assign labels

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Last updated: 2022-12-02

Checks: 6 1

Knit directory: humanCardiacFibroblasts/

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Rmd	e469ce4	mluetge	2022-11-29	add samples with Htrans
html	e469ce4	mluetge	2022-11-29	add samples with Htrans

---

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 SG21 SG24 SG25 SG28 
2740 1268 4439  436 1370 2280 1684 2706 2396  545  781  491 1536 2381 3131 1545 
SG29 SG31 SG32 SG33 SG34 SG35 
2496 1192 1428 6286  620 2363 

assign labels

seurat$label <- "other"
seurat$label[which(seurat$integrated_snn_res.0.6 %in% c("2","9"))] <- "Endothelial"
seurat$label[which(seurat$integrated_snn_res.0.6 %in% c("8"))] <- "EndoEC"
seurat$label[which(seurat$integrated_snn_res.0.6 %in% c("13"))] <- "LEC"
seurat$label[which(seurat$integrated_snn_res.0.6 %in% c("4", "11"))] <- "Tcell"
seurat$label[which(seurat$integrated_snn_res.0.6 %in% c("5"))] <- "Cardiomyocyte"
seurat$label[which(seurat$integrated_snn_res.0.6 %in% c("0", "3"))] <- "Fibroblast"
seurat$label[which(seurat$integrated_snn_res.0.6 %in% c("1"))] <- "Perivascular"
seurat$label[which(seurat$integrated_snn_res.0.6 %in% c("10"))] <- "SMC"
seurat$label[which(seurat$integrated_snn_res.0.6 %in% c("7"))] <- "resMacrophage"
seurat$label[which(seurat$integrated_snn_res.0.6 %in% c("6"))] <- "infMacrophage"
seurat$label[which(seurat$integrated_snn_res.0.6 %in% c("12"))] <- "NeuralCells"
seurat$label[which(seurat$integrated_snn_res.0.6 %in% c("14"))] <- "Adipocytes"

# ## reembedding of cluster 4 (mix of diff cell typpes)
# seuratSub <- subset(seurat, integrated_snn_res.0.4=="4")
# 
# seuratSub <- rerunSeurat3(seuratSub)
# 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$integrated_snn_res.0.4)
# seurat$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))]

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")

Past versions of "vis cluster-1.png"

Version	Author	Date
e469ce4	mluetge	2022-11-29

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

Past versions of "vis cluster-2.png"

Version	Author	Date
e469ce4	mluetge	2022-11-29

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")

Past versions of "vis label-1.png"

Version	Author	Date
e469ce4	mluetge	2022-11-29

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

Past versions of "vis label-2.png"

Version	Author	Date
e469ce4	mluetge	2022-11-29

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")

Past versions of "vis tecnique-1.png"

Version	Author	Date
e469ce4	mluetge	2022-11-29

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")

Past versions of "vis sample-1.png"

Version	Author	Date
e469ce4	mluetge	2022-11-29

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")

Past versions of "vis ID-1.png"

Version	Author	Date
e469ce4	mluetge	2022-11-29

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

Past versions of "vis ID-2.png"

Version	Author	Date
e469ce4	mluetge	2022-11-29

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")

Past versions of "vis origin-1.png"

Version	Author	Date
e469ce4	mluetge	2022-11-29

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

Past versions of "vis origin-2.png"

Version	Author	Date
e469ce4	mluetge	2022-11-29

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")

Past versions of "vis isolation-1.png"

Version	Author	Date
e469ce4	mluetge	2022-11-29

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")

Past versions of "vis cond-1.png"

Version	Author	Date
e469ce4	mluetge	2022-11-29

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")

Past versions of "vis proc-1.png"

Version	Author	Date
e469ce4	mluetge	2022-11-29

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
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	SG21	SG24	SG25	SG28	SG29	SG31	SG32	SG33	SG34	SG35
Adipocytes	0	0	0	0	0	0	1	0	2	1	0	0	1	3	49	3	6	0	5	8	0	0
Cardiomyocyte	207	50	116	25	71	329	106	46	238	44	39	12	96	298	396	73	178	178	40	212	174	304
EndoEC	184	43	172	23	32	211	139	349	215	69	42	59	50	108	116	82	124	67	92	96	23	91
Endothelial	280	279	912	50	161	426	297	890	349	117	203	95	177	377	535	471	440	190	485	160	82	486
Fibroblast	814	317	1123	130	374	627	661	653	847	142	264	155	614	869	844	322	434	357	320	414	117	778
infMacrophage	112	23	110	33	101	25	28	46	55	21	12	30	35	51	98	12	297	39	27	1407	13	74
LEC	6	0	0	0	0	6	6	2	30	0	0	0	1	9	0	1	15	0	4	0	0	0
NeuralCells	58	10	76	7	28	28	28	20	31	7	26	2	7	28	26	26	24	22	26	24	5	27
Perivascular	487	354	1348	20	147	387	270	352	407	62	122	53	318	286	460	432	310	183	241	135	121	328
resMacrophage	188	70	208	20	232	69	56	95	63	33	19	33	120	119	147	8	151	88	43	722	38	125
SMC	186	85	188	18	40	129	48	119	82	22	36	15	72	117	99	94	98	45	67	28	22	65
Tcell	218	37	186	110	184	43	44	134	77	27	18	37	45	116	361	21	419	23	78	3080	25	85

## 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	207	2740	7.5547445
EndoEC	GZ1	184	2740	6.7153285
Endothelial	GZ1	280	2740	10.2189781
Fibroblast	GZ1	814	2740	29.7080292
infMacrophage	GZ1	112	2740	4.0875912
LEC	GZ1	6	2740	0.2189781
NeuralCells	GZ1	58	2740	2.1167883
Perivascular	GZ1	487	2740	17.7737226
resMacrophage	GZ1	188	2740	6.8613139
SMC	GZ1	186	2740	6.7883212
Tcell	GZ1	218	2740	7.9562044
Adipocytes	GZ14	0	1268	0.0000000
Cardiomyocyte	GZ14	50	1268	3.9432177
EndoEC	GZ14	43	1268	3.3911672
Endothelial	GZ14	279	1268	22.0031546
Fibroblast	GZ14	317	1268	25.0000000
infMacrophage	GZ14	23	1268	1.8138801
LEC	GZ14	0	1268	0.0000000
NeuralCells	GZ14	10	1268	0.7886435
Perivascular	GZ14	354	1268	27.9179811
resMacrophage	GZ14	70	1268	5.5205047
SMC	GZ14	85	1268	6.7034700
Tcell	GZ14	37	1268	2.9179811
Adipocytes	GZ15	0	4439	0.0000000
Cardiomyocyte	GZ15	116	4439	2.6132012
EndoEC	GZ15	172	4439	3.8747466
Endothelial	GZ15	912	4439	20.5451678
Fibroblast	GZ15	1123	4439	25.2984907
infMacrophage	GZ15	110	4439	2.4780356
LEC	GZ15	0	4439	0.0000000
NeuralCells	GZ15	76	4439	1.7120973
Perivascular	GZ15	1348	4439	30.3671998
resMacrophage	GZ15	208	4439	4.6857400
SMC	GZ15	188	4439	4.2351881
Tcell	GZ15	186	4439	4.1901329
Adipocytes	GZ16	0	436	0.0000000
Cardiomyocyte	GZ16	25	436	5.7339450
EndoEC	GZ16	23	436	5.2752294
Endothelial	GZ16	50	436	11.4678899
Fibroblast	GZ16	130	436	29.8165138
infMacrophage	GZ16	33	436	7.5688073
LEC	GZ16	0	436	0.0000000
NeuralCells	GZ16	7	436	1.6055046
Perivascular	GZ16	20	436	4.5871560
resMacrophage	GZ16	20	436	4.5871560
SMC	GZ16	18	436	4.1284404
Tcell	GZ16	110	436	25.2293578
Adipocytes	GZ17	0	1370	0.0000000
Cardiomyocyte	GZ17	71	1370	5.1824818
EndoEC	GZ17	32	1370	2.3357664
Endothelial	GZ17	161	1370	11.7518248
Fibroblast	GZ17	374	1370	27.2992701
infMacrophage	GZ17	101	1370	7.3722628
LEC	GZ17	0	1370	0.0000000
NeuralCells	GZ17	28	1370	2.0437956
Perivascular	GZ17	147	1370	10.7299270
resMacrophage	GZ17	232	1370	16.9343066
SMC	GZ17	40	1370	2.9197080
Tcell	GZ17	184	1370	13.4306569
Adipocytes	GZ18	0	2280	0.0000000
Cardiomyocyte	GZ18	329	2280	14.4298246
EndoEC	GZ18	211	2280	9.2543860
Endothelial	GZ18	426	2280	18.6842105
Fibroblast	GZ18	627	2280	27.5000000
infMacrophage	GZ18	25	2280	1.0964912
LEC	GZ18	6	2280	0.2631579
NeuralCells	GZ18	28	2280	1.2280702
Perivascular	GZ18	387	2280	16.9736842
resMacrophage	GZ18	69	2280	3.0263158
SMC	GZ18	129	2280	5.6578947
Tcell	GZ18	43	2280	1.8859649
Adipocytes	GZ2	1	1684	0.0593824
Cardiomyocyte	GZ2	106	1684	6.2945368
EndoEC	GZ2	139	1684	8.2541568
Endothelial	GZ2	297	1684	17.6365796
Fibroblast	GZ2	661	1684	39.2517815
infMacrophage	GZ2	28	1684	1.6627078
LEC	GZ2	6	1684	0.3562945
NeuralCells	GZ2	28	1684	1.6627078
Perivascular	GZ2	270	1684	16.0332542
resMacrophage	GZ2	56	1684	3.3254157
SMC	GZ2	48	1684	2.8503563
Tcell	GZ2	44	1684	2.6128266
Adipocytes	GZ20	0	2706	0.0000000
Cardiomyocyte	GZ20	46	2706	1.6999261
EndoEC	GZ20	349	2706	12.8972653
Endothelial	GZ20	890	2706	32.8898744
Fibroblast	GZ20	653	2706	24.1315595
infMacrophage	GZ20	46	2706	1.6999261
LEC	GZ20	2	2706	0.0739098
NeuralCells	GZ20	20	2706	0.7390983
Perivascular	GZ20	352	2706	13.0081301
resMacrophage	GZ20	95	2706	3.5107169
SMC	GZ20	119	2706	4.3976349
Tcell	GZ20	134	2706	4.9519586
Adipocytes	GZ3	2	2396	0.0834725
Cardiomyocyte	GZ3	238	2396	9.9332220
EndoEC	GZ3	215	2396	8.9732888
Endothelial	GZ3	349	2396	14.5659432
Fibroblast	GZ3	847	2396	35.3505843
infMacrophage	GZ3	55	2396	2.2954925
LEC	GZ3	30	2396	1.2520868
NeuralCells	GZ3	31	2396	1.2938230
Perivascular	GZ3	407	2396	16.9866444
resMacrophage	GZ3	63	2396	2.6293823
SMC	GZ3	82	2396	3.4223706
Tcell	GZ3	77	2396	3.2136895
Adipocytes	GZ4	1	545	0.1834862
Cardiomyocyte	GZ4	44	545	8.0733945
EndoEC	GZ4	69	545	12.6605505
Endothelial	GZ4	117	545	21.4678899
Fibroblast	GZ4	142	545	26.0550459
infMacrophage	GZ4	21	545	3.8532110
LEC	GZ4	0	545	0.0000000
NeuralCells	GZ4	7	545	1.2844037
Perivascular	GZ4	62	545	11.3761468
resMacrophage	GZ4	33	545	6.0550459
SMC	GZ4	22	545	4.0366972
Tcell	GZ4	27	545	4.9541284
Adipocytes	GZ5	0	781	0.0000000
Cardiomyocyte	GZ5	39	781	4.9935980
EndoEC	GZ5	42	781	5.3777209
Endothelial	GZ5	203	781	25.9923175
Fibroblast	GZ5	264	781	33.8028169
infMacrophage	GZ5	12	781	1.5364917
LEC	GZ5	0	781	0.0000000
NeuralCells	GZ5	26	781	3.3290653
Perivascular	GZ5	122	781	15.6209987
resMacrophage	GZ5	19	781	2.4327785
SMC	GZ5	36	781	4.6094750
Tcell	GZ5	18	781	2.3047375
Adipocytes	GZ6	0	491	0.0000000
Cardiomyocyte	GZ6	12	491	2.4439919
EndoEC	GZ6	59	491	12.0162933
Endothelial	GZ6	95	491	19.3482688
Fibroblast	GZ6	155	491	31.5682281
infMacrophage	GZ6	30	491	6.1099796
LEC	GZ6	0	491	0.0000000
NeuralCells	GZ6	2	491	0.4073320
Perivascular	GZ6	53	491	10.7942974
resMacrophage	GZ6	33	491	6.7209776
SMC	GZ6	15	491	3.0549898
Tcell	GZ6	37	491	7.5356415
Adipocytes	SG21	1	1536	0.0651042
Cardiomyocyte	SG21	96	1536	6.2500000
EndoEC	SG21	50	1536	3.2552083
Endothelial	SG21	177	1536	11.5234375
Fibroblast	SG21	614	1536	39.9739583
infMacrophage	SG21	35	1536	2.2786458
LEC	SG21	1	1536	0.0651042
NeuralCells	SG21	7	1536	0.4557292
Perivascular	SG21	318	1536	20.7031250
resMacrophage	SG21	120	1536	7.8125000
SMC	SG21	72	1536	4.6875000
Tcell	SG21	45	1536	2.9296875
Adipocytes	SG24	3	2381	0.1259975
Cardiomyocyte	SG24	298	2381	12.5157497
EndoEC	SG24	108	2381	4.5359093
Endothelial	SG24	377	2381	15.8336833
Fibroblast	SG24	869	2381	36.4972701
infMacrophage	SG24	51	2381	2.1419572
LEC	SG24	9	2381	0.3779924
NeuralCells	SG24	28	2381	1.1759765
Perivascular	SG24	286	2381	12.0117598
resMacrophage	SG24	119	2381	4.9979000
SMC	SG24	117	2381	4.9139017
Tcell	SG24	116	2381	4.8719026
Adipocytes	SG25	49	3131	1.5649952
Cardiomyocyte	SG25	396	3131	12.6477164
EndoEC	SG25	116	3131	3.7048866
Endothelial	SG25	535	3131	17.0871926
Fibroblast	SG25	844	3131	26.9562440
infMacrophage	SG25	98	3131	3.1299904
LEC	SG25	0	3131	0.0000000
NeuralCells	SG25	26	3131	0.8304056
Perivascular	SG25	460	3131	14.6917918
resMacrophage	SG25	147	3131	4.6949856
SMC	SG25	99	3131	3.1619291
Tcell	SG25	361	3131	11.5298627
Adipocytes	SG28	3	1545	0.1941748
Cardiomyocyte	SG28	73	1545	4.7249191
EndoEC	SG28	82	1545	5.3074434
Endothelial	SG28	471	1545	30.4854369
Fibroblast	SG28	322	1545	20.8414239
infMacrophage	SG28	12	1545	0.7766990
LEC	SG28	1	1545	0.0647249
NeuralCells	SG28	26	1545	1.6828479
Perivascular	SG28	432	1545	27.9611650
resMacrophage	SG28	8	1545	0.5177994
SMC	SG28	94	1545	6.0841424
Tcell	SG28	21	1545	1.3592233
Adipocytes	SG29	6	2496	0.2403846
Cardiomyocyte	SG29	178	2496	7.1314103
EndoEC	SG29	124	2496	4.9679487
Endothelial	SG29	440	2496	17.6282051
Fibroblast	SG29	434	2496	17.3878205
infMacrophage	SG29	297	2496	11.8990385
LEC	SG29	15	2496	0.6009615
NeuralCells	SG29	24	2496	0.9615385
Perivascular	SG29	310	2496	12.4198718
resMacrophage	SG29	151	2496	6.0496795
SMC	SG29	98	2496	3.9262821
Tcell	SG29	419	2496	16.7868590
Adipocytes	SG31	0	1192	0.0000000
Cardiomyocyte	SG31	178	1192	14.9328859
EndoEC	SG31	67	1192	5.6208054
Endothelial	SG31	190	1192	15.9395973
Fibroblast	SG31	357	1192	29.9496644
infMacrophage	SG31	39	1192	3.2718121
LEC	SG31	0	1192	0.0000000
NeuralCells	SG31	22	1192	1.8456376
Perivascular	SG31	183	1192	15.3523490
resMacrophage	SG31	88	1192	7.3825503
SMC	SG31	45	1192	3.7751678
Tcell	SG31	23	1192	1.9295302
Adipocytes	SG32	5	1428	0.3501401
Cardiomyocyte	SG32	40	1428	2.8011204
EndoEC	SG32	92	1428	6.4425770
Endothelial	SG32	485	1428	33.9635854
Fibroblast	SG32	320	1428	22.4089636
infMacrophage	SG32	27	1428	1.8907563
LEC	SG32	4	1428	0.2801120
NeuralCells	SG32	26	1428	1.8207283
Perivascular	SG32	241	1428	16.8767507
resMacrophage	SG32	43	1428	3.0112045
SMC	SG32	67	1428	4.6918768
Tcell	SG32	78	1428	5.4621849
Adipocytes	SG33	8	6286	0.1272669
Cardiomyocyte	SG33	212	6286	3.3725740
EndoEC	SG33	96	6286	1.5272033
Endothelial	SG33	160	6286	2.5453388
Fibroblast	SG33	414	6286	6.5860643
infMacrophage	SG33	1407	6286	22.3830735
LEC	SG33	0	6286	0.0000000
NeuralCells	SG33	24	6286	0.3818008
Perivascular	SG33	135	6286	2.1476297
resMacrophage	SG33	722	6286	11.4858416
SMC	SG33	28	6286	0.4454343
Tcell	SG33	3080	6286	48.9977728
Adipocytes	SG34	0	620	0.0000000
Cardiomyocyte	SG34	174	620	28.0645161
EndoEC	SG34	23	620	3.7096774
Endothelial	SG34	82	620	13.2258065
Fibroblast	SG34	117	620	18.8709677
infMacrophage	SG34	13	620	2.0967742
LEC	SG34	0	620	0.0000000
NeuralCells	SG34	5	620	0.8064516
Perivascular	SG34	121	620	19.5161290
resMacrophage	SG34	38	620	6.1290323
SMC	SG34	22	620	3.5483871
Tcell	SG34	25	620	4.0322581
Adipocytes	SG35	0	2363	0.0000000
Cardiomyocyte	SG35	304	2363	12.8650021
EndoEC	SG35	91	2363	3.8510368
Endothelial	SG35	486	2363	20.5670758
Fibroblast	SG35	778	2363	32.9242488
infMacrophage	SG35	74	2363	3.1316124
LEC	SG35	0	2363	0.0000000
NeuralCells	SG35	27	2363	1.1426153
Perivascular	SG35	328	2363	13.8806602
resMacrophage	SG35	125	2363	5.2898857
SMC	SG35	65	2363	2.7507406
Tcell	SG35	85	2363	3.5971223

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)

Past versions of "cnt tables per pat-1.png"

Version	Author	Date
e469ce4	mluetge	2022-11-29

## 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)

Past versions of "cnt tables per pat-2.png"

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e469ce4	mluetge	2022-11-29

assign groups based on T cell fraction

TcellGrp <- read_tsv(paste0(basedir, "/data/assignTcellGrp.txt"))
IDtoTcell <- data.frame(ID=seurat$ID) %>% left_join(., TcellGrp, by="ID")
seurat$TcellGrp <- IDtoTcell$TcellGrp
table(seurat$TcellGrp)

TcellHigh  TcellInt  TcellLow 
    16950     14482     12682 

table(seurat$TcellGrp, seurat$ID)

           
             GZ1 GZ14 GZ15 GZ16 GZ17 GZ18  GZ2 GZ20  GZ3  GZ4  GZ5  GZ6 SG21
  TcellHigh 2740    0    0  436 1370    0    0    0    0    0    0  491    0
  TcellInt     0    0 4439    0    0    0    0 2706    0  545    0    0    0
  TcellLow     0 1268    0    0    0 2280 1684    0 2396    0  781    0 1536
           
            SG24 SG25 SG28 SG29 SG31 SG32 SG33 SG34 SG35
  TcellHigh    0 3131    0 2496    0    0 6286    0    0
  TcellInt  2381    0    0    0    0 1428    0  620 2363
  TcellLow     0    0 1545    0 1192    0    0    0    0

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)

Past versions of "vis sel marker-1.png"

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e469ce4	mluetge	2022-11-29

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)

Past versions of "vis sel marker-2.png"

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e469ce4	mluetge	2022-11-29

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("")

Past versions of dotplot-1.png

Version	Author	Date
e469ce4	mluetge	2022-11-29

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("")

Past versions of dotplot-2.png

Version	Author	Date
e469ce4	mluetge	2022-11-29

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)
})

Past versions of "vlnplt sel marker-1.png"

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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)
})

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e469ce4	mluetge	2022-11-29

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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)
})

Past versions of "feautreplots BMPs-1.png"

Version	Author	Date
e469ce4	mluetge	2022-11-29

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e469ce4	mluetge	2022-11-29

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e469ce4	mluetge	2022-11-29

## 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)
})

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e469ce4	mluetge	2022-11-29

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Version	Author	Date
e469ce4	mluetge	2022-11-29

Feature plots BMPs split by TcellGrp

## plotting loop order=F
pList <- sapply(selGenes$EnsID, function(x){
p <- FeaturePlot(seurat, reduction = "umap", 
            features = x,
            cols=c("lightgrey", "darkred"),
            order = F,
            split.by = "TcellGrp")+
  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,
            split.by = "TcellGrp")+
  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      whisker_0.4            fitdistrplus_1.1-8    
 [91] hms_1.1.2              patchwork_1.1.2        mime_0.12             
 [94] evaluate_0.17          xtable_1.8-4           readxl_1.4.1          
 [97] gridExtra_2.3          compiler_4.2.1         KernSmooth_2.23-20    
[100] crayon_1.5.2           htmltools_0.5.3        mgcv_1.8-41           
[103] later_1.3.0            tzdb_0.3.0             lubridate_1.8.0       
[106] DBI_1.1.3              dbplyr_2.2.1           MASS_7.3-58.1         
[109] Matrix_1.5-1           car_3.1-1              cli_3.4.1             
[112] parallel_4.2.1         igraph_1.3.5           pkgconfig_2.0.3       
[115] plotly_4.10.0          spatstat.sparse_3.0-0  xml2_1.3.3            
[118] vipor_0.4.5            bslib_0.4.0            XVector_0.36.0        
[121] rvest_1.0.3            digest_0.6.30          RcppAnnoy_0.0.19      
[124] spatstat.data_3.0-0    rmarkdown_2.17         cellranger_1.1.0      
[127] leiden_0.4.3           uwot_0.1.14            shiny_1.7.2           
[130] lifecycle_1.0.3        nlme_3.1-160           jsonlite_1.8.3        
[133] carData_3.0-5          fansi_1.0.3            pillar_1.8.1          
[136] lattice_0.20-45        ggrastr_1.0.1          fastmap_1.1.0         
[139] httr_1.4.4             survival_3.4-0         glue_1.6.2            
[142] png_0.1-7              bit_4.0.4              stringi_1.7.8         
[145] sass_0.4.2             irlba_2.3.5.1          future.apply_1.9.1    

date()

[1] "Fri Dec  2 11:27:12 2022"

Session information

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      whisker_0.4            fitdistrplus_1.1-8    
 [91] hms_1.1.2              patchwork_1.1.2        mime_0.12             
 [94] evaluate_0.17          xtable_1.8-4           readxl_1.4.1          
 [97] gridExtra_2.3          compiler_4.2.1         KernSmooth_2.23-20    
[100] crayon_1.5.2           htmltools_0.5.3        mgcv_1.8-41           
[103] later_1.3.0            tzdb_0.3.0             lubridate_1.8.0       
[106] DBI_1.1.3              dbplyr_2.2.1           MASS_7.3-58.1         
[109] Matrix_1.5-1           car_3.1-1              cli_3.4.1             
[112] parallel_4.2.1         igraph_1.3.5           pkgconfig_2.0.3       
[115] plotly_4.10.0          spatstat.sparse_3.0-0  xml2_1.3.3            
[118] vipor_0.4.5            bslib_0.4.0            XVector_0.36.0        
[121] rvest_1.0.3            digest_0.6.30          RcppAnnoy_0.0.19      
[124] spatstat.data_3.0-0    rmarkdown_2.17         cellranger_1.1.0      
[127] leiden_0.4.3           uwot_0.1.14            shiny_1.7.2           
[130] lifecycle_1.0.3        nlme_3.1-160           jsonlite_1.8.3        
[133] carData_3.0-5          fansi_1.0.3            pillar_1.8.1          
[136] lattice_0.20-45        ggrastr_1.0.1          fastmap_1.1.0         
[139] httr_1.4.4             survival_3.4-0         glue_1.6.2            
[142] png_0.1-7              bit_4.0.4              stringi_1.7.8         
[145] sass_0.4.2             irlba_2.3.5.1          future.apply_1.9.1