Merge samples

Last updated: 2022-07-06

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Knit directory: humanCardiacFibroblasts/

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load packages

suppressPackageStartupMessages({
  library(SingleCellExperiment)
  library(tidyverse)
  library(Seurat)
  library(magrittr)
  library(dplyr)
  library(purrr)
  library(ggplot2)
  library(here)
  library(runSeurat3)
  library(ggsci)
  library(ggpubr)
  library(pheatmap)
  library(viridis)
  library(sctransform)
})

load data

basedir <- here()
seurat <- readRDS(file = paste0(basedir, 
                              "/data/humanHeartsPlusGraz_intPatients_merged", 
                              "_seurat.rds"))
Idents(seurat) <- seurat$seurat_clusters

assign labels

seurat$label <- "other"
seurat$label[which(seurat$seurat_clusters %in% c("2","7","9","10"))] <- "Endothelial"
seurat$label[which(seurat$seurat_clusters %in% c("8"))] <- "Tcell"
seurat$label[which(seurat$seurat_clusters %in% c("3"))] <- "Cardiomyocyte"
seurat$label[which(seurat$seurat_clusters %in% c("0"))] <- "Fibroblast"
seurat$label[which(seurat$seurat_clusters %in% c("1","6"))] <- "Perivascular"
seurat$label[which(seurat$seurat_clusters %in% c("4"))] <- "MonocyteMacrophage"

color vectors

colPal <- pal_igv()(length(levels(seurat)))
colTec <- pal_jama()(length(unique(seurat$technique)))
colSmp <- c(pal_uchicago()(8), pal_npg()(8), pal_aaas()(10))[1:length(unique(seurat$dataset))]
colCond <- pal_npg()(length(unique(seurat$cond)))
colID <- c(pal_jco()(10), pal_npg()(10), pal_jama()(10))[1:length(unique(seurat$ID))]
colOrig <- pal_aaas()(length(unique(seurat$origin)))
colIso <- pal_nejm()(length(unique(seurat$isolation)))
colProc <- pal_aaas()(length(unique(seurat$processing)))
colLab <- pal_futurama()(length(unique(seurat$label)))

names(colPal) <- levels(seurat)
names(colTec) <- unique(seurat$technique)
names(colSmp) <- unique(seurat$dataset)
names(colCond) <- unique(seurat$cond)
names(colID) <- unique(seurat$ID)
names(colOrig) <- unique(seurat$origin)
names(colIso) <- unique(seurat$isolation)
names(colProc) <- unique(seurat$processing)
names(colLab) <- unique(seurat$label)

vis data

clusters

DimPlot(seurat, reduction = "umap", cols=colPal)+
  theme_bw() +
  theme(axis.text = element_blank(), axis.ticks = element_blank(), 
        panel.grid.minor = element_blank()) +
  xlab("UMAP1") +
  ylab("UMAP2")

label

DimPlot(seurat, reduction = "umap",  group.by = "label", cols=colLab)+
  theme_bw() +
  theme(axis.text = element_blank(), axis.ticks = element_blank(), 
        panel.grid.minor = element_blank()) +
  xlab("UMAP1") +
  ylab("UMAP2")

technique

DimPlot(seurat, reduction = "umap", group.by = "technique", cols=colTec)+
  theme_bw() +
  theme(axis.text = element_blank(), axis.ticks = element_blank(), 
        panel.grid.minor = element_blank()) +
  xlab("UMAP1") +
  ylab("UMAP2")

Sample

DimPlot(seurat, reduction = "umap", group.by = "dataset", cols=colSmp)+
  theme_bw() +
  theme(axis.text = element_blank(), axis.ticks = element_blank(), 
        panel.grid.minor = element_blank()) +
  xlab("UMAP1") +
  ylab("UMAP2")

ID

DimPlot(seurat, reduction = "umap", group.by = "ID", cols=colID)+
  theme_bw() +
  theme(axis.text = element_blank(), axis.ticks = element_blank(), 
        panel.grid.minor = element_blank()) +
  xlab("UMAP1") +
  ylab("UMAP2")

Origin

DimPlot(seurat, reduction = "umap", group.by = "origin", cols=colOrig)+
  theme_bw() +
  theme(axis.text = element_blank(), axis.ticks = element_blank(), 
        panel.grid.minor = element_blank()) +
  xlab("UMAP1") +
  ylab("UMAP2")

isolation

DimPlot(seurat, reduction = "umap", group.by = "isolation", cols=colIso)+
  theme_bw() +
  theme(axis.text = element_blank(), axis.ticks = element_blank(), 
        panel.grid.minor = element_blank()) +
  xlab("UMAP1") +
  ylab("UMAP2")

cond

DimPlot(seurat, reduction = "umap", group.by = "cond", cols=colCond)+
  theme_bw() +
  theme(axis.text = element_blank(), axis.ticks = element_blank(), 
        panel.grid.minor = element_blank()) +
  xlab("UMAP1") +
  ylab("UMAP2")

processing

DimPlot(seurat, reduction = "umap", group.by = "processing", cols=colProc)+
  theme_bw() +
  theme(axis.text = element_blank(), axis.ticks = element_blank(), 
        panel.grid.minor = element_blank()) +
  xlab("UMAP1") +
  ylab("UMAP2")

cnt tables

per patient

## total cells per patient
knitr::kable(table(seurat$ID))

Var1	Freq
GZ1	2740
GZ10	3731
GZ11	3991
GZ12	3818
GZ2	1684
GZ3	2396
GZ4	545
GZ5	781
GZ6	491
GZ7	653
GZ8	3921
SG16	1321
SG20	1536
SG21	826
SG2224	2381
SG2325	3131
SG26	238
SG27	1545
SG28	1254
SG29	1242
SG30	236
SG31	1192
SG32	1428

## celltype per patient counts
knitr::kable(table(seurat$label, seurat$ID))

	GZ1	GZ10	GZ11	GZ12	GZ2	GZ3	GZ4	GZ5	GZ6	GZ7	GZ8	SG16	SG20	SG21	SG2224	SG2325	SG26	SG27	SG28	SG29	SG30	SG31	SG32
Cardiomyocyte	207	1094	298	114	106	233	44	38	12	88	710	21	96	41	295	396	16	70	78	93	46	178	40
Endothelial	446	398	805	1140	422	568	183	239	153	121	773	103	223	263	455	637	68	552	424	163	73	253	584
Fibroblast	760	982	809	1106	637	819	140	260	150	231	1136	142	606	234	829	774	79	304	256	145	39	347	313
MonocyteMacrophage	291	463	588	219	79	115	56	31	61	16	342	13	153	40	162	230	7	13	38	259	14	126	65
other	131	183	161	151	91	123	11	42	13	71	153	976	22	27	137	164	3	65	58	428	7	33	55
Perivascular	688	497	1202	1051	313	502	85	154	70	121	733	63	392	204	400	574	62	523	363	78	57	232	313
Tcell	217	114	128	37	36	36	26	17	32	5	74	3	44	17	103	356	3	18	37	76	0	23	58

## celltype percentages per patient
datLab <- data.frame(table(seurat$label, seurat$ID))
colnames(datLab) <- c("label", "ID", "cnt")
datPat <- data.frame(table(seurat$ID))
colnames(datPat) <- c("ID", "total")
datFrac <- datLab %>% left_join(., datPat, by="ID") %>% 
  mutate(percentage = cnt*100/total)
knitr::kable(datFrac)

label	ID	cnt	total	percentage
Cardiomyocyte	GZ1	207	2740	7.5547445
Endothelial	GZ1	446	2740	16.2773723
Fibroblast	GZ1	760	2740	27.7372263
MonocyteMacrophage	GZ1	291	2740	10.6204380
other	GZ1	131	2740	4.7810219
Perivascular	GZ1	688	2740	25.1094891
Tcell	GZ1	217	2740	7.9197080
Cardiomyocyte	GZ10	1094	3731	29.3218976
Endothelial	GZ10	398	3731	10.6673814
Fibroblast	GZ10	982	3731	26.3200214
MonocyteMacrophage	GZ10	463	3731	12.4095417
other	GZ10	183	3731	4.9048512
Perivascular	GZ10	497	3731	13.3208255
Tcell	GZ10	114	3731	3.0554811
Cardiomyocyte	GZ11	298	3991	7.4668003
Endothelial	GZ11	805	3991	20.1703834
Fibroblast	GZ11	809	3991	20.2706089
MonocyteMacrophage	GZ11	588	3991	14.7331496
other	GZ11	161	3991	4.0340767
Perivascular	GZ11	1202	3991	30.1177650
Tcell	GZ11	128	3991	3.2072162
Cardiomyocyte	GZ12	114	3818	2.9858565
Endothelial	GZ12	1140	3818	29.8585647
Fibroblast	GZ12	1106	3818	28.9680461
MonocyteMacrophage	GZ12	219	3818	5.7359874
other	GZ12	151	3818	3.9549502
Perivascular	GZ12	1051	3818	27.5275013
Tcell	GZ12	37	3818	0.9690938
Cardiomyocyte	GZ2	106	1684	6.2945368
Endothelial	GZ2	422	1684	25.0593824
Fibroblast	GZ2	637	1684	37.8266033
MonocyteMacrophage	GZ2	79	1684	4.6912114
other	GZ2	91	1684	5.4038005
Perivascular	GZ2	313	1684	18.5866983
Tcell	GZ2	36	1684	2.1377672
Cardiomyocyte	GZ3	233	2396	9.7245409
Endothelial	GZ3	568	2396	23.7061770
Fibroblast	GZ3	819	2396	34.1819699
MonocyteMacrophage	GZ3	115	2396	4.7996661
other	GZ3	123	2396	5.1335559
Perivascular	GZ3	502	2396	20.9515860
Tcell	GZ3	36	2396	1.5025042
Cardiomyocyte	GZ4	44	545	8.0733945
Endothelial	GZ4	183	545	33.5779817
Fibroblast	GZ4	140	545	25.6880734
MonocyteMacrophage	GZ4	56	545	10.2752294
other	GZ4	11	545	2.0183486
Perivascular	GZ4	85	545	15.5963303
Tcell	GZ4	26	545	4.7706422
Cardiomyocyte	GZ5	38	781	4.8655570
Endothelial	GZ5	239	781	30.6017926
Fibroblast	GZ5	260	781	33.2906530
MonocyteMacrophage	GZ5	31	781	3.9692702
other	GZ5	42	781	5.3777209
Perivascular	GZ5	154	781	19.7183099
Tcell	GZ5	17	781	2.1766965
Cardiomyocyte	GZ6	12	491	2.4439919
Endothelial	GZ6	153	491	31.1608961
Fibroblast	GZ6	150	491	30.5498982
MonocyteMacrophage	GZ6	61	491	12.4236253
other	GZ6	13	491	2.6476578
Perivascular	GZ6	70	491	14.2566191
Tcell	GZ6	32	491	6.5173116
Cardiomyocyte	GZ7	88	653	13.4762634
Endothelial	GZ7	121	653	18.5298622
Fibroblast	GZ7	231	653	35.3751914
MonocyteMacrophage	GZ7	16	653	2.4502297
other	GZ7	71	653	10.8728943
Perivascular	GZ7	121	653	18.5298622
Tcell	GZ7	5	653	0.7656968
Cardiomyocyte	GZ8	710	3921	18.1076256
Endothelial	GZ8	773	3921	19.7143586
Fibroblast	GZ8	1136	3921	28.9722010
MonocyteMacrophage	GZ8	342	3921	8.7222647
other	GZ8	153	3921	3.9020658
Perivascular	GZ8	733	3921	18.6942107
Tcell	GZ8	74	3921	1.8872737
Cardiomyocyte	SG16	21	1321	1.5897048
Endothelial	SG16	103	1321	7.7971234
Fibroblast	SG16	142	1321	10.7494322
MonocyteMacrophage	SG16	13	1321	0.9841030
other	SG16	976	1321	73.8834217
Perivascular	SG16	63	1321	4.7691143
Tcell	SG16	3	1321	0.2271007
Cardiomyocyte	SG20	96	1536	6.2500000
Endothelial	SG20	223	1536	14.5182292
Fibroblast	SG20	606	1536	39.4531250
MonocyteMacrophage	SG20	153	1536	9.9609375
other	SG20	22	1536	1.4322917
Perivascular	SG20	392	1536	25.5208333
Tcell	SG20	44	1536	2.8645833
Cardiomyocyte	SG21	41	826	4.9636804
Endothelial	SG21	263	826	31.8401937
Fibroblast	SG21	234	826	28.3292978
MonocyteMacrophage	SG21	40	826	4.8426150
other	SG21	27	826	3.2687651
Perivascular	SG21	204	826	24.6973366
Tcell	SG21	17	826	2.0581114
Cardiomyocyte	SG2224	295	2381	12.3897522
Endothelial	SG2224	455	2381	19.1096178
Fibroblast	SG2224	829	2381	34.8173037
MonocyteMacrophage	SG2224	162	2381	6.8038639
other	SG2224	137	2381	5.7538849
Perivascular	SG2224	400	2381	16.7996640
Tcell	SG2224	103	2381	4.3259135
Cardiomyocyte	SG2325	396	3131	12.6477164
Endothelial	SG2325	637	3131	20.3449377
Fibroblast	SG2325	774	3131	24.7205366
MonocyteMacrophage	SG2325	230	3131	7.3458959
other	SG2325	164	3131	5.2379431
Perivascular	SG2325	574	3131	18.3328010
Tcell	SG2325	356	3131	11.3701693
Cardiomyocyte	SG26	16	238	6.7226891
Endothelial	SG26	68	238	28.5714286
Fibroblast	SG26	79	238	33.1932773
MonocyteMacrophage	SG26	7	238	2.9411765
other	SG26	3	238	1.2605042
Perivascular	SG26	62	238	26.0504202
Tcell	SG26	3	238	1.2605042
Cardiomyocyte	SG27	70	1545	4.5307443
Endothelial	SG27	552	1545	35.7281553
Fibroblast	SG27	304	1545	19.6763754
MonocyteMacrophage	SG27	13	1545	0.8414239
other	SG27	65	1545	4.2071197
Perivascular	SG27	523	1545	33.8511327
Tcell	SG27	18	1545	1.1650485
Cardiomyocyte	SG28	78	1254	6.2200957
Endothelial	SG28	424	1254	33.8118022
Fibroblast	SG28	256	1254	20.4146730
MonocyteMacrophage	SG28	38	1254	3.0303030
other	SG28	58	1254	4.6251994
Perivascular	SG28	363	1254	28.9473684
Tcell	SG28	37	1254	2.9505582
Cardiomyocyte	SG29	93	1242	7.4879227
Endothelial	SG29	163	1242	13.1239936
Fibroblast	SG29	145	1242	11.6747182
MonocyteMacrophage	SG29	259	1242	20.8534622
other	SG29	428	1242	34.4605475
Perivascular	SG29	78	1242	6.2801932
Tcell	SG29	76	1242	6.1191626
Cardiomyocyte	SG30	46	236	19.4915254
Endothelial	SG30	73	236	30.9322034
Fibroblast	SG30	39	236	16.5254237
MonocyteMacrophage	SG30	14	236	5.9322034
other	SG30	7	236	2.9661017
Perivascular	SG30	57	236	24.1525424
Tcell	SG30	0	236	0.0000000
Cardiomyocyte	SG31	178	1192	14.9328859
Endothelial	SG31	253	1192	21.2248322
Fibroblast	SG31	347	1192	29.1107383
MonocyteMacrophage	SG31	126	1192	10.5704698
other	SG31	33	1192	2.7684564
Perivascular	SG31	232	1192	19.4630872
Tcell	SG31	23	1192	1.9295302
Cardiomyocyte	SG32	40	1428	2.8011204
Endothelial	SG32	584	1428	40.8963585
Fibroblast	SG32	313	1428	21.9187675
MonocyteMacrophage	SG32	65	1428	4.5518207
other	SG32	55	1428	3.8515406
Perivascular	SG32	313	1428	21.9187675
Tcell	SG32	58	1428	4.0616246

ggbarplot(datFrac, x="ID", y="percentage",
          fill = "label",
          palette = colLab) +
  rotate_x_text(angle = 90)

per cond

## total cells per cond
knitr::kable(table(seurat$cond))

Var1	Freq
HF	1064
HH	15461
InfCardiomyopathy	1428
inflamation	236
Myocarditis	10532
Perimyocarditis	1192
transplant	11168

## celltype per cond counts
knitr::kable(table(seurat$label, seurat$cond))

	HF	HH	InfCardiomyopathy	inflamation	Myocarditis	Perimyocarditis	transplant
Cardiomyocyte	57	2216	40	46	821	178	956
Endothelial	331	3116	584	73	2295	253	2394
Fibroblast	313	4033	313	39	3142	347	2911
MonocyteMacrophage	47	1612	65	14	908	126	609
other	30	648	55	7	910	33	1422
Perivascular	266	3483	313	57	2011	232	2315
Tcell	20	353	58	0	445	23	561

## celltype percentages per cond
datLab <- data.frame(table(seurat$label, seurat$cond))
colnames(datLab) <- c("label", "cond", "cnt")
datPat <- data.frame(table(seurat$cond))
colnames(datPat) <- c("cond", "total")
datFrac <- datLab %>% left_join(., datPat, by="cond") %>% 
  mutate(percentage = cnt*100/total)
knitr::kable(datFrac)

label	cond	cnt	total	percentage
Cardiomyocyte	HF	57	1064	5.357143
Endothelial	HF	331	1064	31.109023
Fibroblast	HF	313	1064	29.417293
MonocyteMacrophage	HF	47	1064	4.417293
other	HF	30	1064	2.819549
Perivascular	HF	266	1064	25.000000
Tcell	HF	20	1064	1.879699
Cardiomyocyte	HH	2216	15461	14.332838
Endothelial	HH	3116	15461	20.153936
Fibroblast	HH	4033	15461	26.084988
MonocyteMacrophage	HH	1612	15461	10.426234
other	HH	648	15461	4.191191
Perivascular	HH	3483	15461	22.527650
Tcell	HH	353	15461	2.283164
Cardiomyocyte	InfCardiomyopathy	40	1428	2.801120
Endothelial	InfCardiomyopathy	584	1428	40.896358
Fibroblast	InfCardiomyopathy	313	1428	21.918768
MonocyteMacrophage	InfCardiomyopathy	65	1428	4.551821
other	InfCardiomyopathy	55	1428	3.851541
Perivascular	InfCardiomyopathy	313	1428	21.918768
Tcell	InfCardiomyopathy	58	1428	4.061625
Cardiomyocyte	inflamation	46	236	19.491525
Endothelial	inflamation	73	236	30.932203
Fibroblast	inflamation	39	236	16.525424
MonocyteMacrophage	inflamation	14	236	5.932203
other	inflamation	7	236	2.966102
Perivascular	inflamation	57	236	24.152542
Tcell	inflamation	0	236	0.000000
Cardiomyocyte	Myocarditis	821	10532	7.795291
Endothelial	Myocarditis	2295	10532	21.790733
Fibroblast	Myocarditis	3142	10532	29.832890
MonocyteMacrophage	Myocarditis	908	10532	8.621344
other	Myocarditis	910	10532	8.640334
Perivascular	Myocarditis	2011	10532	19.094189
Tcell	Myocarditis	445	10532	4.225218
Cardiomyocyte	Perimyocarditis	178	1192	14.932886
Endothelial	Perimyocarditis	253	1192	21.224832
Fibroblast	Perimyocarditis	347	1192	29.110738
MonocyteMacrophage	Perimyocarditis	126	1192	10.570470
other	Perimyocarditis	33	1192	2.768456
Perivascular	Perimyocarditis	232	1192	19.463087
Tcell	Perimyocarditis	23	1192	1.929530
Cardiomyocyte	transplant	956	11168	8.560172
Endothelial	transplant	2394	11168	21.436246
Fibroblast	transplant	2911	11168	26.065544
MonocyteMacrophage	transplant	609	11168	5.453080
other	transplant	1422	11168	12.732808
Perivascular	transplant	2315	11168	20.728868
Tcell	transplant	561	11168	5.023281

ggbarplot(datFrac, x="cond", y="percentage",
          fill = "label",
          palette = colLab) +
  rotate_x_text(angle = 90)

vis marker for celltype assignment

heatmap

genes <- data.frame(gene=rownames(seurat)) %>% 
    mutate(geneID=gsub("^.*\\.", "", gene)) 

selGenesAll <- read_tsv(file = paste0(basedir,
                                      "/data/markerLabels.txt")) %>% 
  left_join(., genes, by = "geneID")

Idents(seurat) <- seurat$seurat_clusters
pOut <- avgHeatmap(seurat = seurat, selGenes = selGenesAll,
                  colVecIdent = colPal, 
                  ordVec=levels(seurat),
                  gapVecR=NULL, gapVecC=NULL,cc=T,
                  cr=F, condCol=F)

Idents(seurat) <- seurat$label
pOut <- avgHeatmap(seurat = seurat, selGenes = selGenesAll,
                  colVecIdent = colLab, 
                  ordVec=levels(seurat),
                  gapVecR=NULL, gapVecC=NULL,cc=T,
                  cr=F, condCol=F)

Dotplot

DotPlot(seurat, assay="RNA", features = selGenesAll$gene, scale =T,
        cluster.idents = T) +
  scale_color_viridis_c() +
  coord_flip() +
  theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
  scale_x_discrete(breaks=selGenesAll$gene, labels=selGenesAll$geneID) +
  xlab("") + ylab("")

Idents(seurat) <- seurat$seurat_clusters
DotPlot(seurat, assay="RNA", features = selGenesAll$gene, scale =T,
        cluster.idents = T) +
  scale_color_viridis_c() +
  coord_flip() +
  theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
  scale_x_discrete(breaks=selGenesAll$gene, labels=selGenesAll$geneID) +
  xlab("") + ylab("")

save seurat object

saveRDS(seurat, file = paste0(basedir, 
                              "/data/humanHeartsPlusGraz_intPatients_merged", 
                              "labeled_seurat.rds"))

session info

sessionInfo()

R version 4.1.0 (2021-05-18)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur 10.16

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] parallel  stats4    stats     graphics  grDevices utils     datasets 
[8] methods   base     

other attached packages:
 [1] sctransform_0.3.3           viridis_0.6.2              
 [3] viridisLite_0.4.0           pheatmap_1.0.12            
 [5] ggpubr_0.4.0                ggsci_2.9                  
 [7] runSeurat3_0.1.0            here_1.0.1                 
 [9] magrittr_2.0.3              sp_1.4-7                   
[11] SeuratObject_4.1.0          Seurat_4.1.1               
[13] forcats_0.5.1               stringr_1.4.0              
[15] dplyr_1.0.9                 purrr_0.3.4                
[17] readr_2.1.2                 tidyr_1.2.0                
[19] tibble_3.1.7                ggplot2_3.3.6              
[21] tidyverse_1.3.1             SingleCellExperiment_1.14.1
[23] SummarizedExperiment_1.22.0 Biobase_2.52.0             
[25] GenomicRanges_1.44.0        GenomeInfoDb_1.28.4        
[27] IRanges_2.26.0              S4Vectors_0.30.2           
[29] BiocGenerics_0.38.0         MatrixGenerics_1.4.3       
[31] matrixStats_0.62.0         

loaded via a namespace (and not attached):
  [1] utf8_1.2.2             reticulate_1.24        tidyselect_1.1.2      
  [4] htmlwidgets_1.5.4      grid_4.1.0             Rtsne_0.16            
  [7] munsell_0.5.0          codetools_0.2-18       ica_1.0-2             
 [10] future_1.25.0          miniUI_0.1.1.1         withr_2.5.0           
 [13] spatstat.random_2.2-0  colorspace_2.0-3       progressr_0.10.0      
 [16] highr_0.9              knitr_1.39             rstudioapi_0.13       
 [19] ROCR_1.0-11            ggsignif_0.6.3         tensor_1.5            
 [22] listenv_0.8.0          labeling_0.4.2         git2r_0.30.1          
 [25] GenomeInfoDbData_1.2.6 polyclip_1.10-0        bit64_4.0.5           
 [28] farver_2.1.0           rprojroot_2.0.3        parallelly_1.31.1     
 [31] vctrs_0.4.1            generics_0.1.2         xfun_0.30             
 [34] R6_2.5.1               bitops_1.0-7           spatstat.utils_2.3-1  
 [37] DelayedArray_0.18.0    assertthat_0.2.1       vroom_1.5.7           
 [40] promises_1.2.0.1       scales_1.2.0           rgeos_0.5-9           
 [43] gtable_0.3.0           globals_0.15.0         goftest_1.2-3         
 [46] workflowr_1.7.0        rlang_1.0.2            splines_4.1.0         
 [49] rstatix_0.7.0          lazyeval_0.2.2         spatstat.geom_2.4-0   
 [52] broom_0.8.0            yaml_2.3.5             reshape2_1.4.4        
 [55] abind_1.4-5            modelr_0.1.8           backports_1.4.1       
 [58] httpuv_1.6.5           tools_4.1.0            ellipsis_0.3.2        
 [61] spatstat.core_2.4-2    jquerylib_0.1.4        RColorBrewer_1.1-3    
 [64] ggridges_0.5.3         Rcpp_1.0.8.3           plyr_1.8.7            
 [67] zlibbioc_1.38.0        RCurl_1.98-1.6         rpart_4.1.16          
 [70] deldir_1.0-6           pbapply_1.5-0          cowplot_1.1.1         
 [73] zoo_1.8-10             haven_2.5.0            ggrepel_0.9.1         
 [76] cluster_2.1.3          fs_1.5.2               data.table_1.14.2     
 [79] scattermore_0.8        lmtest_0.9-40          reprex_2.0.1          
 [82] RANN_2.6.1             fitdistrplus_1.1-8     hms_1.1.1             
 [85] patchwork_1.1.1        mime_0.12              evaluate_0.15         
 [88] xtable_1.8-4           readxl_1.4.0           gridExtra_2.3         
 [91] compiler_4.1.0         KernSmooth_2.23-20     crayon_1.5.1          
 [94] htmltools_0.5.2        mgcv_1.8-40            later_1.3.0           
 [97] tzdb_0.3.0             lubridate_1.8.0        DBI_1.1.2             
[100] dbplyr_2.1.1           MASS_7.3-57            Matrix_1.4-1          
[103] car_3.0-13             cli_3.3.0              igraph_1.3.1          
[106] pkgconfig_2.0.3        plotly_4.10.0          spatstat.sparse_2.1-1 
[109] xml2_1.3.3             bslib_0.3.1            XVector_0.32.0        
[112] rvest_1.0.2            digest_0.6.29          RcppAnnoy_0.0.19      
[115] spatstat.data_2.2-0    rmarkdown_2.14         cellranger_1.1.0      
[118] leiden_0.3.10          uwot_0.1.11            shiny_1.7.1           
[121] lifecycle_1.0.1        nlme_3.1-157           jsonlite_1.8.0        
[124] carData_3.0-5          fansi_1.0.3            pillar_1.7.0          
[127] lattice_0.20-45        fastmap_1.1.0          httr_1.4.3            
[130] survival_3.3-1         glue_1.6.2             png_0.1-7             
[133] bit_4.0.4              stringi_1.7.6          sass_0.4.1            
[136] irlba_2.3.5            future.apply_1.9.0

date()

[1] "Wed Jul  6 14:03:00 2022"

sessionInfo()

R version 4.1.0 (2021-05-18)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur 10.16

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] parallel  stats4    stats     graphics  grDevices utils     datasets 
[8] methods   base     

other attached packages:
 [1] sctransform_0.3.3           viridis_0.6.2              
 [3] viridisLite_0.4.0           pheatmap_1.0.12            
 [5] ggpubr_0.4.0                ggsci_2.9                  
 [7] runSeurat3_0.1.0            here_1.0.1                 
 [9] magrittr_2.0.3              sp_1.4-7                   
[11] SeuratObject_4.1.0          Seurat_4.1.1               
[13] forcats_0.5.1               stringr_1.4.0              
[15] dplyr_1.0.9                 purrr_0.3.4                
[17] readr_2.1.2                 tidyr_1.2.0                
[19] tibble_3.1.7                ggplot2_3.3.6              
[21] tidyverse_1.3.1             SingleCellExperiment_1.14.1
[23] SummarizedExperiment_1.22.0 Biobase_2.52.0             
[25] GenomicRanges_1.44.0        GenomeInfoDb_1.28.4        
[27] IRanges_2.26.0              S4Vectors_0.30.2           
[29] BiocGenerics_0.38.0         MatrixGenerics_1.4.3       
[31] matrixStats_0.62.0         

loaded via a namespace (and not attached):
  [1] utf8_1.2.2             reticulate_1.24        tidyselect_1.1.2      
  [4] htmlwidgets_1.5.4      grid_4.1.0             Rtsne_0.16            
  [7] munsell_0.5.0          codetools_0.2-18       ica_1.0-2             
 [10] future_1.25.0          miniUI_0.1.1.1         withr_2.5.0           
 [13] spatstat.random_2.2-0  colorspace_2.0-3       progressr_0.10.0      
 [16] highr_0.9              knitr_1.39             rstudioapi_0.13       
 [19] ROCR_1.0-11            ggsignif_0.6.3         tensor_1.5            
 [22] listenv_0.8.0          labeling_0.4.2         git2r_0.30.1          
 [25] GenomeInfoDbData_1.2.6 polyclip_1.10-0        bit64_4.0.5           
 [28] farver_2.1.0           rprojroot_2.0.3        parallelly_1.31.1     
 [31] vctrs_0.4.1            generics_0.1.2         xfun_0.30             
 [34] R6_2.5.1               bitops_1.0-7           spatstat.utils_2.3-1  
 [37] DelayedArray_0.18.0    assertthat_0.2.1       vroom_1.5.7           
 [40] promises_1.2.0.1       scales_1.2.0           rgeos_0.5-9           
 [43] gtable_0.3.0           globals_0.15.0         goftest_1.2-3         
 [46] workflowr_1.7.0        rlang_1.0.2            splines_4.1.0         
 [49] rstatix_0.7.0          lazyeval_0.2.2         spatstat.geom_2.4-0   
 [52] broom_0.8.0            yaml_2.3.5             reshape2_1.4.4        
 [55] abind_1.4-5            modelr_0.1.8           backports_1.4.1       
 [58] httpuv_1.6.5           tools_4.1.0            ellipsis_0.3.2        
 [61] spatstat.core_2.4-2    jquerylib_0.1.4        RColorBrewer_1.1-3    
 [64] ggridges_0.5.3         Rcpp_1.0.8.3           plyr_1.8.7            
 [67] zlibbioc_1.38.0        RCurl_1.98-1.6         rpart_4.1.16          
 [70] deldir_1.0-6           pbapply_1.5-0          cowplot_1.1.1         
 [73] zoo_1.8-10             haven_2.5.0            ggrepel_0.9.1         
 [76] cluster_2.1.3          fs_1.5.2               data.table_1.14.2     
 [79] scattermore_0.8        lmtest_0.9-40          reprex_2.0.1          
 [82] RANN_2.6.1             fitdistrplus_1.1-8     hms_1.1.1             
 [85] patchwork_1.1.1        mime_0.12              evaluate_0.15         
 [88] xtable_1.8-4           readxl_1.4.0           gridExtra_2.3         
 [91] compiler_4.1.0         KernSmooth_2.23-20     crayon_1.5.1          
 [94] htmltools_0.5.2        mgcv_1.8-40            later_1.3.0           
 [97] tzdb_0.3.0             lubridate_1.8.0        DBI_1.1.2             
[100] dbplyr_2.1.1           MASS_7.3-57            Matrix_1.4-1          
[103] car_3.0-13             cli_3.3.0              igraph_1.3.1          
[106] pkgconfig_2.0.3        plotly_4.10.0          spatstat.sparse_2.1-1 
[109] xml2_1.3.3             bslib_0.3.1            XVector_0.32.0        
[112] rvest_1.0.2            digest_0.6.29          RcppAnnoy_0.0.19      
[115] spatstat.data_2.2-0    rmarkdown_2.14         cellranger_1.1.0      
[118] leiden_0.3.10          uwot_0.1.11            shiny_1.7.1           
[121] lifecycle_1.0.1        nlme_3.1-157           jsonlite_1.8.0        
[124] carData_3.0-5          fansi_1.0.3            pillar_1.7.0          
[127] lattice_0.20-45        fastmap_1.1.0          httr_1.4.3            
[130] survival_3.3-1         glue_1.6.2             png_0.1-7             
[133] bit_4.0.4              stringi_1.7.6          sass_0.4.1            
[136] irlba_2.3.5            future.apply_1.9.0