Differential Expression with Limma
Last updated: 2021-01-11
Checks: 6 1
Knit directory: Embryoid_Body_Pilot_Workflowr/analysis/
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| /project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/Pseudobulk_Limma_res0.1_OnevAll_top10Upregby_logFC.csv | ../output/Pseudobulk_Limma_res0.1_OnevAll_top10Upregby_logFC.csv |
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | cbdbc9f | KLRhodes | 2021-01-11 | wflow_publish("analysis/Pseudobulk_Limma_Harmony.BatchIndividual_ClusterRes0.1_minPCT0.2.Rmd") |
| html | 0e51844 | KLRhodes | 2021-01-11 | Build site. |
| Rmd | 63febc0 | KLRhodes | 2021-01-11 | wflow_publish("analysis/Pseudobulk_Limma_Harmony.BatchIndividual_ClusterRes0.1_minPCT0.2.Rmd") |
| html | dd2f60e | KLRhodes | 2020-10-19 | Build site. |
| Rmd | ad4d0f6 | KLRhodes | 2020-10-19 | analysis/index.Rmd |
| html | 7888de8 | KLRhodes | 2020-08-31 | Build site. |
| 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.0.1'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)
| Version | Author | Date |
|---|---|---|
| 7888de8 | KLRhodes | 2020-08-31 |
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$norm.factors) Min. 1st Qu. Median Mean 3rd Qu. Max.
0.8176 0.9349 1.0050 1.0052 1.0540 1.3300 design<- model.matrix(~0+ dge$samples$cluster + dge$samples$batch + dge$samples$ind)v<- voom(dge, design, plot=TRUE)
| Version | Author | Date |
|---|---|---|
| 7888de8 | KLRhodes | 2020-08-31 |
vAn object of class "EList"
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1 1 0 0
2 1 0 0
3 1 0 0
4 1 0 0
5 1 0 0
dge$samples$cluster3 dge$samples$cluster4 dge$samples$cluster5
1 0 0 0
2 0 0 0
3 0 0 0
4 0 0 0
5 0 0 0
dge$samples$cluster6 dge$samples$batchBatch2 dge$samples$batchBatch3
1 0 0 0
2 0 0 0
3 0 0 0
4 0 1 0
5 0 1 0
dge$samples$indNA18858 dge$samples$indNA19160
1 0 0
2 1 0
3 0 1
4 0 0
5 1 0
56 more rows ...fit<- lmFit(v,design)dim(fit)[1] 10185 11head(fit)An object of class "MArrayLM"
$coefficients
dge$samples$cluster0 dge$samples$cluster1 dge$samples$cluster2
NOC2L 6.521581 6.234605 6.220112
HES4 6.202813 8.991581 5.781038
ISG15 5.528388 5.742573 6.459499
AGRN 4.668509 5.076479 4.285475
SDF4 5.895164 6.074760 6.383872
B3GALT6 5.065073 5.569461 5.492295
dge$samples$cluster3 dge$samples$cluster4 dge$samples$cluster5
NOC2L 6.335556 6.036905 6.435496
HES4 6.879074 7.443107 7.624007
ISG15 4.949419 6.996131 4.240453
AGRN 5.440944 5.702992 5.457458
SDF4 6.016760 6.547579 5.870733
B3GALT6 5.594514 5.460039 5.425492
dge$samples$cluster6 dge$samples$batchBatch2 dge$samples$batchBatch3
NOC2L 5.929965 -0.2924504 -0.2539852
HES4 7.351417 -0.3779818 -0.9139473
ISG15 8.240399 -0.1721220 -0.0828577
AGRN 6.084659 -0.2004012 -0.3806297
SDF4 6.267931 -0.3584514 -0.3307362
B3GALT6 5.501144 -0.4206694 -0.5606689
dge$samples$indNA18858 dge$samples$indNA19160
NOC2L -0.13883794 -0.039787837
HES4 -1.06722538 -0.416795987
ISG15 0.10743035 -0.022116613
AGRN -0.15866862 -0.120606924
SDF4 -0.08343974 -0.002033915
B3GALT6 -0.31880604 0.042704121
$stdev.unscaled
dge$samples$cluster0 dge$samples$cluster1 dge$samples$cluster2
NOC2L 0.1208544 0.10048815 0.1270000
HES4 0.1237550 0.08264593 0.1390293
ISG15 0.1283113 0.10401559 0.1219884
AGRN 0.1574168 0.11993347 0.1818788
SDF4 0.1288437 0.10285136 0.1252621
B3GALT6 0.1504900 0.11363853 0.1449090
dge$samples$cluster3 dge$samples$cluster4 dge$samples$cluster5
NOC2L 0.1435807 0.1359875 0.1354571
HES4 0.1397886 0.1140146 0.1223383
ISG15 0.1720368 0.1176188 0.1869172
AGRN 0.1765880 0.1513562 0.1656992
SDF4 0.1547563 0.1279624 0.1473214
B3GALT6 0.1760660 0.1568445 0.1696538
dge$samples$cluster6 dge$samples$batchBatch2 dge$samples$batchBatch3
NOC2L 0.2429432 0.09277119 0.09046154
HES4 0.2015405 0.08360033 0.08479876
ISG15 0.1636313 0.09500493 0.09273144
AGRN 0.2535648 0.11292364 0.11300332
SDF4 0.2268140 0.09552078 0.09324332
B3GALT6 0.2926169 0.10955877 0.10776691
dge$samples$indNA18858 dge$samples$indNA19160
NOC2L 0.1082216 0.08784922
HES4 0.1015368 0.07922947
ISG15 0.1104477 0.08936361
AGRN 0.1391136 0.10823923
SDF4 0.1137425 0.09004569
B3GALT6 0.1370325 0.10302609
$sigma
[1] 0.4845726 1.6703666 1.3986638 0.6227602 0.4840251 0.5306907
$df.residual
[1] 50 50 50 50 50 50
$cov.coefficients
dge$samples$cluster0 dge$samples$cluster1
dge$samples$cluster0 0.17748918 0.06637807
dge$samples$cluster1 0.06637807 0.17748918
dge$samples$cluster2 0.06637807 0.06637807
dge$samples$cluster3 0.06637807 0.06637807
dge$samples$cluster4 0.06637807 0.06637807
dge$samples$cluster2 dge$samples$cluster3
dge$samples$cluster0 0.06637807 0.06637807
dge$samples$cluster1 0.06637807 0.06637807
dge$samples$cluster2 0.17748918 0.06637807
dge$samples$cluster3 0.06637807 0.17748918
dge$samples$cluster4 0.06637807 0.06637807
dge$samples$cluster4 dge$samples$cluster5
dge$samples$cluster0 0.06637807 0.06637807
dge$samples$cluster1 0.06637807 0.06637807
dge$samples$cluster2 0.06637807 0.06637807
dge$samples$cluster3 0.06637807 0.06637807
dge$samples$cluster4 0.17748918 0.06637807
dge$samples$cluster6 dge$samples$batchBatch2
dge$samples$cluster0 0.05627706 -0.04978355
dge$samples$cluster1 0.05627706 -0.04978355
dge$samples$cluster2 0.05627706 -0.04978355
dge$samples$cluster3 0.05627706 -0.04978355
dge$samples$cluster4 0.05627706 -0.04978355
dge$samples$batchBatch3 dge$samples$indNA18858
dge$samples$cluster0 -0.04978355 -0.05194805
dge$samples$cluster1 -0.04978355 -0.05194805
dge$samples$cluster2 -0.04978355 -0.05194805
dge$samples$cluster3 -0.04978355 -0.05194805
dge$samples$cluster4 -0.04978355 -0.05194805
dge$samples$indNA19160
dge$samples$cluster0 -0.04761905
dge$samples$cluster1 -0.04761905
dge$samples$cluster2 -0.04761905
dge$samples$cluster3 -0.04761905
dge$samples$cluster4 -0.04761905
6 more rows ...
$pivot
[1] 1 2 3 4 5 6 7 8 9 10 11
$rank
[1] 11
$Amean
NOC2L HES4 ISG15 AGRN SDF4 B3GALT6
6.020134 6.259862 5.956768 4.883920 5.873302 4.950374
$method
[1] "ls"
$design
dge$samples$cluster0 dge$samples$cluster1 dge$samples$cluster2
1 1 0 0
2 1 0 0
3 1 0 0
4 1 0 0
5 1 0 0
dge$samples$cluster3 dge$samples$cluster4 dge$samples$cluster5
1 0 0 0
2 0 0 0
3 0 0 0
4 0 0 0
5 0 0 0
dge$samples$cluster6 dge$samples$batchBatch2 dge$samples$batchBatch3
1 0 0 0
2 0 0 0
3 0 0 0
4 0 1 0
5 0 1 0
dge$samples$indNA18858 dge$samples$indNA19160
1 0 0
2 1 0
3 0 1
4 0 0
5 1 0
56 more rows ...#all pairwise cluster comparisons
nclust<- length(unique(dge$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 0 0 0 0 0 -1 0 0 0 0 0 -1 0 0 0 0 0
[2,] -1 0 0 0 0 0 1 1 1 1 1 1 0 -1 0 0 0 0 0 -1 0 0 0 0
[3,] 0 -1 0 0 0 0 0 -1 0 0 0 0 1 1 1 1 1 1 0 0 -1 0 0 0
[4,] 0 0 -1 0 0 0 0 0 -1 0 0 0 0 0 -1 0 0 0 1 1 1 1 1 1
[5,] 0 0 0 -1 0 0 0 0 0 -1 0 0 0 0 0 -1 0 0 0 0 0 -1 0 0
[6,] 0 0 0 0 -1 0 0 0 0 0 -1 0 0 0 0 0 -1 0 0 0 0 0 -1 0
[7,] 0 0 0 0 0 -1 0 0 0 0 0 -1 0 0 0 0 0 -1 0 0 0 0 0 -1
[8,] 0 0 0 0 0 0 0 0 0 0 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 0 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 0 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 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,] -1 0 0 0 0 0 -1 0 0 0 0 0 -1 0 0 0 0 0
[2,] 0 -1 0 0 0 0 0 -1 0 0 0 0 0 -1 0 0 0 0
[3,] 0 0 -1 0 0 0 0 0 -1 0 0 0 0 0 -1 0 0 0
[4,] 0 0 0 -1 0 0 0 0 0 -1 0 0 0 0 0 -1 0 0
[5,] 1 1 1 1 1 1 0 0 0 0 -1 0 0 0 0 0 -1 0
[6,] 0 0 0 0 -1 0 1 1 1 1 1 1 0 0 0 0 0 -1
[7,] 0 0 0 0 0 -1 0 0 0 0 0 -1 1 1 1 1 1 1
[8,] 0 0 0 0 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 0 0 0 0 0 0 0 0
[10,] 0 0 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 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)
| Version | Author | Date |
|---|---|---|
| 7888de8 | KLRhodes | 2020-08-31 |
summary(decideTests(efit)) c c c c c c c c c c c c c c
Down 3699 3580 3432 3532 3644 2885 4047 2981 1350 3371 2770 2496 3898 3429
NotSig 2439 2707 2837 3073 2267 4232 2439 3775 7024 3185 4055 4892 2707 3775
Up 4047 3898 3916 3580 4274 3068 3699 3429 1811 3629 3360 2797 3580 2981
c c c c c c c c c c c c c c
Down 2544 2409 3152 1671 3916 1811 2491 3100 2448 2254 3580 3629 2588 3134
NotSig 5150 5188 3617 6975 2837 7024 5150 3951 5019 5665 3073 3185 5188 3951
Up 2491 2588 3416 1539 3432 1350 2544 3134 2718 2266 3532 3371 2409 3100
c c c c c c c c c c c c c c
Down 3462 2013 4274 3360 3416 2718 3644 2700 3068 2797 1539 2266 1904 2690
NotSig 3079 6268 2267 4055 3617 5019 3079 4795 4232 4892 6975 5665 6268 4795
Up 3644 1904 3644 2770 3152 2448 3462 2690 2885 2496 1671 2254 2013 2700topTable(efit, coef=1, sort.by= "P") logFC AveExpr t P.Value adj.P.Val B
TTC3 -4.555146 7.213926 -29.36546 3.374794e-35 3.437227e-31 70.06161
SDK2 -4.259555 3.438961 -28.06537 3.462373e-34 1.763214e-30 67.20281
PLAGL1 -4.916252 4.292829 -27.27100 1.504927e-33 5.109229e-30 65.78618
FZD3 -2.921795 6.088419 -25.82754 2.395099e-32 6.098520e-29 63.55308
LIX1 -7.007386 4.120840 -25.11608 9.838029e-32 1.718866e-28 61.70090
MAPK10 -4.975294 4.251468 -25.10174 1.012587e-31 1.718866e-28 61.85524
DACH1 -5.334254 3.809426 -24.35787 4.604243e-31 6.699173e-28 60.27756
PHC2 -4.707853 5.434183 -23.98984 9.880943e-31 1.084531e-27 59.71449
WLS -4.446207 5.516756 -23.96553 1.039573e-30 1.084531e-27 59.77035
BTBD17 -5.135767 2.561903 -23.95405 1.064831e-30 1.084531e-27 59.25296vol<- topTable(efit, coef=13, n=nrow(fit))
labsig<- vol[abs(vol$logFC) >=9.5 | vol$adj.P.Val < 1e-29,]
labsiggenes<- rownames(labsig)
thresh<- vol$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))
| Version | Author | Date |
|---|---|---|
| 0e51844 | KLRhodes | 2021-01-11 |
vol<- topTable(efit, coef=1, n=nrow(fit))
labsig<- vol[(vol$adj.P.Val < 5e-29) | (vol$logFC> 0 & vol$adj.P.Val < 1e-22) |vol$logFC> 5 |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))
dt<- decideTests(efit)
#which genes are always significantly upreg in cluster 0
c0.common<- which(dt[,1]>0 & dt[,2]>0 & dt[,3]>0 & dt[,4]>0 & dt[,5]>0 & dt[,6]>0)
length(c0.common)[1] 1281genes.common<- rownames(dt[c0.common,])tops0<- topTable(efit, coef=c(1:9), n=nrow(fit))
tops0.common<- tops0[rownames(tops0) %in% genes.common,]
head(tops0.common, n=40) c c.1 c.2 c.3 c.4 c.5 c.6
ADD2 1.0762450 3.191322 1.8612156 3.5196754 0.3805445 1.7427946 -1.0762450
RBPJ 0.5305750 1.251712 0.7478626 1.4086629 0.3196658 1.3547030 -0.5305750
TPD52 2.0292328 3.008293 2.8663648 0.7893244 0.6878505 3.3360945 -2.0292328
TERF1 2.2393122 2.434590 2.4817362 3.5126054 2.1503258 3.2624666 -2.2393122
PHC1 2.0006741 2.324878 2.3995770 2.8016117 2.1013867 2.6855842 -2.0006741
FNDC10 0.8079705 2.096710 1.3363445 2.3715854 1.0572707 1.5331611 -0.8079705
JARID2 1.3737733 1.989100 1.7273883 2.2051412 0.9734495 2.0254508 -1.3737733
DPPA4 1.8142902 2.610799 1.7390857 3.3161013 1.7416934 2.5253857 -1.8142902
TBC1D23 2.0478753 1.588847 1.7311491 1.8175799 1.8344121 1.8614789 -2.0478753
ZNF398 2.2692475 2.228380 2.3656815 2.4200679 2.0681632 1.9467357 -2.2692475
CDCA7L 0.6416218 2.984502 0.9350240 1.8729450 1.0213561 2.8335479 -0.6416218
RAP1GAP2 3.2419153 4.317927 4.1983904 2.7456710 1.4466505 3.0239989 -3.2419153
SEPHS1 1.6680305 2.229236 1.6687873 2.6861440 1.6177193 2.3965022 -1.6680305
VASH2 2.6042807 3.120869 3.2083566 4.3919440 0.8433217 3.7341930 -2.6042807
MYC 3.5117579 1.391381 4.1560120 4.4581655 3.5523878 0.9076810 -3.5117579
TLCD1 2.1086952 1.749002 3.5363316 0.9977126 3.9858821 2.3970504 -2.1086952
GABRB3 2.3208404 3.768053 3.2313303 3.5547162 0.6861179 4.0041570 -2.3208404
VSIG10 2.3878375 3.129086 3.0924190 2.3299073 2.8914969 2.3500252 -2.3878375
HPRT1 0.4465502 1.023549 0.9317182 1.8326043 0.8813376 0.6117789 -0.4465502
ZSCAN2 2.3988085 1.358081 2.1346100 1.9782067 1.3865234 1.7970320 -2.3988085
LRRC47 0.9211033 1.055227 1.1516966 1.2186692 0.8903629 1.1825924 -0.9211033
UGP2 1.9704665 2.309099 2.3103801 2.3595844 1.9650201 2.6661639 -1.9704665
HAGHL 2.4342165 2.140565 2.4994430 2.9061823 2.4119192 2.3955740 -2.4342165
SOCS1 3.5654454 0.886749 3.2648022 2.2054795 3.3592612 2.9800047 -3.5654454
USO1 1.3235493 1.432494 1.2682643 1.4479385 1.2127183 1.5933690 -1.3235493
ZNF589 1.2101177 2.242965 1.3514383 2.3624792 1.6262473 1.8271858 -1.2101177
CARMIL2 3.1635508 4.732247 2.8854415 2.8972600 0.6189099 3.5012907 -3.1635508
ABCA7 2.9648325 1.430140 4.0102640 1.2278926 2.8161351 2.2138990 -2.9648325
ETV4 2.1238857 4.151495 4.1174351 1.9045015 4.3027872 2.5667720 -2.1238857
INTS13 1.2326159 1.382125 0.8589813 1.6839006 1.5046232 1.2874264 -1.2326159
PPARGC1B 3.0023832 2.743984 2.8888505 2.9244727 2.4454271 2.0100548 -3.0023832
DNAJB6 0.7285440 1.198174 0.8268977 1.4398291 0.2467220 0.8386550 -0.7285440
FRAT2 2.1742313 3.125506 2.0458956 2.1917566 1.6379688 1.9355715 -2.1742313
CYCS 1.0291318 1.277607 1.0940186 1.5337022 0.5480318 1.2756189 -1.0291318
PDCD2L 1.3609128 1.698955 1.8522889 1.4132858 1.3142644 1.6903733 -1.3609128
DDX21 1.0950546 1.552972 1.1875878 1.5943816 1.0423820 1.1812003 -1.0950546
NFE2L3 2.3934931 4.868765 3.9734604 1.8859697 3.7752511 3.2608496 -2.3934931
SLC29A1 1.1875736 2.701955 2.2261250 0.9053475 2.3501282 1.0038323 -1.1875736
SKA3 1.6767070 2.094118 1.7426533 2.5976425 3.0334423 2.5320663 -1.6767070
TKT 0.8635789 1.187676 0.6781470 1.1630237 0.9159787 1.3426905 -0.8635789
c.7 c.8 AveExpr F P.Value adj.P.Val
ADD2 2.11507657 0.784970548 4.166766 107.87048 1.643498e-28 6.199640e-27
RBPJ 0.72113718 0.217287526 7.153192 95.38602 3.542209e-27 9.322325e-26
TPD52 0.97905981 0.837132048 4.661170 85.52196 5.247080e-26 1.014070e-24
TERF1 0.19527768 0.242424032 7.578498 82.09970 1.427405e-25 2.485149e-24
PHC1 0.32420361 0.398902919 3.639425 76.61300 7.696156e-25 1.137668e-23
FNDC10 1.28873956 0.528374028 3.830290 76.07478 9.131385e-25 1.330517e-23
JARID2 0.61532665 0.353614961 6.547235 75.55727 1.077407e-24 1.549915e-23
DPPA4 0.79650855 -0.075204536 7.137063 75.08860 1.252621e-24 1.767029e-23
TBC1D23 -0.45902793 -0.316726160 4.673767 73.52998 2.080050e-24 2.794896e-23
ZNF398 -0.04086744 0.096434035 3.653416 72.94447 2.522768e-24 3.323983e-23
CDCA7L 2.34287978 0.293402257 5.217570 72.48083 2.942116e-24 3.788300e-23
RAP1GAP2 1.07601146 0.956475175 1.815557 71.03922 4.772359e-24 5.870347e-23
SEPHS1 0.56120553 0.000756847 7.332304 70.68298 5.385437e-24 6.545426e-23
VASH2 0.51658795 0.604075849 4.588320 68.37019 1.195993e-23 1.357992e-22
MYC -2.12037706 0.644254065 3.868981 66.02013 2.757155e-23 2.910013e-22
TLCD1 -0.35969317 1.427636437 3.433076 64.87588 4.179824e-23 4.240190e-22
GABRB3 1.44721257 0.910489935 4.429625 64.20572 5.349027e-23 5.299595e-22
VSIG10 0.74124880 0.704581582 4.340578 64.13301 5.494831e-23 5.407233e-22
HPRT1 0.57699903 0.485168006 5.420837 63.90241 5.985125e-23 5.838937e-22
ZSCAN2 -1.04072715 -0.264198528 3.785062 63.14985 7.925411e-23 7.529880e-22
LRRC47 0.13412389 0.230593294 5.387962 61.69550 1.374959e-22 1.257088e-21
UGP2 0.33863254 0.339913647 7.393647 61.16606 1.684998e-22 1.516051e-21
HAGHL -0.29365127 0.065226529 4.273916 61.11243 1.720209e-22 1.545003e-21
SOCS1 -2.67869637 -0.300643197 4.376965 60.72917 1.995118e-22 1.774697e-21
USO1 0.10894499 -0.055285055 6.282203 60.68325 2.030982e-22 1.803448e-21
ZNF589 1.03284734 0.141320583 3.631530 60.16624 2.484129e-22 2.160619e-21
CARMIL2 1.56869624 -0.278109295 1.523537 59.73428 2.942726e-22 2.514402e-21
ABCA7 -1.53469269 1.045431499 3.068505 59.45114 3.290210e-22 2.780978e-21
ETV4 2.02760934 1.993549427 4.218631 58.79489 4.269143e-22 3.520747e-21
INTS13 0.14950859 -0.373634588 4.659950 58.52857 4.748469e-22 3.881474e-21
PPARGC1B -0.25839944 -0.113532706 2.214953 58.06650 5.716929e-22 4.602919e-21
DNAJB6 0.46962964 0.098353764 7.211588 57.80936 6.342485e-22 5.070503e-21
FRAT2 0.95127499 -0.128335714 5.295428 57.78769 6.398345e-22 5.107143e-21
CYCS 0.24847475 0.064886819 8.499625 57.06949 8.569848e-22 6.667983e-21
PDCD2L 0.33804242 0.491376149 4.223995 57.06723 8.577761e-22 6.669046e-21
DDX21 0.45791714 0.092533122 7.100406 57.00243 8.808293e-22 6.837841e-21
NFE2L3 2.47527209 1.579967274 3.705582 56.75409 9.752900e-22 7.505264e-21
SLC29A1 1.51438145 1.038551457 4.693604 56.36385 1.145487e-21 8.706559e-21
SKA3 0.41741136 0.065946318 4.590506 56.12904 1.262473e-21 9.468546e-21
TKT 0.32409682 -0.185431989 9.112727 56.03231 1.314203e-21 9.820364e-21"POU5F1" %in% rownames(tops0.common)[1] TRUE"NANOG" %in% rownames(tops0.common)[1] TRUE#upsetr plot showing overlaps in DE genes between cluster 0 and all other clusters
c0v1<- as.vector(rownames(dt[which(dt[,1] != 0),]))
c0v2<-as.vector(rownames(dt[which(dt[,2] != 0),]))
c0v3<-as.vector(rownames(dt[which(dt[,3] != 0),]))
c0v4<-as.vector(rownames(dt[which(dt[,4] != 0),]))
c0v5<-as.vector(rownames(dt[which(dt[,5] != 0),]))
c0v6<-as.vector(rownames(dt[which(dt[,6] != 0),]))
c0v7<-as.vector(rownames(dt[which(dt[,7] != 0),]))
c0v8<-as.vector(rownames(dt[which(dt[,8] != 0),]))
c0v9<-as.vector(rownames(dt[which(dt[,9] != 0),]))
List.0<- list(c0vc1=c0v1,c0vc2=c0v2,c0vc3=c0v3,c0vc4=c0v4,c0vc5=c0v5,c0vc6=c0v6,c0vc7=c0v7,c0vc8=c0v8,c0vc9=c0v9)
#List.0<- list(dt[,1:9])
upset(fromList(List.0), nsets = 9)
1 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
[1,] 6 -1 -1 -1 -1 -1 -1
[2,] -1 6 -1 -1 -1 -1 -1
[3,] -1 -1 6 -1 -1 -1 -1
[4,] -1 -1 -1 6 -1 -1 -1
[5,] -1 -1 -1 -1 6 -1 -1
[6,] -1 -1 -1 -1 -1 6 -1
[7,] -1 -1 -1 -1 -1 -1 6
[8,] 0 0 0 0 0 0 0
[9,] 0 0 0 0 0 0 0
[10,] 0 0 0 0 0 0 0
[11,] 0 0 0 0 0 0 0fit<- contrasts.fit(fit, contrasts= contrasts)efit<- eBayes(fit)plotSA(efit)
c0vall<- topTable(efit, coef=1, n=nrow(fit))
head(c0vall, n=40) logFC AveExpr t P.Value adj.P.Val B
TTC3 -27.577990 7.213926 -33.03441 7.502936e-38 7.641740e-34 71.58323
ASXL1 -12.600599 5.423634 -26.39076 8.011183e-33 4.079695e-29 60.73509
BCL7C -12.415042 6.107591 -25.55948 4.062625e-32 1.194063e-28 60.46400
CST3 -18.385421 7.795340 -24.51773 3.314379e-31 6.751390e-28 59.67992
MAGED2 -11.892399 8.065005 -23.17049 5.604485e-30 7.135209e-27 57.42178
PHC2 -27.285655 5.434183 -25.48709 4.689497e-32 1.194063e-28 57.26356
TERF1 16.081036 7.578498 22.73501 1.439039e-29 1.221384e-26 56.63456
N4BP2L2 -6.602763 7.030785 -22.96675 8.696564e-30 9.841612e-27 56.58471
FAM89B -17.973680 5.685971 -23.74819 1.640065e-30 2.386295e-27 56.27116
TCF25 -7.605582 7.069119 -22.76051 1.361195e-29 1.221384e-26 56.19843
ZNF428 -16.865533 7.151197 -22.40509 2.968995e-29 2.159944e-26 55.21821
VIM -21.799711 10.234124 -21.90020 9.146643e-29 5.419027e-26 55.13542
DDX17 -6.006953 8.026253 -21.91529 8.841506e-29 5.419027e-26 54.88433
VPS28 -9.054529 7.137609 -21.87976 9.577072e-29 5.419027e-26 54.41352
MXD4 -14.985027 6.260003 -21.99581 7.379584e-29 5.010737e-26 53.73964
ADD1 -6.483498 5.894285 -21.53194 2.105681e-28 9.188987e-26 52.98095
NAA38 -8.727147 6.877280 -21.16266 4.914516e-28 1.787655e-25 52.74264
EFNB2 -21.619587 6.072235 -21.70457 1.422415e-28 7.239391e-26 52.74131
YAF2 -22.680261 4.129871 -23.87002 1.269640e-30 2.155214e-27 52.73104
FRMD4A -14.442369 4.560023 -22.56036 2.109197e-29 1.652474e-26 52.58378
C5orf24 -11.871752 5.610553 -21.59132 1.839332e-28 8.515270e-26 52.52431
CIRBP -9.638583 8.336993 -20.68679 1.490531e-27 4.337445e-25 52.21730
TMEM132A -14.904199 5.549007 -21.51969 2.165299e-28 9.188987e-26 52.13800
ARL2BP -10.948403 5.960584 -21.19098 4.603338e-28 1.736481e-25 52.09586
RDX -10.029886 7.663631 -20.68748 1.488133e-27 4.337445e-25 52.00903
NRIP1 -18.338440 5.045814 -21.82277 1.088920e-28 5.837185e-26 51.91901
PLAGL1 -22.403436 4.292829 -22.86631 1.081256e-29 1.101259e-26 51.53936
CCDC50 -11.731418 5.837919 -20.88818 9.297636e-28 3.156547e-25 51.32637
H2AFY -17.341738 7.668655 -20.43437 2.706832e-27 7.068995e-25 51.30182
COMMD3 -15.152130 4.627146 -21.68330 1.492658e-28 7.239391e-26 51.00580
PRTG -34.091343 5.971539 -21.44859 2.547047e-28 1.037667e-25 50.99613
SEPHS1 12.266419 7.332304 20.10262 5.981200e-27 1.428958e-24 50.82337
MSRB2 -8.951263 6.636048 -20.28261 3.885335e-27 9.651740e-25 50.68947
IDH2 -16.395862 6.637623 -20.36733 3.174616e-27 8.083365e-25 50.64952
PGLS -6.059248 7.601152 -19.97400 8.155653e-27 1.767347e-24 50.44106
TCAF1 -17.503481 5.041447 -21.03158 6.658343e-28 2.338456e-25 50.36320
UGP2 13.580714 7.393647 19.89353 9.909455e-27 2.018556e-24 50.32348
HP1BP3 -10.514628 6.668708 -20.09904 6.032912e-27 1.428958e-24 50.26390
MARCKS -18.093872 9.016948 -19.80646 1.224255e-26 2.444909e-24 50.22220
NSD3 -9.257932 6.823146 -19.92764 9.123392e-27 1.896362e-24 49.97306summary(decideTests(efit)) c c c c c c c
Down 3752 3137 2544 2616 3172 3683 1902
NotSig 2211 3557 5124 4972 3989 3248 6232
Up 4222 3491 2517 2597 3024 3254 2051up.0<- which(c0vall$adj.P.Val < 0.05 & c0vall$logFC >0)
length(up.0)[1] 4222head(c0vall[up.0,], n=40) logFC AveExpr t P.Value adj.P.Val B
TERF1 16.081036 7.578498 22.73501 1.439039e-29 1.221384e-26 56.63456
SEPHS1 12.266419 7.332304 20.10262 5.981200e-27 1.428958e-24 50.82337
UGP2 13.580714 7.393647 19.89353 9.909455e-27 2.018556e-24 50.32348
DPPA4 13.747355 7.137063 19.76765 1.345549e-26 2.585738e-24 49.94979
JARID2 10.294303 6.547235 19.44684 2.953992e-26 5.014401e-24 48.99389
PHC1 14.313711 3.639425 20.85504 1.004599e-27 3.300594e-25 48.97870
USO1 8.278334 6.282203 19.33787 3.866947e-26 6.251565e-24 48.63691
TBC1D23 10.881343 4.673767 19.70660 1.561576e-26 2.891756e-24 48.26631
LRRC47 6.419652 5.387962 18.76872 1.608413e-25 2.073631e-23 46.79981
ZNF398 13.298276 3.653416 19.25535 4.745369e-26 7.551811e-24 45.99546
FKBP4 7.614425 7.592202 17.99633 1.171990e-24 1.193672e-22 45.75787
TKT 6.151095 9.112727 17.66536 2.796986e-24 2.498886e-22 45.03825
HAGHL 14.787900 4.273916 18.26502 5.832943e-25 6.457448e-23 44.66419
VSIG10 16.180772 4.340578 18.23523 6.299826e-25 6.899326e-23 44.61177
RBPJ 5.613182 7.153192 17.31340 7.144051e-24 5.774774e-22 43.89780
SKA3 13.676630 4.590506 17.55426 3.755105e-24 3.241165e-22 43.33384
MIB2 8.593087 4.851255 17.41485 5.444658e-24 4.582962e-22 43.19837
DNMT3B 17.309703 6.461688 17.03466 1.515644e-23 1.104834e-21 43.06287
DDX21 7.653578 7.100406 16.89228 2.232917e-23 1.557689e-21 42.80322
PDCD2L 9.330080 4.223995 17.30736 7.260865e-24 5.774919e-22 42.36584
VASH2 17.902965 4.588320 16.79080 2.947246e-23 1.987927e-21 41.39904
ADD2 11.771797 4.166766 16.92599 2.036793e-23 1.440607e-21 41.38411
INTS13 7.949672 4.659950 16.66541 4.159402e-23 2.698313e-21 41.22785
FNDC10 9.203042 3.830290 16.86412 2.411462e-23 1.659510e-21 41.05917
ZNF589 10.620433 3.631530 16.77040 3.116767e-23 2.074251e-21 40.48451
ETV4 19.166876 4.218631 16.40429 8.571677e-23 5.017387e-21 40.27313
FRAT2 13.110930 5.295428 16.13289 1.832472e-22 9.620476e-21 40.25681
CYCS 6.758110 8.499625 15.87657 3.784758e-22 1.826343e-20 40.17016
MAD2L2 8.414309 7.078103 15.76691 5.173945e-22 2.395301e-20 39.75864
TOMM7 7.527693 8.939071 15.69145 6.421164e-22 2.893785e-20 39.65924
PSMG4 6.819823 4.947974 15.91168 3.425297e-22 1.692692e-20 39.43145
TLCD1 14.774674 3.433076 16.46275 7.285782e-23 4.314284e-21 39.38515
POLR3G 22.864011 4.119681 15.97227 2.884359e-22 1.440432e-20 39.08744
MAL2 19.822159 3.837282 16.27068 1.244669e-22 7.003844e-21 38.99007
ATP5PD 5.781917 8.188874 15.45405 1.272274e-21 5.376808e-20 38.96505
TDGF1 22.213829 6.271011 15.50264 1.105521e-21 4.771074e-20 38.85369
ZSCAN2 11.053262 3.785062 15.97217 2.885107e-22 1.440432e-20 38.69981
ZNF90 9.415134 4.781361 15.65464 7.136395e-22 3.173982e-20 38.63426
FGF2 15.592319 4.347030 15.71340 6.029853e-22 2.741248e-20 38.44487
ZNF770 7.097085 6.062098 15.37988 1.577470e-21 6.325407e-20 38.44180vol<- topTable(efit, coef=1, n=nrow(fit))
labsig<- vol[(vol$adj.P.Val < 5e-28) | (vol$logFC> 0 & vol$adj.P.Val < 5e-25) |vol$logFC> 32 |vol$logFC < -35,]
labsiggenes<- rownames(labsig)
thresh<- vol$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))
c1vall<- topTable(efit, coef=2, n=nrow(fit))
head(c1vall, n=40) logFC AveExpr t P.Value adj.P.Val B
TPBG 22.101800 5.718580 25.65465 3.365897e-32 3.428166e-28 61.45532
FGFBP3 19.934064 4.738138 24.43626 3.917992e-31 1.995237e-27 58.27076
FZD3 12.751362 6.088419 23.44222 3.134444e-30 7.981077e-27 57.38200
RUNX1T1 -21.936842 4.583494 -24.16659 6.839176e-31 2.321900e-27 55.93510
LIX1 22.457451 4.120840 19.95297 8.581129e-27 1.456647e-23 48.35581
PLAGL1 12.010326 4.292829 19.45351 2.905798e-26 3.288395e-23 48.32236
DEK 6.350184 8.394821 18.90102 1.151543e-25 1.172847e-22 48.17003
DACH1 17.253507 3.809426 19.71155 1.542837e-26 1.964224e-23 48.11494
SDK2 15.087592 3.438961 20.02142 7.273184e-27 1.456647e-23 47.31320
S100A10 -20.446352 9.208456 -18.46999 3.442476e-25 2.697048e-22 47.08324
ZNF219 8.841571 5.224911 18.79642 1.499533e-25 1.388432e-22 47.02358
WLS 13.119184 5.516756 18.58097 2.592072e-25 2.200021e-22 46.90958
SOX2 15.380397 7.079785 18.21840 6.580037e-25 4.188605e-22 46.19610
PHLDA1 -20.043491 5.389407 -18.39465 4.176733e-25 3.038573e-22 45.59212
BTBD17 22.234310 2.561903 19.83256 1.148984e-26 1.671772e-23 45.12275
KALRN -11.624326 3.744429 -18.23114 6.366739e-25 4.188605e-22 44.15741
CRB2 16.785551 3.443880 18.03772 1.052028e-24 6.302887e-22 43.51480
METRN 11.192753 7.096879 16.85927 2.443614e-23 1.244411e-20 42.77735
NUCKS1 4.380327 10.053209 16.75721 3.231568e-23 1.496069e-20 42.61695
MMP15 -10.687066 4.142716 -17.19833 9.735335e-24 5.218652e-21 42.30404
GLI3 14.209729 4.907882 16.60387 4.928779e-23 2.091651e-20 41.44667
MMRN1 17.981186 2.099102 17.80638 1.928033e-24 1.090946e-21 41.12482
ANP32E 5.032155 8.075231 15.90776 3.463635e-22 1.175904e-19 40.26148
GINS2 7.750517 6.170958 15.96673 2.929923e-22 1.065759e-19 40.24504
RNF175 16.557374 3.530654 16.37330 9.344580e-23 3.806982e-20 39.40595
SOX3 27.530583 2.974193 16.82437 2.688334e-23 1.303842e-20 39.12236
TLE4 13.089684 5.786874 15.37683 1.591517e-21 4.631315e-19 38.45262
DPYSL5 19.134970 2.620642 16.60591 4.901171e-23 2.091651e-20 38.37753
NR2F1 17.845406 5.113486 15.41332 1.431605e-21 4.288500e-19 38.23440
POLD3 6.610797 5.268455 15.31941 1.880603e-21 5.320540e-19 38.19756
POLD2 4.658573 6.727042 15.20406 2.632987e-21 7.057099e-19 38.18097
HES4 12.668030 6.259862 15.13171 3.254429e-21 8.039797e-19 37.96756
CDON 12.508958 3.198887 15.91155 3.426525e-22 1.175904e-19 37.96214
MAPK10 13.372408 4.251468 15.43393 1.348616e-21 4.162321e-19 37.87271
CDH2 13.022568 6.170357 15.10997 3.468836e-21 8.163710e-19 37.81627
BOC 14.524705 3.646838 15.49530 1.129204e-21 3.594045e-19 37.69272
H1FX 5.654379 8.583590 14.96123 5.375262e-21 1.216601e-18 37.54922
ELOVL1 -8.840217 4.741981 -15.13036 3.267363e-21 8.039797e-19 37.35612
DOK4 -13.177945 4.008562 -15.17019 2.907366e-21 7.592698e-19 36.92994
ILDR2 19.790009 1.999447 16.08294 2.109390e-22 7.957089e-20 36.81883c2vall<- topTable(efit, coef=3, n=nrow(fit))
head(c2vall, n=40) logFC AveExpr t P.Value adj.P.Val B
TNNI1 40.70212 2.7228493 40.82299 1.081978e-42 1.101995e-38 70.50955
TMEM88 33.69564 4.8364459 28.95391 6.982264e-35 1.185239e-31 66.23925
COL5A1 25.90639 3.3745876 29.76865 1.669845e-35 5.669123e-32 64.26380
ACTA2 32.29965 3.5890806 29.27456 3.959548e-35 8.065599e-32 63.36468
DOK4 22.03021 4.0085622 28.02692 3.714517e-34 5.404622e-31 63.04533
COL6A3 39.84215 2.1126229 33.14146 6.336918e-38 3.227076e-34 62.26711
COL6A2 23.55907 5.6163531 25.66821 3.277045e-32 3.034245e-29 61.59429
COL3A1 43.51845 4.0150207 25.95085 1.881227e-32 1.916029e-29 60.80590
RGS4 34.80188 2.0793145 29.59594 2.254861e-35 5.741440e-32 58.74099
SLC9A3R1 21.91053 5.6035053 23.88670 1.226031e-30 7.345365e-28 57.83929
PCOLCE 26.17836 4.1215745 24.66241 2.465338e-31 1.931498e-28 57.36847
COL6A1 17.30678 6.2672488 22.75576 1.375353e-29 6.090423e-27 56.28398
LIX1 27.20448 4.1208395 24.04619 8.785181e-31 5.592317e-28 56.14164
PKP2 21.37326 4.0327937 24.51147 3.357222e-31 2.442379e-28 56.14085
CYB5D1 24.74627 2.9393467 25.54047 4.218477e-32 3.580432e-29 55.93821
PARVA 10.72248 4.7540148 23.22540 4.981299e-30 2.709944e-27 55.88896
CDH11 22.05367 4.3415742 23.21852 5.055370e-30 2.709944e-27 55.67428
MFAP4 31.80278 3.0790634 24.35705 4.612005e-31 3.131551e-28 54.62730
SIPA1L2 17.77420 5.1211097 22.04047 6.677321e-29 2.518834e-26 54.19347
SLC40A1 33.83430 1.6515464 27.10250 2.065104e-33 2.629135e-30 53.80100
IL6ST 15.79988 4.2065868 22.08566 6.035637e-29 2.364345e-26 53.37473
ADAMTS9 24.43687 3.2941860 22.57692 2.033881e-29 8.631282e-27 53.01510
KCTD12 26.12105 2.9600572 22.84463 1.133397e-29 5.247112e-27 52.58597
MMP2 17.44437 4.5727593 21.42398 2.694549e-28 8.316359e-26 52.50543
NID2 31.69763 2.5954720 23.12880 6.130034e-30 2.973066e-27 52.37082
HAND1 45.21544 3.0694362 22.40851 2.946668e-29 1.200473e-26 52.32234
KRT19 21.25244 7.6277865 20.60746 1.796873e-27 4.357418e-25 52.03265
HAND2 39.85881 0.7517947 26.39914 7.882933e-33 8.920852e-30 49.98338
BAMBI 20.74585 5.5592107 19.65429 1.774581e-26 3.614822e-24 49.39029
WNT5A 22.53489 2.9577383 20.68730 1.488763e-27 3.790763e-25 48.44260
PRRX1 35.94369 2.1476154 21.07962 5.956042e-28 1.685064e-25 48.06460
ST6GALNAC3 15.15410 3.2022024 20.40967 2.870408e-27 6.798861e-25 47.81639
RGS5 24.04455 4.7808114 19.01821 8.577366e-26 1.432139e-23 47.69149
LEF1 14.42013 3.9269779 19.36670 3.600671e-26 7.052468e-24 47.59629
ADAMTS1 15.94466 3.3625283 19.77366 1.325991e-26 2.756167e-24 47.28734
PDGFRB 23.09537 1.9758235 21.11377 5.502992e-28 1.601371e-25 47.08036
WIPF1 15.04493 3.0135050 19.82846 1.160503e-26 2.514835e-24 46.95906
PMP22 17.87131 3.7248274 19.09079 7.151871e-26 1.255893e-23 46.70140
LUM 47.08760 3.3323636 19.27471 4.522643e-26 8.375113e-24 46.52270
MSRB3 20.40214 1.6928324 21.39009 2.912003e-28 8.723162e-26 46.27155c3vall<- topTable(efit, coef=4, n=nrow(fit))
head(c3vall, n=40) logFC AveExpr t P.Value adj.P.Val B
NR2F1 27.259897 5.113486 22.84719 1.127126e-29 1.147978e-25 55.09757
CNP 12.436813 5.491823 20.54441 2.085419e-27 8.231646e-24 50.74612
FGFBP3 17.781302 4.738138 20.06111 6.609706e-27 1.346397e-23 49.32649
DNAJC1 13.566884 5.886947 19.05404 7.840743e-26 1.140828e-22 47.76852
ATP1A2 23.594471 3.785417 19.83360 1.146095e-26 1.945497e-23 47.21788
ZEB2 19.087833 4.710630 18.97829 9.481171e-26 1.207072e-22 46.97331
METRN 13.448468 7.096879 18.02418 1.089827e-24 1.109988e-21 45.66109
S100B 34.071411 1.899250 20.48076 2.424638e-27 8.231646e-24 44.88785
EDNRA 28.715723 1.937977 20.25544 4.146051e-27 1.055688e-23 44.62876
LMO4 13.409311 6.906923 17.59454 3.374245e-24 2.863890e-21 44.49439
PLEKHA4 15.892794 3.941248 16.66216 4.196826e-23 2.722695e-20 40.18253
NPR3 31.665173 2.001425 17.20553 9.548103e-24 7.480572e-21 39.43758
PHACTR3 29.133146 1.582047 18.16991 7.460755e-25 8.443088e-22 39.40007
PRELP 28.016345 1.397240 17.82957 1.813990e-24 1.679590e-21 38.58424
HDDC2 7.808879 7.067837 15.30764 1.946182e-21 8.259109e-19 38.39282
LSAMP 15.258975 4.274772 15.33662 1.788758e-21 7.921088e-19 37.51913
NRIP1 10.980717 5.045814 15.13992 3.176965e-21 1.244515e-18 37.36601
ERBB3 26.063915 3.772975 15.53613 1.003641e-21 4.646399e-19 37.35704
RFTN2 15.629228 2.333311 16.09497 2.039044e-22 1.221627e-19 37.11441
SOX10 35.357216 1.445070 16.73496 3.435181e-23 2.499094e-20 36.85874
MOXD1 31.301026 1.630318 16.65528 4.277184e-23 2.722695e-20 36.59608
MPZ 37.989571 2.185961 15.74695 5.477763e-22 2.789551e-19 36.23134
CUEDC2 8.812910 6.739784 14.55156 1.821170e-20 5.796443e-18 36.20308
ADAMTS4 16.080498 2.185148 15.80840 4.596037e-22 2.463718e-19 35.86527
SDK2 12.408714 3.438961 15.08119 3.774783e-21 1.423932e-18 35.77713
SMOC1 23.609160 2.764959 15.25646 2.259319e-21 9.204465e-19 35.55062
RNF165 14.605165 3.395552 14.85101 7.449088e-21 2.528965e-18 35.35444
SCRG1 32.287827 2.756581 14.97339 5.185653e-21 1.821237e-18 35.23881
RHOB 9.618255 6.099518 14.10621 7.025470e-20 2.097178e-17 34.81597
CDH6 21.729757 4.433859 14.09788 7.206797e-20 2.097178e-17 34.27559
CMTM5 19.554999 1.322153 15.83496 4.260839e-22 2.410925e-19 34.20197
ADSS 7.531949 6.431948 13.91046 1.281740e-19 3.626255e-17 34.19718
SOX5 15.152791 3.722675 14.23530 4.738359e-20 1.462430e-17 34.14772
KANK4 26.484723 2.501823 14.58598 1.642395e-20 5.396061e-18 33.78969
ASXL1 6.095035 5.423634 13.67716 2.640877e-19 7.269550e-17 33.29193
ITGA4 26.867650 1.080979 15.58144 8.807960e-22 4.271861e-19 32.84077
UBXN2A 5.564068 5.249474 13.47126 5.027079e-19 1.347389e-16 32.57378
COL2A1 15.960632 5.349475 13.37149 6.880597e-19 1.796894e-16 32.40181
FUNDC2 5.210381 6.720313 12.97442 2.429925e-18 5.499730e-16 31.41366
FZD3 8.129218 6.088419 12.92457 2.851092e-18 6.178377e-16 31.15240c4vall<- topTable(efit, coef=5, n=nrow(fit))
head(c4vall, n=40) logFC AveExpr t P.Value adj.P.Val B
S100A16 27.080627 4.866912 27.36428 1.264020e-33 1.287405e-29 63.08509
CST3 14.942772 7.795340 25.47559 4.797783e-32 1.221635e-28 62.22486
KRT19 24.227074 7.627787 23.64002 2.060481e-30 4.197200e-27 58.49467
LGALS3 38.931399 3.708124 26.60588 5.300631e-33 1.799564e-29 58.28405
GATA3 28.257190 2.729964 26.71297 4.320131e-33 1.799564e-29 57.05943
FN1 25.545354 6.483732 21.57219 1.921207e-28 3.261249e-25 54.03110
MGST2 17.991057 4.874313 20.15560 5.266298e-27 4.876113e-24 49.94709
S100A10 18.776220 9.208456 19.56199 2.225056e-26 1.743246e-23 49.84590
DYNLT3 11.823213 4.429137 20.40886 2.875933e-27 3.254597e-24 49.58531
HDHD3 14.391718 3.937994 20.45588 2.572042e-27 3.254597e-24 48.83829
B4GALT1 13.975120 4.286524 19.77059 1.335956e-26 1.133893e-23 48.27729
PKP2 17.912688 4.032794 20.23659 4.337290e-27 4.417530e-24 48.27183
DSP 19.933232 6.193595 18.88281 1.205599e-25 6.821678e-23 47.66443
ACAA1 10.884221 4.937621 18.81365 1.435604e-25 7.695592e-23 46.69846
ANXA3 27.349190 3.958828 19.41961 3.159261e-26 2.013562e-23 46.41686
BCAM 14.764030 4.901107 18.58850 2.542742e-25 1.294891e-22 46.18236
PTGR1 12.793966 6.906708 18.18455 7.182952e-25 3.483732e-22 46.12791
SPINT2 13.341676 7.667458 18.00630 1.141883e-24 5.056556e-22 45.74786
ATP1A1 7.008755 6.677654 17.78639 2.032202e-24 7.665918e-22 44.99305
C12orf75 13.308721 5.519731 17.98375 1.211125e-24 5.139710e-22 44.93519
LYPD6B 22.957248 2.444422 19.51791 2.479643e-26 1.803940e-23 44.61536
KRT8 17.246164 8.333419 17.39495 5.742068e-24 1.840958e-21 44.29065
EPSTI1 36.019508 1.661485 20.99559 7.239140e-28 1.053295e-24 44.24209
STARD10 15.066063 4.457784 17.68847 2.631140e-24 9.570771e-22 43.63256
WFDC2 20.211786 5.987506 17.39223 5.784060e-24 1.840958e-21 43.47320
COL18A1 13.226814 6.099623 17.08565 1.319995e-23 3.841186e-21 43.17723
SMAGP 20.018105 3.310236 17.90978 1.469687e-24 5.987504e-22 42.85857
LAMA1 16.993142 4.218577 17.30335 7.339346e-24 2.265189e-21 42.30523
MPC2 8.990799 7.303518 16.52722 6.092724e-23 1.513522e-20 41.85776
CAMK2D 13.126956 4.828020 16.87222 2.358635e-23 6.492622e-21 41.81023
FREM2 17.644108 3.314543 17.62590 3.104999e-24 1.054147e-21 41.80537
CEBPA 24.897862 1.767795 19.07916 7.362938e-26 4.411266e-23 41.47196
S100A14 46.606615 1.798091 19.41911 3.163181e-26 2.013562e-23 41.28707
AMOT 15.355215 3.625547 16.98462 1.736344e-23 4.912406e-21 41.20077
NFE2L2 7.379952 5.323262 16.50202 6.533384e-23 1.584346e-20 41.17583
SPINT1 21.661490 4.268353 16.80332 2.847831e-23 7.632936e-21 40.89153
AHNAK 20.204225 4.421089 16.41992 8.206873e-23 1.857489e-20 40.78613
ANXA4 16.553834 3.837055 16.65474 4.283459e-23 1.090676e-20 40.43755
PCBD1 14.108516 6.534839 16.01753 2.537481e-22 4.970047e-20 40.35659
MYOF 25.554015 2.071093 17.81479 1.885888e-24 7.387602e-22 40.32981c5vall<- topTable(efit, coef=6, n=nrow(fit))
head(c5vall, n=40) logFC AveExpr t P.Value adj.P.Val B
TAGLN3 39.27827 3.789455 36.91588 2.211015e-40 2.251919e-36 70.92052
RTN1 36.01647 3.277588 36.13899 6.774968e-40 3.450153e-36 68.74791
MLLT11 20.89371 7.328851 28.83936 8.562225e-35 8.720627e-32 67.96028
PCBP4 22.31930 5.195819 30.12132 9.086148e-36 1.322035e-32 67.77555
STMN2 46.66226 4.359924 31.34054 1.162611e-36 2.499848e-33 67.23802
ELAVL2 29.44406 3.476656 31.30791 1.227221e-36 2.499848e-33 64.19872
ELAVL4 36.55510 3.163252 29.94288 1.235271e-35 1.572655e-32 63.08840
KLC1 11.06287 5.725723 26.00295 1.699252e-32 1.018052e-29 61.67888
BASP1 16.82605 8.299298 24.78377 1.925540e-31 6.762629e-29 60.99429
MAP1B 19.80586 8.655748 24.61743 2.702512e-31 9.175029e-29 60.82784
HES6 25.84775 6.033686 25.19891 8.331461e-32 3.689388e-29 60.54604
ACAP3 17.63769 3.784109 26.99525 2.528159e-33 1.839236e-30 60.14522
NHLH1 49.39919 1.853023 35.08292 3.218412e-39 1.092651e-35 59.99047
DCX 36.36060 3.182446 29.70821 1.854585e-35 2.098772e-32 59.34817
PPP1R1A 22.18553 4.854119 25.73511 2.872238e-32 1.539670e-29 59.24161
TUBA1A 20.19511 9.700915 23.66903 1.937973e-30 5.194278e-28 59.07819
GDI1 12.10738 5.553161 24.49601 3.465450e-31 1.138568e-28 58.85907
OLFM1 31.47044 3.200861 27.92111 4.509037e-34 3.827045e-31 58.79719
FNDC5 30.00669 2.213297 30.97722 2.129275e-36 3.614444e-33 58.65621
CDKN2D 20.10065 3.777317 25.26904 7.240447e-32 3.351998e-29 57.64370
CRMP1 24.21700 4.946283 23.80231 1.463591e-30 4.028832e-28 56.96594
DLL3 23.50378 4.195074 24.00489 9.575414e-31 2.868400e-28 56.32955
KLHL35 34.76979 1.911985 27.95029 4.274084e-34 3.827045e-31 55.55411
GNG3 32.81053 2.968788 25.53496 4.264855e-32 2.171878e-29 54.79885
BTBD17 28.18865 2.561903 25.11260 9.907062e-32 4.036137e-29 53.58640
RNF165 21.97121 3.395552 23.47306 2.935422e-30 7.665967e-28 53.56862
GADD45G 18.64181 4.708401 22.01092 7.133971e-29 1.513739e-26 53.47786
BCL7A 12.53809 5.004675 21.80622 1.130333e-28 2.257342e-26 53.04609
TERF2IP 13.98878 6.062423 21.25503 3.971313e-28 6.973762e-26 52.90267
GPC2 15.62513 4.981706 21.59537 1.822503e-28 3.437444e-26 52.71414
NHLH2 28.21825 1.920786 26.25848 1.034235e-32 7.022456e-30 52.46803
INA 32.41813 3.051580 23.80835 1.445128e-30 4.028832e-28 52.03332
SCG3 28.80869 3.886964 22.11342 5.672946e-29 1.256064e-26 51.44738
CADM3 24.69370 2.273654 24.01672 9.341917e-31 2.868400e-28 51.29941
MAP1A 21.25699 4.080536 21.49502 2.290668e-28 4.241901e-26 50.86803
PHF21B 18.89498 3.066879 22.68879 1.591899e-29 3.684885e-27 50.76081
BRSK2 22.22791 2.683183 23.29822 4.261881e-30 1.058714e-27 50.55647
L1CAM 23.17027 2.462770 23.17140 5.593453e-30 1.356412e-27 50.30607
ZBTB20 16.53605 3.853132 20.97404 7.611319e-28 1.292021e-25 50.29049
CNTN2 35.07210 1.547870 25.37668 5.840792e-32 2.832784e-29 50.28118c6vall<- topTable(efit, coef=7, n=nrow(fit))
head(c6vall, n=40) logFC AveExpr t P.Value adj.P.Val B
EGFL7 27.935828 5.8780322 41.89393 2.735599e-43 2.786208e-39 80.53473
GNG11 36.280582 5.1706361 34.96649 3.831685e-39 1.074681e-35 73.40601
RAMP2 31.610475 5.1932897 35.20625 2.677035e-39 1.074681e-35 72.76896
IGFBP4 27.086680 4.9010287 34.90211 4.220644e-39 1.074681e-35 71.11308
S100A16 32.245851 4.8669123 27.45909 1.059188e-33 8.298328e-31 61.69936
S100A4 24.943083 6.3252678 25.39660 5.613631e-32 2.598856e-29 60.36321
PPM1F 20.623640 3.5752285 30.03755 1.049324e-35 2.137472e-32 58.90080
CCDC85B 13.676949 6.9139066 24.59261 2.843196e-31 1.072517e-28 58.67505
MAP4K2 21.197349 3.3771246 28.76027 9.861217e-35 1.255456e-31 56.48165
KDR 41.415348 3.2418243 26.43943 7.294636e-33 4.370345e-30 55.42011
DOCK6 23.057354 3.1436171 27.72007 6.528440e-34 6.649216e-31 55.11032
PLXND1 29.363815 2.9192194 27.49950 9.824661e-34 8.298328e-31 55.08149
SLC9A3R2 23.540806 4.3409448 23.82427 1.397586e-30 4.313457e-28 54.20405
RALB 16.914324 4.7312516 23.86447 1.284522e-30 4.088391e-28 53.77659
MAST4 22.773947 3.3625695 25.78205 2.618960e-32 1.270195e-29 53.61937
TIMP3 27.052457 4.0709583 22.85844 1.099883e-29 2.732271e-27 52.94415
RGL1 20.835340 3.0819442 24.99454 1.256608e-31 5.332729e-29 51.81292
SPTBN1 16.346511 6.5896602 20.62004 1.744319e-27 2.960981e-25 50.79827
LIMS1 13.068649 6.1960830 20.87661 9.552215e-28 1.737309e-25 50.66937
FLT1 40.731797 2.8799215 22.93230 9.370305e-30 2.385914e-27 49.76982
PMP22 25.706666 3.7248274 21.56229 1.964989e-28 4.002682e-26 49.68797
LDB2 25.277510 4.0010548 21.32516 3.379794e-28 6.749647e-26 49.22420
TMEM255B 28.648616 1.9343944 26.57145 5.661778e-33 3.604075e-30 48.49353
JCAD 31.714567 2.0172058 24.39303 4.282439e-31 1.504022e-28 48.19190
IFI16 30.122255 4.6247185 19.60301 2.012033e-26 2.661371e-24 47.72291
ADAM15 20.451034 3.5760753 21.17113 4.819338e-28 9.261313e-26 47.67574
CPNE2 18.756595 4.2381291 20.16864 5.104074e-27 7.534057e-25 47.47991
RCSD1 45.680446 0.5769777 28.98220 6.639939e-35 9.661111e-32 47.44877
HOPX 51.070142 1.4882899 23.09900 6.536220e-30 1.751879e-27 47.27070
BCL6B 33.786968 1.2859310 26.98907 2.557842e-33 1.736775e-30 47.07730
SHANK3 23.620230 2.0559392 24.86990 1.616761e-31 6.333350e-29 46.98638
LYL1 41.845726 1.0664785 26.35787 8.535659e-33 4.829761e-30 46.71356
MSN 15.214456 6.0684067 18.89753 1.161707e-25 1.300218e-23 46.55920
KLF2 43.460135 2.1565324 21.83917 1.049397e-28 2.226689e-26 46.54210
RGS5 29.352348 4.7808114 18.83845 1.348397e-25 1.461002e-23 46.50183
TM4SF18 48.774491 0.8848746 27.37220 1.245457e-33 9.060700e-31 46.46727
SNX3 9.913782 7.9900052 18.47346 3.412005e-25 3.247782e-23 46.41573
AFAP1L1 37.626171 1.7753216 23.75956 1.601284e-30 4.796789e-28 46.03140
SOX7 40.967625 1.4044632 24.44126 3.877910e-31 1.410590e-28 45.65633
MEF2C 32.094321 2.4308036 20.70614 1.424227e-27 2.500991e-25 45.61710#output results from each cluster
TopTable.EachCluster.OnevAll<- list(c0vall, c1vall, c2vall,c3vall,c4vall,c5vall,c6vall)
write.csv(TopTable.EachCluster.OnevAll, "/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/Pseudobulk_Limma_res0.1_OnevAllTopTables.csv")#output table with top 5 upreg DE genes in each cluster by adjusted p
OnevAll.top10.adjP<- NULL
for(i in 1:nclust){
c<- TopTable.EachCluster.OnevAll[[i]]
top10<- c[c$logFC>0,]
top10<- top10[order(top10$adj.P.Val),]
top10<- rownames(top10)[1:10]
OnevAll.top10.adjP<- cbind(OnevAll.top10.adjP, top10)
}
colnames(OnevAll.top10.adjP)<- as.character(0:(nclust-1))
write.csv(OnevAll.top10.adjP, "/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/Pseudobulk_Limma_res0.1_OnevAll_top10Upregby_adjP.csv")#output table with top 5 upreg DE genes in each cluster by logFC
OnevAll.top10.logFC<- NULL
for(i in 1:nclust){
c<- TopTable.EachCluster.OnevAll[[i]]
top10<- c[order(c$logFC, decreasing = T),]
top10<- rownames(top10)[1:10]
OnevAll.top10.logFC<- cbind(OnevAll.top10.logFC, top10)
}
colnames(OnevAll.top10.logFC)<- as.character(0:(nclust-1))
write.csv(OnevAll.top10.logFC, "/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/Pseudobulk_Limma_res0.1_OnevAll_top10Upregby_logFC.csv")batch + ind comparisons
fit<- lmFit(v,design)fit<-contrasts.fit(fit, coefficients = (nclust+1):(nclust+4))efit<- eBayes(fit)plotSA(efit)
topsBatch<- topTable(efit, coef=c(1:2),n=nrow(fit))
head(topsBatch, n=40) dge.samples.batchBatch2 dge.samples.batchBatch3 AveExpr F
EEF1A1 -1.4791738 -1.1167572 9.749261 684.4828
PPP1CB 1.3016015 1.2975520 6.997330 421.2482
SF3A2 -0.9947448 -1.4471413 5.267949 390.3477
LRRC75A 2.1998212 1.4979476 5.947189 348.3474
CAPZA1 1.0415420 1.0795632 6.761536 340.4220
NME2 -1.6596490 -1.2434545 3.944569 330.5270
SMARCB1 -1.5074928 -1.1727631 4.712074 293.5627
TBL1XR1 1.0973611 1.2155075 6.220793 293.4778
GPBP1 0.8362809 0.9114806 6.875262 291.4102
N4BP2L2 0.7686978 0.8243599 7.030785 277.5289
CLIC1 -2.0353258 -1.3288341 4.964395 274.0515
H3F3A -1.3347640 -0.9083080 6.258322 273.0554
AP001267.5 -2.3481522 -1.5381703 3.417069 269.5045
MIF -0.8688510 -0.5720611 8.660693 263.5818
TRA2A 1.0237494 0.9581491 6.127337 238.0555
MRPL42 0.8793849 1.0035707 7.229988 234.4949
SLC25A6 -1.5132971 -1.3065878 4.648292 217.3173
RSL24D1 0.7001932 0.7729428 7.682960 216.9857
TMEM167A 0.6305888 0.8042721 7.370792 206.6786
EIF3E 0.7964242 0.7201832 9.206035 206.4162
USP14 0.5719998 0.7918276 6.914613 206.0501
LYPLA2 -1.0497894 -1.0226395 4.933563 203.8434
C6orf62 0.9171443 0.9140221 5.526238 203.7501
BZW1 0.6007710 0.8522946 7.017364 200.4837
TMED2 0.9137557 0.8730322 6.767068 200.3655
SYNC 1.3436519 1.3901722 4.225984 198.5465
CMTM6 0.7755768 0.8802948 6.703665 192.8530
ACTR2 0.6239884 0.8535300 7.166270 191.7977
HSF1 -0.6855173 -0.8774926 5.377362 189.7591
PSMA4 0.5184325 0.7521368 8.449685 185.8011
YWHAG 0.6880251 0.9595232 6.742297 184.0940
DNAJA2 0.5636804 0.7819193 7.020326 182.4614
EIF4E 1.0723537 0.8565844 5.618441 180.3326
EXOC5 0.6868452 0.7515148 5.693740 179.7984
COPB1 0.5412550 0.6692869 6.673308 176.2850
PRPF31 -1.6305304 -1.0639102 3.137675 173.4246
PEF1 -0.5756065 -0.6952513 5.665333 170.4290
MT-ND1 -0.7758269 -0.8125726 11.379796 167.6785
MTX1 -1.1698806 -0.9499482 4.538119 166.0297
MZT1 0.7710912 0.8157323 6.249351 162.6154
P.Value adj.P.Val
EEF1A1 1.794281e-39 1.827475e-35
PPP1CB 5.555113e-34 2.828941e-30
SF3A2 3.921206e-33 1.331249e-29
LRRC75A 7.139153e-32 1.817807e-28
CAPZA1 1.279942e-31 2.607242e-28
NME2 2.700998e-31 4.584945e-28
SMARCB1 5.345146e-30 6.854543e-27
TBL1XR1 5.384030e-30 6.854543e-27
GPBP1 6.426789e-30 7.272983e-27
N4BP2L2 2.175001e-29 2.215239e-26
CLIC1 2.977627e-29 2.757012e-26
H3F3A 3.260149e-29 2.767051e-26
AP001267.5 4.514790e-29 3.537164e-26
MIF 7.839522e-29 5.703252e-26
TRA2A 9.689002e-28 6.578832e-25
MRPL42 1.402466e-27 8.927575e-25
SLC25A6 8.989789e-27 5.279190e-24
RSL24D1 9.329939e-27 5.279190e-24
TMEM167A 3.036585e-26 1.585285e-23
EIF3E 3.131314e-26 1.585285e-23
USP14 3.268629e-26 1.585285e-23
LYPLA2 4.239612e-26 1.898278e-23
C6orf62 4.286734e-26 1.898278e-23
BZW1 6.329875e-26 2.615694e-23
TMED2 6.420457e-26 2.615694e-23
SYNC 7.997097e-26 3.132709e-23
CMTM6 1.608711e-25 6.068417e-23
ACTR2 1.834841e-25 6.674233e-23
HSF1 2.369908e-25 8.323282e-23
PSMA4 3.922023e-25 1.331527e-22
YWHAG 4.887971e-25 1.605935e-22
DNAJA2 6.043629e-25 1.923574e-22
EIF4E 7.990344e-25 2.466111e-22
EXOC5 8.574077e-25 2.568440e-22
COPB1 1.369603e-24 3.985544e-22
PRPF31 2.017476e-24 5.707776e-22
PEF1 3.044789e-24 8.381399e-22
MT-ND1 4.467737e-24 1.197471e-21
MTX1 5.636908e-24 1.472100e-21
MZT1 9.180328e-24 2.337541e-21topsInd<- topTable(efit, coef=c(3:4),n=nrow(fit))
head(topsInd, n=40) dge.samples.indNA18858 dge.samples.indNA19160 AveExpr F
TYW3 0.25900494 -5.5786091185 3.9153379 1289.7631
CRYZ 0.26953668 -5.0114953983 2.5630224 493.1262
EIF1AY 0.08536481 7.2729620097 2.1264870 488.0851
TRIM61 1.95841374 -4.6331041121 0.5692649 413.6447
CAT -0.76816953 -5.3323629470 2.5536514 391.0041
THOC3 -0.04410403 -1.5652975910 4.2625854 311.3598
USP51 -4.92885108 -0.0084824783 2.9998932 274.8907
RNF187 -1.14008040 -0.0377434330 7.1200184 274.2047
HCCS 1.21423618 -0.0187454376 5.1492210 265.6899
UBLCP1 0.36983263 -0.8610092760 5.3901002 261.1132
DDX3Y 0.63592287 6.8417320935 1.7912693 254.0598
MRPS21 -0.82809392 0.0116661758 8.5068062 251.8755
NXT2 1.42061031 -0.0359131846 3.3454497 242.1401
NDUFS2 -1.10424148 -0.1363851583 7.4857218 238.3313
RAB4A -1.06601365 0.1243146623 6.9336206 232.1540
RRAGB -3.69511078 0.0673397110 2.8059766 231.1645
PRRC2C -0.76791553 -0.0692917671 8.9804387 230.8144
IAH1 2.94603002 3.5084157207 4.7423190 229.3740
HAX1 -0.73716863 0.1521285583 7.0031109 228.5330
TAF9B -0.16815868 -3.4231593635 3.0872820 219.3648
UBA1 0.19050176 -0.8314916148 6.7972613 218.7919
LLGL1 -1.32491454 -1.3623149053 3.4453191 217.6748
MAGEH1 -4.45892874 -0.0008943073 4.0026780 216.6919
MRPL55 -0.69771503 0.1475720506 6.8884481 216.0936
HEPH -2.64148467 0.2748949441 2.9329243 212.1952
NUCKS1 -0.77087961 0.1576923726 10.0532090 211.3153
CDK16 0.07004454 -0.7601982941 6.2405778 210.6172
RBBP7 0.09385378 -0.7309515475 7.1947857 209.7569
ZNF280D 2.96973513 3.6692981471 2.5523706 208.0365
FAM199X 1.23456145 0.0362300439 4.4238350 207.8805
PSMD4 -0.83164888 -0.1028603790 7.9053555 206.5466
TOMM20 -0.69656936 0.2201459624 8.6468736 206.3766
NAXE -0.83283017 0.0212083883 6.9900205 203.6572
KRTCAP2 -0.91592070 0.0603693043 7.0562363 203.2405
NSMCE1 -0.33795416 -1.0650492066 5.0861788 200.9750
QPCT 0.25367332 -3.8935678799 3.1436452 200.7503
PNPLA4 1.40337010 0.1328337504 5.1149287 199.0405
KDM6A 1.17940662 -0.1081981092 4.1317553 195.2829
LYPLAL1 -0.47966710 0.6338695811 5.3940200 194.0716
FDPS -1.24361616 -0.0379751006 8.3715190 193.5837
P.Value adj.P.Val
TYW3 8.625784e-47 8.785361e-43
CRYZ 9.491511e-36 4.206489e-32
EIF1AY 1.239025e-35 4.206489e-32
TRIM61 8.871225e-34 2.258836e-30
CAT 3.756173e-33 7.651324e-30
THOC3 1.219311e-30 2.069781e-27
USP51 2.759348e-29 3.738533e-26
RNF187 2.936501e-29 3.738533e-26
HCCS 6.433319e-29 7.280373e-26
UBLCP1 9.899722e-29 1.008287e-25
DDX3Y 1.949843e-28 1.805378e-25
MRPS21 2.413586e-28 2.048531e-25
NXT2 6.377724e-28 4.996701e-25
NDUFS2 9.417324e-28 6.851104e-25
RAB4A 1.793403e-27 1.217720e-24
RRAGB 1.991170e-27 1.238039e-24
PRRC2C 2.066437e-27 1.238039e-24
IAH1 2.408549e-27 1.362837e-24
HAX1 2.634959e-27 1.412477e-24
TAF9B 7.155685e-27 3.644033e-24
UBA1 7.626000e-27 3.698610e-24
LLGL1 8.637492e-27 3.998766e-24
MAGEH1 9.642455e-27 4.269931e-24
MRPL55 1.031269e-26 4.376449e-24
HEPH 1.604490e-26 6.536693e-24
NUCKS1 1.774575e-26 6.951556e-24
CDK16 1.922779e-26 7.253150e-24
RBBP7 2.123225e-26 7.723231e-24
ZNF280D 2.591743e-26 8.960134e-24
FAM199X 2.639215e-26 8.960134e-24
PSMD4 3.083870e-26 1.001272e-23
TOMM20 3.145873e-26 1.001272e-23
NAXE 4.334138e-26 1.337673e-23
KRTCAP2 4.553802e-26 1.364131e-23
NSMCE1 5.967333e-26 1.734381e-23
QPCT 6.130362e-26 1.734381e-23
PNPLA4 7.532822e-26 2.073562e-23
KDM6A 1.191189e-25 3.192701e-23
LYPLAL1 1.383103e-25 3.612026e-23
FDPS 1.469218e-25 3.740996e-23"UTF1" %in% rownames(topsInd)[1] TRUEtopsInd[rownames(topsInd)== "UTF1",] dge.samples.indNA18858 dge.samples.indNA19160 AveExpr F
UTF1 0.2412929 -0.5291004 5.619477 6.251066
P.Value adj.P.Val
UTF1 0.003586998 0.007964591topsInd[rownames(topsInd)== "TAC3",] dge.samples.indNA18858 dge.samples.indNA19160 AveExpr F
TAC3 1.170806 -0.01437916 3.666656 10.03314
P.Value adj.P.Val
TAC3 0.000193839 0.0006255214plot(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] 7.010019median(topsInd$F)[1] 5.318821
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_3.3.3 reshape2_1.4.4 edgeR_3.28.1
[5] limma_3.42.2 dplyr_1.0.2 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_2.0-0 deldir_0.1-28
[4] ellipsis_0.3.1 ggridges_0.5.2 rprojroot_2.0.2
[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_0.9.0 codetools_0.2-16 splines_3.6.1
[16] lsei_1.2-0 knitr_1.29 polyclip_1.10-0
[19] jsonlite_1.7.2 ica_1.0-2 cluster_2.1.0
[22] png_0.1-7 uwot_0.1.10 shiny_1.5.0
[25] sctransform_0.2.1 compiler_3.6.1 httr_1.4.2
[28] fastmap_1.0.1 lazyeval_0.2.2 later_1.1.0.1
[31] htmltools_0.5.0 tools_3.6.1 rsvd_1.0.3
[34] igraph_1.2.6 gtable_0.3.0 glue_1.4.2
[37] RANN_2.6.1 rappdirs_0.3.1 Rcpp_1.0.5
[40] spatstat_1.64-1 vctrs_0.3.6 gdata_2.18.0
[43] ape_5.4-1 nlme_3.1-140 lmtest_0.9-37
[46] xfun_0.16 stringr_1.4.0 globals_0.12.5
[49] mime_0.9 miniUI_0.1.1.1 lifecycle_0.2.0
[52] irlba_2.3.3 gtools_3.8.2 goftest_1.2-2
[55] future_1.18.0 MASS_7.3-51.4 zoo_1.8-8
[58] scales_1.1.1 promises_1.1.1 spatstat.utils_1.17-0
[61] parallel_3.6.1 RColorBrewer_1.1-2 yaml_2.2.1
[64] reticulate_1.16 pbapply_1.4-2 gridExtra_2.3
[67] rpart_4.1-15 stringi_1.5.3 highr_0.8
[70] caTools_1.18.0 rlang_0.4.10 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_0.4.2 patchwork_1.0.1 htmlwidgets_1.5.1
[82] cowplot_1.1.1 tidyselect_1.1.0 here_0.1-11
[85] RcppAnnoy_0.0.18 plyr_1.8.6 magrittr_2.0.1
[88] R6_2.5.0 gplots_3.0.4 generics_0.1.0
[91] withr_2.3.0 pillar_1.4.7 whisker_0.4
[94] mgcv_1.8-28 fitdistrplus_1.0-14 survival_3.2-3
[97] abind_1.4-5 tibble_3.0.4 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_1.13.4 git2r_0.26.1 digest_0.6.27
[109] xtable_1.8-4 tidyr_1.1.0 httpuv_1.5.4
[112] munsell_0.5.0 viridisLite_0.3.0