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Last updated: 2021-12-08

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Knit directory: Embryoid_Body_Pilot_Workflowr/analysis/

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absolute	relative
/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/mergedObjects/FiveNewLines.rds	../output/mergedObjects/FiveNewLines.rds
/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/figs/NewLinesDimPlotRes0.15.png	../output/figs/NewLinesDimPlotRes0.15.png
/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/figs/NewLinesDotPlotRes.15.CanonicalMarkers.png	../output/figs/NewLinesDotPlotRes.15.CanonicalMarkers.png
/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/figs/NewLinesDotPlotRes.15.LearnedMarkers.png	../output/figs/NewLinesDotPlotRes.15.LearnedMarkers.png
/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/figs/NewLinesQCvlnPlot.png	../output/figs/NewLinesQCvlnPlot.png
/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/5NEWLINES.BroadCellTypeCatAssignment.basedonclustersres0.15.csv	../output/5NEWLINES.BroadCellTypeCatAssignment.basedonclustersres0.15.csv
/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/figs/NewLinesBarPlotCellTypeComp.png	../output/figs/NewLinesBarPlotCellTypeComp.png

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These are the previous versions of the repository in which changes were made to the R Markdown (analysis/NewDataRobustCellTypeComposition.Rmd) and HTML (docs/NewDataRobustCellTypeComposition.html) files. If you've configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File	Version	Author	Date	Message
Rmd	094e7ee	KLRhodes	2021-12-08	Publish aesthetically updated figs and additional line analyses

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library(Seurat)
library(SeuratDisk)

Registered S3 method overwritten by 'cli':
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  method            from  
  as.sparse.H5Group Seurat

library(sctransform)
library(reshape2)
library (ggplot2)

hfile<- LoadH5Seurat("/project2/gilad/katie/ebQTL/Lowpass.3seqbatches.merged.TEMP.h5Seurat", assays= "RNA")

Validating h5Seurat file

Warning: Default assay not requested, using RNA instead

Initializing RNA with data

Adding counts for RNA

Adding command information

Adding cell-level metadata

hfile

An object of class Seurat 
36601 features across 191179 samples within 1 assay 
Active assay: RNA (36601 features, 0 variable features)

#subset to 5 cell lines from the same collection date (10.15.20)
lines<- c("19210", "19159", "19140", "18912", "18856")
date<- 1015
obj<- subset(hfile, subset= individual %in% lines)
obj<- subset(obj, subset= c.date == date)

obj

An object of class Seurat 
36601 features across 46512 samples within 1 assay 
Active assay: RNA (36601 features, 0 variable features)

VlnPlot(obj, features= c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol=3, pt.size=0)

In previous filtering, cells with greater than 15% MT have already been removed. to be consistent with the pilot, I also filter cells with less than 3% MT reads.

obj<- subset(obj, subset= percent.mt > 3)

obj

An object of class Seurat 
36601 features across 26216 samples within 1 assay 
Active assay: RNA (36601 features, 0 variable features)

FeatureScatter(obj, feature1= "nCount_RNA", feature2= "percent.mt")

This data is not sequenced as deeply as the main pilot data, so the Feature threshold will be lower (for main data, kept cells with at least 1,500 genes expressed. Here, will keep cells with at least 1000)

obj<- subset(obj, subset= nFeature_RNA > 1000)

obj

An object of class Seurat 
36601 features across 26216 samples within 1 assay 
Active assay: RNA (36601 features, 0 variable features)

(this didn't actually filter any cells, mt cutoff also removed the low feature cells)

#how many cells total from each individual after filtering?
table(as.character(obj@meta.data$individual))

18856 18912 19140 19159 19210 
 6326  6677  3618  5411  4184 

Normalize with same SCTransform

obj<- SCTransform(obj, verbose=FALSE)

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obj <- RunPCA(obj, npcs=100, verbose = FALSE)

FeaturePlot(obj, reduction= 'pca', features = "POU5F1")

obj<- RunUMAP(obj, dims=1:100, verbose=FALSE)

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

DimPlot(obj, reduction='umap', group.by = 'individual')

#by canonical markers
FeaturePlot(obj, reduction='umap', features = c("POU5F1", "HAND1", "SOX17", "PAX6", "MAP2", "SOX10", "GNG11", "MIXL1", "AFP"))

by top markers in pilot DE analysis (adj. P)

FeaturePlot(obj, reduction='umap', features = c("TERF1", "TPBG", "TNNI1", "NR2F1", "S100A16", "TAGLN3", "EGFL7"))

obj <- FindNeighbors(obj, dims = 1:100, verbose = FALSE)
obj <- FindClusters(obj, resolution= 0.1, verbose = FALSE)
V<- DimPlot(obj, label = TRUE) + NoLegend()
V

saveRDS(obj, "/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/mergedObjects/FiveNewLines.rds")

G<- DotPlot(obj, features= c("POU5F1", "HAND1", "SOX17", "PAX6", "MAP2", "SOX10", "GNG11", "MIXL1", "AFP"))
G

DotPlot(obj, features= c("TERF1", "TPBG", "TNNI1", "NR2F1", "S100A16", "TAGLN3", "EGFL7"))

Where do clusters 2 and 4 fit into original cell type anno?

#DimPlot colored by more of the top DE genes from Neuron cluster in OG pilot data
FeaturePlot(obj, reduction='umap', features = c("TAGLN3", "RTN1", "NEUROD1", "NHLH1", "STMN2"))

Decent evidence that these two clusters are both similar to the "Neuron" cluster in OG data

obj <- FindClusters(obj, resolution= 0.15, verbose = FALSE)
G<- DimPlot(obj, label = TRUE) + NoLegend()
G

png(file= "/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/figs/NewLinesDimPlotRes0.15.png", width= 5, height=5, units= "in", res= 1080)

G

dev.off()

V<- DotPlot(obj, features= c("POU5F1", "HAND1", "SOX17", "PAX6", "MAP2", "SOX10", "GNG11"))
V

png(file= "/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/figs/NewLinesDotPlotRes.15.CanonicalMarkers.png", width= 7, height=5, units= "in", res= 1080)

V

dev.off()

B<- DotPlot(obj, features= c("TERF1", "TNNI1", "S100A16", "TPBG", "TAGLN3", "NR2F1", "EGFL7"))
B

png(file= "/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/figs/NewLinesDotPlotRes.15.LearnedMarkers.png", width= 7, height=5, units= "in", res= 1080)

B

dev.off()

obj <- FindClusters(obj, resolution= 0.06, verbose = FALSE)
DimPlot(obj, label = TRUE) + NoLegend()

DotPlot(obj, features= c("POU5F1", "HAND1", "SOX17", "PAX6", "MAP2", "SOX10", "GNG11", "MIXL1", "AFP"))

DotPlot(obj, features= c("TERF1", "TNNI1", "S100A16", "TPBG", "TAGLN3", "NR2F1", "EGFL7"))

M<-VlnPlot(obj, features= c("nFeature_RNA", "nCount_RNA", "percent.mt"), group.by= 'individual', ncol=3, pt.size=0)
M

png(file= "/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/figs/NewLinesQCvlnPlot.png", width= 5, height=4, units= "in", res= 1080)

M

dev.off()

quick FindMarkers to see whether any of these are distinct from the OG cell types using res 0.1

Idents(obj)<- obj@meta.data$SCT_snn_res.0.15

c0mark<-FindMarkers(obj, ident.1= 0)
head(c0mark, 20)

          p_val avg_logFC pct.1 pct.2 p_val_adj
HES5          0 1.0861886 0.737 0.171         0
RSPO2         0 1.0709754 0.452 0.062         0
ID4           0 0.9872308 0.691 0.322         0
COL4A6        0 0.9405951 0.829 0.302         0
C1orf61       0 0.9209276 0.579 0.074         0
SOX2-OT       0 0.8956804 0.416 0.189         0
LINC00461     0 0.8927213 0.550 0.113         0
GPC3          0 0.8641882 0.704 0.313         0
EMX2          0 0.8601119 0.654 0.060         0
MECOM         0 0.8365233 0.697 0.208         0
WNT5B         0 0.8156181 0.668 0.162         0
TRPM3         0 0.8039140 0.625 0.226         0
DACH1         0 0.7425357 0.918 0.517         0
SLIT2         0 0.6757451 0.783 0.362         0
GPM6A         0 0.6676135 0.652 0.240         0
OLFM3         0 0.6613416 0.526 0.108         0
CACHD1        0 0.6582560 0.745 0.376         0
FABP7         0 0.6383351 0.661 0.341         0
TPBG          0 0.6286699 0.653 0.268         0
COL4A5        0 0.6184229 0.803 0.479         0

c1mark<-FindMarkers(obj, ident.1= 1)
head(c1mark, 20)

       p_val avg_logFC pct.1 pct.2 p_val_adj
LMO4       0 1.1384901 0.843 0.320         0
TIMP3      0 1.0912554 0.664 0.168         0
PLP1       0 1.0872261 0.780 0.147         0
ROBO1      0 1.0536350 0.941 0.555         0
LSAMP      0 0.9852867 0.972 0.678         0
DLGAP1     0 0.9649397 0.901 0.581         0
SMOC1      0 0.9571335 0.639 0.139         0
MOXD1      0 0.9433660 0.565 0.020         0
IGFBP5     0 0.8970008 0.690 0.276         0
NTM        0 0.8910920 0.611 0.161         0
KANK4      0 0.8894128 0.645 0.061         0
CDH6       0 0.8757557 0.860 0.353         0
TFAP2B     0 0.8749704 0.789 0.130         0
DNAJC1     0 0.8319087 0.830 0.321         0
ZEB2       0 0.8290513 0.821 0.388         0
PAX3       0 0.8256959 0.817 0.274         0
CCSER1     0 0.7721004 0.551 0.174         0
MAML2      0 0.7504680 0.728 0.324         0
SEMA3C     0 0.7463829 0.566 0.138         0
NR2F1      0 0.7392974 0.915 0.509         0

c2mark<-FindMarkers(obj, ident.1= 2)
head(c2mark, 20)

         p_val avg_logFC pct.1 pct.2 p_val_adj
RMST         0 1.0462278 0.866 0.478         0
PTN          0 0.9730542 0.894 0.500         0
ADAMTS3      0 0.8244967 0.526 0.079         0
MIR99AHG     0 0.7897001 0.774 0.363         0
ID2          0 0.6679245 0.751 0.376         0
FGFBP3       0 0.6662026 0.624 0.254         0
ERBB4        0 0.6643301 0.699 0.386         0
ADGRV1       0 0.6258005 0.781 0.436         0
ZIC1         0 0.6181693 0.668 0.218         0
FREM1        0 0.6150697 0.571 0.201         0
RFX4         0 0.6024881 0.667 0.232         0
PTPRM        0 0.5792304 0.562 0.255         0
PRTG         0 0.5568951 0.945 0.689         0
NPAS3        0 0.5458955 0.688 0.378         0
TPBG         0 0.5242331 0.677 0.328         0
RIPPLY3      0 0.5226054 0.268 0.022         0
RBFOX1       0 0.5083071 0.626 0.321         0
CDON         0 0.5082526 0.683 0.354         0
TMEM132C     0 0.4935586 0.681 0.319         0
FAM181B      0 0.4864708 0.380 0.079         0

c3mark<-FindMarkers(obj, ident.1= 3)
head(c3mark, 20)

           p_val avg_logFC pct.1 pct.2 p_val_adj
POU5F1         0  2.153375 0.890 0.109         0
GRID2          0  1.953455 0.864 0.244         0
XACT           0  1.808333 0.851 0.114         0
L1TD1          0  1.758341 0.934 0.246         0
AC022140.1     0  1.572051 0.835 0.086         0
TERF1          0  1.492221 0.843 0.368         0
ESRG           0  1.489324 0.819 0.048         0
KIAA0825       0  1.364973 0.668 0.120         0
TDGF1          0  1.364787 0.764 0.048         0
GAL            0  1.361415 0.610 0.065         0
SEMA6A         0  1.221667 0.911 0.405         0
AC009446.1     0  1.220684 0.795 0.021         0
SNHG14         0  1.208214 0.958 0.703         0
LINC01194      0  1.207547 0.535 0.016         0
DPPA4          0  1.202006 0.838 0.254         0
LINC01356      0  1.199534 0.546 0.084         0
AC005062.1     0  1.194825 0.706 0.034         0
DNMT3B         0  1.172429 0.806 0.156         0
KRT18          0  1.163628 0.924 0.280         0
UGP2           0  1.117417 0.864 0.276         0

c4mark<-FindMarkers(obj, ident.1= 4)
head(c4mark)

       p_val avg_logFC pct.1 pct.2 p_val_adj
THSD7B     0  2.817742 0.808 0.044         0
SDK1       0  2.163928 0.928 0.565         0
CADM1      0  2.117758 0.988 0.536         0
STMN2      0  1.810880 0.800 0.170         0
PCDH15     0  1.784029 0.791 0.082         0
PRKG1      0  1.753416 0.822 0.453         0

c5mark<-FindMarkers(obj, ident.1= 5)
head(c5mark)

       p_val avg_logFC pct.1 pct.2 p_val_adj
HAND1      0  1.801715 0.818 0.015         0
HAPLN1     0  1.715140 0.665 0.064         0
COL3A1     0  1.692152 0.645 0.019         0
KRT19      0  1.670763 0.893 0.244         0
KIF26B     0  1.661946 0.901 0.160         0
TMEM88     0  1.636375 0.813 0.100         0

c6mark<- FindMarkers(obj, ident.1=6)
head(c6mark, 20)

         p_val avg_logFC pct.1 pct.2 p_val_adj
CRABP1       0 2.7519681 0.703 0.356         0
DCC          0 1.9049625 0.833 0.318         0
STMN2        0 1.6902833 0.638 0.195         0
TUBB3        0 1.4417459 0.865 0.543         0
MAP1B        0 1.2944386 0.972 0.816         0
TUBA1A       0 1.2250424 0.995 0.913         0
TAGLN3       0 1.2084022 0.662 0.173         0
CACNA2D1     0 1.1402375 0.655 0.281         0
NEFM         0 1.0880185 0.449 0.120         0
EBF2         0 1.0854734 0.549 0.079         0
AKAP6        0 1.0578978 0.518 0.101         0
MLLT11       0 0.9701026 0.758 0.421         0
TMEM163      0 0.9040895 0.409 0.045         0
KIF5C        0 0.8686720 0.687 0.326         0
KCNH7        0 0.8615666 0.439 0.086         0
RGS16        0 0.7899902 0.341 0.069         0
DLL3         0 0.7893436 0.445 0.088         0
DCX          0 0.7771801 0.626 0.199         0
IGFBPL1      0 0.7762339 0.531 0.137         0
RTN1         0 0.7330614 0.451 0.114         0

c7mark<- FindMarkers(obj, ident.1=7)
head(c7mark, 20)

           p_val avg_logFC pct.1 pct.2 p_val_adj
APOA1          0  3.879597 0.855 0.153         0
APOA2          0  3.474856 0.785 0.110         0
TTR            0  2.893806 0.489 0.122         0
FN1            0  2.647526 0.952 0.218         0
CST1           0  2.618879 0.611 0.025         0
S100A10        0  2.422552 0.936 0.402         0
APOE           0  2.206223 0.989 0.559         0
RBP4           0  2.092523 0.343 0.023         0
SERPINE2       0  1.943455 0.893 0.237         0
APOC1          0  1.853571 0.797 0.129         0
KRT19          0  1.702184 0.967 0.258         0
APOB           0  1.687632 0.461 0.013         0
FGB            0  1.680620 0.343 0.011         0
SLC2A3         0  1.572469 0.870 0.175         0
S100A14        0  1.452102 0.447 0.015         0
EPCAM          0  1.426321 0.913 0.126         0
CST3           0  1.382077 0.890 0.510         0
APOC3          0  1.375009 0.279 0.021         0
TMEM141        0  1.273297 0.862 0.267         0
AC084816.1     0  1.227068 0.553 0.019         0

c8mark<- FindMarkers(obj, ident.1=8)
head(c8mark, 20)

         p_val avg_logFC pct.1 pct.2 p_val_adj
FLT1         0 2.0703908 0.954 0.048         0
KDR          0 1.8046174 0.944 0.047         0
FLI1         0 1.7028039 0.954 0.009         0
RASGRP3      0 1.2639977 0.824 0.010         0
CRHBP        0 1.2624930 0.324 0.006         0
HOPX         0 1.2594838 0.657 0.018         0
ERG          0 1.2566469 0.833 0.005         0
PLVAP        0 1.2207059 0.787 0.002         0
ECSCR        0 1.1549165 0.796 0.004         0
CD34         0 1.1421850 0.750 0.002         0
SAMSN1       0 1.0359535 0.648 0.005         0
DYSF         0 1.0270250 0.731 0.017         0
TEK          0 0.9295784 0.713 0.017         0
KLF2         0 0.8714894 0.611 0.006         0
AFAP1L1      0 0.8582102 0.611 0.015         0
ADGRL4       0 0.8132077 0.556 0.005         0
PECAM1       0 0.7993008 0.611 0.003         0
PPP1R16B     0 0.7847968 0.620 0.020         0
ICAM2        0 0.7767651 0.620 0.001         0
ESAM         0 0.7544554 0.583 0.001         0

#label them with labels from pilot ( Res 0.15:combining 0 and 2 to early ectoderm, combining 4 and 6 to Neuron. )
Easy<- as.character(obj@meta.data$SCT_snn_res.0.15)
Easy[Easy=="0"]<- "Early Ectoderm"
Easy[Easy=="1"]<- "Neural Crest"
Easy[Easy=="2"]<- "Early Ectoderm"
Easy[Easy=="3"]<- "Pluripotent"
Easy[Easy=="4"]<- "Neuron"
Easy[Easy=="5"]<- "Mesoderm"
Easy[Easy=="6"]<- "Neuron"
Easy[Easy=="7"]<- "Endoderm"
Easy[Easy=="8"]<- "Endothelial"

obj<- AddMetaData(obj, metadata = Easy, col.name = "Pilot.Ann")

tab<- cbind(rownames(obj@meta.data), obj@meta.data$Pilot.Ann)
write.csv(tab, "/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/5NEWLINES.BroadCellTypeCatAssignment.basedonclustersres0.15.csv")

#calculate proportion of cells in each pilot annotation group
indtot<- table(as.character(obj@meta.data$individual))
indbyann<- table(as.character(obj@meta.data$individual), obj@meta.data$Pilot.Ann)
indbyann

       
        Early Ectoderm Endoderm Endothelial Mesoderm Neural Crest Neuron
  18856           2918      375          39      247         1715    926
  18912           2298      361          39     1180         1244    785
  19140           2020       61          22      183          448    749
  19159           3624       26           1      152          682    665
  19210           1761      319           7       43          672    476
       
        Pluripotent
  18856         106
  18912         770
  19140         135
  19159         261
  19210         906

prop<- indbyann/as.vector(indtot)
prop

       
        Early Ectoderm     Endoderm  Endothelial     Mesoderm Neural Crest
  18856   0.4612709453 0.0592791653 0.0061650332 0.0390452102 0.2711033829
  18912   0.3441665419 0.0540661974 0.0058409465 0.1767260746 0.1863112176
  19140   0.5583195135 0.0168601437 0.0060807076 0.0505804312 0.1238253179
  19159   0.6697468120 0.0048050268 0.0001848087 0.0280909259 0.1260395491
  19210   0.4208891013 0.0762428298 0.0016730402 0.0102772467 0.1606118547
       
              Neuron  Pluripotent
  18856 0.1463800190 0.0167562441
  18912 0.1175677700 0.1153212521
  19140 0.2070204533 0.0373134328
  19159 0.1228978008 0.0482350767
  19210 0.1137667304 0.2165391969

dat<- melt(prop)
dat$Var2<- factor(dat$Var2, levels = c("Endothelial", "Neuron", "Endoderm", "Neural Crest", "Mesoderm", "Early Ectoderm", "Pluripotent"))

clrs2<- c("#8dd3c7", "#ffffb3", "#bebada", "#fb8072", "#80b1d3", "#fdb462", "#b3de69", "#fccde5", "#bc80bd", "#ccebc5", "#ffed6f", "#a6cee3", "#1f78b4", "midnightblue", "#33a02c", "#fb9a99", "#e31a1c", "#fdbf6f", "#ff7f00", "#cab2d6", "#6a3d9a", "#ffff99", "#b15928", "darkseagreen4", "darkorange3", "darkorchid4", "palevioletred2", "khaki3", "cornsilk3")
V<- ggplot(dat, aes(x= factor(Var1), y=value))+
  geom_col(aes(fill=Var2), width = 0.8)+
  xlab("individual")+
  ylab("cell type proportion")+
  scale_fill_manual(values = clrs2)+
  theme(axis.text.x = element_text(angle=45, hjust=1))+
  labs(fill = "Cell Type")


V

png(file= "/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/figs/NewLinesBarPlotCellTypeComp.png", width= 6, height=4, units= "in", res= 1080)

V

dev.off()

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] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] ggplot2_3.3.5         reshape2_1.4.4        sctransform_0.2.1    
[4] SeuratDisk_0.0.0.9013 Seurat_3.2.0          workflowr_1.6.2      

loaded via a namespace (and not attached):
  [1] Rtsne_0.15            colorspace_2.0-2      deldir_0.1-28        
  [4] ellipsis_0.3.2        ggridges_0.5.2        rprojroot_2.0.2      
  [7] fs_1.4.2              spatstat.data_1.4-3   farver_2.1.0         
 [10] leiden_0.3.3          listenv_0.8.0         npsurv_0.4-0         
 [13] ggrepel_0.9.0         bit64_4.0.5           RSpectra_0.16-0      
 [16] fansi_0.5.0           codetools_0.2-16      splines_3.6.1        
 [19] lsei_1.2-0            knitr_1.29            polyclip_1.10-0      
 [22] jsonlite_1.7.2        ica_1.0-2             cluster_2.1.0        
 [25] png_0.1-7             uwot_0.1.10           shiny_1.5.0          
 [28] compiler_3.6.1        httr_1.4.2            Matrix_1.2-18        
 [31] fastmap_1.0.1         lazyeval_0.2.2        limma_3.42.2         
 [34] cli_3.0.1             later_1.1.0.1         htmltools_0.5.0      
 [37] tools_3.6.1           rsvd_1.0.3            igraph_1.2.6         
 [40] gtable_0.3.0          glue_1.4.2            RANN_2.6.1           
 [43] dplyr_1.0.2           rappdirs_0.3.3        Rcpp_1.0.6           
 [46] spatstat_1.64-1       vctrs_0.3.8           ape_5.4-1            
 [49] nlme_3.1-140          lmtest_0.9-37         xfun_0.16            
 [52] stringr_1.4.0         globals_0.12.5        mime_0.9             
 [55] miniUI_0.1.1.1        lifecycle_1.0.1       irlba_2.3.3          
 [58] goftest_1.2-2         future_1.18.0         MASS_7.3-51.4        
 [61] zoo_1.8-8             scales_1.1.1          promises_1.1.1       
 [64] spatstat.utils_1.17-0 parallel_3.6.1        RColorBrewer_1.1-2   
 [67] yaml_2.2.1            reticulate_1.20       pbapply_1.4-2        
 [70] gridExtra_2.3         rpart_4.1-15          stringi_1.5.3        
 [73] highr_0.8             rlang_0.4.11          pkgconfig_2.0.3      
 [76] evaluate_0.14         lattice_0.20-38       ROCR_1.0-11          
 [79] purrr_0.3.4           tensor_1.5            labeling_0.4.2       
 [82] patchwork_1.1.1       htmlwidgets_1.5.1     bit_4.0.4            
 [85] cowplot_1.1.1         tidyselect_1.1.0      RcppAnnoy_0.0.18     
 [88] plyr_1.8.6            magrittr_2.0.1        R6_2.5.1             
 [91] generics_0.1.0        withr_2.4.2           pillar_1.6.3         
 [94] whisker_0.4           mgcv_1.8-28           fitdistrplus_1.0-14  
 [97] survival_3.2-3        abind_1.4-5           tibble_3.1.5         
[100] future.apply_1.6.0    crayon_1.4.1          hdf5r_1.3.1          
[103] KernSmooth_2.23-15    utf8_1.2.2            plotly_4.9.2.1       
[106] rmarkdown_2.3         grid_3.6.1            data.table_1.13.4    
[109] git2r_0.26.1          digest_0.6.28         xtable_1.8-4         
[112] tidyr_1.1.0           httpuv_1.5.4          munsell_0.5.0        
[115] viridisLite_0.4.0    

Session information

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] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] ggplot2_3.3.5         reshape2_1.4.4        sctransform_0.2.1    
[4] SeuratDisk_0.0.0.9013 Seurat_3.2.0          workflowr_1.6.2      

loaded via a namespace (and not attached):
  [1] Rtsne_0.15            colorspace_2.0-2      deldir_0.1-28        
  [4] ellipsis_0.3.2        ggridges_0.5.2        rprojroot_2.0.2      
  [7] fs_1.4.2              spatstat.data_1.4-3   farver_2.1.0         
 [10] leiden_0.3.3          listenv_0.8.0         npsurv_0.4-0         
 [13] ggrepel_0.9.0         bit64_4.0.5           RSpectra_0.16-0      
 [16] fansi_0.5.0           codetools_0.2-16      splines_3.6.1        
 [19] lsei_1.2-0            knitr_1.29            polyclip_1.10-0      
 [22] jsonlite_1.7.2        ica_1.0-2             cluster_2.1.0        
 [25] png_0.1-7             uwot_0.1.10           shiny_1.5.0          
 [28] compiler_3.6.1        httr_1.4.2            Matrix_1.2-18        
 [31] fastmap_1.0.1         lazyeval_0.2.2        limma_3.42.2         
 [34] cli_3.0.1             later_1.1.0.1         htmltools_0.5.0      
 [37] tools_3.6.1           rsvd_1.0.3            igraph_1.2.6         
 [40] gtable_0.3.0          glue_1.4.2            RANN_2.6.1           
 [43] dplyr_1.0.2           rappdirs_0.3.3        Rcpp_1.0.6           
 [46] spatstat_1.64-1       vctrs_0.3.8           ape_5.4-1            
 [49] nlme_3.1-140          lmtest_0.9-37         xfun_0.16            
 [52] stringr_1.4.0         globals_0.12.5        mime_0.9             
 [55] miniUI_0.1.1.1        lifecycle_1.0.1       irlba_2.3.3          
 [58] goftest_1.2-2         future_1.18.0         MASS_7.3-51.4        
 [61] zoo_1.8-8             scales_1.1.1          promises_1.1.1       
 [64] spatstat.utils_1.17-0 parallel_3.6.1        RColorBrewer_1.1-2   
 [67] yaml_2.2.1            reticulate_1.20       pbapply_1.4-2        
 [70] gridExtra_2.3         rpart_4.1-15          stringi_1.5.3        
 [73] highr_0.8             rlang_0.4.11          pkgconfig_2.0.3      
 [76] evaluate_0.14         lattice_0.20-38       ROCR_1.0-11          
 [79] purrr_0.3.4           tensor_1.5            labeling_0.4.2       
 [82] patchwork_1.1.1       htmlwidgets_1.5.1     bit_4.0.4            
 [85] cowplot_1.1.1         tidyselect_1.1.0      RcppAnnoy_0.0.18     
 [88] plyr_1.8.6            magrittr_2.0.1        R6_2.5.1             
 [91] generics_0.1.0        withr_2.4.2           pillar_1.6.3         
 [94] whisker_0.4           mgcv_1.8-28           fitdistrplus_1.0-14  
 [97] survival_3.2-3        abind_1.4-5           tibble_3.1.5         
[100] future.apply_1.6.0    crayon_1.4.1          hdf5r_1.3.1          
[103] KernSmooth_2.23-15    utf8_1.2.2            plotly_4.9.2.1       
[106] rmarkdown_2.3         grid_3.6.1            data.table_1.13.4    
[109] git2r_0.26.1          digest_0.6.28         xtable_1.8-4         
[112] tidyr_1.1.0           httpuv_1.5.4          munsell_0.5.0        
[115] viridisLite_0.4.0