Differential Expression with Limma
Last updated: 2020-08-31
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Knit directory: Embryoid_Body_Pilot_Workflowr/analysis/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 162da72 | KLRhodes | 2020-08-31 | wflow_publish("analysis/Pseudobulk_Limma_Harmony.BatchIndividual_ClusterRes0.*") |
library(Seurat)
library(Matrix)
library(dplyr)
library(edgeR)
library(limma)
library(reshape2)
library(ggplot2)
library(UpSetR)choose parameters (integration type, clustering res, min pct threshold)
f<- 'Harmony.Batchindividual'
pct<-0.2
res<- 'SCT_snn_res.0.5'path<- here::here("output/DGELists/")
dge<- readRDS(paste0(path,"Pseudobulk_dge_",f, "_", res,"_minPCT",pct,".rds"))cpm<- cpmByGroup(dge, group=dge$samples$cluster)
lcpm<- cpmByGroup(dge, group=dge$samples$cluster, log=TRUE)hist(lcpm)
L<- mean(dge$samples$lib.size) *1e-6
M<- median(dge$samples$lib.size) *1e-6genes.ribo <- grep('^RP',rownames(dge),value=T)
genes.no.ribo <- rownames(dge)[which(!(rownames(dge) %in% genes.ribo))]
dge$counts <- dge$counts[which(rownames(dge$counts) %in% genes.no.ribo),] #remove ribosomal genesdge<- calcNormFactors(dge, method="TMM")
summary(dge$samples$norm.factors) Min. 1st Qu. Median Mean 3rd Qu. Max.
0.7736 0.8998 0.9755 1.0174 1.0565 2.1383 design<- model.matrix(~0+ dge$samples$cluster + dge$samples$batch + dge$samples$ind)v<- voom(dge, design, plot=TRUE)
vAn object of class "EList"
$targets
group lib.size norm.factors cluster batch ind
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153 more rows ...
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ISG15 5.5398831 5.7896444 4.935965
8.Batch1.SNG-NA18858 8.Batch1.SNG-NA19160 8.Batch2.SNG-NA18511
SAMD11 2.5725796 3.0196698 2.910340
NOC2L 5.6478677 5.9582693 5.894180
PLEKHN1 -0.2347754 -0.8872208 -3.747871
HES4 7.7367682 8.6278219 8.975576
ISG15 4.8945077 4.8941389 5.209231
8.Batch2.SNG-NA18858 8.Batch2.SNG-NA19160 8.Batch3.SNG-NA18511
SAMD11 2.352117 1.509119 2.128847
NOC2L 5.393145 5.583467 5.698313
PLEKHN1 -1.348322 -2.191320 -3.156555
HES4 7.065306 8.344927 8.441962
ISG15 4.801425 4.774464 5.741290
8.Batch3.SNG-NA18858 8.Batch3.SNG-NA19160 9.Batch1.SNG-NA18511
SAMD11 3.4885531 3.896876 4.0997227
NOC2L 6.0387501 5.771345 6.5538986
PLEKHN1 0.6811981 -1.594977 -0.5441335
HES4 6.5140881 7.557308 6.6357756
ISG15 5.8906515 5.299841 3.9794285
9.Batch1.SNG-NA18858 9.Batch1.SNG-NA19160 9.Batch2.SNG-NA18511
SAMD11 3.717622 3.1891642 0.01814573
NOC2L 6.440088 6.6459964 5.30354795
PLEKHN1 1.395694 -0.2991222 0.01814573
HES4 5.919256 6.7220120 6.73239125
ISG15 6.821959 2.7207773 4.10560857
9.Batch2.SNG-NA18858 9.Batch2.SNG-NA19160 9.Batch3.SNG-NA18511
SAMD11 5.938852 2.7629643 3.440982
NOC2L 5.201886 6.1343611 5.969361
PLEKHN1 3.616923 0.7961311 1.325505
HES4 6.424278 6.2703902 5.295131
ISG15 3.616923 2.5461529 4.495430
9.Batch3.SNG-NA18858 9.Batch3.SNG-NA19160
SAMD11 2.305067 3.9919915
NOC2L 5.764498 6.2076266
PLEKHN1 2.305067 -2.7494755
HES4 3.890029 5.5405434
ISG15 3.890029 0.9509643
10910 more rows ...
$weights
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[,100] [,101] [,102] [,103] [,104] [,105] [,106]
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[3,] 0.5358885 0.6489274 0.7115043 1.044922 2.310630 1.183572 2.377239
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[2,] 4.0759616 11.498286 7.566451 2.6869020 5.3439359 8.392817 1.6776752
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[5,] 9.251173 10.255212 3.8924813 15.325287 1.3077562 8.623161 1.9743526
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[2,] 2.3556930 13.570135 4.0969404 5.3533541 11.100627 5.0859999 8.164818
[3,] 0.2201520 5.595792 0.4245559 0.5206975 3.600769 0.4716777 0.276699
[4,] 1.9934519 11.382466 3.2993097 3.9330690 8.124000 3.4280556 15.364851
[5,] 1.5322707 12.523126 3.1001201 4.2384504 10.652753 4.0396019 6.398980
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[2,] 3.1769970 6.8903612 9.3386884 4.2766794 5.8384105 8.1830512 2.1983235
[3,] 0.2080015 0.2144732 0.4022401 0.2524875 0.2080015 0.2776995 0.2080015
[4,] 6.7875551 12.7021814 17.3764770 8.6299043 11.2267058 13.8394955 4.0259093
[5,] 2.8636735 5.3315572 7.5034786 3.7699647 4.4474726 6.8141108 2.0481007
[,149] [,150] [,151] [,152] [,153] [,154] [,155]
[1,] 1.6834395 1.451467 0.3757177 4.7933313 0.8696654 0.2080015 3.2600460
[2,] 4.9254779 4.117287 1.9341539 9.6632011 3.1036773 0.4856851 7.2767409
[3,] 0.2080015 0.270927 0.2103745 0.9950778 0.2272734 0.2080015 0.6793922
[4,] 8.7711245 4.539112 1.8183528 9.7506262 3.3779583 0.4642358 7.3879275
[5,] 3.9212741 1.465926 0.5836596 4.5117504 0.8926882 0.2080015 3.1281272
[,156] [,157] [,158]
[1,] 0.9652068 0.2243569 3.2504393
[2,] 3.4678662 0.9942515 7.6310988
[3,] 0.2262572 0.2080015 0.6142165
[4,] 3.2502862 0.7209098 6.7372140
[5,] 1.2356906 0.3870356 3.4842327
10910 more rows ...
$design
dge$samples$cluster0 dge$samples$cluster1 dge$samples$cluster10
1 1 0 0
2 1 0 0
3 1 0 0
4 1 0 0
5 1 0 0
dge$samples$cluster11 dge$samples$cluster12 dge$samples$cluster13
1 0 0 0
2 0 0 0
3 0 0 0
4 0 0 0
5 0 0 0
dge$samples$cluster14 dge$samples$cluster15 dge$samples$cluster16
1 0 0 0
2 0 0 0
3 0 0 0
4 0 0 0
5 0 0 0
dge$samples$cluster17 dge$samples$cluster2 dge$samples$cluster3
1 0 0 0
2 0 0 0
3 0 0 0
4 0 0 0
5 0 0 0
dge$samples$cluster4 dge$samples$cluster5 dge$samples$cluster6
1 0 0 0
2 0 0 0
3 0 0 0
4 0 0 0
5 0 0 0
dge$samples$cluster7 dge$samples$cluster8 dge$samples$cluster9
1 0 0 0
2 0 0 0
3 0 0 0
4 0 0 0
5 0 0 0
dge$samples$batchBatch2 dge$samples$batchBatch3 dge$samples$indNA18858
1 0 0 0
2 0 0 1
3 0 0 0
4 1 0 0
5 1 0 1
dge$samples$indNA19160
1 0
2 0
3 1
4 0
5 0
153 more rows ...fit<- lmFit(v,design)dim(fit)[1] 10915 22head(fit)An object of class "MArrayLM"
$coefficients
dge$samples$cluster0 dge$samples$cluster1 dge$samples$cluster10
SAMD11 2.039547 2.903009 1.68361012
NOC2L 6.601111 6.317593 6.59850892
PLEKHN1 1.366940 -2.569173 0.09687398
HES4 6.091080 9.327304 6.55930710
ISG15 5.516810 5.692514 5.35451655
AGRN 4.638427 5.196508 4.91542082
dge$samples$cluster11 dge$samples$cluster12 dge$samples$cluster13
SAMD11 3.4308878 5.3386018 2.4901493
NOC2L 6.0467517 6.3992387 6.2339779
PLEKHN1 -0.1325727 -0.7095955 -0.3166955
HES4 9.4357719 8.0883432 9.1886717
ISG15 6.1381676 5.0606985 5.6324846
AGRN 4.8435596 5.5984149 4.9787540
dge$samples$cluster14 dge$samples$cluster15 dge$samples$cluster16
SAMD11 4.092883 4.7559097 4.077276
NOC2L 6.568888 6.1465359 5.817145
PLEKHN1 2.350744 -0.2790673 4.743175
HES4 6.201964 8.3902760 7.503147
ISG15 5.605723 5.1086110 6.361162
AGRN 4.550076 5.0486762 5.581236
dge$samples$cluster17 dge$samples$cluster2 dge$samples$cluster3
SAMD11 0.8967904 2.648612 3.0724273
NOC2L 5.9924455 6.391168 6.2698416
PLEKHN1 0.3427823 -1.419643 -0.1581311
HES4 7.3922018 8.973141 5.7856626
ISG15 8.1802810 5.959436 6.4758084
AGRN 6.0862275 5.173724 4.3579876
dge$samples$cluster4 dge$samples$cluster5 dge$samples$cluster6
SAMD11 3.578533 2.9893043 4.415488
NOC2L 6.406420 6.2026735 6.135993
PLEKHN1 -0.851034 0.8743783 1.466009
HES4 6.879054 6.6115354 7.441633
ISG15 4.987376 5.6848112 6.937661
AGRN 5.489668 4.9059639 5.768351
dge$samples$cluster7 dge$samples$cluster8 dge$samples$cluster9
SAMD11 2.317608 2.722964 3.1401664
NOC2L 6.305509 5.986355 6.4242972
PLEKHN1 1.076852 -1.943810 0.2048845
HES4 6.510843 9.029302 6.8856946
ISG15 5.393732 5.183517 2.8572391
AGRN 4.843317 5.201753 5.2229414
dge$samples$batchBatch2 dge$samples$batchBatch3 dge$samples$indNA18858
SAMD11 -0.2548973 -0.05847588 -0.6346015
NOC2L -0.2757671 -0.23017741 -0.1502711
PLEKHN1 -0.1105059 -0.30372459 0.5629640
HES4 -0.3748454 -0.88895687 -0.9404706
ISG15 -0.1651807 -0.00497113 0.1605749
AGRN -0.2307388 -0.40896790 -0.0967634
dge$samples$indNA19160
SAMD11 0.285904750
NOC2L -0.043523959
PLEKHN1 -0.183604175
HES4 -0.402249461
ISG15 -0.003643458
AGRN -0.104599642
$stdev.unscaled
dge$samples$cluster0 dge$samples$cluster1 dge$samples$cluster10
SAMD11 0.2322535 0.18898937 0.3636159
NOC2L 0.1267344 0.12357008 0.1692158
PLEKHN1 0.2960554 0.59421904 0.4358723
HES4 0.1355938 0.09652449 0.1913966
ISG15 0.1366195 0.13099722 0.1908121
AGRN 0.1608399 0.14384702 0.2255630
dge$samples$cluster11 dge$samples$cluster12 dge$samples$cluster13
SAMD11 0.2930570 0.2159402 0.3548537
NOC2L 0.1916652 0.1641372 0.2017703
PLEKHN1 0.5841662 0.6751429 0.6330931
HES4 0.1314009 0.1437320 0.1543271
ISG15 0.1844721 0.1952834 0.2314222
AGRN 0.2317738 0.1915827 0.2472419
dge$samples$cluster14 dge$samples$cluster15 dge$samples$cluster16
SAMD11 0.2796605 0.3144652 0.2759645
NOC2L 0.2082347 0.2114792 0.2017567
PLEKHN1 0.4071854 0.4917892 0.2965377
HES4 0.2279887 0.1574974 0.1694633
ISG15 0.2260604 0.2455725 0.1840256
AGRN 0.2735778 0.2467756 0.2191045
dge$samples$cluster17 dge$samples$cluster2 dge$samples$cluster3
SAMD11 0.6404207 0.2122587 0.2074759
NOC2L 0.2265150 0.1277402 0.1372941
PLEKHN1 0.7871239 0.5331781 0.4075145
HES4 0.1949124 0.1037864 0.1489913
ISG15 0.1664771 0.1331899 0.1340033
AGRN 0.2348244 0.1507746 0.1814178
dge$samples$cluster4 dge$samples$cluster5 dge$samples$cluster6
SAMD11 0.2245118 0.2140278 0.2025002
NOC2L 0.1492784 0.1361666 0.1494785
PLEKHN1 0.5196002 0.3607772 0.3325962
HES4 0.1478987 0.1335288 0.1311130
ISG15 0.1755689 0.1428023 0.1371503
AGRN 0.1774187 0.1640277 0.1632010
dge$samples$cluster7 dge$samples$cluster8 dge$samples$cluster9
SAMD11 0.2965805 0.2475024 0.2787242
NOC2L 0.1584611 0.1498020 0.1749093
PLEKHN1 0.3700782 0.6878972 0.5619642
HES4 0.1686929 0.1136341 0.1726474
ISG15 0.1717216 0.1650105 0.2620069
AGRN 0.1990146 0.1712245 0.2091758
dge$samples$batchBatch2 dge$samples$batchBatch3 dge$samples$indNA18858
SAMD11 0.13040195 0.13064964 0.16958673
NOC2L 0.08250074 0.08151828 0.09260831
PLEKHN1 0.20401977 0.20754047 0.23890155
HES4 0.07241979 0.07474088 0.08333524
ISG15 0.08686597 0.08491277 0.09687370
AGRN 0.09747244 0.09904809 0.11437090
dge$samples$indNA19160
SAMD11 0.12139635
NOC2L 0.07889245
PLEKHN1 0.23507541
HES4 0.06974736
ISG15 0.08316557
AGRN 0.09455957
$sigma
[1] 1.2252715 0.5038468 0.9030387 1.4842538 1.2917626 0.7213480
$df.residual
[1] 136 136 136 136 136 136
$cov.coefficients
dge$samples$cluster0 dge$samples$cluster1
dge$samples$cluster0 0.13640916 0.02529804
dge$samples$cluster1 0.02529804 0.13640916
dge$samples$cluster10 0.02529804 0.02529804
dge$samples$cluster11 0.02529804 0.02529804
dge$samples$cluster12 0.02529804 0.02529804
dge$samples$cluster10 dge$samples$cluster11
dge$samples$cluster0 0.02529804 0.02529804
dge$samples$cluster1 0.02529804 0.02529804
dge$samples$cluster10 0.13640916 0.02529804
dge$samples$cluster11 0.02529804 0.13640916
dge$samples$cluster12 0.02529804 0.02529804
dge$samples$cluster12 dge$samples$cluster13
dge$samples$cluster0 0.02529804 0.02440965
dge$samples$cluster1 0.02529804 0.02440965
dge$samples$cluster10 0.02529804 0.02440965
dge$samples$cluster11 0.02529804 0.02440965
dge$samples$cluster12 0.13640916 0.02440965
dge$samples$cluster14 dge$samples$cluster15
dge$samples$cluster0 0.02529804 0.02529804
dge$samples$cluster1 0.02529804 0.02529804
dge$samples$cluster10 0.02529804 0.02529804
dge$samples$cluster11 0.02529804 0.02529804
dge$samples$cluster12 0.02529804 0.02529804
dge$samples$cluster16 dge$samples$cluster17
dge$samples$cluster0 0.02529804 0.02163229
dge$samples$cluster1 0.02529804 0.02163229
dge$samples$cluster10 0.02529804 0.02163229
dge$samples$cluster11 0.02529804 0.02163229
dge$samples$cluster12 0.02529804 0.02163229
dge$samples$cluster2 dge$samples$cluster3
dge$samples$cluster0 0.02529804 0.02529804
dge$samples$cluster1 0.02529804 0.02529804
dge$samples$cluster10 0.02529804 0.02529804
dge$samples$cluster11 0.02529804 0.02529804
dge$samples$cluster12 0.02529804 0.02529804
dge$samples$cluster4 dge$samples$cluster5
dge$samples$cluster0 0.02529804 0.02529804
dge$samples$cluster1 0.02529804 0.02529804
dge$samples$cluster10 0.02529804 0.02529804
dge$samples$cluster11 0.02529804 0.02529804
dge$samples$cluster12 0.02529804 0.02529804
dge$samples$cluster6 dge$samples$cluster7
dge$samples$cluster0 0.02529804 0.02529804
dge$samples$cluster1 0.02529804 0.02529804
dge$samples$cluster10 0.02529804 0.02529804
dge$samples$cluster11 0.02529804 0.02529804
dge$samples$cluster12 0.02529804 0.02529804
dge$samples$cluster8 dge$samples$cluster9
dge$samples$cluster0 0.02529804 0.02529804
dge$samples$cluster1 0.02529804 0.02529804
dge$samples$cluster10 0.02529804 0.02529804
dge$samples$cluster11 0.02529804 0.02529804
dge$samples$cluster12 0.02529804 0.02529804
dge$samples$batchBatch2 dge$samples$batchBatch3
dge$samples$cluster0 -0.0188226 -0.01897346
dge$samples$cluster1 -0.0188226 -0.01897346
dge$samples$cluster10 -0.0188226 -0.01897346
dge$samples$cluster11 -0.0188226 -0.01897346
dge$samples$cluster12 -0.0188226 -0.01897346
dge$samples$indNA18858 dge$samples$indNA19160
dge$samples$cluster0 -0.01923015 -0.01886792
dge$samples$cluster1 -0.01923015 -0.01886792
dge$samples$cluster10 -0.01923015 -0.01886792
dge$samples$cluster11 -0.01923015 -0.01886792
dge$samples$cluster12 -0.01923015 -0.01886792
17 more rows ...
$pivot
[1] 1 2 3 4 5
17 more elements ...
$rank
[1] 22
$Amean
SAMD11 NOC2L PLEKHN1 HES4 ISG15 AGRN
3.1654089 5.9847813 0.6963754 6.5936954 5.6389559 4.8249547
$method
[1] "ls"
$design
dge$samples$cluster0 dge$samples$cluster1 dge$samples$cluster10
1 1 0 0
2 1 0 0
3 1 0 0
4 1 0 0
5 1 0 0
dge$samples$cluster11 dge$samples$cluster12 dge$samples$cluster13
1 0 0 0
2 0 0 0
3 0 0 0
4 0 0 0
5 0 0 0
dge$samples$cluster14 dge$samples$cluster15 dge$samples$cluster16
1 0 0 0
2 0 0 0
3 0 0 0
4 0 0 0
5 0 0 0
dge$samples$cluster17 dge$samples$cluster2 dge$samples$cluster3
1 0 0 0
2 0 0 0
3 0 0 0
4 0 0 0
5 0 0 0
dge$samples$cluster4 dge$samples$cluster5 dge$samples$cluster6
1 0 0 0
2 0 0 0
3 0 0 0
4 0 0 0
5 0 0 0
dge$samples$cluster7 dge$samples$cluster8 dge$samples$cluster9
1 0 0 0
2 0 0 0
3 0 0 0
4 0 0 0
5 0 0 0
dge$samples$batchBatch2 dge$samples$batchBatch3 dge$samples$indNA18858
1 0 0 0
2 0 0 1
3 0 0 0
4 1 0 0
5 1 0 1
dge$samples$indNA19160
1 0
2 0
3 1
4 0
5 0
153 more rows ...#all pairwise cluster comparisons
nclust<- length(unique(dge$samples$cluster))
nterms<- ncol(fit)
contrasts<- NULL
for (b in 1:nclust){
for (i in 1:nclust){
c<- rep(0,nterms)
c[b]<- 1
c[i]<- -1
contrasts<- cbind(contrasts, c)
}
}
selfcols<- c()
el<- 1
while (length(selfcols) <= nclust){
selfcols<- c(selfcols, el)
el<- el + nclust + 1
}
contrasts<- contrasts[,-selfcols]contrasts c c c c c c c c c c c c c c c c c c c c c c c c
[1,] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 -1 0 0 0 0 0 0
[2,] -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1
[3,] 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0
[4,] 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0
[5,] 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0
[6,] 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0
[7,] 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0
[8,] 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1
[9,] 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[10,] 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[11,] 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[12,] 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0
[13,] 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0
[14,] 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0
[15,] 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0
[16,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0
[17,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0
[18,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0
[19,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[20,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[21,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[22,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
c c c c c c c c c c c c c c c c c c c c c c c c
[1,] 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0
[2,] 1 1 1 1 1 1 1 1 1 1 0 -1 0 0 0 0 0 0 0 0 0 0 0 0
[3,] 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[4,] 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0
[5,] 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0
[6,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0
[7,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0
[8,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0
[9,] -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0
[10,] 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0
[11,] 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0
[12,] 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0
[13,] 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0
[14,] 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0
[15,] 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1
[16,] 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[17,] 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[18,] 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[19,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[20,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[21,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[22,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
c c c c c c c c c c c c c c c c c c c c c c c c
[1,] 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0
[2,] 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0
[3,] 1 1 1 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0
[4,] 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 -1
[5,] 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1
[6,] 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[7,] 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[8,] 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[9,] 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0
[10,] 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0
[11,] 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0
[12,] 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0
[13,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0
[14,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0
[15,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0
[16,] -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0
[17,] 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0
[18,] 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0
[19,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[20,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[21,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[22,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
c c c c c c c c c c c c c c c c c c c c c c c c
[1,] 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0
[2,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0
[3,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0
[4,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0
[5,] 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 -1 0 0 0 0 0 0
[6,] -1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1
[7,] 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0
[8,] 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0
[9,] 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0
[10,] 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0
[11,] 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0
[12,] 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1
[13,] 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[14,] 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[15,] 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[16,] 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0
[17,] 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0
[18,] 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0
[19,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[20,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[21,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[22,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
c c c c c c c c c c c c c c c c c c c c c c c c
[1,] 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1
[2,] 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[3,] 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[4,] 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[5,] 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0
[6,] 1 1 1 1 1 1 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0
[7,] 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0
[8,] 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 1
[9,] 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0
[10,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0
[11,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0
[12,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0
[13,] -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0
[14,] 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0
[15,] 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0
[16,] 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0
[17,] 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0
[18,] 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0
[19,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[20,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[21,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[22,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
c c c c c c c c c c c c c c c c c c c c c c c c
[1,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0
[2,] -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0
[3,] 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0
[4,] 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0
[5,] 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0
[6,] 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0
[7,] 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0
[8,] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 -1
[9,] 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1
[10,] 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[11,] 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[12,] 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[13,] 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0
[14,] 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0
[15,] 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0
[16,] 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0
[17,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0
[18,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0
[19,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[20,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[21,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[22,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
c c c c c c c c c c c c c c c c c c c c c c c c
[1,] 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[2,] 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0
[3,] 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0
[4,] 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0
[5,] 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0
[6,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0
[7,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0
[8,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0
[9,] 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0
[10,] -1 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[11,] 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0
[12,] 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0
[13,] 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0
[14,] 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0
[15,] 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0
[16,] 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1
[17,] 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[18,] 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[19,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[20,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[21,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[22,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
c c c c c c c c c c c c c c c c c c c c c c c c
[1,] 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0
[2,] 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0
[3,] 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0
[4,] 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0
[5,] 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1
[6,] 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[7,] 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[8,] 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[9,] 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0
[10,] 1 1 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0
[11,] 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0
[12,] 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 1 1 1 1 1
[13,] 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0
[14,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0
[15,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0
[16,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0
[17,] -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0
[18,] 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0
[19,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[20,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[21,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[22,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
c c c c c c c c c c c c c c c c c c c c c c c c
[1,] 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0
[2,] 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0
[3,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0
[4,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0
[5,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0
[6,] -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0
[7,] 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0
[8,] 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0
[9,] 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0
[10,] 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0
[11,] 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0
[12,] 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 -1
[13,] 0 0 0 0 0 0 -1 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1
[14,] 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[15,] 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[16,] 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[17,] 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0
[18,] 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0
[19,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[20,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[21,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[22,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
c c c c c c c c c c c c c c c c c c c c c c c c
[1,] 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0
[2,] 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1
[3,] 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[4,] 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[5,] 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[6,] 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0
[7,] 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0
[8,] 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0
[9,] 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0
[10,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0
[11,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0
[12,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0
[13,] 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0
[14,] -1 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0
[15,] 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 1 1
[16,] 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0
[17,] 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0
[18,] 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0
[19,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[20,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[21,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[22,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
c c c c c c c c c c c c c c c c c c c c c c c c
[1,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0
[2,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0
[3,] -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0
[4,] 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0
[5,] 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0
[6,] 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0
[7,] 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0
[8,] 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0
[9,] 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1
[10,] 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[11,] 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[12,] 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[13,] 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0
[14,] 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0
[15,] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0
[16,] 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 1 1 1 1 1 1 1 1 1
[17,] 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0
[18,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0
[19,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[20,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[21,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[22,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
c c c c c c c c c c c c c c c c c c c c c c c c
[1,] 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[2,] 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[3,] 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0
[4,] 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0
[5,] 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0
[6,] 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0
[7,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0
[8,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0
[9,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0
[10,] -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0
[11,] 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0
[12,] 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0
[13,] 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0
[14,] 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0
[15,] 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0
[16,] 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1
[17,] 0 0 0 0 0 0 -1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[18,] 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[19,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[20,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[21,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[22,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
c c c c c c c c c c c c c c c c c c
[1,] 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[2,] 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[3,] 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[4,] 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0
[5,] 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0
[6,] 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0
[7,] 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0
[8,] 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0
[9,] 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0
[10,] 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0
[11,] 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0
[12,] 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0
[13,] 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0
[14,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0 0
[15,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 0
[16,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0
[17,] 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1
[18,] -1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[19,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[20,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[21,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[22,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0#first, testing all pairwise cluster comparisons.
fit<- contrasts.fit(fit, contrasts= contrasts)efit<- eBayes(fit)plotSA(efit)
summary(decideTests(efit)) c c c c c c c c c c c c
Down 3765 1749 3385 3599 3353 735 3218 3395 3287 3722 3782 3513
NotSig 2792 7433 3500 2777 3632 9477 3883 4245 4351 2734 3210 3356
Up 4358 1733 4030 4539 3930 703 3814 3275 3277 4459 3923 4046
c c c c c c c c c c c c
Down 2503 3610 976 3723 3518 4358 3375 1519 2940 192 4158 1005
NotSig 5923 3828 9088 3020 3380 2792 4252 7609 4448 10429 3251 8745
Up 2489 3477 851 4172 4017 3765 3288 1787 3527 294 3506 1165
c c c c c c c c c c c c
Down 3697 3202 1171 3496 1808 3498 3734 4072 880 2678 1733 3288
NotSig 3822 4679 8465 4161 6996 4390 3743 3314 8721 5299 7433 4252
Up 3396 3034 1279 3258 2111 3027 3438 3529 1314 2938 1749 3375
c c c c c c c c c c c c
Down 2809 3155 2855 2505 2715 3352 3008 3182 3464 2957 2429 3466
NotSig 4969 3750 5080 6307 5226 4503 4996 4341 4197 4858 6460 4420
Up 3137 4010 2980 2103 2974 3060 2911 3392 3254 3100 2026 3029
c c c c c c c c c c c c
Down 2002 3346 3055 4030 1787 3137 2303 892 3860 598 2789 2529
NotSig 7160 4170 4529 3500 7609 4969 5809 9244 3947 9710 5542 6165
Up 1753 3399 3331 3385 1519 2809 2803 779 3108 607 2584 2221
c c c c c c c c c c c c
Down 992 2599 1543 3075 3033 3825 1640 2181 4539 3527 4010 2803
NotSig 8903 6199 7803 5396 5357 4014 7637 6578 2777 4448 3750 5809
Up 1020 2117 1569 2444 2525 3076 1638 2156 3599 2940 3155 2303
c c c c c c c c c c c c
Down 2886 4305 2748 3768 3463 3295 3946 3331 4148 4116 4361 3291
NotSig 5737 3261 5794 4061 4719 5266 4039 4940 3621 3645 3166 4809
Up 2292 3349 2373 3086 2733 2354 2930 2644 3146 3154 3388 2815
c c c c c c c c c c c c
Down 2265 3930 294 2980 779 2292 3787 493 3023 2775 446 2786
NotSig 6853 3632 10429 5080 9244 5737 3930 9841 5012 5547 9951 5566
Up 1797 3353 192 2855 892 2886 3198 581 2880 2593 518 2563
c c c c c c c c c c c c
Down 1289 2883 3162 3696 758 2069 703 3506 2103 3108 3349 3198
NotSig 8194 5578 4982 4116 9418 6654 9477 3251 6307 3947 3261 3930
Up 1432 2454 2771 3103 739 2192 735 4158 2505 3860 4305 3787
c c c c c c c c c c c c
Down 2966 2888 3016 3501 3337 3265 2281 3128 1316 3420 3295 3814
NotSig 4287 4980 4794 3140 4014 3815 6311 4581 8391 3523 3803 3883
Up 3662 3047 3105 4274 3564 3835 2323 3206 1208 3972 3817 3218
c c c c c c c c c c c c
Down 1165 2974 607 2373 581 3662 2591 2385 1096 2252 1106 2615
NotSig 8745 5226 9710 5794 9841 4287 5976 6512 8861 6872 8512 6277
Up 1005 2715 598 2748 493 2966 2348 2018 958 1791 1297 2023
c c c c c c c c c c c c
Down 2612 3530 1112 1877 3275 3396 3060 2584 3086 2880 3047 2348
NotSig 6123 4540 8680 7144 4245 3822 4503 5542 4061 5012 4980 5976
Up 2180 2845 1123 1894 3395 3697 3352 2789 3768 3023 2888 2591
c c c c c c c c c c c c
Down 2084 3273 2507 2912 2950 1926 3161 3176 2997 3277 3034 2911
NotSig 6971 4037 6172 5053 5155 7179 4649 4328 4898 4351 4679 4996
Up 1860 3605 2236 2950 2810 1810 3105 3411 3020 3287 3202 3008
c c c c c c c c c c c c
Down 2221 2733 2593 3105 2018 1860 2973 1706 2335 2806 2211 3030
NotSig 6165 4719 5547 4794 6512 6971 4727 7370 6035 5458 6503 4893
Up 2529 3463 2775 3016 2385 2084 3215 1839 2545 2651 2201 2992
c c c c c c c c c c c c
Down 2821 2381 4459 1279 3392 1020 2354 518 4274 958 3605 3215
NotSig 5063 5948 2734 8465 4341 8903 5266 9951 3140 8861 4037 4727
Up 3031 2586 3722 1171 3182 992 3295 446 3501 1096 3273 2973
c c c c c c c c c c c c
Down 3508 1940 3781 3835 4244 1399 2519 3923 3258 3254 2117 2930
NotSig 4233 6918 3987 3783 3210 7702 5708 3210 4161 4197 6199 4039
Up 3174 2057 3147 3297 3461 1814 2688 3782 3496 3464 2599 3946
c c c c c c c c c c c c
Down 2563 3564 1791 2236 1839 3174 2325 3272 2381 3588 2893 2606
NotSig 5566 4014 6872 6172 7370 4233 6058 4709 6213 3909 4809 5405
Up 2786 3337 2252 2507 1706 3508 2532 2934 2321 3418 3213 2904
c c c c c c c c c c c c
Down 4046 2111 3100 1569 2644 1432 3835 1297 2950 2545 2057 2532
NotSig 3356 6996 4858 7803 4940 8194 3815 8512 5053 6035 6918 6058
Up 3513 1808 2957 1543 3331 1289 3265 1106 2912 2335 1940 2325
c c c c c c c c c c c c
Down 3064 3187 3751 2087 1747 2489 3027 2026 2444 3146 2454 2323
NotSig 5311 4795 3940 6821 7221 5923 4390 6460 5396 3621 5578 6311
Up 2540 2933 3224 2007 1947 2503 3498 2429 3075 4148 2883 2281
c c c c c c c c c c c c
Down 2023 2810 2651 3147 2934 2540 2830 1961 2720 2763 3477 3438
NotSig 6277 5155 5458 3987 4709 5311 5148 7082 5065 4770 3828 3743
Up 2615 2950 2806 3781 3272 3064 2937 1872 3130 3382 3610 3734
c c c c c c c c c c c c
Down 3029 2525 3154 2771 3206 2180 1810 2201 3297 2321 2933 2937
NotSig 4420 5357 3645 4982 4581 6123 7179 6503 3783 6213 4795 5148
Up 3466 3033 4116 3162 3128 2612 1926 2211 3835 2381 3187 2830
c c c c c c c c c c c c
Down 3174 3096 2905 851 3529 1753 3076 3388 3103 1208 2845 3105
NotSig 4529 4293 4792 9088 3314 7160 4014 3166 4116 8391 4540 4649
Up 3212 3526 3218 976 4072 2002 3825 4361 3696 1316 3530 3161
c c c c c c c c c c c c
Down 2992 3461 3418 3224 1872 3212 3397 3275 4172 1314 3399 1638
NotSig 4893 3210 3909 3940 7082 4529 3633 3861 3020 8721 4170 7637
Up 3030 4244 3588 3751 1961 3174 3885 3779 3723 880 3346 1640
c c c c c c c c c c c c
Down 2815 739 3972 1123 3411 3031 1814 3213 2007 3130 3526 3885
NotSig 4809 9418 3523 8680 4328 5063 7702 4809 6821 5065 4293 3633
Up 3291 758 3420 1112 3176 2821 1399 2893 2087 2720 3096 3397
c c c c c c c c c c c c
Down 2592 4017 2938 3331 2156 1797 2192 3817 1894 3020 2586 2688
NotSig 5639 3380 5299 4529 6578 6853 6654 3803 7144 4898 5948 5708
Up 2684 3518 2678 3055 2181 2265 2069 3295 1877 2997 2381 2519
c c c c c c
Down 2904 1947 3382 3218 3779 2684
NotSig 5405 7221 4770 4792 3861 5639
Up 2606 1747 2763 2905 3275 2592qqt(efit$t, df=efit$df.prior+efit$df.residual, pch=16, cex=0.2)
abline(0,1)
topTable(efit, coef=1, sort.by= "P") logFC AveExpr t P.Value adj.P.Val B
FZD3 -3.404693 6.513489 -24.70435 1.299931e-53 1.300176e-49 111.6844
TTC3 -5.057673 6.918583 -24.57621 2.382366e-53 1.300176e-49 111.0577
PHC2 -5.154735 5.269648 -23.34270 8.981254e-51 3.267680e-47 104.9972
PRXL2A -3.136003 7.314321 -23.11730 2.709053e-50 6.845237e-47 104.0708
MAP2 -5.692043 4.260302 -23.05504 3.679043e-50 6.845237e-47 103.5493
PALLD -4.300937 5.169170 -23.02776 4.207686e-50 6.845237e-47 103.5634
ZBTB16 -5.987047 3.883852 -23.01914 4.389983e-50 6.845237e-47 103.3315
ZNF428 -3.465697 7.012878 -22.81956 1.175760e-49 1.604177e-46 102.6136
GLI3 -4.484837 5.195618 -22.67594 2.396374e-49 2.906270e-46 101.8227
SDK2 -4.455940 3.877262 -22.53306 4.878435e-49 5.324812e-46 100.9614Comparing the subclusters of the pluripotent blob
clust0 v clust14
topTable(efit, coef=14, sort.by= "P", n=20) logFC AveExpr t P.Value adj.P.Val B
TTC3 -5.352604 6.918583 -25.07959 2.230402e-54 2.434483e-50 113.36131
PTGR1 -3.599433 6.780217 -24.83916 6.887584e-54 3.758899e-50 112.31318
CST3 -4.939164 7.495076 -22.43631 7.906047e-49 2.876484e-45 100.71467
FLRT3 -7.712076 4.311649 -22.28859 1.656019e-48 4.518863e-45 99.55888
CDH2 -5.448870 6.197210 -22.05197 5.443866e-48 1.188396e-44 98.72946
RHOBTB3 -5.878735 5.392051 -20.29636 4.636106e-44 8.433849e-41 89.65605
COL2A1 -5.611429 5.435774 -19.44717 4.244770e-42 6.618810e-39 85.21978
PHC2 -4.439581 5.269648 -19.03671 3.891777e-41 5.309844e-38 82.99682
MPC2 -2.143289 7.228280 -18.65724 3.075490e-40 3.640250e-37 80.97868
COL18A1 -3.730244 5.704533 -18.63466 3.480021e-40 3.640250e-37 80.90336
BCL7C -2.461359 5.929437 -18.62502 3.668598e-40 3.640250e-37 80.85449
HK1 2.935070 5.689100 18.41009 1.193350e-39 1.021752e-36 79.66775
PCBD1 -2.740295 6.446632 -18.40654 1.216929e-39 1.021752e-36 79.63198
PRXL2A -2.688058 7.314321 -18.38587 1.363497e-39 1.032295e-36 79.52085
FBN2 -5.060464 4.224214 -18.37867 1.418638e-39 1.032295e-36 79.42775
DPPA4 3.361593 7.359877 18.33871 1.767914e-39 1.206049e-36 79.26890
TERF1 3.552635 7.872473 18.30851 2.088087e-39 1.340675e-36 79.07947
ADD3 -2.339852 5.815396 -18.08948 7.006485e-39 4.248654e-36 77.91191
DERA -3.014595 5.220743 -18.06398 8.070064e-39 4.636040e-36 77.77857
ADD2 3.546186 4.466847 18.00880 1.095976e-38 5.981290e-36 77.34588vol<- topTable(efit, coef=14, n=nrow(fit))
labsig<- vol[(vol$logFC) >=3 & vol$adj.P.Val < 5e-30 | vol$logFC <=-5 & vol$adj.P.Val < 1e-35,]
labsiggenes<- rownames(labsig)
thresh<- vol$adj.P.Val < 0.05
vol<-cbind(vol, thresh)
ggplot(vol, aes(x=logFC, y= -log10(adj.P.Val))) +
geom_point(aes(colour=thresh), show.legend = FALSE) +
scale_colour_manual(values = c("TRUE" = "red", "FALSE" = "black")) +
geom_text(data=labsig, aes(label=labsiggenes))
some<-topTable(efit, coef=14, sort.by= "P", n=60)
up<- some[some$logFC>0,]
down<-some[some$logFC<0,]head(up,10) logFC AveExpr t P.Value adj.P.Val B
HK1 2.935070 5.689100 18.41009 1.193350e-39 1.021752e-36 79.66775
DPPA4 3.361593 7.359877 18.33871 1.767914e-39 1.206049e-36 79.26890
TERF1 3.552635 7.872473 18.30851 2.088087e-39 1.340675e-36 79.07947
ADD2 3.546186 4.466847 18.00880 1.095976e-38 5.981290e-36 77.34588
THY1 4.302143 6.547327 17.57590 1.225013e-37 5.571259e-35 75.07868
SEPHS1 2.591606 7.554782 17.28595 6.247399e-37 2.351392e-34 73.39475
DNMT3B 3.907227 6.674119 16.46802 6.519906e-35 1.779119e-32 68.81935
JARID2 2.057235 6.738158 16.13605 4.392113e-34 8.877762e-32 66.89643
MYC 5.228513 3.732291 16.04987 7.220768e-34 1.382714e-31 66.33822
POLR3G 4.707196 4.260883 16.03340 7.940930e-34 1.438252e-31 66.33874head(down,10) logFC AveExpr t P.Value adj.P.Val B
TTC3 -5.352604 6.918583 -25.07959 2.230402e-54 2.434483e-50 113.36131
PTGR1 -3.599433 6.780217 -24.83916 6.887584e-54 3.758899e-50 112.31318
CST3 -4.939164 7.495076 -22.43631 7.906047e-49 2.876484e-45 100.71467
FLRT3 -7.712076 4.311649 -22.28859 1.656019e-48 4.518863e-45 99.55888
CDH2 -5.448870 6.197210 -22.05197 5.443866e-48 1.188396e-44 98.72946
RHOBTB3 -5.878735 5.392051 -20.29636 4.636106e-44 8.433849e-41 89.65605
COL2A1 -5.611429 5.435774 -19.44717 4.244770e-42 6.618810e-39 85.21978
PHC2 -4.439581 5.269648 -19.03671 3.891777e-41 5.309844e-38 82.99682
MPC2 -2.143289 7.228280 -18.65724 3.075490e-40 3.640250e-37 80.97868
COL18A1 -3.730244 5.704533 -18.63466 3.480021e-40 3.640250e-37 80.90336clust0 v clust10
vol<- topTable(efit, coef=10, n=nrow(fit))
labsig<- vol[(vol$adj.P.Val < 5e-45) | (vol$logFC> 0 & vol$adj.P.Val < 1e-30) |vol$logFC> 7 |vol$logFC < -7,]
labsiggenes<- rownames(labsig)
thresh<- vol$adj.P.Val < 0.05
vol<-cbind(vol, thresh)
ggplot(vol, aes(x=logFC, y= -log10(adj.P.Val))) +
geom_point(aes(colour=thresh), show.legend = FALSE) +
scale_colour_manual(values = c("TRUE" = "red", "FALSE" = "black")) +
geom_text(data=labsig, aes(label=labsiggenes))
some<-topTable(efit, coef=10, sort.by= "P", n=200)
up<- some[some$logFC>0,]
down<-some[some$logFC<0,]head(up,10) logFC AveExpr t P.Value adj.P.Val B
SKA3 4.000412 4.683639 20.67823 6.264567e-45 2.442063e-42 91.69270
POLR3G 4.512689 4.260883 16.69633 1.768181e-35 1.754518e-33 70.13741
ESCO2 3.238793 4.228161 16.53727 4.386124e-35 3.956574e-33 69.19999
PKMYT1 2.958103 3.972926 16.32128 1.512901e-34 1.260559e-32 67.99548
ARRB1 3.886548 3.638653 16.26005 2.150960e-34 1.726303e-32 67.62550
SOCS1 3.969757 4.318091 16.08709 5.824945e-34 4.415227e-32 66.66986
PIF1 4.457772 4.732559 15.95008 1.285305e-33 9.169350e-32 65.87761
MYC 4.447913 3.732291 15.83589 2.489411e-33 1.666989e-31 65.22051
TEAD4 3.850521 3.667473 15.60049 9.766757e-33 5.988998e-31 63.86067
AUNIP 2.755692 3.310330 15.46787 2.114846e-32 1.227848e-30 63.06869head(down,10) logFC AveExpr t P.Value adj.P.Val B
TTC3 -5.079898 6.918583 -24.46025 4.128689e-53 4.506464e-49 110.5114
MAPK10 -5.344690 4.486971 -24.03208 3.188848e-52 1.740314e-48 108.2798
PHC2 -5.310932 5.269648 -23.93672 5.042892e-52 1.834772e-48 107.8429
FZD3 -3.332954 6.513489 -23.86247 7.210753e-52 1.967634e-48 107.6864
ZNF428 -3.626402 7.012878 -23.73323 1.345891e-51 2.938080e-48 107.0666
MAP2 -5.829260 4.260302 -23.48411 4.507438e-51 8.199781e-48 105.6220
SDK2 -4.655060 3.877262 -23.44048 5.574245e-51 8.691841e-48 105.3686
CDH2 -5.556189 6.197210 -23.16302 2.164450e-50 2.953121e-47 104.2561
TLE4 -3.916044 6.198220 -22.99731 4.888275e-50 5.928392e-47 103.4821
KMT2E -2.641580 6.699401 -22.97569 5.437974e-50 5.935549e-47 103.3818clust0 v clust7
vol<- topTable(efit, coef=7, n=nrow(fit))
labsig<- vol[(vol$adj.P.Val < 5e-37) | (vol$logFC> 0 & vol$adj.P.Val < 1e-21) |vol$logFC> 6 |vol$logFC < -7,]
labsiggenes<- rownames(labsig)
thresh<- vol$adj.P.Val < 0.05
vol<-cbind(vol, thresh)
ggplot(vol, aes(x=logFC, y= -log10(adj.P.Val))) +
geom_point(aes(colour=thresh), show.legend = FALSE) +
scale_colour_manual(values = c("TRUE" = "red", "FALSE" = "black")) +
geom_text(data=labsig, aes(label=labsiggenes))
some<-topTable(efit, coef=7, sort.by= "P", n=300)
up<- some[some$logFC>0,]
down<-some[some$logFC<0,]head(up,10) logFC AveExpr t P.Value adj.P.Val B
L1TD1 4.078318 7.936582 12.90836 8.383958e-26 6.354655e-24 47.93565
TERF1 2.808480 7.872473 12.58365 5.924013e-25 4.066705e-23 45.93276
JARID2 2.082664 6.738158 12.49413 1.016141e-24 6.672461e-23 45.52516
ESRP1 6.151823 3.313360 12.39767 1.817980e-24 1.140417e-22 44.91822
UGP2 2.569624 7.611443 12.35363 2.371135e-24 1.478911e-22 44.58794
CYCS 1.511861 8.549251 11.98598 2.180865e-23 1.144430e-21 42.27029
DPPA4 2.522185 7.359877 11.93308 3.001498e-23 1.567529e-21 42.07324
PDLIM1 3.423607 6.674474 11.91445 3.358907e-23 1.721243e-21 42.09967
DNMT3B 3.501431 6.674119 11.86661 4.483610e-23 2.234639e-21 41.77162
HTATIP2 5.317395 4.378024 11.78004 7.562030e-23 3.652193e-21 41.22032head(down, 10) logFC AveExpr t P.Value adj.P.Val B
TTC3 -5.455607 6.918583 -23.37193 7.786981e-51 8.499490e-47 105.25649
COL2A1 -6.360132 5.435774 -20.97251 1.356663e-45 7.403986e-42 93.18050
PHC2 -5.203011 5.269648 -20.79681 3.377529e-45 1.228858e-41 92.21278
PALLD -4.304422 5.169170 -19.74562 8.588625e-43 2.343621e-39 86.81091
SDK2 -4.557873 3.877262 -19.32911 8.011512e-42 1.748913e-38 84.40667
EFNA5 -5.100087 5.437646 -18.66424 2.959962e-40 4.661925e-37 81.06530
ZBTB16 -5.756527 3.883852 -18.66240 2.989783e-40 4.661925e-37 80.85787
VIM -4.626255 9.934601 -18.56505 5.095523e-40 6.952204e-37 80.38134
CDH2 -5.034243 6.197210 -18.52564 6.324842e-40 7.078335e-37 80.30312
ZNF428 -3.342604 7.012878 -18.52109 6.484961e-40 7.078335e-37 80.28518comparing the two mature neuron clusters
clust9 v clust12
vol<- topTable(efit, coef=165, n=nrow(fit))
labsig<- vol[(vol$adj.P.Val < 5e-37) | (vol$logFC< 0 & vol$adj.P.Val < 1e-15) |vol$logFC> 9.5 |vol$logFC < -6,]
labsiggenes<- rownames(labsig)
thresh<- vol$adj.P.Val < 0.05
vol<-cbind(vol, thresh)
ggplot(vol, aes(x=logFC, y= -log10(adj.P.Val))) +
geom_point(aes(colour=thresh), show.legend = FALSE) +
scale_colour_manual(values = c("TRUE" = "red", "FALSE" = "black")) +
geom_text(data=labsig, aes(label=labsiggenes))
some<-topTable(efit, coef=165, sort.by= "P", n=400)
up<- some[some$logFC>0,]
down<-some[some$logFC<0,]head(up,10) logFC AveExpr t P.Value adj.P.Val B
EGFL7 5.013369 5.480814 29.21823 2.316511e-62 2.528471e-58 131.71827
IGFBP4 4.622808 4.468332 27.18708 1.514547e-58 8.265642e-55 122.93935
RAMP2 5.577156 4.671294 24.10036 2.298396e-52 8.362330e-49 108.81136
GNG11 4.952769 4.650268 20.90612 1.914072e-45 5.223024e-42 92.92592
SLC9A3R2 4.990341 4.042906 20.79781 3.359921e-45 7.334709e-42 92.35900
KDR 8.868834 2.717682 19.78715 6.882520e-43 1.252045e-39 86.73472
RGS5 6.667662 3.677650 18.84047 1.130997e-40 1.763548e-37 82.02311
FLT1 8.511865 2.096625 18.80700 1.357320e-40 1.851894e-37 81.56069
DOCK6 4.179149 2.976909 18.67041 2.861699e-40 3.470605e-37 81.00127
MAP4K2 3.851620 3.225441 18.50198 7.202053e-40 7.861041e-37 80.12463head(down,10) logFC AveExpr t P.Value adj.P.Val B
HDDC2 -2.150967 7.007206 -12.469897 1.176032e-24 9.438522e-23 45.25070
LMO4 -2.704546 6.830333 -11.486277 4.454955e-22 2.628423e-20 39.35081
METRN -3.453564 7.392618 -11.182589 2.782654e-21 1.439463e-19 37.55896
FZD3 -2.656501 6.513489 -10.877425 1.748821e-20 8.054168e-19 35.83649
CRABP2 -3.970496 8.339630 -10.237889 8.109766e-19 3.031442e-17 31.81122
CDH6 -5.030988 4.838455 -10.143413 1.425929e-18 5.205356e-17 31.63891
COL2A1 -4.798300 5.435774 -9.920974 5.367535e-18 1.854008e-16 30.31351
TLE1 -2.256331 6.125347 -9.720027 1.769998e-17 5.718115e-16 28.94028
CNP -1.907485 5.413204 -9.582488 3.994990e-17 1.231789e-15 28.12738
ATP1A2 -5.421017 4.104727 -9.505743 6.285654e-17 1.895246e-15 27.95163cluster 4 v12
vol<- topTable(efit, coef=80, n=nrow(fit))
labsig<- vol[(vol$adj.P.Val < 5e-40) |vol$logFC> 7 |vol$logFC < -6 | vol$adj.P.Val<1e-27 & vol$logFC<0,]
labsiggenes<- rownames(labsig)
thresh<- vol$adj.P.Val < 0.05
vol<-cbind(vol, thresh)
ggplot(vol, aes(x=logFC, y= -log10(adj.P.Val))) +
geom_point(aes(colour=thresh), show.legend = FALSE) +
scale_colour_manual(values = c("TRUE" = "red", "FALSE" = "black")) +
geom_text(data=labsig, aes(label=labsiggenes))
some<-topTable(efit, coef=80, sort.by= "P", n=400)
up<- some[some$logFC>0,]
down<-some[some$logFC<0,]head(up,10) logFC AveExpr t P.Value adj.P.Val B
RTN1 6.283233 3.322440 29.23024 2.202083e-62 2.403574e-58 131.43213
INA 6.968998 3.226732 25.01275 3.049435e-54 1.664229e-50 112.44418
STMN2 7.840766 4.084364 23.27740 1.235857e-50 4.496458e-47 104.83451
GNG3 5.999507 3.215638 21.30742 2.410473e-46 6.577577e-43 94.81926
FNDC5 5.667737 2.061072 20.59407 9.722261e-45 2.122369e-41 91.04387
KLHL35 5.894091 1.987637 20.54765 1.239471e-44 2.254804e-41 90.84930
IGFBPL1 5.737944 4.520933 20.50719 1.531928e-44 2.367077e-41 90.88251
MAP6 5.755501 3.308765 20.48345 1.734917e-44 2.367077e-41 90.65277
ELAVL3 7.351680 2.596420 20.36873 3.168151e-44 3.842263e-41 89.90741
TAGLN3 4.661624 3.936643 19.37929 6.114721e-42 6.674218e-39 84.91877head(down,10) logFC AveExpr t P.Value adj.P.Val B
GPX8 -3.296792 5.747707 -16.38695 1.037754e-34 3.236309e-32 68.38254
GINS4 -3.246664 4.658249 -16.10846 5.149545e-34 1.405182e-31 66.76782
ARPC1B -3.484451 4.736997 -15.34056 4.446607e-32 8.666914e-30 62.36856
DNAJC1 -2.796057 5.593647 -14.55309 4.558586e-30 6.219621e-28 57.67779
ATP1A2 -5.714578 4.104727 -14.53399 5.103827e-30 6.877564e-28 57.63630
YAP1 -2.825628 5.669665 -14.50000 6.240852e-30 8.279520e-28 57.46226
UACA -3.979332 5.445227 -14.49649 6.371779e-30 8.279520e-28 57.44869
MDFI -4.659454 5.432176 -14.42558 9.695961e-30 1.230598e-27 57.03341
CNP -2.357814 5.413204 -14.06274 8.360898e-29 8.609358e-27 54.80104
MCM10 -4.400485 4.677969 -14.06063 8.466354e-29 8.636472e-27 54.84023cluster2 v cluster13
vol<- topTable(efit, coef=47, n=nrow(fit))
labsig<- vol[(vol$adj.P.Val < 5e-15) |vol$logFC> 3 |vol$logFC < -4,]
labsiggenes<- rownames(labsig)
thresh<- vol$adj.P.Val < 0.05
vol<-cbind(vol, thresh)
ggplot(vol, aes(x=logFC, y= -log10(adj.P.Val))) +
geom_point(aes(colour=thresh), show.legend = FALSE) +
scale_colour_manual(values = c("TRUE" = "red", "FALSE" = "black")) +
geom_text(data=labsig, aes(label=labsiggenes))
some<-topTable(efit, coef=47, sort.by= "P", n=400)
up<- some[some$logFC>0,]
down<-some[some$logFC<0,]head(up,10) logFC AveExpr t P.Value adj.P.Val B
MT-CO2 1.1583742 13.873780 12.501636 9.712033e-25 1.060068e-20 45.33755
MT-ND4 1.0221558 12.565437 11.255825 1.789275e-21 5.832717e-18 37.89834
EIF2S3 0.9893087 8.163179 10.444154 2.359706e-19 4.292699e-16 33.33784
MT-ND4L 1.1128059 7.687674 10.362095 3.857638e-19 4.678457e-16 32.88074
MT-ND5 0.9355394 10.245649 9.995515 3.444192e-18 3.132780e-15 30.53457
MT-ND2 0.8907165 10.782929 9.944795 4.658255e-18 3.389657e-15 30.19041
TOMM7 1.1603328 8.961618 9.845063 8.428573e-18 5.749867e-15 29.73764
SLC25A39 1.0181265 7.083068 9.702886 1.959231e-17 1.188056e-14 29.03117
LRRC75A 1.6189398 6.067424 9.677037 2.283408e-17 1.311758e-14 28.91758
USO1 1.1629757 6.410516 9.636840 2.896813e-17 1.437214e-14 28.67141head(down,10) logFC AveExpr t P.Value adj.P.Val B
MAPK10 -4.360461 4.486971 -11.943649 2.815950e-23 1.536805e-19 41.34367
PRTG -4.224031 5.708449 -11.226336 2.137505e-21 5.832717e-18 37.91330
BOC -3.423740 3.962865 -10.509912 1.590931e-19 3.473003e-16 33.28337
ZEB2 -3.968027 4.429497 -10.410542 2.886147e-19 4.500328e-16 32.83816
SDK2 -3.110266 3.877262 -10.384955 3.364141e-19 4.589950e-16 32.53557
MAP2 -4.200851 4.260302 -10.047809 2.522143e-18 2.733613e-15 30.45636
ZNF518B -2.408133 3.903056 -10.032998 2.754901e-18 2.733613e-15 30.59062
YAF2 -3.675311 4.041744 -9.969237 4.027572e-18 3.228978e-15 29.96532
FGFBP3 -2.771643 5.387173 -9.964547 4.141612e-18 3.228978e-15 30.61020
EPHA4 -2.993781 4.152873 -9.723684 1.732049e-17 1.112077e-14 29.036341 v all contrasts
fit<- lmFit(v,design)contrasts<- NULL
for (i in 1:nclust){
c<- c(rep(-1,nclust),0,0,0,0)
c[i]<- nclust-1
contrasts<- cbind(contrasts, c)
}contrasts c c c c c c c c c c c c c c c c c c
[1,] 17 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1
[2,] -1 17 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1
[3,] -1 -1 17 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1
[4,] -1 -1 -1 17 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1
[5,] -1 -1 -1 -1 17 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1
[6,] -1 -1 -1 -1 -1 17 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1
[7,] -1 -1 -1 -1 -1 -1 17 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1
[8,] -1 -1 -1 -1 -1 -1 -1 17 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1
[9,] -1 -1 -1 -1 -1 -1 -1 -1 17 -1 -1 -1 -1 -1 -1 -1 -1 -1
[10,] -1 -1 -1 -1 -1 -1 -1 -1 -1 17 -1 -1 -1 -1 -1 -1 -1 -1
[11,] -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 17 -1 -1 -1 -1 -1 -1 -1
[12,] -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 17 -1 -1 -1 -1 -1 -1
[13,] -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 17 -1 -1 -1 -1 -1
[14,] -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 17 -1 -1 -1 -1
[15,] -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 17 -1 -1 -1
[16,] -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 17 -1 -1
[17,] -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 17 -1
[18,] -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 17
[19,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[20,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[21,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[22,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0#first, testing all pairwise cluster comparisons.
fit<- contrasts.fit(fit, contrasts= contrasts)efit<- eBayes(fit)plotSA(efit)
qqt(efit$t, df=efit$df.prior+efit$df.residual, pch=16, cex=0.2)
abline(0,1)
summary(decideTests(efit)) c c c c c c c c c c c c c c
Down 3750 3515 3003 2004 3949 2029 3079 1365 2784 2278 3530 2713 2403 2065
NotSig 2813 4002 4701 6910 3840 6607 3965 8035 5121 5996 4046 5138 5977 5783
Up 4352 3398 3211 2001 3126 2279 3871 1515 3010 2641 3339 3064 2535 3067
c c c c
Down 2889 3194 3141 2776
NotSig 4709 3841 4775 5519
Up 3317 3880 2999 2620c16vall<- topTable(efit, coef=17, sort.by= "P", n=nrow(fit))
head(c16vall, n=30) logFC AveExpr t P.Value adj.P.Val B
NEK2 52.63998 4.830189 27.61041 2.339853e-59 2.553949e-55 90.94901
CKAP2L 47.26989 4.411568 25.25623 9.783802e-55 5.339510e-51 81.08220
CENPF 46.46481 8.689377 24.93375 4.416354e-54 1.606817e-50 100.65144
ECT2 35.92285 5.452767 24.85592 6.365568e-54 1.737004e-50 84.92280
H2AFX 34.48278 6.793425 24.71820 1.217685e-53 2.658207e-50 91.99501
PSRC1 48.73518 5.034690 24.60589 2.070114e-53 3.765882e-50 83.68168
CENPE 46.59960 5.745943 24.55099 2.684582e-53 4.186031e-50 87.64523
HMGB2 34.61832 8.685212 24.41265 5.176596e-53 7.062818e-50 97.65636
BUB3 25.99295 7.111377 24.12989 1.995135e-52 2.419656e-49 89.77597
CCNB2 39.16759 6.823961 23.90708 5.816110e-52 6.348284e-49 89.44644
ARL6IP1 32.72680 7.250500 23.78154 1.065606e-51 1.057372e-48 90.03132
PBK 46.65631 4.455516 23.68767 1.677901e-51 1.526191e-48 77.65680
TOP2A 47.79335 7.630618 23.62612 2.261016e-51 1.898384e-48 92.60334
KPNA2 35.62962 8.432579 23.49655 4.242623e-51 3.307730e-48 93.37520
ASPM 47.38279 6.735890 23.44058 5.571612e-51 4.054276e-48 88.79310
MXD3 49.42145 3.792698 23.27443 1.253942e-50 8.554234e-48 72.19251
PRR11 44.06991 4.277712 23.11040 2.802465e-50 1.799347e-47 73.33055
CDCA8 46.52676 5.010388 23.02524 4.260111e-50 2.583284e-47 78.66194
KIFC1 41.47115 4.189600 22.92123 7.113761e-50 4.086669e-47 72.60899
KIF14 43.86825 4.675526 22.83414 1.093970e-49 5.970339e-47 75.65319
GPSM2 39.65333 4.276758 22.75962 1.582170e-49 8.223519e-47 72.50739
KNSTRN 37.12816 5.612926 22.53221 4.899035e-49 2.430589e-46 79.31939
KIF18A 43.77790 3.996648 22.49239 5.975223e-49 2.835633e-46 69.97055
NDC80 42.16411 5.204179 22.33322 1.324143e-48 5.884742e-46 77.21708
NUF2 40.30199 6.214613 22.31840 1.426144e-48 5.884742e-46 81.79203
GTSE1 42.70245 5.393118 22.31730 1.434054e-48 5.884742e-46 78.13370
CKAP2 33.83509 6.072220 22.31431 1.455685e-48 5.884742e-46 80.89015
FAM83D 45.23841 4.088318 22.17147 2.982018e-48 1.162454e-45 69.88796
TROAP 39.35532 4.986507 22.15790 3.192716e-48 1.201672e-45 75.42388
CDCA3 50.62851 5.039881 22.12639 3.741268e-48 1.361198e-45 76.90545vol<- topTable(efit, coef=17, n=nrow(fit))
labsig<- vol[(vol$adj.P.Val < 5e-50) | (vol$logFC< 0 & vol$adj.P.Val < 5e-20) |vol$logFC> 65 |vol$logFC < -55,]
labsiggenes<- rownames(labsig)
thresh<- vol$adj.P.Val < 0.05
vol<-cbind(vol, thresh)
ggplot(vol, aes(x=logFC, y= -log10(adj.P.Val))) +
geom_point(aes(colour=thresh), show.legend = FALSE) +
scale_colour_manual(values = c("TRUE" = "red", "FALSE" = "black")) +
geom_text(data=labsig, aes(label=labsiggenes))
c12vall<- topTable(efit, coef=13, sort.by="P",n=nrow(fit))
head(c12vall, n=40) logFC AveExpr t P.Value adj.P.Val B
S100B 86.77566 2.1297724 23.59344 2.649432e-51 2.891855e-47 60.38339
NPR3 100.83128 1.3195987 22.46218 6.947619e-49 3.271570e-45 50.65431
EDNRA 86.05856 1.5675030 22.41055 8.991947e-49 3.271570e-45 50.60999
ERBB3 74.13220 3.4417554 21.84957 1.515032e-47 4.134145e-44 64.02814
SOX10 101.81566 1.1720426 21.75720 2.421158e-47 5.285388e-44 46.51703
DNAJC1 41.23444 5.5936472 21.49547 9.193175e-47 1.672392e-43 74.99844
PHACTR3 82.29166 1.3836614 21.27013 2.919713e-46 4.552666e-43 46.43938
MPZ 109.29321 1.7629100 21.14529 5.553749e-46 7.577396e-43 51.51075
ZEB2 57.91105 4.4294971 20.96913 1.380650e-45 1.674422e-42 68.11718
SCRG1 94.21982 2.4322644 20.93373 1.658689e-45 1.810459e-42 56.21624
ATP1A2 56.35268 4.1047267 20.32266 4.036653e-44 4.005461e-41 64.47439
PRELP 80.64422 1.3023938 19.67936 1.223389e-42 1.112774e-39 44.16888
KANK4 83.96534 1.9754425 19.56751 2.225701e-42 1.868733e-39 49.89094
CNP 33.74355 5.4132040 19.33849 7.616532e-42 5.938174e-39 64.95289
MOXD1 91.94808 1.2674002 18.60692 4.050861e-40 2.947676e-37 41.39881
CDH6 52.33876 4.8384554 18.28204 2.416229e-39 1.648322e-36 61.54456
LMO4 35.53733 6.8303330 18.17881 4.273225e-39 2.743662e-36 66.19360
TFAP2B 95.18319 2.3665878 18.04160 9.135990e-39 5.539963e-36 51.05426
PLEKHA4 39.80359 4.1138458 17.60786 1.024229e-37 5.883925e-35 53.41998
PRSS56 111.56675 0.4937154 17.59039 1.129537e-37 6.164448e-35 34.80450
NR2F1 64.62671 5.4087644 17.03619 2.561962e-36 1.331610e-33 59.50517
TFAP2A 74.10051 3.2682912 16.73238 1.439645e-35 7.142604e-33 50.88564
ITGA4 83.30224 0.8937784 16.38121 1.072449e-34 5.089468e-32 31.86468
RHOB 32.31834 5.7224898 16.30481 1.662998e-34 7.563175e-32 55.89868
BCHE 68.33868 2.0975778 16.24588 2.333595e-34 1.018847e-31 40.28551
SNAI2 65.81066 1.7635782 16.23643 2.464005e-34 1.034408e-31 39.71319
ABCC2 81.47828 0.6166620 15.97622 1.104975e-33 4.466963e-31 28.77660
SMOC1 59.95529 2.9949411 15.59847 9.882470e-33 3.852399e-30 42.91014
ADAMTS4 50.73641 2.0053885 15.54126 1.378839e-32 5.189665e-30 35.52102
ROPN1 101.34272 -0.2122844 15.44117 2.471229e-32 8.991154e-30 23.60729
MEF2C 61.10032 1.7901830 15.41274 2.917140e-32 1.027115e-29 36.18023
PLAT 66.65920 2.4173335 15.23226 8.377905e-32 2.828164e-29 39.66642
COL2A1 41.71693 5.4357744 15.22878 8.550565e-32 2.828164e-29 51.13801
PRDM12 81.75199 0.5413568 15.22295 8.847577e-32 2.840332e-29 27.59441
CMTM5 57.73733 1.3880257 14.68747 2.060778e-30 6.426682e-28 30.70686
LSAMP 42.83652 4.1862570 14.67433 2.227030e-30 6.752232e-28 43.68183
RFTN2 47.05674 2.2719455 14.66626 2.335711e-30 6.890347e-28 35.40663
S100A1 58.44306 2.0321371 14.64483 2.650882e-30 7.614311e-28 32.91760
CUEDC2 25.17400 6.6188366 14.63521 2.805784e-30 7.852599e-28 50.04919
OAF 42.78513 2.7984324 14.56259 4.309601e-30 1.175982e-27 38.86536batch + ind comparisons
fit<- lmFit(v,design)fit<-contrasts.fit(fit, coefficients = (nclust+1):(nclust+4))efit<- eBayes(fit)plotSA(efit)
qqt(efit$t, df=efit$df.prior+efit$df.residual, pch=16, cex=0.2)
abline(0,1)
topsBatch<- topTable(efit, coef=c(1:2),n=nrow(fit))
head(topsBatch, n=40) dge.samples.batchBatch2 dge.samples.batchBatch3 AveExpr F
EEF1A1 -1.4306147 -1.0525243 9.730178 579.4724
PPP1CB 1.2935540 1.2786922 7.003696 475.8166
CLIC1 -2.0033220 -1.3258816 4.937045 434.9431
AP001267.5 -2.3869624 -1.5947683 3.336455 431.5869
MRPL42 0.8772992 1.0076378 7.310103 394.4952
SMARCB1 -1.5068365 -1.1857060 4.862201 390.5907
LRRC75A 2.1737607 1.4443018 6.067424 385.9050
SF3A2 -0.9778686 -1.4569135 5.324960 381.1228
CAPZA1 1.0209149 1.0578690 6.715245 380.1256
TBL1XR1 1.0696723 1.1951648 6.236839 340.5775
GPBP1 0.8186246 0.8675824 6.945178 340.5608
H3F3A -1.3164993 -0.9010471 6.185463 335.1198
EIF3E 0.8194785 0.7601219 9.223865 334.4057
TRA2A 1.0150333 0.9439583 6.162212 319.3388
N4BP2L2 0.8283410 0.8872747 6.970941 305.5450
NME2 -1.6074268 -1.2508633 3.912930 301.2705
PGAM1 -0.9598645 -0.7759151 6.859031 297.9188
SLC25A6 -1.5197161 -1.3622162 4.555480 290.5582
MED21 0.9763726 1.1246960 5.653105 288.3694
DNAJA2 0.5822158 0.8000127 7.104472 266.8075
PRPF31 -1.6766705 -1.0900384 3.254492 265.2866
EIF4E 1.0612566 0.8219131 5.676749 264.6764
MT-ND1 -0.7769798 -0.8078985 11.390268 259.2355
RSL24D1 0.6786157 0.7681192 7.681722 255.6105
YWHAG 0.6674777 0.9599464 6.694974 238.3306
TMEM167A 0.6064178 0.8207721 7.355623 237.5583
CMTM6 0.7656784 0.8676582 6.715668 235.5807
TMED2 0.9196650 0.9181669 6.787542 232.4091
BZW1 0.5689984 0.8313351 7.002194 232.1843
MTX1 -1.1896438 -1.0046155 4.560252 229.7192
CAPZA2 0.7409954 0.7839070 6.718293 228.0493
LYPLA2 -1.0300137 -1.0242190 4.859538 225.5143
ZFAND6 0.7986269 0.8499418 6.782021 225.0197
BTF3L4 0.7078889 0.7197503 7.468313 219.7953
HNRNPH1 0.8324717 0.7110226 8.237763 218.6944
SKIL 1.0342334 1.3569664 6.659637 216.6658
ACTR2 0.6112190 0.8560656 7.072977 216.4177
C6orf62 0.9176043 0.9107224 5.519504 213.5445
MOB4 0.7597861 0.6440750 6.320181 213.3128
SYNC 1.3706567 1.3937707 4.325719 211.3674
P.Value adj.P.Val
EEF1A1 1.480432e-69 1.615891e-65
PPP1CB 3.838659e-64 2.094948e-60
CLIC1 1.015459e-61 3.694578e-58
AP001267.5 1.637488e-61 4.468294e-58
MRPL42 4.021745e-59 8.779470e-56
SMARCB1 7.362015e-59 1.339273e-55
LRRC75A 1.531336e-58 2.387791e-55
SF3A2 3.258819e-58 4.446251e-55
CAPZA1 3.818456e-58 4.630939e-55
TBL1XR1 2.764705e-55 2.751336e-52
GPBP1 2.772762e-55 2.751336e-52
H3F3A 7.205907e-55 6.554373e-52
EIF3E 8.175899e-55 6.864611e-52
TRA2A 1.238292e-53 9.654254e-51
N4BP2L2 1.636557e-52 1.190868e-49
NME2 3.712529e-52 2.532641e-49
PGAM1 7.103619e-52 4.560941e-49
SLC25A6 3.015867e-51 1.828788e-48
MED21 4.662299e-51 2.678368e-48
DNAJA2 3.946716e-49 2.153920e-46
PRPF31 5.455084e-49 2.835345e-46
EIF4E 6.213982e-49 3.082982e-46
MT-ND1 2.006403e-48 9.521692e-46
RSL24D1 4.428213e-48 2.013914e-45
YWHAG 2.183968e-46 9.535204e-44
TMEM167A 2.612714e-46 1.096838e-43
CMTM6 4.143245e-46 1.674945e-43
TMED2 8.733381e-46 3.404459e-43
BZW1 9.210209e-46 3.466532e-43
MTX1 1.653865e-45 6.017312e-43
CAPZA2 2.465526e-45 8.681037e-43
LYPLA2 4.539335e-45 1.548339e-42
ZFAND6 5.116664e-45 1.692375e-42
BTF3L4 1.834197e-44 5.888310e-42
HNRNPH1 2.407388e-44 7.507612e-42
SKIL 3.984293e-44 1.208015e-41
ACTR2 4.238569e-44 1.250378e-41
C6orf62 8.710304e-44 2.501920e-41
MOB4 9.234317e-44 2.584425e-41
SYNC 1.510670e-43 4.122240e-41topsInd<- topTable(efit, coef=c(3:4),n=nrow(fit))
head(topsInd, n=40) dge.samples.indNA18858 dge.samples.indNA19160 AveExpr F
TYW3 0.26928141 -5.55543495 4.113800 1525.8094
EIF1AY 0.20508973 6.93681076 2.326674 1048.0792
CRYZ 0.17882404 -4.81958907 2.839771 690.2228
AC004556.3 -5.75534452 -6.17888060 1.835405 656.6054
CAT -0.73782305 -5.07969345 2.867017 585.1321
DDX3Y 0.56220247 6.34465982 1.945796 513.0746
IAH1 2.86642172 3.50414363 4.724134 461.5977
MAGEH1 -4.21921787 0.08695672 3.723956 439.4305
USP9Y 0.30535465 6.70590345 1.684035 411.8934
USP51 -4.63927891 0.02424610 2.975420 348.2111
THOC3 -0.07080983 -1.56049342 4.320177 334.3516
ZNF280D 2.84062273 3.51733872 2.725728 328.2798
SNRPE -0.93855774 0.02260109 8.972366 325.5767
RRAGB -3.48527180 0.15316671 2.720922 321.5495
TOMM20 -0.73295274 0.15607105 8.609516 314.5744
TAF9B -0.07278787 -3.59417724 3.157304 312.6982
NUCKS1 -0.71312420 0.16623544 10.180196 310.5760
FDPS -1.21877291 -0.02684859 8.334725 297.3570
RNF187 -1.03317535 -0.02179477 7.155469 293.5132
TIMM17B 0.95128537 -0.01098697 7.209957 293.1374
KRTCAP2 -0.91311441 0.06066995 7.057066 288.3349
QPCT 0.13830166 -3.76249787 3.000958 284.8849
TPR -0.93976183 0.06625741 8.182364 282.8973
SP5 -3.11295951 1.40660934 3.943575 273.6559
TRIM61 1.61407622 -3.62478665 1.202615 271.3424
ILF2 -0.89329881 -0.15709871 8.864239 265.5155
NDUFS2 -1.05704830 -0.14211365 7.471435 262.9533
HAX1 -0.73733217 0.16587815 6.932201 253.0943
HCCS 1.16016653 -0.01690698 5.092590 249.0936
UBA1 0.20645551 -0.77137417 6.782157 245.0465
TCEAL5 1.58799720 -2.89834113 3.157009 244.2397
NXT2 1.43401585 0.02192887 3.535435 239.9512
RAB4A -1.01107992 0.11777263 6.862365 237.8658
USP9X 0.71474988 -0.50252712 6.863537 236.8376
CHCHD2 0.70387819 -2.35692826 7.107940 234.0863
MRPS21 -0.81878449 0.02949131 8.499517 233.0113
UBLCP1 0.35068073 -0.87261236 5.383690 232.8228
ZNF717 0.81324572 -3.22149694 1.204613 232.5085
ZNF300 2.57911387 -1.77563074 1.358216 232.4810
IRX2 4.05239853 4.61757793 3.704031 225.9666
P.Value adj.P.Val
TYW3 1.394143e-97 1.521707e-93
EIF1AY 1.742628e-86 9.510390e-83
CRYZ 1.843357e-74 6.706745e-71
AC004556.3 4.728545e-73 1.290302e-69
CAT 7.946161e-70 1.734647e-66
DDX3Y 3.377413e-66 6.144077e-63
IAH1 2.545447e-63 3.969079e-60
MAGEH1 5.386846e-62 7.349679e-59
USP9Y 2.886984e-60 3.501270e-57
USP51 7.394120e-56 8.070682e-53
THOC3 8.254718e-55 8.190932e-52
ZNF280D 2.438121e-54 2.217674e-51
SNRPE 3.969633e-54 3.332965e-51
RRAGB 8.257078e-54 6.437572e-51
TOMM20 2.988980e-53 2.174981e-50
TAF9B 4.241582e-53 2.893554e-50
NUCKS1 6.314727e-53 4.054426e-50
FDPS 7.924399e-52 4.805267e-49
RNF187 1.681918e-51 9.662179e-49
TIMM17B 1.811086e-51 9.884003e-49
KRTCAP2 4.694532e-51 2.440039e-48
QPCT 9.379221e-51 4.653373e-48
TPR 1.401716e-50 6.652056e-48
SP5 9.354124e-50 4.254178e-47
TRIM61 1.516399e-49 6.620598e-47
ILF2 5.195226e-49 2.180996e-46
NDUFS2 8.988518e-49 3.633691e-46
HAX1 7.711098e-48 3.005951e-45
HCCS 1.879321e-47 7.073376e-45
UBA1 4.680632e-47 1.702970e-44
TCEAL5 5.622262e-47 1.979580e-44
NXT2 1.501420e-46 5.121249e-44
RAB4A 2.432635e-46 8.046124e-44
USP9X 3.089731e-46 9.918947e-44
CHCHD2 5.881745e-46 1.834264e-43
MRPS21 7.575898e-46 2.296970e-43
UBLCP1 7.920531e-46 2.336557e-43
ZNF717 8.530743e-46 2.403061e-43
ZNF300 8.586293e-46 2.403061e-43
IRX2 4.069474e-45 1.110458e-42"UTF1" %in% rownames(topsInd)[1] TRUEtopsInd[rownames(topsInd)== "UTF1",] dge.samples.indNA18858 dge.samples.indNA19160 AveExpr F
UTF1 0.122897 -0.5988935 5.844886 11.48102
P.Value adj.P.Val
UTF1 2.36572e-05 6.909775e-05plot(density(topsBatch$adj.P.Val))
lines(density(topsInd$adj.P.Val), col= "red")
boxplot(-log10(topsBatch$adj.P.Val), -log10(topsInd$adj.P.Val), names= c("Batch", "Individual"), main="Distribution of p values from F tests", ylab="-log10(adjusted.p.val)")
median(topsBatch$F)[1] 8.997543median(topsInd$F)[1] 6.817397
sessionInfo()R version 3.6.1 (2019-07-05)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Scientific Linux 7.4 (Nitrogen)
Matrix products: default
BLAS/LAPACK: /software/openblas-0.2.19-el7-x86_64/lib/libopenblas_haswellp-r0.2.19.so
locale:
[1] C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] UpSetR_1.4.0 ggplot2_3.3.2 reshape2_1.4.4 edgeR_3.28.1
[5] limma_3.42.2 dplyr_1.0.0 Matrix_1.2-18 Seurat_3.2.0
[9] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] Rtsne_0.15 colorspace_1.4-1 deldir_0.1-28
[4] ellipsis_0.3.1 ggridges_0.5.2 rprojroot_1.3-2
[7] fs_1.4.2 spatstat.data_1.4-3 farver_2.0.3
[10] leiden_0.3.3 listenv_0.8.0 npsurv_0.4-0
[13] ggrepel_0.8.2 codetools_0.2-16 splines_3.6.1
[16] lsei_1.2-0 knitr_1.29 polyclip_1.10-0
[19] jsonlite_1.7.0 ica_1.0-2 cluster_2.1.0
[22] png_0.1-7 uwot_0.1.8 shiny_1.5.0
[25] sctransform_0.2.1 compiler_3.6.1 httr_1.4.2
[28] backports_1.1.8 fastmap_1.0.1 lazyeval_0.2.2
[31] later_1.1.0.1 htmltools_0.5.0 tools_3.6.1
[34] rsvd_1.0.3 igraph_1.2.5 gtable_0.3.0
[37] glue_1.4.1 RANN_2.6.1 rappdirs_0.3.1
[40] Rcpp_1.0.5 spatstat_1.64-1 vctrs_0.3.2
[43] gdata_2.18.0 ape_5.3 nlme_3.1-140
[46] lmtest_0.9-37 xfun_0.16 stringr_1.4.0
[49] globals_0.12.5 mime_0.9 miniUI_0.1.1.1
[52] lifecycle_0.2.0 irlba_2.3.3 gtools_3.8.2
[55] goftest_1.2-2 future_1.18.0 MASS_7.3-51.4
[58] zoo_1.8-8 scales_1.1.1 promises_1.1.1
[61] spatstat.utils_1.17-0 parallel_3.6.1 RColorBrewer_1.1-2
[64] yaml_2.2.1 reticulate_1.16 pbapply_1.4-2
[67] gridExtra_2.3 rpart_4.1-15 stringi_1.4.6
[70] caTools_1.18.0 rlang_0.4.7 pkgconfig_2.0.3
[73] bitops_1.0-6 evaluate_0.14 lattice_0.20-38
[76] ROCR_1.0-7 purrr_0.3.4 tensor_1.5
[79] labeling_0.3 patchwork_1.0.1 htmlwidgets_1.5.1
[82] cowplot_1.0.0 tidyselect_1.1.0 here_0.1-11
[85] RcppAnnoy_0.0.16 plyr_1.8.6 magrittr_1.5
[88] R6_2.4.1 gplots_3.0.4 generics_0.0.2
[91] withr_2.2.0 pillar_1.4.6 whisker_0.4
[94] mgcv_1.8-28 fitdistrplus_1.0-14 survival_3.2-3
[97] abind_1.4-5 tibble_3.0.3 future.apply_1.6.0
[100] crayon_1.3.4 KernSmooth_2.23-15 plotly_4.9.2.1
[103] rmarkdown_2.3 locfit_1.5-9.4 grid_3.6.1
[106] data.table_1.13.0 git2r_0.26.1 digest_0.6.25
[109] xtable_1.8-4 tidyr_1.1.0 httpuv_1.5.4
[112] munsell_0.5.0 viridisLite_0.3.0