Last updated: 2021-07-05

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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_JustEndoderm.csv ../output/TranferredAnnotations_ReferenceInt_JustEndoderm.csv

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library(Seurat)
library(edgeR)
library(dplyr)
library(tidyr)
library(tibble)
library(purrr)
library(harmony)
library(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 endoderm cells (cluster 4, res 0.1)

merged<- subset(merged, idents = 4)
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))
#EB9.obj<- suppressWarnings(SCTransform(EB9.obj, variable.features.n=5000,verbose=F))
#EB18.obj<- suppressWarnings(SCTransform(EB18.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$orig.ident<- "scHCL.hESC"
#EB9.obj$orig.ident<- "scHCL.EB9"
#EB18.obj$orig.ident<- "scHCL.EB18"
EB20.obj$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)
FeatureScatter(merge.all, feature1 = "nCount_SCT", feature2 = "nFeature_SCT", group.by = "orig.ident")

FeatureScatter(merge.all, feature1 = "nCount_RNA", feature2 = "nFeature_RNA", group.by = "orig.ident")

merge.all<- SCTransform(merge.all, variable.features.n = 5000, vars.to.regress = c("orig.ident"), assay= "SCT")

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  |==========================                                            |  38%
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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 matrix
merge.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 1/10
Harmony 2/10
Harmony 3/10
Harmony 4/10
Harmony 5/10
Harmony converged after 5 iterations
Warning: 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")
20:33:01 UMAP embedding parameters a = 0.9922 b = 1.112
20:33:01 Read 48616 rows and found 100 numeric columns
20:33:01 Using Annoy for neighbor search, n_neighbors = 30
20:33:01 Building Annoy index with metric = cosine, n_trees = 50
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
20:33:20 Writing NN index file to temp file /tmp/jobs/12040951/Rtmp2izUBz/file19c771d43f480
20:33:20 Searching Annoy index using 1 thread, search_k = 3000
20:33:49 Annoy recall = 91.98%
20:33:51 Commencing smooth kNN distance calibration using 1 thread
20:33:53 Initializing from normalized Laplacian + noise
20:34:05 Commencing optimization for 200 epochs, with 2282618 positive edges
20:35:07 Optimization finished
V<- 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.
V
Warning: ggrepel: 9 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()

V
Warning: ggrepel: 9 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. The EB data isnt useful here because cell type annotations are not available and annotation based on primary cell types will be more informative.

There a ton of tools available for label transfer/label propogation/full integration of data sets. But, Since I have already taken care of integration, all I really want to do here and label an EB cell based on it's nearest neighbor (in harmony space) from the reference set.

#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        65
3                                       Acinar cells       175
4                               Adrenocortical cells         0
5                                     Amacrine cells         0
6                           Antigen presenting cells         0
7                                         Astrocytes         0
8                                      Bipolar cells         0
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        52
14                                  Chromaffin cells         0
15                         Ciliated epithelial cells        88
16         Corneal and conjunctival epithelial cells         5
17                                      Ductal cells         0
18                         ELF3_AGBL2 positive cells         0
19                                          ENS glia         0
20                                       ENS neurons         0
21                                 Endocardial cells         0
22                              Epicardial fat cells         7
23                                     Erythroblasts         8
24                                Excitatory neurons         0
25                         Extravillous trophoblasts        42
26                                    Ganglion cells         0
27                                      Goblet cells         0
28                                   Granule neurons         0
29                          Hematopoietic stem cells         0
30                                      Hepatoblasts       145
31                                  Horizontal cells         0
32                        IGFBP1_DKK1 positive cells        47
33                           Inhibitory interneurons         0
34                                Inhibitory neurons         0
35                       Intestinal epithelial cells       246
36                             Islet endocrine cells         0
37                                  Lens fibre cells         1
38                             Limbic system neurons         0
39                       Lymphatic endothelial cells         0
40                                    Lymphoid cells         0
41                        MUC13_DMBT1 positive cells        79
42                                    Megakaryocytes         2
43                                   Mesangial cells         9
44                                 Mesothelial cells         1
45                                 Metanephric cells         1
46                                         Microglia         0
47                                     Myeloid cells         0
48                              Neuroendocrine cells        11
49                                  Oligodendrocytes         1
50                         PAEP_MECOM positive cells         0
51                     PDE11A_FAM19A2 positive cells         1
52                        PDE1C_ACSM3 positive cells         0
53                          Parietal and chief cells         0
54                               Photoreceptor cells         0
55                                  Purkinje neurons         0
56                             Retinal pigment cells         1
57               Retinal progenitors and Muller glia         0
58                        SATB2_LRRC7 positive cells         2
59                        SKOR2_NPSR1 positive cells         0
60                      SLC24A4_PEX5L positive cells         0
61                       SLC26A4_PAEP positive cells         0
62                          STC2_TLX1 positive cells         0
63                                   Satellite cells         3
64                                     Schwann cells         0
65                             Skeletal muscle cells         2
66                               Smooth muscle cells         0
67                         Squamous epithelial cells        82
68                                    Stellate cells        66
69                                     Stromal cells         0
70                                    Sympathoblasts         0
71 Syncytiotrophoblasts and villous cytotrophoblasts         1
72                           Thymic epithelial cells         0
73                                        Thymocytes        30
74                           Trophoblast giant cells         0
75                              Unipolar brush cells        93
76                                Ureteric bud cells         0
77                        Vascular endothelial cells         0
78                                  Visceral neurons         0
80                                        scHCL.hESC      1102
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, 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, it make provide a more robust annotation is 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_JustEndoderm.csv")
b<- table(CommonAnnDF$Annotation)

b

                   AFP_ALB positive cells 
                                       38 
                             Acinar cells 
                                      160 
                           Cardiomyocytes 
                                       22 
                Ciliated epithelial cells 
                                       97 
Corneal and conjunctival epithelial cells 
                                        1 
                     Epicardial fat cells 
                                        1 
                            Erythroblasts 
                                        1 
                Extravillous trophoblasts 
                                       19 
                             Hepatoblasts 
                                      167 
               IGFBP1_DKK1 positive cells 
                                       39 
              Intestinal epithelial cells 
                                      276 
               MUC13_DMBT1 positive cells 
                                       31 
                          Mesangial cells 
                                        3 
                     Neuroendocrine cells 
                                        2 
                    Skeletal muscle cells 
                                        1 
                Squamous epithelial cells 
                                      125 
                           Stellate cells 
                                       72 
                               Thymocytes 
                                        5 
                     Unipolar brush cells 
                                       81 
                               scHCL.hESC 
                                     1101 
                                uncertain 
                                      126 
#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] "Retinal progenitors and Muller glia"              
 [2] "Ganglion cells"                                   
 [3] "Retinal pigment cells"                            
 [4] "Horizontal cells"                                 
 [5] "Photoreceptor cells"                              
 [6] "Lens fibre cells"                                 
 [7] "Amacrine cells"                                   
 [8] "PDE11A_FAM19A2 positive cells"                    
 [9] "Bipolar cells"                                    
[10] "Stromal cells"                                    
[11] "Microglia"                                        
[12] "Vascular endothelial cells"                       
[13] "Smooth muscle cells"                              
[14] "Adrenocortical cells"                             
[15] "Chromaffin cells"                                 
[16] "Myeloid cells"                                    
[17] "Megakaryocytes"                                   
[18] "Lymphoid cells"                                   
[19] "Sympathoblasts"                                   
[20] "Schwann cells"                                    
[21] "SLC26A4_PAEP positive cells"                      
[22] "CSH1_CSH2 positive cells"                         
[23] "Inhibitory interneurons"                          
[24] "SLC24A4_PEX5L positive cells"                     
[25] "Purkinje neurons"                                 
[26] "Astrocytes"                                       
[27] "Granule neurons"                                  
[28] "Oligodendrocytes"                                 
[29] "SKOR2_NPSR1 positive cells"                       
[30] "Excitatory neurons"                               
[31] "Inhibitory neurons"                               
[32] "Limbic system neurons"                            
[33] "CLC_IL5RA positive cells"                         
[34] "SATB2_LRRC7 positive cells"                       
[35] "ELF3_AGBL2 positive cells"                        
[36] "Endocardial cells"                                
[37] "Lymphatic endothelial cells"                      
[38] "Visceral neurons"                                 
[39] "Mesothelial cells"                                
[40] "ENS neurons"                                      
[41] "ENS glia"                                         
[42] "Metanephric cells"                                
[43] "Ureteric bud cells"                               
[44] "Hematopoietic stem cells"                         
[45] "Bronchiolar and alveolar epithelial cells"        
[46] "Satellite cells"                                  
[47] "CCL19_CCL21 positive cells"                       
[48] "Ductal cells"                                     
[49] "Islet endocrine cells"                            
[50] "Trophoblast giant cells"                          
[51] "PAEP_MECOM positive cells"                        
[52] "Syncytiotrophoblasts and villous cytotrophoblasts"
[53] "STC2_TLX1 positive cells"                         
[54] "PDE1C_ACSM3 positive cells"                       
[55] "Parietal and chief cells"                         
[56] "Goblet cells"                                     
[57] "Antigen presenting cells"                         
[58] "Thymic epithelial cells"                          
[59] 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