assign labels

Last updated: 2022-11-29

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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$seurat_clusters
table(seurat$ID)


 GZ1 GZ14 GZ15 GZ16 GZ17 GZ18  GZ2 GZ20  GZ3  GZ4  GZ5  GZ6  GZ7 SG17 SG21 SG24 
2740 1268 4439  436 1370 2280 1684 2706 2396  545  781  491  653 1321 1536 2381 
SG25 SG28 SG29 SG31 SG32 SG33 SG34 SG35 
3131 1545 2496 1192 1428 6286  620 2363

assign labels

seurat$label <- "other"
seurat$label[which(seurat$integrated_snn_res.0.4 %in% c("2","9"))] <- "Endothelial"
seurat$label[which(seurat$integrated_snn_res.0.4 %in% c("8"))] <- "EndoEC"
seurat$label[which(seurat$integrated_snn_res.0.4 %in% c("6"))] <- "Tcell"
seurat$label[which(seurat$integrated_snn_res.0.4 %in% c("5"))] <- "Cardiomyocyte"
seurat$label[which(seurat$integrated_snn_res.0.4 %in% c("0"))] <- "Fibroblast"
seurat$label[which(seurat$integrated_snn_res.0.4 %in% c("1"))] <- "Perivascular"
seurat$label[which(seurat$integrated_snn_res.0.4 %in% c("7"))] <- "SMC"
seurat$label[which(seurat$integrated_snn_res.0.4 %in% c("3"))] <- "resMacrophage"
seurat$label[which(seurat$integrated_snn_res.0.6 %in% c("8"))] <- "infMacrophage"
seurat$label[which(seurat$integrated_snn_res.0.4 %in% c("10"))] <- "NeuralCells"

## reembedding of cluster 4 (mix of diff cell typpes)
seuratSub <- subset(seurat, integrated_snn_res.0.4=="4")

seuratSub <- rerunSeurat3(seuratSub)

Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 3471
Number of edges: 127742

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9495
Number of communities: 12
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 3471
Number of edges: 127742

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9033
Number of communities: 15
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 3471
Number of edges: 127742

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8787
Number of communities: 17
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 3471
Number of edges: 127742

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9290
Number of communities: 14
Elapsed time: 0 seconds

Tcell <- colnames(seuratSub)[which(seuratSub$RNA_snn_res.0.4 %in% c("0", "3","6", "5", "11"))]
endo1 <- colnames(seuratSub)[which(seuratSub$RNA_snn_res.0.4 %in% c("1"))]
fibro <- colnames(seuratSub)[which(seuratSub$RNA_snn_res.0.4 %in% c("2"))]
peri <- colnames(seuratSub)[which(seuratSub$RNA_snn_res.0.4 %in% c("9", "12"))]
Adipo <- colnames(seuratSub)[which(seuratSub$RNA_snn_res.0.4 %in% c("7"))]
resmacro <- colnames(seuratSub)[which(seuratSub$RNA_snn_res.0.4 %in% c("4", "13"))]
infmacro <- colnames(seuratSub)[which(seuratSub$RNA_snn_res.0.4 %in% c("8"))]
LEC <- colnames(seuratSub)[which(seuratSub$RNA_snn_res.0.4 %in% c("10"))]


seurat$label[which(colnames(seurat) %in% Tcell)] <- "Tcell"
seurat$label[which(colnames(seurat) %in% peri)] <- "Perivascular"
seurat$label[which(colnames(seurat) %in% endo1)] <- "Endothelial"
seurat$label[which(colnames(seurat) %in% fibro)] <- "Fibroblast"
seurat$label[which(colnames(seurat) %in% Adipo)] <- "Adipocytes"
seurat$label[which(colnames(seurat) %in% resmacro)] <- "resMacrophage"
seurat$label[which(colnames(seurat) %in% infmacro)] <- "infMacrophage"
seurat$label[which(colnames(seurat) %in% LEC)] <- "LEC"

unique(seurat$integrated_snn_res.0.4)

 [1] 8  2  4  3  6  0  1  5  7  9  10
Levels: 0 1 2 3 4 5 6 7 8 9 10

seurat$clust <- as.character(seurat$integrated_snn_res.0.4)
seurat$clust[which(seurat$integrated_snn_res.0.6 == "8")] <- "11"
seurat$clust[which(colnames(seurat) %in% Tcell)] <- "12"
seurat$clust[which(colnames(seurat) %in% peri)] <- "13"
seurat$clust[which(colnames(seurat) %in% endo1)] <- "14"
seurat$clust[which(colnames(seurat) %in% fibro)] <- "15"
seurat$clust[which(colnames(seurat) %in% Adipo)] <- "16"
seurat$clust[which(colnames(seurat) %in% resmacro)] <- "17"
seurat$clust[which(colnames(seurat) %in% infmacro)] <- "18"
seurat$clust[which(colnames(seurat) %in% LEC)] <- "19"

color vectors

colPal <- c(pal_igv()(12),
            pal_aaas()(10))[1:length(levels(seurat))]
colTec <- pal_jama()(length(unique(seurat$technique)))
colSmp <- c(pal_uchicago()(9), pal_npg()(10), pal_aaas()(10), 
            pal_jama()(7))[1:length(unique(seurat$dataset))]
colCond <- pal_npg()(length(unique(seurat$cond)))
colID <- c(pal_jco()(10), pal_npg()(10), pal_futurama()(10),
           pal_d3()(10))[1:length(unique(seurat$ID))]
colOrig <- pal_aaas()(length(unique(seurat$origin)))
colIso <- pal_nejm()(length(unique(seurat$isolation)))
colProc <- pal_aaas()(length(unique(seurat$processing)))
colLab <- c(pal_futurama()(8), pal_uchicago()(6))[1:length(unique(seurat$label))]
colClust <- c(pal_igv()(12),
            pal_aaas()(10))[1:length(unique(seurat$clust))]

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)
names(colClust) <- unique(seurat$clust)

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")

DimPlot(seurat, reduction = "umap", cols=colPal,
        shuffle = T)+
  theme_void()

cluster plus re-embedding

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

DimPlot(seurat, reduction = "umap", cols=colClust, group.by = "clust",
        shuffle = F)+
  theme_void()

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")

DimPlot(seurat, reduction = "umap", group.by = "label", cols=colLab,
        shuffle = T)+
  theme_void()

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, shuffle = T)+
  theme_bw() +
  theme(axis.text = element_blank(), axis.ticks = element_blank(), 
        panel.grid.minor = element_blank()) +
  xlab("UMAP1") +
  ylab("UMAP2")

DimPlot(seurat, reduction = "umap", group.by = "ID", cols=colID,
        shuffle = T)+
  theme_void()

Origin

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

DimPlot(seurat, reduction = "umap", group.by = "origin", cols=colOrig,
        shuffle = T)+
  theme_void()

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
GZ14	1268
GZ15	4439
GZ16	436
GZ17	1370
GZ18	2280
GZ2	1684
GZ20	2706
GZ3	2396
GZ4	545
GZ5	781
GZ6	491
GZ7	653
SG17	1321
SG21	1536
SG24	2381
SG25	3131
SG28	1545
SG29	2496
SG31	1192
SG32	1428
SG33	6286
SG34	620
SG35	2363

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

	GZ1	GZ14	GZ15	GZ16	GZ17	GZ18	GZ2	GZ20	GZ3	GZ4	GZ5	GZ6	GZ7	SG17	SG21	SG24	SG25	SG28	SG29	SG31	SG32	SG33	SG34	SG35
Adipocytes	0	0	0	0	0	0	1	0	2	1	0	0	45	1	1	3	49	3	1	0	5	8	0	0
Cardiomyocyte	208	51	115	25	71	329	107	46	238	45	39	12	90	56	96	299	397	71	182	178	40	216	177	303
EndoEC	173	32	78	21	20	182	127	301	192	64	39	46	12	23	39	92	98	48	99	60	73	82	18	61
Endothelial	290	290	961	54	167	456	312	933	374	119	207	108	111	826	186	376	548	506	461	197	511	189	82	522
Fibroblast	813	321	1185	126	377	630	667	652	867	143	263	156	240	165	613	882	871	316	422	354	324	414	120	794
infMacrophage	76	22	149	32	135	23	20	38	39	20	10	15	5	114	29	53	102	10	379	35	15	1393	8	55
LEC	5	0	0	0	0	6	5	2	30	0	0	0	3	1	1	9	0	0	9	0	2	0	0	0
NeuralCells	58	10	76	6	28	28	28	20	33	7	26	2	8	19	7	27	27	26	21	22	27	38	5	28
Perivascular	473	337	1299	18	144	380	264	337	401	55	117	54	63	46	302	285	437	406	297	179	240	178	122	322
resMacrophage	225	72	174	24	200	73	63	104	80	34	21	47	12	13	126	118	150	8	391	91	55	651	42	145
SMC	224	102	256	22	45	144	57	149	108	31	41	19	58	40	93	139	139	134	113	51	82	35	22	73
Tcell	195	31	146	108	183	29	33	124	32	26	18	32	6	17	43	98	313	17	121	25	54	3082	24	60

## 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
Adipocytes	GZ1	0	2740	0.0000000
Cardiomyocyte	GZ1	208	2740	7.5912409
EndoEC	GZ1	173	2740	6.3138686
Endothelial	GZ1	290	2740	10.5839416
Fibroblast	GZ1	813	2740	29.6715328
infMacrophage	GZ1	76	2740	2.7737226
LEC	GZ1	5	2740	0.1824818
NeuralCells	GZ1	58	2740	2.1167883
Perivascular	GZ1	473	2740	17.2627737
resMacrophage	GZ1	225	2740	8.2116788
SMC	GZ1	224	2740	8.1751825
Tcell	GZ1	195	2740	7.1167883
Adipocytes	GZ14	0	1268	0.0000000
Cardiomyocyte	GZ14	51	1268	4.0220820
EndoEC	GZ14	32	1268	2.5236593
Endothelial	GZ14	290	1268	22.8706625
Fibroblast	GZ14	321	1268	25.3154574
infMacrophage	GZ14	22	1268	1.7350158
LEC	GZ14	0	1268	0.0000000
NeuralCells	GZ14	10	1268	0.7886435
Perivascular	GZ14	337	1268	26.5772871
resMacrophage	GZ14	72	1268	5.6782334
SMC	GZ14	102	1268	8.0441640
Tcell	GZ14	31	1268	2.4447950
Adipocytes	GZ15	0	4439	0.0000000
Cardiomyocyte	GZ15	115	4439	2.5906736
EndoEC	GZ15	78	4439	1.7571525
Endothelial	GZ15	961	4439	21.6490200
Fibroblast	GZ15	1185	4439	26.6952016
infMacrophage	GZ15	149	4439	3.3566118
LEC	GZ15	0	4439	0.0000000
NeuralCells	GZ15	76	4439	1.7120973
Perivascular	GZ15	1299	4439	29.2633476
resMacrophage	GZ15	174	4439	3.9198018
SMC	GZ15	256	4439	5.7670647
Tcell	GZ15	146	4439	3.2890291
Adipocytes	GZ16	0	436	0.0000000
Cardiomyocyte	GZ16	25	436	5.7339450
EndoEC	GZ16	21	436	4.8165138
Endothelial	GZ16	54	436	12.3853211
Fibroblast	GZ16	126	436	28.8990826
infMacrophage	GZ16	32	436	7.3394495
LEC	GZ16	0	436	0.0000000
NeuralCells	GZ16	6	436	1.3761468
Perivascular	GZ16	18	436	4.1284404
resMacrophage	GZ16	24	436	5.5045872
SMC	GZ16	22	436	5.0458716
Tcell	GZ16	108	436	24.7706422
Adipocytes	GZ17	0	1370	0.0000000
Cardiomyocyte	GZ17	71	1370	5.1824818
EndoEC	GZ17	20	1370	1.4598540
Endothelial	GZ17	167	1370	12.1897810
Fibroblast	GZ17	377	1370	27.5182482
infMacrophage	GZ17	135	1370	9.8540146
LEC	GZ17	0	1370	0.0000000
NeuralCells	GZ17	28	1370	2.0437956
Perivascular	GZ17	144	1370	10.5109489
resMacrophage	GZ17	200	1370	14.5985401
SMC	GZ17	45	1370	3.2846715
Tcell	GZ17	183	1370	13.3576642
Adipocytes	GZ18	0	2280	0.0000000
Cardiomyocyte	GZ18	329	2280	14.4298246
EndoEC	GZ18	182	2280	7.9824561
Endothelial	GZ18	456	2280	20.0000000
Fibroblast	GZ18	630	2280	27.6315789
infMacrophage	GZ18	23	2280	1.0087719
LEC	GZ18	6	2280	0.2631579
NeuralCells	GZ18	28	2280	1.2280702
Perivascular	GZ18	380	2280	16.6666667
resMacrophage	GZ18	73	2280	3.2017544
SMC	GZ18	144	2280	6.3157895
Tcell	GZ18	29	2280	1.2719298
Adipocytes	GZ2	1	1684	0.0593824
Cardiomyocyte	GZ2	107	1684	6.3539192
EndoEC	GZ2	127	1684	7.5415677
Endothelial	GZ2	312	1684	18.5273159
Fibroblast	GZ2	667	1684	39.6080760
infMacrophage	GZ2	20	1684	1.1876485
LEC	GZ2	5	1684	0.2969121
NeuralCells	GZ2	28	1684	1.6627078
Perivascular	GZ2	264	1684	15.6769596
resMacrophage	GZ2	63	1684	3.7410926
SMC	GZ2	57	1684	3.3847981
Tcell	GZ2	33	1684	1.9596200
Adipocytes	GZ20	0	2706	0.0000000
Cardiomyocyte	GZ20	46	2706	1.6999261
EndoEC	GZ20	301	2706	11.1234294
Endothelial	GZ20	933	2706	34.4789357
Fibroblast	GZ20	652	2706	24.0946046
infMacrophage	GZ20	38	2706	1.4042868
LEC	GZ20	2	2706	0.0739098
NeuralCells	GZ20	20	2706	0.7390983
Perivascular	GZ20	337	2706	12.4538064
resMacrophage	GZ20	104	2706	3.8433112
SMC	GZ20	149	2706	5.5062823
Tcell	GZ20	124	2706	4.5824095
Adipocytes	GZ3	2	2396	0.0834725
Cardiomyocyte	GZ3	238	2396	9.9332220
EndoEC	GZ3	192	2396	8.0133556
Endothelial	GZ3	374	2396	15.6093489
Fibroblast	GZ3	867	2396	36.1853088
infMacrophage	GZ3	39	2396	1.6277129
LEC	GZ3	30	2396	1.2520868
NeuralCells	GZ3	33	2396	1.3772955
Perivascular	GZ3	401	2396	16.7362270
resMacrophage	GZ3	80	2396	3.3388982
SMC	GZ3	108	2396	4.5075125
Tcell	GZ3	32	2396	1.3355593
Adipocytes	GZ4	1	545	0.1834862
Cardiomyocyte	GZ4	45	545	8.2568807
EndoEC	GZ4	64	545	11.7431193
Endothelial	GZ4	119	545	21.8348624
Fibroblast	GZ4	143	545	26.2385321
infMacrophage	GZ4	20	545	3.6697248
LEC	GZ4	0	545	0.0000000
NeuralCells	GZ4	7	545	1.2844037
Perivascular	GZ4	55	545	10.0917431
resMacrophage	GZ4	34	545	6.2385321
SMC	GZ4	31	545	5.6880734
Tcell	GZ4	26	545	4.7706422
Adipocytes	GZ5	0	781	0.0000000
Cardiomyocyte	GZ5	39	781	4.9935980
EndoEC	GZ5	39	781	4.9935980
Endothelial	GZ5	207	781	26.5044814
Fibroblast	GZ5	263	781	33.6747759
infMacrophage	GZ5	10	781	1.2804097
LEC	GZ5	0	781	0.0000000
NeuralCells	GZ5	26	781	3.3290653
Perivascular	GZ5	117	781	14.9807939
resMacrophage	GZ5	21	781	2.6888604
SMC	GZ5	41	781	5.2496799
Tcell	GZ5	18	781	2.3047375
Adipocytes	GZ6	0	491	0.0000000
Cardiomyocyte	GZ6	12	491	2.4439919
EndoEC	GZ6	46	491	9.3686354
Endothelial	GZ6	108	491	21.9959267
Fibroblast	GZ6	156	491	31.7718941
infMacrophage	GZ6	15	491	3.0549898
LEC	GZ6	0	491	0.0000000
NeuralCells	GZ6	2	491	0.4073320
Perivascular	GZ6	54	491	10.9979633
resMacrophage	GZ6	47	491	9.5723014
SMC	GZ6	19	491	3.8696538
Tcell	GZ6	32	491	6.5173116
Adipocytes	GZ7	45	653	6.8912711
Cardiomyocyte	GZ7	90	653	13.7825421
EndoEC	GZ7	12	653	1.8376723
Endothelial	GZ7	111	653	16.9984686
Fibroblast	GZ7	240	653	36.7534456
infMacrophage	GZ7	5	653	0.7656968
LEC	GZ7	3	653	0.4594181
NeuralCells	GZ7	8	653	1.2251149
Perivascular	GZ7	63	653	9.6477795
resMacrophage	GZ7	12	653	1.8376723
SMC	GZ7	58	653	8.8820827
Tcell	GZ7	6	653	0.9188361
Adipocytes	SG17	1	1321	0.0757002
Cardiomyocyte	SG17	56	1321	4.2392127
EndoEC	SG17	23	1321	1.7411052
Endothelial	SG17	826	1321	62.5283876
Fibroblast	SG17	165	1321	12.4905375
infMacrophage	SG17	114	1321	8.6298259
LEC	SG17	1	1321	0.0757002
NeuralCells	SG17	19	1321	1.4383043
Perivascular	SG17	46	1321	3.4822104
resMacrophage	SG17	13	1321	0.9841030
SMC	SG17	40	1321	3.0280091
Tcell	SG17	17	1321	1.2869039
Adipocytes	SG21	1	1536	0.0651042
Cardiomyocyte	SG21	96	1536	6.2500000
EndoEC	SG21	39	1536	2.5390625
Endothelial	SG21	186	1536	12.1093750
Fibroblast	SG21	613	1536	39.9088542
infMacrophage	SG21	29	1536	1.8880208
LEC	SG21	1	1536	0.0651042
NeuralCells	SG21	7	1536	0.4557292
Perivascular	SG21	302	1536	19.6614583
resMacrophage	SG21	126	1536	8.2031250
SMC	SG21	93	1536	6.0546875
Tcell	SG21	43	1536	2.7994792
Adipocytes	SG24	3	2381	0.1259975
Cardiomyocyte	SG24	299	2381	12.5577488
EndoEC	SG24	92	2381	3.8639227
Endothelial	SG24	376	2381	15.7916842
Fibroblast	SG24	882	2381	37.0432591
infMacrophage	SG24	53	2381	2.2259555
LEC	SG24	9	2381	0.3779924
NeuralCells	SG24	27	2381	1.1339773
Perivascular	SG24	285	2381	11.9697606
resMacrophage	SG24	118	2381	4.9559009
SMC	SG24	139	2381	5.8378832
Tcell	SG24	98	2381	4.1159177
Adipocytes	SG25	49	3131	1.5649952
Cardiomyocyte	SG25	397	3131	12.6796551
EndoEC	SG25	98	3131	3.1299904
Endothelial	SG25	548	3131	17.5023954
Fibroblast	SG25	871	3131	27.8185883
infMacrophage	SG25	102	3131	3.2577451
LEC	SG25	0	3131	0.0000000
NeuralCells	SG25	27	3131	0.8623443
Perivascular	SG25	437	3131	13.9572022
resMacrophage	SG25	150	3131	4.7908017
SMC	SG25	139	3131	4.4394762
Tcell	SG25	313	3131	9.9968061
Adipocytes	SG28	3	1545	0.1941748
Cardiomyocyte	SG28	71	1545	4.5954693
EndoEC	SG28	48	1545	3.1067961
Endothelial	SG28	506	1545	32.7508091
Fibroblast	SG28	316	1545	20.4530744
infMacrophage	SG28	10	1545	0.6472492
LEC	SG28	0	1545	0.0000000
NeuralCells	SG28	26	1545	1.6828479
Perivascular	SG28	406	1545	26.2783172
resMacrophage	SG28	8	1545	0.5177994
SMC	SG28	134	1545	8.6731392
Tcell	SG28	17	1545	1.1003236
Adipocytes	SG29	1	2496	0.0400641
Cardiomyocyte	SG29	182	2496	7.2916667
EndoEC	SG29	99	2496	3.9663462
Endothelial	SG29	461	2496	18.4695513
Fibroblast	SG29	422	2496	16.9070513
infMacrophage	SG29	379	2496	15.1842949
LEC	SG29	9	2496	0.3605769
NeuralCells	SG29	21	2496	0.8413462
Perivascular	SG29	297	2496	11.8990385
resMacrophage	SG29	391	2496	15.6650641
SMC	SG29	113	2496	4.5272436
Tcell	SG29	121	2496	4.8477564
Adipocytes	SG31	0	1192	0.0000000
Cardiomyocyte	SG31	178	1192	14.9328859
EndoEC	SG31	60	1192	5.0335570
Endothelial	SG31	197	1192	16.5268456
Fibroblast	SG31	354	1192	29.6979866
infMacrophage	SG31	35	1192	2.9362416
LEC	SG31	0	1192	0.0000000
NeuralCells	SG31	22	1192	1.8456376
Perivascular	SG31	179	1192	15.0167785
resMacrophage	SG31	91	1192	7.6342282
SMC	SG31	51	1192	4.2785235
Tcell	SG31	25	1192	2.0973154
Adipocytes	SG32	5	1428	0.3501401
Cardiomyocyte	SG32	40	1428	2.8011204
EndoEC	SG32	73	1428	5.1120448
Endothelial	SG32	511	1428	35.7843137
Fibroblast	SG32	324	1428	22.6890756
infMacrophage	SG32	15	1428	1.0504202
LEC	SG32	2	1428	0.1400560
NeuralCells	SG32	27	1428	1.8907563
Perivascular	SG32	240	1428	16.8067227
resMacrophage	SG32	55	1428	3.8515406
SMC	SG32	82	1428	5.7422969
Tcell	SG32	54	1428	3.7815126
Adipocytes	SG33	8	6286	0.1272669
Cardiomyocyte	SG33	216	6286	3.4362074
EndoEC	SG33	82	6286	1.3044862
Endothelial	SG33	189	6286	3.0066815
Fibroblast	SG33	414	6286	6.5860643
infMacrophage	SG33	1393	6286	22.1603563
LEC	SG33	0	6286	0.0000000
NeuralCells	SG33	38	6286	0.6045180
Perivascular	SG33	178	6286	2.8316895
resMacrophage	SG33	651	6286	10.3563474
SMC	SG33	35	6286	0.5567929
Tcell	SG33	3082	6286	49.0295896
Adipocytes	SG34	0	620	0.0000000
Cardiomyocyte	SG34	177	620	28.5483871
EndoEC	SG34	18	620	2.9032258
Endothelial	SG34	82	620	13.2258065
Fibroblast	SG34	120	620	19.3548387
infMacrophage	SG34	8	620	1.2903226
LEC	SG34	0	620	0.0000000
NeuralCells	SG34	5	620	0.8064516
Perivascular	SG34	122	620	19.6774194
resMacrophage	SG34	42	620	6.7741935
SMC	SG34	22	620	3.5483871
Tcell	SG34	24	620	3.8709677
Adipocytes	SG35	0	2363	0.0000000
Cardiomyocyte	SG35	303	2363	12.8226830
EndoEC	SG35	61	2363	2.5814642
Endothelial	SG35	522	2363	22.0905628
Fibroblast	SG35	794	2363	33.6013542
infMacrophage	SG35	55	2363	2.3275497
LEC	SG35	0	2363	0.0000000
NeuralCells	SG35	28	2363	1.1849344
Perivascular	SG35	322	2363	13.6267457
resMacrophage	SG35	145	2363	6.1362675
SMC	SG35	73	2363	3.0892933
Tcell	SG35	60	2363	2.5391452

ordVec <- datFrac %>% dplyr::filter(label=="Tcell") %>% 
  arrange(., percentage)
ordBar <- c("Tcell","infMacrophage","resMacrophage","Fibroblast","Perivascular",
            "SMC","Endothelial","EndoEC","LEC","Cardiomyocyte","Adipocytes",
            "NeuralCells")
datFrac <-datFrac %>% mutate(labelFac=factor(label, levels = ordBar))
ggbarplot(datFrac, x="ID", y="percentage",
          fill = "labelFac",
          palette = colLab,
          order= ordVec$ID) +
  rotate_x_text(angle = 90)

## only Myocarditis patients
selMyo <- unique(seurat$ID[which(seurat$cond != "HH")])
datFracSel <- datFrac %>% filter(ID %in% selMyo)
ggbarplot(datFracSel, x="ID", y="percentage",
          fill = "labelFac",
          palette = colLab,
          order= ordVec$ID) +
  rotate_x_text(angle = 90)

assign groups based on T cell fraction

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("")

violin plots selected marker

genesDat <- data.frame(EnsID=rownames(seurat)) %>% 
  mutate(gene=gsub(".*\\.", "", EnsID))
selGenes <- data.frame(gene=c("CD2", "TNNT2", "PECAM1", "NRG1", "PROX1",
                              "PDGFRA", "RGS5", "MYH11", "C1QA", "NRXN1",
                              "PLIN1")) %>% 
  left_join(., genesDat, by="gene")

pList <- sapply(selGenes$EnsID, function(x){
  p <- VlnPlot(object = seurat, features = x,
               group.by = "label",
               cols = colLab, pt.size = 0
               ) + 
    theme(legend.position = "none")
    
  plot(p)
})

pList <- sapply(selGenes$EnsID, function(x){
  p <- VlnPlot(object = seurat, features = x,
               group.by = "label",
               cols = colLab, pt.size = 0.3
               ) + 
    theme(legend.position = "none")
    
  plot(p)
})

Feature plots BMPs

## list with all gene names for mapping of EnsIDs
genesDat <- data.frame(EnsID=rownames(seurat)) %>% 
  mutate(gene=gsub(".*\\.", "", EnsID))

## selected genes to plot
selGenes <- data.frame(gene=c("BMP2", "BMP4", "BMPR1A", "BMPR2")) %>% 
  left_join(., genesDat, by="gene")

## plotting loop order=F
pList <- sapply(selGenes$EnsID, function(x){
p <- FeaturePlot(seurat, reduction = "umap", 
            features = x,
            cols=c("lightgrey", "darkred"),
            order = F)+
  theme(legend.position="right")
  plot(p)
})

## plotting loop order=T
pList <- sapply(selGenes$EnsID, function(x){
p <- FeaturePlot(seurat, reduction = "umap", 
            features = x,
            cols=c("lightgrey", "darkred"),
            order = T)+
  theme(legend.position="right")
  plot(p)
})

average counts BMPs in Fibroblasts

seuratSub <- subset(seurat, label=="Fibroblast")

## assay data
clusterAssigned <- as.data.frame(seuratSub$ID) %>%
  dplyr::mutate(cell=rownames(.))
colnames(clusterAssigned)[1] <- "ident"
seuratDat <- GetAssayData(seuratSub)
  
  ## genes of interest
genes <- data.frame(gene=rownames(seuratSub)) %>% 
  mutate(geneID=gsub("^.*\\.", "", gene)) %>% filter(geneID %in% selGenes$gene)

  ## matrix with averaged cnts per ident
logNormExpres <- as.data.frame(t(as.matrix(
  seuratDat[which(rownames(seuratDat) %in% genes$gene),])))
logNormExpres <- logNormExpres %>% dplyr::mutate(cell=rownames(.)) %>%
  dplyr::left_join(.,clusterAssigned, by=c("cell")) %>%
  dplyr::select(-cell) %>% dplyr::group_by(ident) %>%
  dplyr::summarise_all(mean)

write.table(logNormExpres, 
            file=paste0(basedir, "/data/BmpCntsFibroblastsPerPatient_woHH.txt"),
            row.names = F, col.names = T, sep = "\t", quote = F)

save seurat object

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

session info

sessionInfo()

R version 4.2.1 (2022-06-23)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur ... 10.16

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.2/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] stats4    stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
 [1] sctransform_0.3.5           viridis_0.6.2              
 [3] viridisLite_0.4.1           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_1.5-0                   
[11] SeuratObject_4.1.2          Seurat_4.2.0               
[13] forcats_0.5.2               stringr_1.4.1              
[15] dplyr_1.0.10                purrr_0.3.5                
[17] readr_2.1.3                 tidyr_1.2.1                
[19] tibble_3.1.8                ggplot2_3.3.6              
[21] tidyverse_1.3.2             SingleCellExperiment_1.18.1
[23] SummarizedExperiment_1.26.1 Biobase_2.56.0             
[25] GenomicRanges_1.48.0        GenomeInfoDb_1.32.4        
[27] IRanges_2.30.1              S4Vectors_0.34.0           
[29] BiocGenerics_0.42.0         MatrixGenerics_1.8.1       
[31] matrixStats_0.62.0         

loaded via a namespace (and not attached):
  [1] utf8_1.2.2             reticulate_1.26        tidyselect_1.2.0      
  [4] htmlwidgets_1.5.4      grid_4.2.1             Rtsne_0.16            
  [7] munsell_0.5.0          codetools_0.2-18       ica_1.0-3             
 [10] future_1.28.0          miniUI_0.1.1.1         withr_2.5.0           
 [13] spatstat.random_2.2-0  colorspace_2.0-3       progressr_0.11.0      
 [16] highr_0.9              knitr_1.40             rstudioapi_0.14       
 [19] ROCR_1.0-11            ggsignif_0.6.4         tensor_1.5            
 [22] listenv_0.8.0          labeling_0.4.2         git2r_0.30.1          
 [25] GenomeInfoDbData_1.2.8 polyclip_1.10-4        bit64_4.0.5           
 [28] farver_2.1.1           rprojroot_2.0.3        parallelly_1.32.1     
 [31] vctrs_0.5.0            generics_0.1.3         xfun_0.34             
 [34] R6_2.5.1               ggbeeswarm_0.6.0       bitops_1.0-7          
 [37] spatstat.utils_3.0-1   cachem_1.0.6           DelayedArray_0.22.0   
 [40] assertthat_0.2.1       vroom_1.6.0            promises_1.2.0.1      
 [43] scales_1.2.1           googlesheets4_1.0.1    beeswarm_0.4.0        
 [46] rgeos_0.5-9            gtable_0.3.1           globals_0.16.1        
 [49] goftest_1.2-3          workflowr_1.7.0        rlang_1.0.6           
 [52] splines_4.2.1          rstatix_0.7.0          lazyeval_0.2.2        
 [55] gargle_1.2.1           spatstat.geom_2.4-0    broom_1.0.1           
 [58] yaml_2.3.6             reshape2_1.4.4         abind_1.4-5           
 [61] modelr_0.1.9           backports_1.4.1        httpuv_1.6.6          
 [64] tools_4.2.1            ellipsis_0.3.2         spatstat.core_2.4-4   
 [67] jquerylib_0.1.4        RColorBrewer_1.1-3     ggridges_0.5.4        
 [70] Rcpp_1.0.9             plyr_1.8.7             zlibbioc_1.42.0       
 [73] RCurl_1.98-1.9         rpart_4.1.19           deldir_1.0-6          
 [76] pbapply_1.5-0          cowplot_1.1.1          zoo_1.8-11            
 [79] haven_2.5.1            ggrepel_0.9.1          cluster_2.1.4         
 [82] fs_1.5.2               data.table_1.14.4      scattermore_0.8       
 [85] lmtest_0.9-40          reprex_2.0.2           RANN_2.6.1            
 [88] googledrive_2.0.0      fitdistrplus_1.1-8     hms_1.1.2             
 [91] patchwork_1.1.2        mime_0.12              evaluate_0.17         
 [94] xtable_1.8-4           readxl_1.4.1           gridExtra_2.3         
 [97] compiler_4.2.1         KernSmooth_2.23-20     crayon_1.5.2          
[100] htmltools_0.5.3        mgcv_1.8-41            later_1.3.0           
[103] tzdb_0.3.0             lubridate_1.8.0        DBI_1.1.3             
[106] dbplyr_2.2.1           MASS_7.3-58.1          Matrix_1.5-1          
[109] car_3.1-1              cli_3.4.1              parallel_4.2.1        
[112] igraph_1.3.5           pkgconfig_2.0.3        plotly_4.10.0         
[115] spatstat.sparse_3.0-0  xml2_1.3.3             vipor_0.4.5           
[118] bslib_0.4.0            XVector_0.36.0         rvest_1.0.3           
[121] digest_0.6.30          RcppAnnoy_0.0.19       spatstat.data_3.0-0   
[124] rmarkdown_2.17         cellranger_1.1.0       leiden_0.4.3          
[127] uwot_0.1.14            shiny_1.7.2            lifecycle_1.0.3       
[130] nlme_3.1-160           jsonlite_1.8.3         carData_3.0-5         
[133] fansi_1.0.3            pillar_1.8.1           lattice_0.20-45       
[136] ggrastr_1.0.1          fastmap_1.1.0          httr_1.4.4            
[139] survival_3.4-0         glue_1.6.2             png_0.1-7             
[142] bit_4.0.4              stringi_1.7.8          sass_0.4.2            
[145] irlba_2.3.5.1          future.apply_1.9.1

date()

[1] "Tue Nov 29 15:51:21 2022"

sessionInfo()

R version 4.2.1 (2022-06-23)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur ... 10.16

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.2/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] stats4    stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
 [1] sctransform_0.3.5           viridis_0.6.2              
 [3] viridisLite_0.4.1           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_1.5-0                   
[11] SeuratObject_4.1.2          Seurat_4.2.0               
[13] forcats_0.5.2               stringr_1.4.1              
[15] dplyr_1.0.10                purrr_0.3.5                
[17] readr_2.1.3                 tidyr_1.2.1                
[19] tibble_3.1.8                ggplot2_3.3.6              
[21] tidyverse_1.3.2             SingleCellExperiment_1.18.1
[23] SummarizedExperiment_1.26.1 Biobase_2.56.0             
[25] GenomicRanges_1.48.0        GenomeInfoDb_1.32.4        
[27] IRanges_2.30.1              S4Vectors_0.34.0           
[29] BiocGenerics_0.42.0         MatrixGenerics_1.8.1       
[31] matrixStats_0.62.0         

loaded via a namespace (and not attached):
  [1] utf8_1.2.2             reticulate_1.26        tidyselect_1.2.0      
  [4] htmlwidgets_1.5.4      grid_4.2.1             Rtsne_0.16            
  [7] munsell_0.5.0          codetools_0.2-18       ica_1.0-3             
 [10] future_1.28.0          miniUI_0.1.1.1         withr_2.5.0           
 [13] spatstat.random_2.2-0  colorspace_2.0-3       progressr_0.11.0      
 [16] highr_0.9              knitr_1.40             rstudioapi_0.14       
 [19] ROCR_1.0-11            ggsignif_0.6.4         tensor_1.5            
 [22] listenv_0.8.0          labeling_0.4.2         git2r_0.30.1          
 [25] GenomeInfoDbData_1.2.8 polyclip_1.10-4        bit64_4.0.5           
 [28] farver_2.1.1           rprojroot_2.0.3        parallelly_1.32.1     
 [31] vctrs_0.5.0            generics_0.1.3         xfun_0.34             
 [34] R6_2.5.1               ggbeeswarm_0.6.0       bitops_1.0-7          
 [37] spatstat.utils_3.0-1   cachem_1.0.6           DelayedArray_0.22.0   
 [40] assertthat_0.2.1       vroom_1.6.0            promises_1.2.0.1      
 [43] scales_1.2.1           googlesheets4_1.0.1    beeswarm_0.4.0        
 [46] rgeos_0.5-9            gtable_0.3.1           globals_0.16.1        
 [49] goftest_1.2-3          workflowr_1.7.0        rlang_1.0.6           
 [52] splines_4.2.1          rstatix_0.7.0          lazyeval_0.2.2        
 [55] gargle_1.2.1           spatstat.geom_2.4-0    broom_1.0.1           
 [58] yaml_2.3.6             reshape2_1.4.4         abind_1.4-5           
 [61] modelr_0.1.9           backports_1.4.1        httpuv_1.6.6          
 [64] tools_4.2.1            ellipsis_0.3.2         spatstat.core_2.4-4   
 [67] jquerylib_0.1.4        RColorBrewer_1.1-3     ggridges_0.5.4        
 [70] Rcpp_1.0.9             plyr_1.8.7             zlibbioc_1.42.0       
 [73] RCurl_1.98-1.9         rpart_4.1.19           deldir_1.0-6          
 [76] pbapply_1.5-0          cowplot_1.1.1          zoo_1.8-11            
 [79] haven_2.5.1            ggrepel_0.9.1          cluster_2.1.4         
 [82] fs_1.5.2               data.table_1.14.4      scattermore_0.8       
 [85] lmtest_0.9-40          reprex_2.0.2           RANN_2.6.1            
 [88] googledrive_2.0.0      fitdistrplus_1.1-8     hms_1.1.2             
 [91] patchwork_1.1.2        mime_0.12              evaluate_0.17         
 [94] xtable_1.8-4           readxl_1.4.1           gridExtra_2.3         
 [97] compiler_4.2.1         KernSmooth_2.23-20     crayon_1.5.2          
[100] htmltools_0.5.3        mgcv_1.8-41            later_1.3.0           
[103] tzdb_0.3.0             lubridate_1.8.0        DBI_1.1.3             
[106] dbplyr_2.2.1           MASS_7.3-58.1          Matrix_1.5-1          
[109] car_3.1-1              cli_3.4.1              parallel_4.2.1        
[112] igraph_1.3.5           pkgconfig_2.0.3        plotly_4.10.0         
[115] spatstat.sparse_3.0-0  xml2_1.3.3             vipor_0.4.5           
[118] bslib_0.4.0            XVector_0.36.0         rvest_1.0.3           
[121] digest_0.6.30          RcppAnnoy_0.0.19       spatstat.data_3.0-0   
[124] rmarkdown_2.17         cellranger_1.1.0       leiden_0.4.3          
[127] uwot_0.1.14            shiny_1.7.2            lifecycle_1.0.3       
[130] nlme_3.1-160           jsonlite_1.8.3         carData_3.0-5         
[133] fansi_1.0.3            pillar_1.8.1           lattice_0.20-45       
[136] ggrastr_1.0.1          fastmap_1.1.0          httr_1.4.4            
[139] survival_3.4-0         glue_1.6.2             png_0.1-7             
[142] bit_4.0.4              stringi_1.7.8          sass_0.4.2            
[145] irlba_2.3.5.1          future.apply_1.9.1

assign labels

Mechthild Lütge

31 July 2022

load packages

load data

assign labels

color vectors

vis data

clusters

cluster plus re-embedding

label

technique

Sample

ID

Origin

isolation

cond

processing

cnt tables

per patient

assign groups based on T cell fraction

vis marker for celltype assignment

heatmap

Dotplot

violin plots selected marker

Feature plots BMPs

average counts BMPs in Fibroblasts

save seurat object

session info