Merge samples

Last updated: 2022-09-26

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Knit directory: humanCardiacFibroblasts/

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File	Version	Author	Date	Message
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$seurat_clusters %in% c("2","6","7"))] <- "Endothelial"
seurat$label[which(seurat$seurat_clusters %in% c("5"))] <- "Tcell"
seurat$label[which(seurat$seurat_clusters %in% c("4"))] <- "Cardiomyocyte"
seurat$label[which(seurat$seurat_clusters %in% c("0"))] <- "Fibroblast"
seurat$label[which(seurat$seurat_clusters %in% c("1","8"))] <- "Perivascular"
seurat$label[which(seurat$seurat_clusters %in% c("3"))] <- "MonocyteMacrophage"

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
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
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
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
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
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
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
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
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
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	116	2206	50	116	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	442	411	810	1152	2259	323	1067	72	193	643	30	425	1242	258	273	226	285	592	180	244	155	131	783	1025	165	74	257	581	257	104	581
Fibroblast	718	984	841	1110	2222	316	1095	90	368	624	16	640	624	99	314	68	268	815	136	261	150	220	1146	1468	141	42	347	307	402	115	759
MonocyteMacrophage	299	487	659	221	898	91	330	56	331	93	17	85	137	184	444	123	106	118	54	31	63	18	359	638	321	14	124	65	2170	51	204
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	746	523	1201	1096	1813	439	1536	80	190	517	33	322	497	803	728	337	657	523	90	156	72	123	742	999	103	55	234	320	170	145	397
Tcell	268	135	154	101	279	39	218	102	189	46	13	74	140	25	50	26	36	77	32	23	36	20	108	94	388	4	29	82	3043	26	89

## 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	442	2740	16.1313869
Fibroblast	GZ1	718	2740	26.2043796
MonocyteMacrophage	GZ1	299	2740	10.9124088
other	GZ1	59	2740	2.1532847
Perivascular	GZ1	746	2740	27.2262774
Tcell	GZ1	268	2740	9.7810219
Cardiomyocyte	GZ10	1098	3731	29.4291075
Endothelial	GZ10	411	3731	11.0158135
Fibroblast	GZ10	984	3731	26.3736264
MonocyteMacrophage	GZ10	487	3731	13.0528009
other	GZ10	93	3731	2.4926293
Perivascular	GZ10	523	3731	14.0176896
Tcell	GZ10	135	3731	3.6183329
Cardiomyocyte	GZ11	299	3991	7.4918567
Endothelial	GZ11	810	3991	20.2956652
Fibroblast	GZ11	841	3991	21.0724129
MonocyteMacrophage	GZ11	659	3991	16.5121523
other	GZ11	27	3991	0.6765222
Perivascular	GZ11	1201	3991	30.0927086
Tcell	GZ11	154	3991	3.8586820
Cardiomyocyte	GZ12	116	3818	3.0382399
Endothelial	GZ12	1152	3818	30.1728654
Fibroblast	GZ12	1110	3818	29.0728130
MonocyteMacrophage	GZ12	221	3818	5.7883709
other	GZ12	22	3818	0.5762179
Perivascular	GZ12	1096	3818	28.7061289
Tcell	GZ12	101	3818	2.6453641
Cardiomyocyte	GZ13	2206	9882	22.3234163
Endothelial	GZ13	2259	9882	22.8597450
Fibroblast	GZ13	2222	9882	22.4853269
MonocyteMacrophage	GZ13	898	9882	9.0872293
other	GZ13	205	9882	2.0744789
Perivascular	GZ13	1813	9882	18.3464886
Tcell	GZ13	279	9882	2.8233151
Cardiomyocyte	GZ14	50	1268	3.9432177
Endothelial	GZ14	323	1268	25.4731861
Fibroblast	GZ14	316	1268	24.9211356
MonocyteMacrophage	GZ14	91	1268	7.1766562
other	GZ14	10	1268	0.7886435
Perivascular	GZ14	439	1268	34.6214511
Tcell	GZ14	39	1268	3.0757098
Cardiomyocyte	GZ15	116	4439	2.6132012
Endothelial	GZ15	1067	4439	24.0369453
Fibroblast	GZ15	1095	4439	24.6677180
MonocyteMacrophage	GZ15	330	4439	7.4341068
other	GZ15	77	4439	1.7346249
Perivascular	GZ15	1536	4439	34.6023879
Tcell	GZ15	218	4439	4.9110160
Cardiomyocyte	GZ16	25	436	5.7339450
Endothelial	GZ16	72	436	16.5137615
Fibroblast	GZ16	90	436	20.6422018
MonocyteMacrophage	GZ16	56	436	12.8440367
other	GZ16	11	436	2.5229358
Perivascular	GZ16	80	436	18.3486239
Tcell	GZ16	102	436	23.3944954
Cardiomyocyte	GZ17	71	1370	5.1824818
Endothelial	GZ17	193	1370	14.0875912
Fibroblast	GZ17	368	1370	26.8613139
MonocyteMacrophage	GZ17	331	1370	24.1605839
other	GZ17	28	1370	2.0437956
Perivascular	GZ17	190	1370	13.8686131
Tcell	GZ17	189	1370	13.7956204
Cardiomyocyte	GZ18	328	2280	14.3859649
Endothelial	GZ18	643	2280	28.2017544
Fibroblast	GZ18	624	2280	27.3684211
MonocyteMacrophage	GZ18	93	2280	4.0789474
other	GZ18	29	2280	1.2719298
Perivascular	GZ18	517	2280	22.6754386
Tcell	GZ18	46	2280	2.0175439
Cardiomyocyte	GZ19	1	111	0.9009009
Endothelial	GZ19	30	111	27.0270270
Fibroblast	GZ19	16	111	14.4144144
MonocyteMacrophage	GZ19	17	111	15.3153153
other	GZ19	1	111	0.9009009
Perivascular	GZ19	33	111	29.7297297
Tcell	GZ19	13	111	11.7117117
Cardiomyocyte	GZ2	108	1684	6.4133017
Endothelial	GZ2	425	1684	25.2375297
Fibroblast	GZ2	640	1684	38.0047506
MonocyteMacrophage	GZ2	85	1684	5.0475059
other	GZ2	30	1684	1.7814727
Perivascular	GZ2	322	1684	19.1211401
Tcell	GZ2	74	1684	4.3942993
Cardiomyocyte	GZ20	46	2706	1.6999261
Endothelial	GZ20	1242	2706	45.8980044
Fibroblast	GZ20	624	2706	23.0598670
MonocyteMacrophage	GZ20	137	2706	5.0628234
other	GZ20	20	2706	0.7390983
Perivascular	GZ20	497	2706	18.3665928
Tcell	GZ20	140	2706	5.1736881
Cardiomyocyte	GZ21	58	1442	4.0221914
Endothelial	GZ21	258	1442	17.8918169
Fibroblast	GZ21	99	1442	6.8654646
MonocyteMacrophage	GZ21	184	1442	12.7600555
other	GZ21	15	1442	1.0402219
Perivascular	GZ21	803	1442	55.6865465
Tcell	GZ21	25	1442	1.7337032
Cardiomyocyte	GZ22	159	1998	7.9579580
Endothelial	GZ22	273	1998	13.6636637
Fibroblast	GZ22	314	1998	15.7157157
MonocyteMacrophage	GZ22	444	1998	22.2222222
other	GZ22	30	1998	1.5015015
Perivascular	GZ22	728	1998	36.4364364
Tcell	GZ22	50	1998	2.5025025
Cardiomyocyte	GZ23	54	841	6.4209275
Endothelial	GZ23	226	841	26.8727705
Fibroblast	GZ23	68	841	8.0856124
MonocyteMacrophage	GZ23	123	841	14.6254459
other	GZ23	7	841	0.8323424
Perivascular	GZ23	337	841	40.0713436
Tcell	GZ23	26	841	3.0915577
Cardiomyocyte	GZ24	117	1480	7.9054054
Endothelial	GZ24	285	1480	19.2567568
Fibroblast	GZ24	268	1480	18.1081081
MonocyteMacrophage	GZ24	106	1480	7.1621622
other	GZ24	11	1480	0.7432432
Perivascular	GZ24	657	1480	44.3918919
Tcell	GZ24	36	1480	2.4324324
Cardiomyocyte	GZ3	234	2396	9.7662771
Endothelial	GZ3	592	2396	24.7078464
Fibroblast	GZ3	815	2396	34.0150250
MonocyteMacrophage	GZ3	118	2396	4.9248748
other	GZ3	37	2396	1.5442404
Perivascular	GZ3	523	2396	21.8280467
Tcell	GZ3	77	2396	3.2136895
Cardiomyocyte	GZ4	45	545	8.2568807
Endothelial	GZ4	180	545	33.0275229
Fibroblast	GZ4	136	545	24.9541284
MonocyteMacrophage	GZ4	54	545	9.9082569
other	GZ4	8	545	1.4678899
Perivascular	GZ4	90	545	16.5137615
Tcell	GZ4	32	545	5.8715596
Cardiomyocyte	GZ5	39	781	4.9935980
Endothelial	GZ5	244	781	31.2419974
Fibroblast	GZ5	261	781	33.4186940
MonocyteMacrophage	GZ5	31	781	3.9692702
other	GZ5	27	781	3.4571063
Perivascular	GZ5	156	781	19.9743918
Tcell	GZ5	23	781	2.9449424
Cardiomyocyte	GZ6	12	491	2.4439919
Endothelial	GZ6	155	491	31.5682281
Fibroblast	GZ6	150	491	30.5498982
MonocyteMacrophage	GZ6	63	491	12.8309572
other	GZ6	3	491	0.6109980
Perivascular	GZ6	72	491	14.6639511
Tcell	GZ6	36	491	7.3319756
Cardiomyocyte	GZ7	88	653	13.4762634
Endothelial	GZ7	131	653	20.0612557
Fibroblast	GZ7	220	653	33.6906585
MonocyteMacrophage	GZ7	18	653	2.7565084
other	GZ7	53	653	8.1163859
Perivascular	GZ7	123	653	18.8361409
Tcell	GZ7	20	653	3.0627871
Cardiomyocyte	GZ8	714	3921	18.2096404
Endothelial	GZ8	783	3921	19.9693956
Fibroblast	GZ8	1146	3921	29.2272379
MonocyteMacrophage	GZ8	359	3921	9.1558276
other	GZ8	69	3921	1.7597552
Perivascular	GZ8	742	3921	18.9237439
Tcell	GZ8	108	3921	2.7543994
Cardiomyocyte	GZ9	650	4908	13.2436838
Endothelial	GZ9	1025	4908	20.8842706
Fibroblast	GZ9	1468	4908	29.9103504
MonocyteMacrophage	GZ9	638	4908	12.9991850
other	GZ9	34	4908	0.6927465
Perivascular	GZ9	999	4908	20.3545232
Tcell	GZ9	94	4908	1.9152404
Cardiomyocyte	SG29	109	1242	8.7761675
Endothelial	SG29	165	1242	13.2850242
Fibroblast	SG29	141	1242	11.3526570
MonocyteMacrophage	SG29	321	1242	25.8454106
other	SG29	15	1242	1.2077295
Perivascular	SG29	103	1242	8.2930757
Tcell	SG29	388	1242	31.2399356
Cardiomyocyte	SG30	47	236	19.9152542
Endothelial	SG30	74	236	31.3559322
Fibroblast	SG30	42	236	17.7966102
MonocyteMacrophage	SG30	14	236	5.9322034
other	SG30	0	236	0.0000000
Perivascular	SG30	55	236	23.3050847
Tcell	SG30	4	236	1.6949153
Cardiomyocyte	SG31	178	1192	14.9328859
Endothelial	SG31	257	1192	21.5604027
Fibroblast	SG31	347	1192	29.1107383
MonocyteMacrophage	SG31	124	1192	10.4026846
other	SG31	23	1192	1.9295302
Perivascular	SG31	234	1192	19.6308725
Tcell	SG31	29	1192	2.4328859
Cardiomyocyte	SG32	40	1428	2.8011204
Endothelial	SG32	581	1428	40.6862745
Fibroblast	SG32	307	1428	21.4985994
MonocyteMacrophage	SG32	65	1428	4.5518207
other	SG32	33	1428	2.3109244
Perivascular	SG32	320	1428	22.4089636
Tcell	SG32	82	1428	5.7422969
Cardiomyocyte	SG33	212	6286	3.3725740
Endothelial	SG33	257	6286	4.0884505
Fibroblast	SG33	402	6286	6.3951639
MonocyteMacrophage	SG33	2170	6286	34.5211581
other	SG33	32	6286	0.5090678
Perivascular	SG33	170	6286	2.7044225
Tcell	SG33	3043	6286	48.4091632
Cardiomyocyte	SG34	173	620	27.9032258
Endothelial	SG34	104	620	16.7741935
Fibroblast	SG34	115	620	18.5483871
MonocyteMacrophage	SG34	51	620	8.2258065
other	SG34	6	620	0.9677419
Perivascular	SG34	145	620	23.3870968
Tcell	SG34	26	620	4.1935484
Cardiomyocyte	SG35	304	2363	12.8650021
Endothelial	SG35	581	2363	24.5873889
Fibroblast	SG35	759	2363	32.1201862
MonocyteMacrophage	SG35	204	2363	8.6330935
other	SG35	29	2363	1.2272535
Perivascular	SG35	397	2363	16.8006771
Tcell	SG35	89	2363	3.7663986

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

Version	Author	Date
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	5471	40	47	2169	178
Endothelial	7482	581	74	6846	257
Fibroblast	8520	307	42	7490	347
MonocyteMacrophage	4119	65	14	4469	124
other	513	33	0	475	23
Perivascular	8899	320	55	6139	234
Tcell	1008	82	4	4823	29

## 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	5471	36012	15.192158
Endothelial	HH	7482	36012	20.776408
Fibroblast	HH	8520	36012	23.658780
MonocyteMacrophage	HH	4119	36012	11.437854
other	HH	513	36012	1.424525
Perivascular	HH	8899	36012	24.711207
Tcell	HH	1008	36012	2.799067
Cardiomyocyte	InfCardiomyopathy	40	1428	2.801120
Endothelial	InfCardiomyopathy	581	1428	40.686275
Fibroblast	InfCardiomyopathy	307	1428	21.498599
MonocyteMacrophage	InfCardiomyopathy	65	1428	4.551821
other	InfCardiomyopathy	33	1428	2.310924
Perivascular	InfCardiomyopathy	320	1428	22.408964
Tcell	InfCardiomyopathy	82	1428	5.742297
Cardiomyocyte	inflamation	47	236	19.915254
Endothelial	inflamation	74	236	31.355932
Fibroblast	inflamation	42	236	17.796610
MonocyteMacrophage	inflamation	14	236	5.932203
other	inflamation	0	236	0.000000
Perivascular	inflamation	55	236	23.305085
Tcell	inflamation	4	236	1.694915
Cardiomyocyte	Myocarditis	2169	32411	6.692172
Endothelial	Myocarditis	6846	32411	21.122458
Fibroblast	Myocarditis	7490	32411	23.109438
MonocyteMacrophage	Myocarditis	4469	32411	13.788529
other	Myocarditis	475	32411	1.465552
Perivascular	Myocarditis	6139	32411	18.941100
Tcell	Myocarditis	4823	32411	14.880750
Cardiomyocyte	Perimyocarditis	178	1192	14.932886
Endothelial	Perimyocarditis	257	1192	21.560403
Fibroblast	Perimyocarditis	347	1192	29.110738
MonocyteMacrophage	Perimyocarditis	124	1192	10.402685
other	Perimyocarditis	23	1192	1.929530
Perivascular	Perimyocarditis	234	1192	19.630872
Tcell	Perimyocarditis	29	1192	2.432886

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

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

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
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
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
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
cd902dd	mluetge	2022-09-12
141fae8	mluetge	2022-07-06

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_0.3.4           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_4.1.1          Seurat_4.1.1               
[13] forcats_0.5.2               stringr_1.4.1              
[15] dplyr_1.0.10                purrr_0.3.4                
[17] readr_2.1.2                 tidyr_1.2.1                
[19] tibble_3.1.8                ggplot2_3.3.6              
[21] tidyverse_1.3.2             SingleCellExperiment_1.18.0
[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_1.1.2      
  [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_0.4.1            generics_0.1.3         xfun_0.32             
 [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_1.5.7            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_1.0.5            splines_4.2.1          rstatix_0.7.0         
 [52] lazyeval_0.2.2         gargle_1.2.0           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_0.5.3         Rcpp_1.0.9             plyr_1.8.7            
 [70] zlibbioc_1.42.0        RCurl_1.98-1.8         rpart_4.1.16          
 [73] deldir_1.0-6           pbapply_1.5-0          cowplot_1.1.1         
 [76] zoo_1.8-10             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_0.16          xtable_1.8-4          
 [94] readxl_1.4.1           gridExtra_2.3          compiler_4.2.1        
 [97] KernSmooth_2.23-20     crayon_1.5.1           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_1.4-1           car_3.1-0             
[109] cli_3.4.0              parallel_4.2.1         igraph_1.3.4          
[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_2.16         cellranger_1.1.0      
[124] leiden_0.4.2           uwot_0.1.14            shiny_1.7.2           
[127] lifecycle_1.0.2        nlme_3.1-159           jsonlite_1.8.0        
[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            future.apply_1.9.0

date()

[1] "Mon Sep 26 17:13:41 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_0.3.4           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_4.1.1          Seurat_4.1.1               
[13] forcats_0.5.2               stringr_1.4.1              
[15] dplyr_1.0.10                purrr_0.3.4                
[17] readr_2.1.2                 tidyr_1.2.1                
[19] tibble_3.1.8                ggplot2_3.3.6              
[21] tidyverse_1.3.2             SingleCellExperiment_1.18.0
[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_1.1.2      
  [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_0.4.1            generics_0.1.3         xfun_0.32             
 [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_1.5.7            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_1.0.5            splines_4.2.1          rstatix_0.7.0         
 [52] lazyeval_0.2.2         gargle_1.2.0           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_0.5.3         Rcpp_1.0.9             plyr_1.8.7            
 [70] zlibbioc_1.42.0        RCurl_1.98-1.8         rpart_4.1.16          
 [73] deldir_1.0-6           pbapply_1.5-0          cowplot_1.1.1         
 [76] zoo_1.8-10             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_0.16          xtable_1.8-4          
 [94] readxl_1.4.1           gridExtra_2.3          compiler_4.2.1        
 [97] KernSmooth_2.23-20     crayon_1.5.1           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_1.4-1           car_3.1-0             
[109] cli_3.4.0              parallel_4.2.1         igraph_1.3.4          
[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_2.16         cellranger_1.1.0      
[124] leiden_0.4.2           uwot_0.1.14            shiny_1.7.2           
[127] lifecycle_1.0.2        nlme_3.1-159           jsonlite_1.8.0        
[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            future.apply_1.9.0