ReferenceIntegration
Last updated: 2021-07-05
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Knit directory: Embryoid_Body_Pilot_Workflowr/analysis/
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| /project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/mergedObjects/Harmony.Batchindividual.rds | ../output/mergedObjects/Harmony.Batchindividual.rds |
| /project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/data/dge/hESC1.rawdge.txt.gz | ../data/dge/hESC1.rawdge.txt.gz |
| /project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/data/dge/iPS-to-EB_20Day_dge.txt.gz | ../data/dge/iPS-to-EB_20Day_dge.txt.gz |
| /project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/CaoEtAl.Obj.CellsOfAllClusters.ProteinCodingGenes.rds | ../output/CaoEtAl.Obj.CellsOfAllClusters.ProteinCodingGenes.rds |
| /project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/TranferredAnnotations_ReferenceInt_JustEarlyEcto.csv | ../output/TranferredAnnotations_ReferenceInt_JustEarlyEcto.csv |
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library(Seurat)
library(edgeR)Loading required package: limmalibrary(dplyr)
Attaching package: 'dplyr'The following objects are masked from 'package:stats':
filter, lagThe following objects are masked from 'package:base':
intersect, setdiff, setequal, unionlibrary(tidyr)
library(tibble)
library(purrr)
library(harmony)Loading required package: Rcpplibrary(ggplot2)loading data
first, my data
merged<- readRDS("/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/mergedObjects/Harmony.Batchindividual.rds")subset to only early ectoderm cells (cluster 1, res 0.1)
merged<- subset(merged, idents = 1)DimPlot(merged)
loading in hESC and iPS-to-EB raw dges from scHCL reference set
hESC<- read.table("/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/data/dge/hESC1.rawdge.txt.gz", header=T, row.names = 1)
iPStoEBday20<- read.table("/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/data/dge/iPS-to-EB_20Day_dge.txt.gz", header=T, row.names = 1)Note: there is no available metadata for these iPS to EB differentiations (no cell annotations online)
make a seurat objects with all of the data
hESC.obj<- CreateSeuratObject(hESC)Warning: Feature names cannot have underscores ('_'), replacing with dashes
('-')EB20.obj<- CreateSeuratObject(iPStoEBday20)Warning: Feature names cannot have underscores ('_'), replacing with dashes
('-')#normalizing each dataset
hESC.obj<- suppressWarnings(SCTransform(hESC.obj, variable.features.n=5000,verbose=F))
EB20.obj<-suppressWarnings(SCTransform(EB20.obj, variable.features.n=5000,verbose=F))Cao.obj<-readRDS("/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/CaoEtAl.Obj.CellsOfAllClusters.ProteinCodingGenes.rds")Cao.obj<- RunPCA(Cao.obj, npcs= 100, verbose = F)
Cao.obj<- FindNeighbors(Cao.obj, dims = 1:30, verbose = F)
Cao.obj<- RunUMAP(Cao.obj, dims=1:30, verbose = F)Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session#rename Cao metadata so none match with EB (just need to replace Batch)
colnames(Cao.obj@meta.data) [1] "orig.ident" "nCount_RNA" "nFeature_RNA"
[4] "Assay" "Batch" "Experiment_batch"
[7] "Main_cluster_name" "Fetus_id" "Sex"
[10] "nCount_SCT" "nFeature_SCT" colnames(Cao.obj@meta.data)[5]<-"Batch_week"#rename orig.idents
hESC.obj$orig.ident<- "scHCL.hESC"
EB20.obj$orig.ident<- "scHCL.EB20"
merged$orig.ident<- "EB.Pilot"
Cao.obj$orig.ident<- "Cao.EtAl"
#merge objects
obj.list<- list(Cao.obj, merged, hESC.obj, EB20.obj)
merge.all<- merge(x=obj.list[[1]], y=c(obj.list[[2]], obj.list[[3]], obj.list[[4]]), merge.data=T)merge.all<- SCTransform(merge.all, variable.features.n = 5000, vars.to.regress = c("orig.ident"), assay= "SCT")
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|
|================== | 25%Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached
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|========================== | 38%Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached
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|=================================== | 50%Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached
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|============================================ | 62%Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached
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|==================================================== | 75%Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached
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|============================================================= | 88%Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached
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|======================================================================| 100%all.genes= rownames(merge.all)
merge.all<-FindVariableFeatures(merge.all,selection.method="vst", nfeatures = 5000)
merge.all<- ScaleData(merge.all, features = all.genes, assay = "SCT")Centering and scaling data matrixmerge.all<-RunPCA(merge.all, npcs = 100, verbose=F, Assay="SCT")DimPlot(merge.all, reduction = "pca", group.by = "orig.ident")
merge.all<- RunHarmony(merge.all, c("orig.ident", "individual", "Batch"), theta = c(3,1,1), plot_convergence = T, assay.use = "SCT")Harmony 1/10Harmony 2/10Harmony 3/10Harmony 4/10Harmony 5/10Harmony converged after 5 iterationsWarning: Invalid name supplied, making object name syntactically valid. New
object name is Seurat..ProjectDim.SCT.harmony; see ?make.names for more details
on syntax validity
DimPlot(merge.all, group.by= "orig.ident", reduction= "harmony")
merge.all<- RunUMAP(merge.all,dims=1:100, reduction="harmony")19:00:59 UMAP embedding parameters a = 0.9922 b = 1.11219:00:59 Read 60631 rows and found 100 numeric columns19:00:59 Using Annoy for neighbor search, n_neighbors = 3019:00:59 Building Annoy index with metric = cosine, n_trees = 500% 10 20 30 40 50 60 70 80 90 100%[----|----|----|----|----|----|----|----|----|----|**************************************************|
19:01:17 Writing NN index file to temp file /tmp/jobs/12040951/Rtmpw2P0Kc/file178ff7f5a81a4
19:01:17 Searching Annoy index using 1 thread, search_k = 3000
19:02:00 Annoy recall = 100%
19:02:02 Commencing smooth kNN distance calibration using 1 thread
19:02:05 Initializing from normalized Laplacian + noise
19:02:12 Commencing optimization for 200 epochs, with 2893712 positive edges
19:03:30 Optimization finishedV<- DimPlot(merge.all, group.by = "Main_cluster_name", label = T, label.size = 2.5, repel = T)+NoLegend()Warning: Using `as.character()` on a quosure is deprecated as of rlang 0.3.0.
Please use `as_label()` or `as_name()` instead.
This warning is displayed once per session.VWarning: ggrepel: 33 unlabeled data points (too many overlaps). Consider
increasing max.overlaps
Add new metadata to include Main_cluster_name as well as cluster labels from EB Pilot DE results, and orig ident of the HCL data
merge.all<- AddMetaData(merge.all, col.name = "all.labels", metadata = merge.all@meta.data$Main_cluster_name)
#for cells with NA for main_cluster_name, replace label from SCT_snn_res 0.1
for (i in (1:nrow(merge.all@meta.data))){
if (is.na(merge.all@meta.data$all.labels[i]) == T){
merge.all@meta.data$all.labels[i]<- merge.all@meta.data$SCT_snn_res.0.1[i]
}
}
#and now replace remaining NAs with orig.ident
for (i in (1:nrow(merge.all@meta.data))){
if (is.na(merge.all@meta.data$all.labels[i]) == T){
merge.all@meta.data$all.labels[i]<- merge.all@meta.data$orig.ident[i]
}
}V<- DimPlot(merge.all, group.by = "all.labels", label = T, label.size = 2.5, repel = T)+NoLegend()
VWarning: ggrepel: 34 unlabeled data points (too many overlaps). Consider
increasing max.overlaps
V<- DimPlot(merge.all, group.by = "orig.ident", label = F, order= c("scHCL.hESC","scHCL.EB20", "EB.Pilot","Cao.EtAl"))
V
DimPlot(merge.all, split.by = "orig.ident", label = F, order= c("scHCL.hESC","scHCL.EB20", "EB.Pilot","Cao.EtAl"))
options(ggrepel.max.overlaps = Inf)#subset object to just my data and Cao reference, plot UMAP
Idents(merge.all)<- "orig.ident"
sub<- subset(merge.all, idents= c("Cao.EtAl", "EB.Pilot"))
V<- DimPlot(sub, group.by = "all.labels", label = T, repel = T, label.size = 2.5)+NoLegend()
V
V<- DimPlot(sub, group.by = "Main_cluster_name", label = T, repel = T, label.size = 2.5)+NoLegend()
V
V<- DimPlot(sub, group.by = "SCT_snn_res.0.1", label = T, repel = T, label.size = 2.5)+NoLegend()
V
DimPlot(sub, group.by = "orig.ident")
DimPlot(sub, group.by = "orig.ident", order="Cao.EtAl")
#Subset object to just my data and HCL references, plot UMAP
sub<- subset(merge.all, idents= c("scHCL.EB20", "EB.Pilot"))
V<- DimPlot(sub, group.by = "orig.ident", label = F)
V
sub<- subset(merge.all, idents= c("scHCL.EB20", "EB.Pilot", "Cao.EtAl"))
DimPlot(sub, group.by="orig.ident", pt.size = 0.2, label=F) 
DimPlot(merge.all, split.by="orig.ident",group.by = "all.labels", pt.size = 0.2, label=F) +NoLegend()
Now, will transfer labels for Cao Et Al and hESC onto my data.
#subset to remove scHCL.EB20
sub<- subset(merge.all, idents= c("Cao.EtAl", "EB.Pilot", "scHCL.hESC"))
#compute distance matrix based on harmony embeddings, dims 1:100
har_embeds<- sub@reductions$harmony@cell.embeddings
har_distmat<- as.matrix(dist(har_embeds, method="euclidean", upper=TRUE))#vectors with cell ids from Cao.EtAl, EB.pilot, and scHCL.hESC
EB.pilot.ids<-rownames(merge.all@meta.data[merge.all@meta.data$orig.ident == "EB.Pilot",])
#subset rows to only cells in EB.pilot
sub_har_distmat<- har_distmat[rownames(har_distmat) %in% EB.pilot.ids,]
#subset cols to only cells not in EB.pilot
'%notin%'<- Negate('%in%')
sub_har_distmat<- sub_har_distmat[,colnames(sub_har_distmat) %notin% EB.pilot.ids]nearest.ref.cell.id<- NULL
nearest.ref.cell.dist<- NULL
#for loop, loop through each row
for (i in 1:nrow(sub_har_distmat)){
nearest.ref.cell.dist[i]<- min(sub_har_distmat[i,])
nearest.ref.cell.id[i]<- names(which.min(sub_har_distmat[i,]))
}
nearest.ref.table<- cbind(rownames(sub_har_distmat), nearest.ref.cell.id,nearest.ref.cell.dist)
colnames(nearest.ref.table)<- c("EB.cell.id", "nearest.ref.cell.id", "harmony.dist.to.nearest.ref.cell")#add annotation
ann<- as.data.frame(merge.all@meta.data$all.labels)
ann<- cbind(rownames(merge.all@meta.data), ann)
colnames(ann)<- c("nearest.ref.cell.id", "annotation")
nearest.ref.table<- as.data.frame(nearest.ref.table)
nearest.ann<- left_join(nearest.ref.table, ann, by=c("nearest.ref.cell.id"))a<- as.data.frame(table(nearest.ann$annotation))
a<- a[a$Var1 != "scHCL.EB20",]
a<- a[a$Var1 != "0",]
a<- a[a$Var1 != "1",]
a<- a[a$Var1 != "2",]
a<- a[a$Var1 != "3",]
a<- a[a$Var1 != "4",]
a<- a[a$Var1 != "5",]
a<- a[a$Var1 != "6",]
a<- a[a$Var1 != "7",]
colnames(a)<- c("reference.cell.type", "Frequency")
a reference.cell.type Frequency
2 AFP_ALB positive cells 0
3 Acinar cells 80
4 Adrenocortical cells 4
5 Amacrine cells 134
6 Antigen presenting cells 11
7 Astrocytes 8
8 Bipolar cells 18
9 Bronchiolar and alveolar epithelial cells 0
10 CCL19_CCL21 positive cells 0
11 CLC_IL5RA positive cells 0
12 CSH1_CSH2 positive cells 0
13 Cardiomyocytes 1
14 Chromaffin cells 1
15 Ciliated epithelial cells 302
16 Corneal and conjunctival epithelial cells 2
17 Ductal cells 16
18 ELF3_AGBL2 positive cells 1
19 ENS glia 4
20 ENS neurons 4
21 Endocardial cells 11
22 Epicardial fat cells 0
23 Erythroblasts 14
24 Excitatory neurons 0
25 Extravillous trophoblasts 2
26 Ganglion cells 0
27 Goblet cells 1
28 Granule neurons 4
29 Hematopoietic stem cells 1
30 Hepatoblasts 8
31 Horizontal cells 68
32 IGFBP1_DKK1 positive cells 9
33 Inhibitory interneurons 9
34 Inhibitory neurons 4
35 Intestinal epithelial cells 0
36 Islet endocrine cells 12
37 Lens fibre cells 7
38 Limbic system neurons 1
39 Lymphatic endothelial cells 2
40 Lymphoid cells 8
41 MUC13_DMBT1 positive cells 2
42 Megakaryocytes 2
43 Mesangial cells 2
44 Mesothelial cells 1
45 Metanephric cells 25
46 Microglia 27
47 Myeloid cells 6
48 Neuroendocrine cells 0
49 Oligodendrocytes 12
50 PAEP_MECOM positive cells 0
51 PDE11A_FAM19A2 positive cells 4
52 PDE1C_ACSM3 positive cells 0
53 Parietal and chief cells 13
54 Photoreceptor cells 43
55 Purkinje neurons 26
56 Retinal pigment cells 220
57 Retinal progenitors and Muller glia 10
58 SATB2_LRRC7 positive cells 0
59 SKOR2_NPSR1 positive cells 3
60 SLC24A4_PEX5L positive cells 9
61 SLC26A4_PAEP positive cells 0
62 STC2_TLX1 positive cells 0
63 Satellite cells 0
64 Schwann cells 5
65 Skeletal muscle cells 3
66 Smooth muscle cells 2
67 Squamous epithelial cells 13
68 Stellate cells 2
69 Stromal cells 1
70 Sympathoblasts 0
71 Syncytiotrophoblasts and villous cytotrophoblasts 2
72 Thymic epithelial cells 0
73 Thymocytes 47
74 Trophoblast giant cells 7
75 Unipolar brush cells 2
76 Ureteric bud cells 2
77 Vascular endothelial cells 0
78 Visceral neurons 1
80 scHCL.hESC 13144sub<- subset(merge.all, idents= c("EB.Pilot"))
EB.cell.id<- rownames(sub@meta.data)
sub@meta.data<- cbind(sub@meta.data, EB.cell.id)
sub@meta.data<- full_join(sub@meta.data, nearest.ann, by= c("EB.cell.id"))
rownames(sub@meta.data)<- EB.cell.id
V<- DimPlot(sub, group.by="annotation", pt.size = 0.2, label.size = 2.5,label=T, repel=T) +NoLegend()
V
Instead of matching to just one nearest cell, we will get a more robust annotation if we check a group of nearest neighbors for common annotations
mostcommon.ann<- NULL
maxann.FIVEnearest<- NULL
#for loop, loop through each row
for (i in 1:nrow(sub_har_distmat)){
cell<- sub_har_distmat[i,]
cell<- cell[order(cell)]
topfive<- names(cell[1:5])
#get the annotations of the nearest 5 reference cells
topfiveann<- merge.all@meta.data$all.labels[rownames(merge.all@meta.data) %in% topfive]
#if/else at least 3/5 match annotations
maxann<- max(table(topfiveann))
finalann<- names(which.max(table(topfiveann)))
maxann.FIVEnearest[i]<- maxann
if(maxann >= 3){
mostcommon.ann[i]<- finalann
} else {
mostcommon.ann[i]<- "uncertain"
}
}
CommonAnnDF<- as.data.frame(cbind(rownames(sub_har_distmat), mostcommon.ann, maxann.FIVEnearest))
colnames(CommonAnnDF)<- c("EB.cell.id", "Annotation", "NoutofFIVErefneighborsWithSameAnnotation")write.csv(CommonAnnDF,"/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/TranferredAnnotations_ReferenceInt_JustEarlyEcto.csv")b<- table(CommonAnnDF$Annotation)
b
Acinar cells
24
Amacrine cells
133
Astrocytes
4
Bipolar cells
3
Chromaffin cells
1
Ciliated epithelial cells
291
Ductal cells
3
ENS glia
3
Endocardial cells
6
Erythroblasts
4
Hepatoblasts
7
Horizontal cells
69
IGFBP1_DKK1 positive cells
3
Inhibitory neurons
1
Islet endocrine cells
3
Lens fibre cells
6
Megakaryocytes
2
Mesangial cells
1
Metanephric cells
1
Microglia
21
Oligodendrocytes
7
Parietal and chief cells
2
Photoreceptor cells
5
Purkinje neurons
14
Retinal pigment cells
375
Retinal progenitors and Muller glia
4
SKOR2_NPSR1 positive cells
2
SLC24A4_PEX5L positive cells
7
Schwann cells
2
Skeletal muscle cells
3
Smooth muscle cells
3
Squamous epithelial cells
9
Stellate cells
1
Stromal cells
1
Syncytiotrophoblasts and villous cytotrophoblasts
1
Thymocytes
12
Trophoblast giant cells
2
scHCL.hESC
13064
uncertain
283 #print fetal celltypes not present in EB data
unique(merge.all@meta.data$Main_cluster_name)[unique(merge.all@meta.data$Main_cluster_name) %notin% names(b)] [1] "Ganglion cells"
[2] "Corneal and conjunctival epithelial cells"
[3] "PDE11A_FAM19A2 positive cells"
[4] "Vascular endothelial cells"
[5] "Adrenocortical cells"
[6] "Myeloid cells"
[7] "Lymphoid cells"
[8] "Sympathoblasts"
[9] "SLC26A4_PAEP positive cells"
[10] "CSH1_CSH2 positive cells"
[11] "Inhibitory interneurons"
[12] "Granule neurons"
[13] "Unipolar brush cells"
[14] "Excitatory neurons"
[15] "Limbic system neurons"
[16] "CLC_IL5RA positive cells"
[17] "SATB2_LRRC7 positive cells"
[18] "Epicardial fat cells"
[19] "Cardiomyocytes"
[20] "ELF3_AGBL2 positive cells"
[21] "Lymphatic endothelial cells"
[22] "Visceral neurons"
[23] "Mesothelial cells"
[24] "ENS neurons"
[25] "Intestinal epithelial cells"
[26] "Ureteric bud cells"
[27] "Hematopoietic stem cells"
[28] "Neuroendocrine cells"
[29] "Bronchiolar and alveolar epithelial cells"
[30] "Satellite cells"
[31] "CCL19_CCL21 positive cells"
[32] "AFP_ALB positive cells"
[33] "Extravillous trophoblasts"
[34] "PAEP_MECOM positive cells"
[35] "STC2_TLX1 positive cells"
[36] "PDE1C_ACSM3 positive cells"
[37] "MUC13_DMBT1 positive cells"
[38] "Goblet cells"
[39] "Antigen presenting cells"
[40] "Thymic epithelial cells"
[41] NA sub<- subset(merge.all, idents= c("EB.Pilot"))
EB.cell.id<- rownames(sub@meta.data)
sub@meta.data<- cbind(sub@meta.data, EB.cell.id)
sub@meta.data<- full_join(sub@meta.data, CommonAnnDF, by= c("EB.cell.id"))
rownames(sub@meta.data)<- EB.cell.id
V<- DimPlot(sub, group.by="Annotation", pt.size = 0.2, label.size = 2.5,label=T, repel=T) +NoLegend()
V
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] ggplot2_3.3.3 harmony_1.0 Rcpp_1.0.6 purrr_0.3.4
[5] tibble_3.0.4 tidyr_1.1.0 dplyr_1.0.2 edgeR_3.28.1
[9] limma_3.42.2 Seurat_3.2.0 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 RSpectra_0.16-0 codetools_0.2-16
[16] splines_3.6.1 lsei_1.2-0 knitr_1.29
[19] polyclip_1.10-0 jsonlite_1.7.2 ica_1.0-2
[22] cluster_2.1.0 png_0.1-7 uwot_0.1.10
[25] shiny_1.5.0 sctransform_0.2.1 compiler_3.6.1
[28] httr_1.4.2 Matrix_1.2-18 fastmap_1.0.1
[31] lazyeval_0.2.2 later_1.1.0.1 htmltools_0.5.0
[34] tools_3.6.1 rsvd_1.0.3 igraph_1.2.6
[37] gtable_0.3.0 glue_1.4.2 RANN_2.6.1
[40] reshape2_1.4.4 rappdirs_0.3.3 spatstat_1.64-1
[43] vctrs_0.3.6 gdata_2.18.0 ape_5.4-1
[46] nlme_3.1-140 lmtest_0.9-37 xfun_0.16
[49] stringr_1.4.0 globals_0.12.5 mime_0.9
[52] miniUI_0.1.1.1 lifecycle_0.2.0 irlba_2.3.3
[55] gtools_3.8.2 goftest_1.2-2 future_1.18.0
[58] MASS_7.3-51.4 zoo_1.8-8 scales_1.1.1
[61] promises_1.1.1 spatstat.utils_1.17-0 parallel_3.6.1
[64] RColorBrewer_1.1-2 yaml_2.2.1 reticulate_1.20
[67] pbapply_1.4-2 gridExtra_2.3 rpart_4.1-15
[70] stringi_1.5.3 highr_0.8 caTools_1.18.0
[73] rlang_0.4.10 pkgconfig_2.0.3 bitops_1.0-6
[76] evaluate_0.14 lattice_0.20-38 ROCR_1.0-7
[79] tensor_1.5 labeling_0.4.2 patchwork_1.1.1
[82] htmlwidgets_1.5.1 cowplot_1.1.1 tidyselect_1.1.0
[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 future.apply_1.6.0 crayon_1.3.4
[100] KernSmooth_2.23-15 plotly_4.9.2.1 rmarkdown_2.3
[103] locfit_1.5-9.4 grid_3.6.1 data.table_1.13.4
[106] git2r_0.26.1 digest_0.6.27 xtable_1.8-4
[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] ggplot2_3.3.3 harmony_1.0 Rcpp_1.0.6 purrr_0.3.4
[5] tibble_3.0.4 tidyr_1.1.0 dplyr_1.0.2 edgeR_3.28.1
[9] limma_3.42.2 Seurat_3.2.0 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 RSpectra_0.16-0 codetools_0.2-16
[16] splines_3.6.1 lsei_1.2-0 knitr_1.29
[19] polyclip_1.10-0 jsonlite_1.7.2 ica_1.0-2
[22] cluster_2.1.0 png_0.1-7 uwot_0.1.10
[25] shiny_1.5.0 sctransform_0.2.1 compiler_3.6.1
[28] httr_1.4.2 Matrix_1.2-18 fastmap_1.0.1
[31] lazyeval_0.2.2 later_1.1.0.1 htmltools_0.5.0
[34] tools_3.6.1 rsvd_1.0.3 igraph_1.2.6
[37] gtable_0.3.0 glue_1.4.2 RANN_2.6.1
[40] reshape2_1.4.4 rappdirs_0.3.3 spatstat_1.64-1
[43] vctrs_0.3.6 gdata_2.18.0 ape_5.4-1
[46] nlme_3.1-140 lmtest_0.9-37 xfun_0.16
[49] stringr_1.4.0 globals_0.12.5 mime_0.9
[52] miniUI_0.1.1.1 lifecycle_0.2.0 irlba_2.3.3
[55] gtools_3.8.2 goftest_1.2-2 future_1.18.0
[58] MASS_7.3-51.4 zoo_1.8-8 scales_1.1.1
[61] promises_1.1.1 spatstat.utils_1.17-0 parallel_3.6.1
[64] RColorBrewer_1.1-2 yaml_2.2.1 reticulate_1.20
[67] pbapply_1.4-2 gridExtra_2.3 rpart_4.1-15
[70] stringi_1.5.3 highr_0.8 caTools_1.18.0
[73] rlang_0.4.10 pkgconfig_2.0.3 bitops_1.0-6
[76] evaluate_0.14 lattice_0.20-38 ROCR_1.0-7
[79] tensor_1.5 labeling_0.4.2 patchwork_1.1.1
[82] htmlwidgets_1.5.1 cowplot_1.1.1 tidyselect_1.1.0
[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 future.apply_1.6.0 crayon_1.3.4
[100] KernSmooth_2.23-15 plotly_4.9.2.1 rmarkdown_2.3
[103] locfit_1.5-9.4 grid_3.6.1 data.table_1.13.4
[106] git2r_0.26.1 digest_0.6.27 xtable_1.8-4
[109] httpuv_1.5.4 munsell_0.5.0 viridisLite_0.3.0