Merge samples

Last updated: 2022-10-13

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Knit directory: humanCardiacFibroblasts/

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File	Version	Author	Date	Message
Rmd	e6d0c3c	mluetge	2022-09-26	add final human samples
html	e6d0c3c	mluetge	2022-09-26	add final human samples
Rmd	cd902dd	mluetge	2022-09-12	add human cardiac samples GZ and SG
html	cd902dd	mluetge	2022-09-12	add human cardiac samples GZ and SG
Rmd	141fae8	mluetge	2022-07-06	assign labels
html	141fae8	mluetge	2022-07-06	assign labels

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$integrated_snn_res.0.4 %in% c("2","9","7","10","12"))] <- "Endothelial"
seurat$label[which(seurat$integrated_snn_res.0.4 %in% c("4"))] <- "Tcell"
seurat$label[which(seurat$integrated_snn_res.0.4 %in% c("3"))] <- "Cardiomyocyte"
seurat$label[which(seurat$integrated_snn_res.0.4 %in% c("0"))] <- "Fibroblast"
seurat$label[which(seurat$integrated_snn_res.0.4 %in% c("1","8"))] <- "Perivascular"
seurat$label[which(seurat$integrated_snn_res.0.4 %in% c("5"))] <- "resMacrophage"
seurat$label[which(seurat$integrated_snn_res.0.4 %in% c("6"))] <- "infMacrophage"

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$processing)))
colLab <- pal_futurama()(length(unique(seurat$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")

Version	Author	Date
e6d0c3c	mluetge	2022-09-26
cd902dd	mluetge	2022-09-12
141fae8	mluetge	2022-07-06

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")

Version	Author	Date
e6d0c3c	mluetge	2022-09-26
cd902dd	mluetge	2022-09-12
141fae8	mluetge	2022-07-06

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")

Version	Author	Date
e6d0c3c	mluetge	2022-09-26
cd902dd	mluetge	2022-09-12
141fae8	mluetge	2022-07-06

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")

Version	Author	Date
e6d0c3c	mluetge	2022-09-26
cd902dd	mluetge	2022-09-12
141fae8	mluetge	2022-07-06

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")

Version	Author	Date
e6d0c3c	mluetge	2022-09-26
cd902dd	mluetge	2022-09-12
141fae8	mluetge	2022-07-06

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")

Version	Author	Date
e6d0c3c	mluetge	2022-09-26
cd902dd	mluetge	2022-09-12
141fae8	mluetge	2022-07-06

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")

Version	Author	Date
e6d0c3c	mluetge	2022-09-26
cd902dd	mluetge	2022-09-12
141fae8	mluetge	2022-07-06

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")

Version	Author	Date
e6d0c3c	mluetge	2022-09-26
cd902dd	mluetge	2022-09-12
141fae8	mluetge	2022-07-06

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")

Version	Author	Date
e6d0c3c	mluetge	2022-09-26
cd902dd	mluetge	2022-09-12
141fae8	mluetge	2022-07-06

cnt tables

per patient

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

Var1	Freq
GZ1	2740
GZ10	3731
GZ11	3991
GZ12	3818
GZ13	9882
GZ14	1268
GZ15	4439
GZ16	436
GZ17	1370
GZ18	2280
GZ19	111
GZ2	1684
GZ20	2706
GZ21	1442
GZ22	1998
GZ23	841
GZ24	1480
GZ3	2396
GZ4	545
GZ5	781
GZ6	491
GZ7	653
GZ8	3921
GZ9	4908
SG29	1242
SG30	236
SG31	1192
SG32	1428
SG33	6286
SG34	620
SG35	2363

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

	GZ1	GZ10	GZ11	GZ12	GZ13	GZ14	GZ15	GZ16	GZ17	GZ18	GZ19	GZ2	GZ20	GZ21	GZ22	GZ23	GZ24	GZ3	GZ4	GZ5	GZ6	GZ7	GZ8	GZ9	SG29	SG30	SG31	SG32	SG33	SG34	SG35
Cardiomyocyte	208	1098	299	117	2207	50	117	25	71	328	1	108	46	58	159	54	117	234	45	39	12	88	714	650	109	47	178	40	212	173	304
Endothelial	471	421	819	1188	2352	326	1098	73	195	650	30	456	1256	258	279	226	286	616	184	245	155	139	795	1037	168	74	259	593	257	105	598
Fibroblast	715	983	841	1110	2217	316	1095	88	368	623	16	638	622	99	314	68	268	812	135	261	150	220	1144	1466	140	41	347	306	401	115	759
infMacrophage	162	108	275	65	288	19	109	32	106	18	7	29	36	38	113	31	29	43	22	11	19	5	120	228	187	5	36	19	1270	10	76
other	59	93	27	22	205	10	77	11	28	29	1	30	20	15	30	7	11	37	8	27	3	53	69	34	15	0	23	33	32	6	29
Perivascular	749	523	1200	1096	1817	439	1535	82	190	518	33	323	499	803	728	337	657	525	91	156	72	123	744	1000	104	56	234	321	171	145	396
resMacrophage	137	380	384	154	611	72	223	24	225	75	10	56	101	143	331	91	77	76	32	20	44	13	240	409	136	9	88	46	898	41	129
Tcell	239	125	146	66	185	36	185	101	187	39	13	44	126	28	44	27	35	53	28	22	36	12	95	84	383	4	27	70	3045	25	72

## 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	208	2740	7.5912409
Endothelial	GZ1	471	2740	17.1897810
Fibroblast	GZ1	715	2740	26.0948905
infMacrophage	GZ1	162	2740	5.9124088
other	GZ1	59	2740	2.1532847
Perivascular	GZ1	749	2740	27.3357664
resMacrophage	GZ1	137	2740	5.0000000
Tcell	GZ1	239	2740	8.7226277
Cardiomyocyte	GZ10	1098	3731	29.4291075
Endothelial	GZ10	421	3731	11.2838381
Fibroblast	GZ10	983	3731	26.3468239
infMacrophage	GZ10	108	3731	2.8946663
other	GZ10	93	3731	2.4926293
Perivascular	GZ10	523	3731	14.0176896
resMacrophage	GZ10	380	3731	10.1849370
Tcell	GZ10	125	3731	3.3503082
Cardiomyocyte	GZ11	299	3991	7.4918567
Endothelial	GZ11	819	3991	20.5211726
Fibroblast	GZ11	841	3991	21.0724129
infMacrophage	GZ11	275	3991	6.8905036
other	GZ11	27	3991	0.6765222
Perivascular	GZ11	1200	3991	30.0676522
resMacrophage	GZ11	384	3991	9.6216487
Tcell	GZ11	146	3991	3.6582310
Cardiomyocyte	GZ12	117	3818	3.0644316
Endothelial	GZ12	1188	3818	31.1157674
Fibroblast	GZ12	1110	3818	29.0728130
infMacrophage	GZ12	65	3818	1.7024620
other	GZ12	22	3818	0.5762179
Perivascular	GZ12	1096	3818	28.7061289
resMacrophage	GZ12	154	3818	4.0335254
Tcell	GZ12	66	3818	1.7286537
Cardiomyocyte	GZ13	2207	9882	22.3335357
Endothelial	GZ13	2352	9882	23.8008500
Fibroblast	GZ13	2217	9882	22.4347298
infMacrophage	GZ13	288	9882	2.9143898
other	GZ13	205	9882	2.0744789
Perivascular	GZ13	1817	9882	18.3869662
resMacrophage	GZ13	611	9882	6.1829589
Tcell	GZ13	185	9882	1.8720907
Cardiomyocyte	GZ14	50	1268	3.9432177
Endothelial	GZ14	326	1268	25.7097792
Fibroblast	GZ14	316	1268	24.9211356
infMacrophage	GZ14	19	1268	1.4984227
other	GZ14	10	1268	0.7886435
Perivascular	GZ14	439	1268	34.6214511
resMacrophage	GZ14	72	1268	5.6782334
Tcell	GZ14	36	1268	2.8391167
Cardiomyocyte	GZ15	117	4439	2.6357288
Endothelial	GZ15	1098	4439	24.7353007
Fibroblast	GZ15	1095	4439	24.6677180
infMacrophage	GZ15	109	4439	2.4555080
other	GZ15	77	4439	1.7346249
Perivascular	GZ15	1535	4439	34.5798603
resMacrophage	GZ15	223	4439	5.0236540
Tcell	GZ15	185	4439	4.1676053
Cardiomyocyte	GZ16	25	436	5.7339450
Endothelial	GZ16	73	436	16.7431193
Fibroblast	GZ16	88	436	20.1834862
infMacrophage	GZ16	32	436	7.3394495
other	GZ16	11	436	2.5229358
Perivascular	GZ16	82	436	18.8073394
resMacrophage	GZ16	24	436	5.5045872
Tcell	GZ16	101	436	23.1651376
Cardiomyocyte	GZ17	71	1370	5.1824818
Endothelial	GZ17	195	1370	14.2335766
Fibroblast	GZ17	368	1370	26.8613139
infMacrophage	GZ17	106	1370	7.7372263
other	GZ17	28	1370	2.0437956
Perivascular	GZ17	190	1370	13.8686131
resMacrophage	GZ17	225	1370	16.4233577
Tcell	GZ17	187	1370	13.6496350
Cardiomyocyte	GZ18	328	2280	14.3859649
Endothelial	GZ18	650	2280	28.5087719
Fibroblast	GZ18	623	2280	27.3245614
infMacrophage	GZ18	18	2280	0.7894737
other	GZ18	29	2280	1.2719298
Perivascular	GZ18	518	2280	22.7192982
resMacrophage	GZ18	75	2280	3.2894737
Tcell	GZ18	39	2280	1.7105263
Cardiomyocyte	GZ19	1	111	0.9009009
Endothelial	GZ19	30	111	27.0270270
Fibroblast	GZ19	16	111	14.4144144
infMacrophage	GZ19	7	111	6.3063063
other	GZ19	1	111	0.9009009
Perivascular	GZ19	33	111	29.7297297
resMacrophage	GZ19	10	111	9.0090090
Tcell	GZ19	13	111	11.7117117
Cardiomyocyte	GZ2	108	1684	6.4133017
Endothelial	GZ2	456	1684	27.0783848
Fibroblast	GZ2	638	1684	37.8859857
infMacrophage	GZ2	29	1684	1.7220903
other	GZ2	30	1684	1.7814727
Perivascular	GZ2	323	1684	19.1805226
resMacrophage	GZ2	56	1684	3.3254157
Tcell	GZ2	44	1684	2.6128266
Cardiomyocyte	GZ20	46	2706	1.6999261
Endothelial	GZ20	1256	2706	46.4153732
Fibroblast	GZ20	622	2706	22.9859571
infMacrophage	GZ20	36	2706	1.3303769
other	GZ20	20	2706	0.7390983
Perivascular	GZ20	499	2706	18.4405026
resMacrophage	GZ20	101	2706	3.7324464
Tcell	GZ20	126	2706	4.6563193
Cardiomyocyte	GZ21	58	1442	4.0221914
Endothelial	GZ21	258	1442	17.8918169
Fibroblast	GZ21	99	1442	6.8654646
infMacrophage	GZ21	38	1442	2.6352288
other	GZ21	15	1442	1.0402219
Perivascular	GZ21	803	1442	55.6865465
resMacrophage	GZ21	143	1442	9.9167822
Tcell	GZ21	28	1442	1.9417476
Cardiomyocyte	GZ22	159	1998	7.9579580
Endothelial	GZ22	279	1998	13.9639640
Fibroblast	GZ22	314	1998	15.7157157
infMacrophage	GZ22	113	1998	5.6556557
other	GZ22	30	1998	1.5015015
Perivascular	GZ22	728	1998	36.4364364
resMacrophage	GZ22	331	1998	16.5665666
Tcell	GZ22	44	1998	2.2022022
Cardiomyocyte	GZ23	54	841	6.4209275
Endothelial	GZ23	226	841	26.8727705
Fibroblast	GZ23	68	841	8.0856124
infMacrophage	GZ23	31	841	3.6860880
other	GZ23	7	841	0.8323424
Perivascular	GZ23	337	841	40.0713436
resMacrophage	GZ23	91	841	10.8204518
Tcell	GZ23	27	841	3.2104637
Cardiomyocyte	GZ24	117	1480	7.9054054
Endothelial	GZ24	286	1480	19.3243243
Fibroblast	GZ24	268	1480	18.1081081
infMacrophage	GZ24	29	1480	1.9594595
other	GZ24	11	1480	0.7432432
Perivascular	GZ24	657	1480	44.3918919
resMacrophage	GZ24	77	1480	5.2027027
Tcell	GZ24	35	1480	2.3648649
Cardiomyocyte	GZ3	234	2396	9.7662771
Endothelial	GZ3	616	2396	25.7095159
Fibroblast	GZ3	812	2396	33.8898164
infMacrophage	GZ3	43	2396	1.7946578
other	GZ3	37	2396	1.5442404
Perivascular	GZ3	525	2396	21.9115192
resMacrophage	GZ3	76	2396	3.1719533
Tcell	GZ3	53	2396	2.2120200
Cardiomyocyte	GZ4	45	545	8.2568807
Endothelial	GZ4	184	545	33.7614679
Fibroblast	GZ4	135	545	24.7706422
infMacrophage	GZ4	22	545	4.0366972
other	GZ4	8	545	1.4678899
Perivascular	GZ4	91	545	16.6972477
resMacrophage	GZ4	32	545	5.8715596
Tcell	GZ4	28	545	5.1376147
Cardiomyocyte	GZ5	39	781	4.9935980
Endothelial	GZ5	245	781	31.3700384
Fibroblast	GZ5	261	781	33.4186940
infMacrophage	GZ5	11	781	1.4084507
other	GZ5	27	781	3.4571063
Perivascular	GZ5	156	781	19.9743918
resMacrophage	GZ5	20	781	2.5608195
Tcell	GZ5	22	781	2.8169014
Cardiomyocyte	GZ6	12	491	2.4439919
Endothelial	GZ6	155	491	31.5682281
Fibroblast	GZ6	150	491	30.5498982
infMacrophage	GZ6	19	491	3.8696538
other	GZ6	3	491	0.6109980
Perivascular	GZ6	72	491	14.6639511
resMacrophage	GZ6	44	491	8.9613035
Tcell	GZ6	36	491	7.3319756
Cardiomyocyte	GZ7	88	653	13.4762634
Endothelial	GZ7	139	653	21.2863706
Fibroblast	GZ7	220	653	33.6906585
infMacrophage	GZ7	5	653	0.7656968
other	GZ7	53	653	8.1163859
Perivascular	GZ7	123	653	18.8361409
resMacrophage	GZ7	13	653	1.9908116
Tcell	GZ7	12	653	1.8376723
Cardiomyocyte	GZ8	714	3921	18.2096404
Endothelial	GZ8	795	3921	20.2754399
Fibroblast	GZ8	1144	3921	29.1762306
infMacrophage	GZ8	120	3921	3.0604438
other	GZ8	69	3921	1.7597552
Perivascular	GZ8	744	3921	18.9747513
resMacrophage	GZ8	240	3921	6.1208875
Tcell	GZ8	95	3921	2.4228513
Cardiomyocyte	GZ9	650	4908	13.2436838
Endothelial	GZ9	1037	4908	21.1287694
Fibroblast	GZ9	1466	4908	29.8696007
infMacrophage	GZ9	228	4908	4.6454768
other	GZ9	34	4908	0.6927465
Perivascular	GZ9	1000	4908	20.3748981
resMacrophage	GZ9	409	4908	8.3333333
Tcell	GZ9	84	4908	1.7114914
Cardiomyocyte	SG29	109	1242	8.7761675
Endothelial	SG29	168	1242	13.5265700
Fibroblast	SG29	140	1242	11.2721417
infMacrophage	SG29	187	1242	15.0563607
other	SG29	15	1242	1.2077295
Perivascular	SG29	104	1242	8.3735910
resMacrophage	SG29	136	1242	10.9500805
Tcell	SG29	383	1242	30.8373591
Cardiomyocyte	SG30	47	236	19.9152542
Endothelial	SG30	74	236	31.3559322
Fibroblast	SG30	41	236	17.3728814
infMacrophage	SG30	5	236	2.1186441
other	SG30	0	236	0.0000000
Perivascular	SG30	56	236	23.7288136
resMacrophage	SG30	9	236	3.8135593
Tcell	SG30	4	236	1.6949153
Cardiomyocyte	SG31	178	1192	14.9328859
Endothelial	SG31	259	1192	21.7281879
Fibroblast	SG31	347	1192	29.1107383
infMacrophage	SG31	36	1192	3.0201342
other	SG31	23	1192	1.9295302
Perivascular	SG31	234	1192	19.6308725
resMacrophage	SG31	88	1192	7.3825503
Tcell	SG31	27	1192	2.2651007
Cardiomyocyte	SG32	40	1428	2.8011204
Endothelial	SG32	593	1428	41.5266106
Fibroblast	SG32	306	1428	21.4285714
infMacrophage	SG32	19	1428	1.3305322
other	SG32	33	1428	2.3109244
Perivascular	SG32	321	1428	22.4789916
resMacrophage	SG32	46	1428	3.2212885
Tcell	SG32	70	1428	4.9019608
Cardiomyocyte	SG33	212	6286	3.3725740
Endothelial	SG33	257	6286	4.0884505
Fibroblast	SG33	401	6286	6.3792555
infMacrophage	SG33	1270	6286	20.2036271
other	SG33	32	6286	0.5090678
Perivascular	SG33	171	6286	2.7203309
resMacrophage	SG33	898	6286	14.2857143
Tcell	SG33	3045	6286	48.4409800
Cardiomyocyte	SG34	173	620	27.9032258
Endothelial	SG34	105	620	16.9354839
Fibroblast	SG34	115	620	18.5483871
infMacrophage	SG34	10	620	1.6129032
other	SG34	6	620	0.9677419
Perivascular	SG34	145	620	23.3870968
resMacrophage	SG34	41	620	6.6129032
Tcell	SG34	25	620	4.0322581
Cardiomyocyte	SG35	304	2363	12.8650021
Endothelial	SG35	598	2363	25.3068134
Fibroblast	SG35	759	2363	32.1201862
infMacrophage	SG35	76	2363	3.2162505
other	SG35	29	2363	1.2272535
Perivascular	SG35	396	2363	16.7583580
resMacrophage	SG35	129	2363	5.4591621
Tcell	SG35	72	2363	3.0469742

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

Version	Author	Date
e6d0c3c	mluetge	2022-09-26
cd902dd	mluetge	2022-09-12
141fae8	mluetge	2022-07-06

per cond

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

Var1	Freq
HH	36012
InfCardiomyopathy	1428
inflamation	236
Myocarditis	32411
Perimyocarditis	1192

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

	HH	InfCardiomyopathy	inflamation	Myocarditis	Perimyocarditis
Cardiomyocyte	5473	40	47	2170	178
Endothelial	7661	593	74	7022	259
Fibroblast	8510	306	41	7474	347
infMacrophage	1295	19	5	2161	36
other	513	33	0	475	23
Perivascular	8905	321	56	6151	234
resMacrophage	2820	46	9	2312	88
Tcell	835	70	4	4646	27

## 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	HH	5473	36012	15.197712
Endothelial	HH	7661	36012	21.273464
Fibroblast	HH	8510	36012	23.631012
infMacrophage	HH	1295	36012	3.596023
other	HH	513	36012	1.424525
Perivascular	HH	8905	36012	24.727868
resMacrophage	HH	2820	36012	7.830723
Tcell	HH	835	36012	2.318672
Cardiomyocyte	InfCardiomyopathy	40	1428	2.801120
Endothelial	InfCardiomyopathy	593	1428	41.526611
Fibroblast	InfCardiomyopathy	306	1428	21.428571
infMacrophage	InfCardiomyopathy	19	1428	1.330532
other	InfCardiomyopathy	33	1428	2.310924
Perivascular	InfCardiomyopathy	321	1428	22.478992
resMacrophage	InfCardiomyopathy	46	1428	3.221288
Tcell	InfCardiomyopathy	70	1428	4.901961
Cardiomyocyte	inflamation	47	236	19.915254
Endothelial	inflamation	74	236	31.355932
Fibroblast	inflamation	41	236	17.372881
infMacrophage	inflamation	5	236	2.118644
other	inflamation	0	236	0.000000
Perivascular	inflamation	56	236	23.728814
resMacrophage	inflamation	9	236	3.813559
Tcell	inflamation	4	236	1.694915
Cardiomyocyte	Myocarditis	2170	32411	6.695258
Endothelial	Myocarditis	7022	32411	21.665484
Fibroblast	Myocarditis	7474	32411	23.060072
infMacrophage	Myocarditis	2161	32411	6.667489
other	Myocarditis	475	32411	1.465552
Perivascular	Myocarditis	6151	32411	18.978125
resMacrophage	Myocarditis	2312	32411	7.133381
Tcell	Myocarditis	4646	32411	14.334639
Cardiomyocyte	Perimyocarditis	178	1192	14.932886
Endothelial	Perimyocarditis	259	1192	21.728188
Fibroblast	Perimyocarditis	347	1192	29.110738
infMacrophage	Perimyocarditis	36	1192	3.020134
other	Perimyocarditis	23	1192	1.929530
Perivascular	Perimyocarditis	234	1192	19.630872
resMacrophage	Perimyocarditis	88	1192	7.382550
Tcell	Perimyocarditis	27	1192	2.265101

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

Version	Author	Date
e6d0c3c	mluetge	2022-09-26
cd902dd	mluetge	2022-09-12
141fae8	mluetge	2022-07-06

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)


     Ctrl TcellHigh  TcellLow 
    36012     24777     10254

table(seurat$TcellGrp, seurat$ID)

           
             GZ1 GZ10 GZ11 GZ12 GZ13 GZ14 GZ15 GZ16 GZ17 GZ18 GZ19  GZ2 GZ20
  Ctrl         0 3731 3991 3818 9882    0    0    0    0    0    0    0    0
  TcellHigh 2740    0    0    0    0    0 4439  436 1370    0  111    0 2706
  TcellLow     0    0    0    0    0 1268    0    0    0 2280    0 1684    0
           
            GZ21 GZ22 GZ23 GZ24  GZ3  GZ4  GZ5  GZ6  GZ7  GZ8  GZ9 SG29 SG30
  Ctrl      1442 1998  841 1480    0    0    0    0    0 3921 4908    0    0
  TcellHigh    0    0    0    0    0  545    0  491    0    0    0 1242    0
  TcellLow     0    0    0    0 2396    0  781    0  653    0    0    0    0
           
            SG31 SG32 SG33 SG34 SG35
  Ctrl         0    0    0    0    0
  TcellHigh    0 1428 6286  620 2363
  TcellLow  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)

Version	Author	Date
e6d0c3c	mluetge	2022-09-26
cd902dd	mluetge	2022-09-12
141fae8	mluetge	2022-07-06

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)

Version	Author	Date
e6d0c3c	mluetge	2022-09-26
cd902dd	mluetge	2022-09-12
141fae8	mluetge	2022-07-06

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("")

Version	Author	Date
e6d0c3c	mluetge	2022-09-26
cd902dd	mluetge	2022-09-12
141fae8	mluetge	2022-07-06

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("")

Version	Author	Date
e6d0c3c	mluetge	2022-09-26
cd902dd	mluetge	2022-09-12
141fae8	mluetge	2022-07-06

exclude two patients with very low nuclei

## two patients with <250 nuclei --> exclude from downstream analysis
table(seurat$ID)


 GZ1 GZ10 GZ11 GZ12 GZ13 GZ14 GZ15 GZ16 GZ17 GZ18 GZ19  GZ2 GZ20 GZ21 GZ22 GZ23 
2740 3731 3991 3818 9882 1268 4439  436 1370 2280  111 1684 2706 1442 1998  841 
GZ24  GZ3  GZ4  GZ5  GZ6  GZ7  GZ8  GZ9 SG29 SG30 SG31 SG32 SG33 SG34 SG35 
1480 2396  545  781  491  653 3921 4908 1242  236 1192 1428 6286  620 2363

seurat <- subset(seurat, ID %in% c("GZ19", "SG30"), invert=T)
table(seurat$ID)


 GZ1 GZ10 GZ11 GZ12 GZ13 GZ14 GZ15 GZ16 GZ17 GZ18  GZ2 GZ20 GZ21 GZ22 GZ23 GZ24 
2740 3731 3991 3818 9882 1268 4439  436 1370 2280 1684 2706 1442 1998  841 1480 
 GZ3  GZ4  GZ5  GZ6  GZ7  GZ8  GZ9 SG29 SG31 SG32 SG33 SG34 SG35 
2396  545  781  491  653 3921 4908 1242 1192 1428 6286  620 2363

save seurat object

saveRDS(seurat, file = paste0(basedir, 
                              "/data/humanHeartsPlusGraz_intPatients_merged", 
                              "labeled_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_0.6.3         tensor_1.5            
 [22] listenv_0.8.0          labeling_0.4.2         git2r_0.30.1          
 [25] GenomeInfoDbData_1.2.8 polyclip_1.10-0        bit64_4.0.5           
 [28] farver_2.1.1           rprojroot_2.0.3        parallelly_1.32.1     
 [31] vctrs_0.4.2            generics_0.1.3         xfun_0.33             
 [34] R6_2.5.1               bitops_1.0-7           spatstat.utils_2.3-1  
 [37] cachem_1.0.6           DelayedArray_0.22.0    assertthat_0.2.1      
 [40] vroom_1.6.0            promises_1.2.0.1       scales_1.2.1          
 [43] googlesheets4_1.0.1    rgeos_0.5-9            gtable_0.3.1          
 [46] globals_0.16.1         goftest_1.2-3          workflowr_1.7.0       
 [49] rlang_1.0.6            splines_4.2.1          rstatix_0.7.0         
 [52] lazyeval_0.2.2         gargle_1.2.1           spatstat.geom_2.4-0   
 [55] broom_1.0.1            yaml_2.3.5             reshape2_1.4.4        
 [58] abind_1.4-5            modelr_0.1.9           backports_1.4.1       
 [61] httpuv_1.6.6           tools_4.2.1            ellipsis_0.3.2        
 [64] spatstat.core_2.4-4    jquerylib_0.1.4        RColorBrewer_1.1-3    
 [67] ggridges_0.5.4         Rcpp_1.0.9             plyr_1.8.7            
 [70] zlibbioc_1.42.0        RCurl_1.98-1.9         rpart_4.1.16          
 [73] deldir_1.0-6           pbapply_1.5-0          cowplot_1.1.1         
 [76] zoo_1.8-11             haven_2.5.1            ggrepel_0.9.1         
 [79] cluster_2.1.4          fs_1.5.2               data.table_1.14.2     
 [82] scattermore_0.8        lmtest_0.9-40          reprex_2.0.2          
 [85] RANN_2.6.1             googledrive_2.0.0      whisker_0.4           
 [88] fitdistrplus_1.1-8     hms_1.1.2              patchwork_1.1.2       
 [91] mime_0.12              evaluate_0.17          xtable_1.8-4          
 [94] readxl_1.4.1           gridExtra_2.3          compiler_4.2.1        
 [97] KernSmooth_2.23-20     crayon_1.5.2           htmltools_0.5.3       
[100] mgcv_1.8-40            later_1.3.0            tzdb_0.3.0            
[103] lubridate_1.8.0        DBI_1.1.3              dbplyr_2.2.1          
[106] MASS_7.3-58.1          Matrix_1.5-1           car_3.1-0             
[109] cli_3.4.1              parallel_4.2.1         igraph_1.3.5          
[112] pkgconfig_2.0.3        plotly_4.10.0          spatstat.sparse_2.1-1 
[115] xml2_1.3.3             bslib_0.4.0            XVector_0.36.0        
[118] rvest_1.0.3            digest_0.6.29          RcppAnnoy_0.0.19      
[121] spatstat.data_2.2-0    rmarkdown_2.17         cellranger_1.1.0      
[124] leiden_0.4.3           uwot_0.1.14            shiny_1.7.2           
[127] lifecycle_1.0.3        nlme_3.1-159           jsonlite_1.8.2        
[130] carData_3.0-5          fansi_1.0.3            pillar_1.8.1          
[133] lattice_0.20-45        fastmap_1.1.0          httr_1.4.4            
[136] survival_3.4-0         glue_1.6.2             png_0.1-7             
[139] bit_4.0.4              stringi_1.7.8          sass_0.4.2            
[142] irlba_2.3.5.1          future.apply_1.9.1

date()

[1] "Thu Oct 13 09:59:49 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_0.6.3         tensor_1.5            
 [22] listenv_0.8.0          labeling_0.4.2         git2r_0.30.1          
 [25] GenomeInfoDbData_1.2.8 polyclip_1.10-0        bit64_4.0.5           
 [28] farver_2.1.1           rprojroot_2.0.3        parallelly_1.32.1     
 [31] vctrs_0.4.2            generics_0.1.3         xfun_0.33             
 [34] R6_2.5.1               bitops_1.0-7           spatstat.utils_2.3-1  
 [37] cachem_1.0.6           DelayedArray_0.22.0    assertthat_0.2.1      
 [40] vroom_1.6.0            promises_1.2.0.1       scales_1.2.1          
 [43] googlesheets4_1.0.1    rgeos_0.5-9            gtable_0.3.1          
 [46] globals_0.16.1         goftest_1.2-3          workflowr_1.7.0       
 [49] rlang_1.0.6            splines_4.2.1          rstatix_0.7.0         
 [52] lazyeval_0.2.2         gargle_1.2.1           spatstat.geom_2.4-0   
 [55] broom_1.0.1            yaml_2.3.5             reshape2_1.4.4        
 [58] abind_1.4-5            modelr_0.1.9           backports_1.4.1       
 [61] httpuv_1.6.6           tools_4.2.1            ellipsis_0.3.2        
 [64] spatstat.core_2.4-4    jquerylib_0.1.4        RColorBrewer_1.1-3    
 [67] ggridges_0.5.4         Rcpp_1.0.9             plyr_1.8.7            
 [70] zlibbioc_1.42.0        RCurl_1.98-1.9         rpart_4.1.16          
 [73] deldir_1.0-6           pbapply_1.5-0          cowplot_1.1.1         
 [76] zoo_1.8-11             haven_2.5.1            ggrepel_0.9.1         
 [79] cluster_2.1.4          fs_1.5.2               data.table_1.14.2     
 [82] scattermore_0.8        lmtest_0.9-40          reprex_2.0.2          
 [85] RANN_2.6.1             googledrive_2.0.0      whisker_0.4           
 [88] fitdistrplus_1.1-8     hms_1.1.2              patchwork_1.1.2       
 [91] mime_0.12              evaluate_0.17          xtable_1.8-4          
 [94] readxl_1.4.1           gridExtra_2.3          compiler_4.2.1        
 [97] KernSmooth_2.23-20     crayon_1.5.2           htmltools_0.5.3       
[100] mgcv_1.8-40            later_1.3.0            tzdb_0.3.0            
[103] lubridate_1.8.0        DBI_1.1.3              dbplyr_2.2.1          
[106] MASS_7.3-58.1          Matrix_1.5-1           car_3.1-0             
[109] cli_3.4.1              parallel_4.2.1         igraph_1.3.5          
[112] pkgconfig_2.0.3        plotly_4.10.0          spatstat.sparse_2.1-1 
[115] xml2_1.3.3             bslib_0.4.0            XVector_0.36.0        
[118] rvest_1.0.3            digest_0.6.29          RcppAnnoy_0.0.19      
[121] spatstat.data_2.2-0    rmarkdown_2.17         cellranger_1.1.0      
[124] leiden_0.4.3           uwot_0.1.14            shiny_1.7.2           
[127] lifecycle_1.0.3        nlme_3.1-159           jsonlite_1.8.2        
[130] carData_3.0-5          fansi_1.0.3            pillar_1.8.1          
[133] lattice_0.20-45        fastmap_1.1.0          httr_1.4.4            
[136] survival_3.4-0         glue_1.6.2             png_0.1-7             
[139] bit_4.0.4              stringi_1.7.8          sass_0.4.2            
[142] irlba_2.3.5.1          future.apply_1.9.1