TreatNGbenfo TreatHGbenfo
1        1       0            0            0
2        1       0            0            0
3        1       0            0            0
4        0       1            0            0
5        0       1            0            0
6        0       1            0            0
7        0       0            1            0
8        0       0            1            0
9        0       0            1            0
10       0       0            0            1
11       0       0            0            1
12       0       0            0            1
attr(,"assign")
[1] 1 1 1 1
attr(,"contrasts")
attr(,"contrasts")$Treat
[1] "contr.treatment"

> 
> fit <- lmFit(dataSet_Norm_exp_log2, design)
> fit2 <- eBayes(fit)
> 
> top1 <- topTable(fit2, coef= 1, number = 20, adjust= "BH", sort.by="p")
> top1
         ID    logFC  AveExpr        t      P.Value    adj.P.Val        B
29377 29377 14.91596 14.92944 516.8806 7.735991e-23 3.558893e-18 35.95591
7792   7792 14.77356 14.80328 467.7334 2.036389e-22 3.558893e-18 35.77056
46748 46748 14.54269 14.56708 453.1413 2.768223e-22 3.558893e-18 35.70514
7793   7793 14.90254 14.90698 441.9898 3.523945e-22 3.558893e-18 35.65132
28882 28882 14.28753 14.29856 435.5766 4.059943e-22 3.558893e-18 35.61875
33208 33208 14.14750 14.13732 426.6177 4.965377e-22 3.558893e-18 35.57112
28580 28580 14.87370 14.83600 424.1510 5.252285e-22 3.558893e-18 35.55755
41917 41917 14.03380 14.05301 419.5783 5.833773e-22 3.558893e-18 35.53186
4769   4769 14.41497 14.44560 410.1179 7.276004e-22 3.945535e-18 35.47637
41907 41907 13.77121 13.74080 400.9056 9.067137e-22 4.313991e-18 35.41912
26904 26904 14.29154 14.33758 398.0242 9.723362e-22 4.313991e-18 35.40053
46749 46749 14.50236 14.47209 393.3830 1.089334e-21 4.430320e-18 35.36986
29730 29730 14.34559 14.32901 383.3841 1.397896e-21 5.020324e-18 35.30060
42046 42046 14.48283 14.51144 380.4832 1.504625e-21 5.020324e-18 35.27966
43841 43841 13.71735 13.74382 378.2740 1.591940e-21 5.020324e-18 35.26344
41965 41965 14.45261 14.38145 376.9752 1.645873e-21 5.020324e-18 35.25380
42047 42047 14.37644 14.35028 372.5960 1.843121e-21 5.216996e-18 35.22067
42019 42019 14.27887 14.32325 370.1575 1.964159e-21 5.216996e-18 35.20180
32161 32161 13.95997 14.02817 368.8802 2.031041e-21 5.216996e-18 35.19179
42021 42021 14.23735 14.15566 365.6109 2.213963e-21 5.402512e-18 35.16579

> top2 <- topTable(fit2, coef= 2, number = 20, adjust= "BH", sort.by="p")
> top2
         ID    logFC  AveExpr        t      P.Value    adj.P.Val        B
29377 29377 14.94293 14.92944 517.8155 7.601752e-23 3.468187e-18 35.95899
7792   7792 14.81788 14.80328 469.1366 1.978147e-22 3.468187e-18 35.77657
46748 46748 14.56020 14.56708 453.6869 2.736145e-22 3.468187e-18 35.70769
7793   7793 14.90254 14.90698 441.9898 3.523945e-22 3.468187e-18 35.65132
28882 28882 14.31171 14.29856 436.3137 3.993984e-22 3.468187e-18 35.62256
33208 33208 14.14569 14.13732 426.5633 4.971519e-22 3.468187e-18 35.57082
28580 28580 14.78715 14.83600 421.6828 5.557769e-22 3.468187e-18 35.54377
41917 41917 14.07125 14.05301 420.6980 5.685086e-22 3.468187e-18 35.53822
4769   4769 14.39271 14.44560 409.4848 7.385709e-22 4.005024e-18 35.47254
41907 41907 13.70521 13.74080 398.9841 9.499104e-22 4.251956e-18 35.40676
26904 26904 14.31293 14.33758 398.6198 9.583542e-22 4.251956e-18 35.40440
46749 46749 14.44334 14.47209 391.7820 1.133228e-21 4.608840e-18 35.35906
29730 29730 14.32854 14.32901 382.9285 1.414093e-21 5.099289e-18 35.29734
42046 42046 14.51584 14.51144 381.3506 1.471801e-21 5.099289e-18 35.28596
43841 43841 13.70964 13.74382 378.0612 1.600642e-21 5.099289e-18 35.26187
41965 41965 14.39025 14.38145 375.3486 1.716279e-21 5.099289e-18 35.24160
42019 42019 14.38992 14.32325 373.0365 1.822145e-21 5.099289e-18 35.22404
42047 42047 14.28467 14.35028 370.2178 1.961065e-21 5.099289e-18 35.20227
32161 32161 13.99289 14.02817 369.7503 1.985216e-21 5.099289e-18 35.19862
42021 42021 14.15355 14.15566 363.4590 2.344258e-21 5.720459e-18 35.14836

> top3 <- topTable(fit2, coef= 3, number = 20, adjust= "BH", sort.by="p")
> top3
         ID    logFC  AveExpr        t      P.Value    adj.P.Val        B
29377 29377 14.96991 14.92944 518.7503 7.470078e-23 3.536707e-18 35.96205
7792   7792 14.83129 14.80328 469.5612 1.960886e-22 3.536707e-18 35.77838
46748 46748 14.60366 14.56708 455.0410 2.658281e-22 3.536707e-18 35.71397
7793   7793 14.85291 14.90698 440.5176 3.639697e-22 3.536707e-18 35.64396
28882 28882 14.30565 14.29856 436.1290 4.010404e-22 3.536707e-18 35.62160
33208 33208 14.18397 14.13732 427.7176 4.843054e-22 3.536707e-18 35.57711
28580 28580 14.84366 14.83600 423.2943 5.356163e-22 3.536707e-18 35.55279
41917 41917 14.04286 14.05301 419.8492 5.797406e-22 3.536707e-18 35.53340
4769   4769 14.48384 14.44560 412.0774 6.947685e-22 3.767498e-18 35.48813
41907 41907 13.69640 13.74080 398.7278 9.558428e-22 4.265616e-18 35.40510
26904 26904 14.30819 14.33758 398.4878 9.614331e-22 4.265616e-18 35.40354
46749 46749 14.47947 14.47209 392.7620 1.106132e-21 4.498641e-18 35.36568
29730 29730 14.33028 14.32901 382.9748 1.412436e-21 5.031474e-18 35.29767
42046 42046 14.54513 14.51144 382.1202 1.443337e-21 5.031474e-18 35.29153
43841 43841 13.75475 13.74382 379.3054 1.550501e-21 5.042971e-18 35.27104
41965 41965 14.35647 14.38145 374.4674 1.755804e-21 5.042971e-18 35.23494
32161 32161 14.08381 14.02817 372.1527 1.864498e-21 5.042971e-18 35.21726
42047 42047 14.34979 14.35028 371.9053 1.876547e-21 5.042971e-18 35.21535
42019 42019 14.27952 14.32325 370.1744 1.963291e-21 5.042971e-18 35.20193
42021 42021 14.15562 14.15566 363.5122 2.340936e-21 5.712351e-18 35.14880
########################################################################

> sessionInfo()
R version 2.7.1 (2008-06-23) 
i386-pc-mingw32 

locale:
LC_COLLATE=English_United Kingdom.1252;LC_CTYPE=English_United Kingdom.1252;LC_MONETARY=English_United Kingdom.1252;LC_NUMERIC=C;LC_TIME=English_United Kingdom.1252

attached base packages:
[1] splines   tools     stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] illuminaHumanv3ProbeID.db_1.1.1 statmod_1.3.6                   GOstats_2.6.0                   Category_2.6.0                  genefilter_1.20.0               survival_2.34-1               
 [7] RBGL_1.16.0                     graph_1.18.1                    annaffy_1.12.1                  KEGG.db_2.2.0                   GO.db_2.2.0                     annotate_1.18.0               
[13] AnnotationDbi_1.2.2             RSQLite_0.6-9                   DBI_0.2-4                       xtable_1.5-2                    RColorBrewer_1.0-2              limma_2.14.5                   
[19] lumi_1.6.2                      mgcv_1.4-0                      affy_1.18.2                     preprocessCore_1.2.0            affyio_1.8.0                    Biobase_2.0.1                  

loaded via a namespace (and not attached):
[1] cluster_1.11.11
>



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