Differential Expression with Limma
Last updated: 2021-07-05
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Knit directory: Embryoid_Body_Pilot_Workflowr/analysis/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| 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.0.5'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)
| 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$norm.factors) Min. 1st Qu. Median Mean 3rd Qu. Max.
0.7736 0.8998 0.9755 1.0174 1.0565 2.1383 design<- model.matrix(~0+ dge$samples$cluster + dge$samples$batch + dge$samples$ind)v<- voom(dge, design, plot=TRUE)
| Version | Author | Date |
|---|---|---|
| 7888de8 | KLRhodes | 2020-08-31 |
vAn object of class "EList"
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PLEKHN1 3.969458 0.01188828 3.100060
HES4 6.015751 7.23105680 5.907415
ISG15 5.278786 9.65754671 5.421988
17.Batch1.SNG-NA19160 17.Batch2.SNG-NA18511 17.Batch2.SNG-NA19160
SAMD11 1.6766868 0.2434696 -0.8078316
NOC2L 5.7297981 6.1261127 5.7920813
PLEKHN1 -0.6452413 0.2434696 -0.8078316
HES4 6.9472157 6.0763597 6.8575044
ISG15 8.1588897 8.0700181 7.5185979
17.Batch3.SNG-NA18511 17.Batch3.SNG-NA19160 2.Batch1.SNG-NA18511
SAMD11 -0.03310476 0.79402418 2.2563784
NOC2L 5.58160508 5.71050082 6.4543889
PLEKHN1 -0.03310476 0.05705858 -0.9586345
HES4 7.02217767 6.45508966 9.1634539
ISG15 8.07018305 8.38648122 5.7789750
2.Batch1.SNG-NA18858 2.Batch1.SNG-NA19160 2.Batch2.SNG-NA18511
SAMD11 3.371089 3.347964 1.689570
NOC2L 6.146383 6.216891 6.145876
PLEKHN1 1.255612 -1.442113 -2.702747
HES4 7.277980 8.816453 8.803891
ISG15 5.601387 6.054186 5.847999
2.Batch2.SNG-NA18858 2.Batch2.SNG-NA19160 2.Batch3.SNG-NA18511
SAMD11 2.732835 2.168768 2.184671
NOC2L 5.786350 6.170406 6.126751
PLEKHN1 -0.371502 -2.027630 -1.833251
HES4 6.812381 8.443707 8.169846
ISG15 6.051404 5.688904 5.803374
2.Batch3.SNG-NA18858 2.Batch3.SNG-NA19160 3.Batch1.SNG-NA18511
SAMD11 1.6522064 4.053993 2.8900071
NOC2L 6.0445238 6.321567 6.2101960
PLEKHN1 -0.6697217 -2.840825 0.4218583
HES4 6.7978838 7.646010 5.4836345
ISG15 6.2727928 6.361299 6.4263597
3.Batch1.SNG-NA18858 3.Batch1.SNG-NA19160 3.Batch2.SNG-NA18511
SAMD11 3.314066 3.219328 3.2494233
NOC2L 6.519180 6.247685 5.7109353
PLEKHN1 1.729103 -1.185427 -0.4059285
HES4 5.925500 5.927842 4.9516235
ISG15 6.802352 6.621343 6.4545378
3.Batch2.SNG-NA18858 3.Batch2.SNG-NA19160 3.Batch3.SNG-NA18511
SAMD11 4.442953 2.984379 2.97513203
NOC2L 5.580457 5.967890 6.02099904
PLEKHN1 2.121025 -0.234045 -0.04116978
HES4 3.705987 5.197372 3.81228156
ISG15 6.208488 6.316998 6.22901935
3.Batch3.SNG-NA18858 3.Batch3.SNG-NA19160 4.Batch1.SNG-NA18511
SAMD11 2.342371 3.308837 4.64058645
NOC2L 5.801803 6.135238 6.43920657
PLEKHN1 2.342371 -1.269431 0.07580184
HES4 5.149726 4.920394 7.03387116
ISG15 6.865933 6.403780 6.03773380
4.Batch1.SNG-NA18858 4.Batch1.SNG-NA19160 4.Batch2.SNG-NA18511
SAMD11 4.1871834 3.048785 5.1585532
NOC2L 6.5606418 6.254799 6.2580889
PLEKHN1 0.7277518 -1.373747 -0.4561566
HES4 6.8362763 6.582387 6.3120277
ISG15 4.8152147 5.112753 5.8836934
4.Batch2.SNG-NA18858 4.Batch2.SNG-NA19160 4.Batch3.SNG-NA18511
SAMD11 2.596729 3.410961 2.4756421
NOC2L 5.766654 6.181896 6.0794637
PLEKHN1 2.596729 -1.486280 -0.1873229
HES4 5.404084 6.501269 4.4945012
ISG15 4.918657 4.943336 4.6706581
4.Batch3.SNG-NA18858 4.Batch3.SNG-NA19160 5.Batch1.SNG-NA18511
SAMD11 1.904391 4.451682 3.9378483
NOC2L 6.659279 6.030057 6.2301309
PLEKHN1 1.904391 -2.741433 0.3771334
HES4 5.811282 5.590977 8.2413195
ISG15 5.074316 4.232026 5.5709051
5.Batch1.SNG-NA18858 5.Batch1.SNG-NA19160 5.Batch2.SNG-NA18511
SAMD11 4.491454 1.8299187 2.9613083
NOC2L 6.055807 6.0645800 6.0063548
PLEKHN1 1.258794 -0.3717152 0.5956588
HES4 5.240646 6.6124184 6.6450885
ISG15 5.731281 5.6595036 5.2745975
5.Batch2.SNG-NA18858 5.Batch2.SNG-NA19160 5.Batch3.SNG-NA18511
SAMD11 3.147236 3.0621076 2.2925337
NOC2L 5.639475 5.7920754 5.9991685
PLEKHN1 1.416052 0.6104116 0.4180646
HES4 5.523740 4.7756142 5.0289947
ISG15 6.136331 5.3715717 5.5704144
5.Batch3.SNG-NA18858 5.Batch3.SNG-NA19160 6.Batch1.SNG-NA18511
SAMD11 2.512142 3.1373449 4.7206961
NOC2L 5.710922 6.0961633 6.0154881
PLEKHN1 2.222636 0.8453183 0.8492108
HES4 4.983448 5.2655485 7.0815227
ISG15 6.018495 5.7705874 6.9728620
6.Batch1.SNG-NA18858 6.Batch1.SNG-NA19160 6.Batch2.SNG-NA18511
SAMD11 2.725273 4.488108 4.579490
NOC2L 5.760897 5.937718 5.942410
PLEKHN1 5.447739 3.376215 1.132604
HES4 6.812736 7.025645 7.144364
ISG15 6.778384 7.517415 6.783314
6.Batch2.SNG-NA18858 6.Batch2.SNG-NA19160 6.Batch3.SNG-NA18511
SAMD11 4.369502 3.789253 4.9796225
NOC2L 5.287039 5.771975 6.0276697
PLEKHN1 3.521505 1.467325 0.7085187
HES4 6.625841 6.756246 5.7050465
ISG15 5.954464 7.131645 6.2678222
6.Batch3.SNG-NA18858 6.Batch3.SNG-NA19160 7.Batch1.SNG-NA18511
SAMD11 3.334450 4.4266903 3.718819
NOC2L 5.812497 5.9923199 6.151778
PLEKHN1 2.112058 0.1496654 2.981853
HES4 7.213596 6.4079655 6.606344
ISG15 7.241341 7.0967132 5.303781
7.Batch1.SNG-NA18858 7.Batch1.SNG-NA19160 7.Batch2.SNG-NA18511
SAMD11 1.860589 2.202334 3.227946
NOC2L 6.238745 5.661766 6.332282
PLEKHN1 1.098748 0.617372 2.490980
HES4 5.538082 4.076804 5.153945
ISG15 5.110462 6.109225 4.365449
7.Batch2.SNG-NA18858 7.Batch2.SNG-NA19160 7.Batch3.SNG-NA18511
SAMD11 1.070192 3.885427 2.816636
NOC2L 5.884888 6.141767 6.316902
PLEKHN1 1.597821 0.715502 2.028141
HES4 5.643396 5.013183 5.708260
ISG15 5.347403 5.759896 5.666741
7.Batch3.SNG-NA18858 7.Batch3.SNG-NA19160 8.Batch1.SNG-NA18511
SAMD11 0.9598028 2.8576628 2.707524
NOC2L 5.7741038 6.0609464 5.889967
PLEKHN1 1.3650593 0.1946978 -2.964902
HES4 5.2797800 4.7995599 9.128846
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 ...
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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
[,142] [,143] [,144] [,145] [,146] [,147] [,148]
[1,] 0.6215747 2.8507008 3.7922673 0.9739062 2.3586125 3.0353457 0.3454113
[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$indNA19160
1 0
2 0
3 1
4 0
5 0
153 more rows ...1 v all contrasts
fit<- lmFit(v,design)nclust<- length(unique(dge$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 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)
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 2620output all OneVall cluster comparisons
output.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_res0.5_OnevAllTopTables.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 leiden_0.3.3
[10] listenv_0.8.0 npsurv_0.4-0 ggrepel_0.9.0
[13] codetools_0.2-16 splines_3.6.1 lsei_1.2-0
[16] knitr_1.29 polyclip_1.10-0 jsonlite_1.7.2
[19] ica_1.0-2 cluster_2.1.0 png_0.1-7
[22] uwot_0.1.10 shiny_1.5.0 sctransform_0.2.1
[25] compiler_3.6.1 httr_1.4.2 fastmap_1.0.1
[28] lazyeval_0.2.2 later_1.1.0.1 htmltools_0.5.0
[31] tools_3.6.1 rsvd_1.0.3 igraph_1.2.6
[34] gtable_0.3.0 glue_1.4.2 RANN_2.6.1
[37] rappdirs_0.3.3 Rcpp_1.0.6 spatstat_1.64-1
[40] vctrs_0.3.6 gdata_2.18.0 ape_5.4-1
[43] nlme_3.1-140 lmtest_0.9-37 xfun_0.16
[46] stringr_1.4.0 globals_0.12.5 mime_0.9
[49] miniUI_0.1.1.1 lifecycle_0.2.0 irlba_2.3.3
[52] gtools_3.8.2 goftest_1.2-2 future_1.18.0
[55] MASS_7.3-51.4 zoo_1.8-8 scales_1.1.1
[58] promises_1.1.1 spatstat.utils_1.17-0 parallel_3.6.1
[61] RColorBrewer_1.1-2 yaml_2.2.1 reticulate_1.20
[64] pbapply_1.4-2 gridExtra_2.3 rpart_4.1-15
[67] stringi_1.5.3 highr_0.8 caTools_1.18.0
[70] rlang_0.4.10 pkgconfig_2.0.3 bitops_1.0-6
[73] evaluate_0.14 lattice_0.20-38 ROCR_1.0-7
[76] purrr_0.3.4 tensor_1.5 patchwork_1.1.1
[79] htmlwidgets_1.5.1 cowplot_1.1.1 tidyselect_1.1.0
[82] here_0.1-11 RcppAnnoy_0.0.18 plyr_1.8.6
[85] magrittr_2.0.1 R6_2.5.0 gplots_3.0.4
[88] generics_0.1.0 withr_2.4.2 pillar_1.4.7
[91] whisker_0.4 mgcv_1.8-28 fitdistrplus_1.0-14
[94] survival_3.2-3 abind_1.4-5 tibble_3.0.4
[97] future.apply_1.6.0 crayon_1.3.4 KernSmooth_2.23-15
[100] plotly_4.9.2.1 rmarkdown_2.3 locfit_1.5-9.4
[103] grid_3.6.1 data.table_1.13.4 git2r_0.26.1
[106] digest_0.6.27 xtable_1.8-4 tidyr_1.1.0
[109] httpuv_1.5.4 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 leiden_0.3.3
[10] listenv_0.8.0 npsurv_0.4-0 ggrepel_0.9.0
[13] codetools_0.2-16 splines_3.6.1 lsei_1.2-0
[16] knitr_1.29 polyclip_1.10-0 jsonlite_1.7.2
[19] ica_1.0-2 cluster_2.1.0 png_0.1-7
[22] uwot_0.1.10 shiny_1.5.0 sctransform_0.2.1
[25] compiler_3.6.1 httr_1.4.2 fastmap_1.0.1
[28] lazyeval_0.2.2 later_1.1.0.1 htmltools_0.5.0
[31] tools_3.6.1 rsvd_1.0.3 igraph_1.2.6
[34] gtable_0.3.0 glue_1.4.2 RANN_2.6.1
[37] rappdirs_0.3.3 Rcpp_1.0.6 spatstat_1.64-1
[40] vctrs_0.3.6 gdata_2.18.0 ape_5.4-1
[43] nlme_3.1-140 lmtest_0.9-37 xfun_0.16
[46] stringr_1.4.0 globals_0.12.5 mime_0.9
[49] miniUI_0.1.1.1 lifecycle_0.2.0 irlba_2.3.3
[52] gtools_3.8.2 goftest_1.2-2 future_1.18.0
[55] MASS_7.3-51.4 zoo_1.8-8 scales_1.1.1
[58] promises_1.1.1 spatstat.utils_1.17-0 parallel_3.6.1
[61] RColorBrewer_1.1-2 yaml_2.2.1 reticulate_1.20
[64] pbapply_1.4-2 gridExtra_2.3 rpart_4.1-15
[67] stringi_1.5.3 highr_0.8 caTools_1.18.0
[70] rlang_0.4.10 pkgconfig_2.0.3 bitops_1.0-6
[73] evaluate_0.14 lattice_0.20-38 ROCR_1.0-7
[76] purrr_0.3.4 tensor_1.5 patchwork_1.1.1
[79] htmlwidgets_1.5.1 cowplot_1.1.1 tidyselect_1.1.0
[82] here_0.1-11 RcppAnnoy_0.0.18 plyr_1.8.6
[85] magrittr_2.0.1 R6_2.5.0 gplots_3.0.4
[88] generics_0.1.0 withr_2.4.2 pillar_1.4.7
[91] whisker_0.4 mgcv_1.8-28 fitdistrplus_1.0-14
[94] survival_3.2-3 abind_1.4-5 tibble_3.0.4
[97] future.apply_1.6.0 crayon_1.3.4 KernSmooth_2.23-15
[100] plotly_4.9.2.1 rmarkdown_2.3 locfit_1.5-9.4
[103] grid_3.6.1 data.table_1.13.4 git2r_0.26.1
[106] digest_0.6.27 xtable_1.8-4 tidyr_1.1.0
[109] httpuv_1.5.4 munsell_0.5.0 viridisLite_0.3.0