Differential Expression with Limma
Last updated: 2021-07-05
Checks: 5 2
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_OnevAllTopTables.csv | ../output/Pseudobulk_Limma_res0.1_OnevAllTopTables.csv |
| /project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/Pseudobulk_Limma_res0.1_OnevAll_top10Upregby_adjP.csv | ../output/Pseudobulk_Limma_res0.1_OnevAll_top10Upregby_adjP.csv |
| /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 |
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| html | 56c37c9 | KLRhodes | 2021-01-11 | Build site. |
| 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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[4,] 10.04298 21.07287 9.32857 20.98907 21.36202 12.99572 16.19713 18.80142
[5,] 16.41662 26.84389 14.04097 27.13414 26.36484 17.66351 20.77130 25.26352
[,17] [,18] [,19] [,20] [,21] [,22] [,23]
[1,] 9.134733 22.80686 13.667003 2.5520182 19.241403 9.217620 0.6980291
[2,] 15.484326 35.64761 11.203415 1.2808396 15.670514 6.558968 0.4335242
[3,] 8.719828 20.10535 15.531270 3.4625833 20.420649 11.403381 0.9609097
[4,] 4.556935 15.39915 5.968453 0.7479667 9.926134 3.957180 0.3158993
[5,] 8.073718 21.51586 14.760670 2.9102387 20.730546 9.708264 0.7309726
[,24] [,25] [,26] [,27] [,28] [,29] [,30]
[1,] 14.176676 12.661618 0.6563529 16.932067 6.431747 2.0237300 19.77768
[2,] 10.349558 7.889203 0.3536638 10.941530 7.352719 1.7630311 20.55233
[3,] 15.859331 14.927886 0.8818901 18.529036 3.832212 1.0393622 12.62874
[4,] 6.263387 4.724821 0.2718365 7.032874 4.208964 0.9955042 14.59363
[5,] 14.943063 13.075462 0.6789117 17.597051 5.154360 1.4536564 17.45418
[,31] [,32] [,33] [,34] [,35] [,36] [,37]
[1,] 3.728046 0.6475818 14.517105 6.425352 0.8934026 17.27681 10.132333
[2,] 3.884228 0.5584429 14.378407 5.549849 0.6336634 15.04216 16.762215
[3,] 1.972346 0.4461850 8.819443 3.962643 0.5858969 11.00485 15.790206
[4,] 2.190698 0.4137049 10.059942 3.773516 0.4831896 11.08245 9.451264
[5,] 2.845599 0.5090940 12.013245 4.856936 0.6777276 14.44734 12.619581
[,38] [,39] [,40] [,41] [,42] [,43] [,44]
[1,] 5.551504 7.791019 10.68701 2.447371 10.025377 9.944092 3.254204
[2,] 8.783537 13.245523 16.58118 3.694754 15.119523 13.671604 3.801898
[3,] 10.728431 12.235040 17.09536 4.900442 15.462474 16.056730 6.047476
[4,] 4.811975 7.139187 10.04651 1.964783 9.321564 8.057447 2.232151
[5,] 7.265883 10.269233 12.71600 3.190897 12.204877 11.832167 3.873585
[,45] [,46] [,47] [,48] [,49] [,50] [,51]
[1,] 18.01059 10.222377 3.138945 15.444373 10.530066 1.1815080 10.304070
[2,] 21.96642 13.928699 3.679351 19.016687 13.489610 1.3268478 12.610055
[3,] 24.76003 4.571048 1.147407 7.124721 4.964830 0.6028348 4.532964
[4,] 15.56020 7.030687 1.655454 11.581170 7.325607 0.7382867 7.076254
[5,] 20.62655 8.434597 2.402367 13.524820 8.295530 0.8877284 8.239576
[,52] [,53] [,54] [,55] [,56] [,57] [,58]
[1,] 9.745966 1.3454345 11.867007 2.648834 0.4577756 3.572129 1.742307
[2,] 10.820897 1.1826829 12.357834 4.453690 0.6094011 5.362935 3.333483
[3,] 4.543722 0.6518845 5.190990 6.684286 1.2943609 8.171292 5.347110
[4,] 5.758106 0.7087039 7.233630 2.678536 0.4337226 3.572191 1.779529
[5,] 7.494726 0.9758391 9.590081 3.275818 0.5301035 4.177155 2.125773
[,59] [,60] [,61]
[1,] 3.436238 2.369097 4.525188
[2,] 4.793791 3.356727 5.524276
[3,] 8.319574 6.458774 11.269555
[4,] 3.453803 1.933325 4.058538
[5,] 3.890978 2.791188 5.100664
10180 more rows ...
$design
dge$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$indNA19160
1 0 0
2 1 0
3 0 1
4 0 0
5 1 0
56 more rows ...1 v all contrasts
fit<- lmFit(v,design)nclust<- length(unique(dge$samples$cluster))
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 45.61710output.list<- list()
for (i in 1:nclust){
ta<- topTable(efit, coef=i,n=nrow(fit))
output.list[[i]]<- ta
}
listnames<- c(paste0("Cluster", unique(dge$samples$cluster)))
names(output.list)<- as.vector(listnames)
write.csv(output.list, "/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/Pseudobulk_Limma_res0.1_OnevAllTopTables.csv")#output table with top 5 upreg DE genes in each cluster by adjusted p
OnevAll.top10.adjP<- NULL
for(i in 1:nclust){
c<- output.list[[i]]
top10<- c[c$logFC>0,]
top10<- top10[order(top10$adj.P.Val),]
top10<- rownames(top10)[1:10]
OnevAll.top10.adjP<- cbind(OnevAll.top10.adjP, top10)
}
colnames(OnevAll.top10.adjP)<- as.character(0:(nclust-1))
write.csv(OnevAll.top10.adjP, "/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/Pseudobulk_Limma_res0.1_OnevAll_top10Upregby_adjP.csv")#output table with top 5 upreg DE genes in each cluster by logFC
OnevAll.top10.logFC<- NULL
for(i in 1:nclust){
c<- output.list[[i]]
top10<- c[order(c$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")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_0.3.3 Rcpp_1.0.6
[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_1.20 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_1.1.1 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_2.4.2 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
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_0.3.3 Rcpp_1.0.6
[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_1.20 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_1.1.1 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_2.4.2 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