Last updated: 2021-07-04

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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_JustNeuralCrest.csv ../output/TranferredAnnotations_ReferenceInt_JustNeuralCrest.csv

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library(Seurat)
library(edgeR)
Loading required package: limma
library(dplyr)

Attaching package: 'dplyr'
The following objects are masked from 'package:stats':

    filter, lag
The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union
library(tidyr)
library(tibble)
library(purrr)
library(harmony)
Loading required package: Rcpp
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 neural crest (cluster 3, res 0.1)

merged<- subset(merged, idents = 3)
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)
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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  |                                                                            
  |=========                                                             |  12%
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  |==================                                                    |  25%
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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")
17:33:43 UMAP embedding parameters a = 0.9922 b = 1.112
17:33:43 Read 48921 rows and found 100 numeric columns
17:33:43 Using Annoy for neighbor search, n_neighbors = 30
17:33:43 Building Annoy index with metric = cosine, n_trees = 50
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:34:02 Writing NN index file to temp file /tmp/jobs/12028895/RtmpbRzQpu/file3d010542d0173
17:34:02 Searching Annoy index using 1 thread, search_k = 3000
17:34:32 Annoy recall = 99.7%
17:34:32 Commencing smooth kNN distance calibration using 1 thread
17:34:35 Initializing from normalized Laplacian + noise
17:34:58 Commencing optimization for 200 epochs, with 2295848 positive edges
17:36:03 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: 25 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: 25 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         1
3                                       Acinar cells         0
4                               Adrenocortical cells         0
5                                     Amacrine cells         0
6                           Antigen presenting cells         0
7                                         Astrocytes         4
8                                      Bipolar cells         0
9          Bronchiolar and alveolar epithelial cells         3
10                        CCL19_CCL21 positive cells        13
11                          CLC_IL5RA positive cells         0
12                          CSH1_CSH2 positive cells         0
13                                    Cardiomyocytes         0
14                                  Chromaffin cells         0
15                         Ciliated epithelial cells         6
16         Corneal and conjunctival epithelial cells         0
17                                      Ductal cells         0
18                         ELF3_AGBL2 positive cells         0
19                                          ENS glia        45
20                                       ENS neurons         0
21                                 Endocardial cells        24
22                              Epicardial fat cells         0
23                                     Erythroblasts        28
24                                Excitatory neurons         0
25                         Extravillous trophoblasts         2
26                                    Ganglion cells         0
27                                      Goblet cells         0
28                                   Granule neurons         0
29                          Hematopoietic stem cells         0
30                                      Hepatoblasts         2
31                                  Horizontal cells         0
32                        IGFBP1_DKK1 positive cells        15
33                           Inhibitory interneurons         0
34                                Inhibitory neurons         2
35                       Intestinal epithelial cells         0
36                             Islet endocrine cells         5
37                                  Lens fibre cells         2
38                             Limbic system neurons         0
39                       Lymphatic endothelial cells         0
40                                    Lymphoid cells         0
41                        MUC13_DMBT1 positive cells         0
42                                    Megakaryocytes         0
43                                   Mesangial cells         0
44                                 Mesothelial cells         0
45                                 Metanephric cells         9
46                                         Microglia         1
47                                     Myeloid cells         1
48                              Neuroendocrine cells         0
49                                  Oligodendrocytes        12
50                         PAEP_MECOM positive cells         0
51                     PDE11A_FAM19A2 positive cells        25
52                        PDE1C_ACSM3 positive cells         0
53                          Parietal and chief cells         0
54                               Photoreceptor cells         0
55                                  Purkinje neurons        17
56                             Retinal pigment cells        20
57               Retinal progenitors and Muller glia         3
58                        SATB2_LRRC7 positive cells         1
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         0
64                                     Schwann cells        55
65                             Skeletal muscle cells         0
66                               Smooth muscle cells         0
67                         Squamous epithelial cells        14
68                                    Stellate cells         0
69                                     Stromal cells         0
70                                    Sympathoblasts         0
71 Syncytiotrophoblasts and villous cytotrophoblasts         0
72                           Thymic epithelial cells         0
73                                        Thymocytes         0
74                           Trophoblast giant cells         1
75                              Unipolar brush cells         0
76                                Ureteric bud cells         0
77                        Vascular endothelial cells         1
78                                  Visceral neurons         0
80                                        scHCL.hESC      2361
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_JustNeuralCrest.csv")
b<- table(CommonAnnDF$Annotation)

b

                   Astrocytes    CCL19_CCL21 positive cells 
                            1                             3 
    Ciliated epithelial cells                      ENS glia 
                            6                            12 
            Endocardial cells                  Hepatoblasts 
                           14                             3 
   IGFBP1_DKK1 positive cells            Inhibitory neurons 
                            4                            25 
        Islet endocrine cells              Lens fibre cells 
                            1                             2 
            Metanephric cells              Oligodendrocytes 
                            3                             5 
PDE11A_FAM19A2 positive cells         Retinal pigment cells 
                            1                            18 
                Schwann cells     Squamous epithelial cells 
                           57                             9 
      Trophoblast giant cells    Vascular endothelial cells 
                            1                             1 
                   scHCL.hESC                     uncertain 
                         2339                           168 
#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] "Horizontal cells"                                 
 [4] "Photoreceptor cells"                              
 [5] "Amacrine cells"                                   
 [6] "Corneal and conjunctival epithelial cells"        
 [7] "Bipolar cells"                                    
 [8] "Stromal cells"                                    
 [9] "Microglia"                                        
[10] "Skeletal muscle cells"                            
[11] "Smooth muscle cells"                              
[12] "Adrenocortical cells"                             
[13] "Chromaffin cells"                                 
[14] "Myeloid cells"                                    
[15] "Megakaryocytes"                                   
[16] "Lymphoid cells"                                   
[17] "Sympathoblasts"                                   
[18] "Erythroblasts"                                    
[19] "SLC26A4_PAEP positive cells"                      
[20] "CSH1_CSH2 positive cells"                         
[21] "Inhibitory interneurons"                          
[22] "SLC24A4_PEX5L positive cells"                     
[23] "Purkinje neurons"                                 
[24] "Granule neurons"                                  
[25] "Unipolar brush cells"                             
[26] "SKOR2_NPSR1 positive cells"                       
[27] "Excitatory neurons"                               
[28] "Limbic system neurons"                            
[29] "CLC_IL5RA positive cells"                         
[30] "SATB2_LRRC7 positive cells"                       
[31] "Epicardial fat cells"                             
[32] "Cardiomyocytes"                                   
[33] "ELF3_AGBL2 positive cells"                        
[34] "Lymphatic endothelial cells"                      
[35] "Visceral neurons"                                 
[36] "Mesothelial cells"                                
[37] "ENS neurons"                                      
[38] "Intestinal epithelial cells"                      
[39] "Mesangial cells"                                  
[40] "Ureteric bud cells"                               
[41] "Stellate cells"                                   
[42] "Hematopoietic stem cells"                         
[43] "Neuroendocrine cells"                             
[44] "Bronchiolar and alveolar epithelial cells"        
[45] "Satellite cells"                                  
[46] "Ductal cells"                                     
[47] "Acinar cells"                                     
[48] "AFP_ALB positive cells"                           
[49] "Extravillous trophoblasts"                        
[50] "PAEP_MECOM positive cells"                        
[51] "Syncytiotrophoblasts and villous cytotrophoblasts"
[52] "STC2_TLX1 positive cells"                         
[53] "PDE1C_ACSM3 positive cells"                       
[54] "MUC13_DMBT1 positive cells"                       
[55] "Parietal and chief cells"                         
[56] "Goblet cells"                                     
[57] "Antigen presenting cells"                         
[58] "Thymic epithelial cells"                          
[59] "Thymocytes"                                       
[60] 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          mgcv_1.8-28          
 [94] fitdistrplus_1.0-14   survival_3.2-3        abind_1.4-5          
 [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          httpuv_1.5.4         
[109] 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          mgcv_1.8-28          
 [94] fitdistrplus_1.0-14   survival_3.2-3        abind_1.4-5          
 [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          httpuv_1.5.4         
[109] munsell_0.5.0         viridisLite_0.3.0