Merge samples

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Last updated: 2022-10-19

Checks: 6 1

Knit directory: humanCardiacFibroblasts/

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These are the previous versions of the repository in which changes were made to the R Markdown (analysis/assignLabelshumanHeartsPlusGrazInt.Rmd) and HTML (docs/assignLabelshumanHeartsPlusGrazInt.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File	Version	Author	Date	Message
Rmd	3ac3709	mluetge	2022-10-13	add macrophage markers to heatmap
html	3ac3709	mluetge	2022-10-13	add macrophage markers to heatmap
Rmd	8f79da0	mluetge	2022-10-13	update label
html	8f79da0	mluetge	2022-10-13	update label
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

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 

assign labels

seurat$label <- "other"
seurat$label[which(seurat$integrated_snn_res.0.4 %in% c("2","9","7"))] <- "Endothelial"
seurat$label[which(seurat$integrated_snn_res.0.4 %in% c("10"))] <- "EndoEC"
seurat$label[which(seurat$integrated_snn_res.0.4 %in% c("12"))] <- "LEC"
seurat$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$integrated_snn_res.0.4 %in% c("1"))] <- "Perivascular"
seurat$label[which(seurat$integrated_snn_res.0.4 %in% c("8"))] <- "SMC"
seurat$label[which(seurat$integrated_snn_res.0.4 %in% c("5"))] <- "resMacrophage"
seurat$label[which(seurat$integrated_snn_res.0.4 %in% c("6"))] <- "infMacrophage"
seurat$label[which(seurat$integrated_snn_res.0.4 %in% c("11"))] <- "NeuralCells"
seurat$label[which(seurat$integrated_snn_res.0.4 %in% c("13"))] <- "Adipocytes"

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
3ac3709	mluetge	2022-10-13
e6d0c3c	mluetge	2022-09-26
cd902dd	mluetge	2022-09-12
141fae8	mluetge	2022-07-06

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

label

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

Past versions of "vis label-1.png"

Version	Author	Date
3ac3709	mluetge	2022-10-13
8f79da0	mluetge	2022-10-13
e6d0c3c	mluetge	2022-09-26
cd902dd	mluetge	2022-09-12
141fae8	mluetge	2022-07-06

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

technique

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

Past versions of "vis tecnique-1.png"

Version	Author	Date
3ac3709	mluetge	2022-10-13
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")

Past versions of "vis sample-1.png"

Version	Author	Date
3ac3709	mluetge	2022-10-13
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, 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
3ac3709	mluetge	2022-10-13
e6d0c3c	mluetge	2022-09-26
cd902dd	mluetge	2022-09-12
141fae8	mluetge	2022-07-06

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

Origin

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

Past versions of "vis origin-1.png"

Version	Author	Date
3ac3709	mluetge	2022-10-13
e6d0c3c	mluetge	2022-09-26
cd902dd	mluetge	2022-09-12
141fae8	mluetge	2022-07-06

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

isolation

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

Past versions of "vis isolation-1.png"

Version	Author	Date
3ac3709	mluetge	2022-10-13
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")

Past versions of "vis cond-1.png"

Version	Author	Date
3ac3709	mluetge	2022-10-13
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")

Past versions of "vis proc-1.png"

Version	Author	Date
3ac3709	mluetge	2022-10-13
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
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
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	GZ2	GZ20	GZ21	GZ22	GZ23	GZ24	GZ3	GZ4	GZ5	GZ6	GZ7	GZ8	GZ9	SG29	SG31	SG32	SG33	SG34	SG35
Adipocytes	0	61	0	0	3	0	0	0	0	0	1	0	0	0	0	1	2	1	0	0	41	4	2	0	0	5	8	0	0
Cardiomyocyte	208	1098	299	117	2207	50	117	25	71	328	108	46	58	159	54	117	234	45	39	12	88	714	650	109	178	40	212	173	304
EndoEC	94	25	33	23	174	4	8	14	3	133	93	159	8	3	9	4	163	55	27	37	5	16	349	19	51	29	36	12	30
Endothelial	335	381	775	1123	2065	319	1057	58	190	503	326	1081	250	270	211	272	403	125	217	118	121	752	657	147	206	550	219	92	550
Fibroblast	715	983	841	1110	2217	316	1095	88	368	623	638	622	99	314	68	268	812	135	261	150	220	1144	1466	140	347	306	401	115	759
infMacrophage	162	108	275	65	288	19	109	32	106	18	29	36	38	113	31	29	43	22	11	19	5	120	228	187	36	19	1270	10	76
LEC	42	15	11	42	113	3	33	1	2	14	37	16	0	6	6	10	50	4	1	0	13	27	31	2	2	14	2	1	18
NeuralCells	59	32	27	22	202	10	77	11	28	29	29	20	15	30	7	10	35	7	27	3	12	65	32	15	23	28	24	6	29
Perivascular	562	452	1084	965	1516	347	1321	66	151	394	274	374	724	645	299	586	432	68	118	55	68	584	864	86	191	254	142	122	335
resMacrophage	137	380	384	154	611	72	223	24	225	75	56	101	143	331	91	77	76	32	20	44	13	240	409	136	88	46	898	41	129
SMC	187	71	116	131	301	92	214	16	39	124	49	125	79	83	38	71	93	23	38	17	55	160	136	18	43	67	29	23	61
Tcell	239	125	146	66	185	36	185	101	187	39	44	126	28	44	27	35	53	28	22	36	12	95	84	383	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
Adipocytes	GZ1	0	2740	0.0000000
Cardiomyocyte	GZ1	208	2740	7.5912409
EndoEC	GZ1	94	2740	3.4306569
Endothelial	GZ1	335	2740	12.2262774
Fibroblast	GZ1	715	2740	26.0948905
infMacrophage	GZ1	162	2740	5.9124088
LEC	GZ1	42	2740	1.5328467
NeuralCells	GZ1	59	2740	2.1532847
Perivascular	GZ1	562	2740	20.5109489
resMacrophage	GZ1	137	2740	5.0000000
SMC	GZ1	187	2740	6.8248175
Tcell	GZ1	239	2740	8.7226277
Adipocytes	GZ10	61	3731	1.6349504
Cardiomyocyte	GZ10	1098	3731	29.4291075
EndoEC	GZ10	25	3731	0.6700616
Endothelial	GZ10	381	3731	10.2117395
Fibroblast	GZ10	983	3731	26.3468239
infMacrophage	GZ10	108	3731	2.8946663
LEC	GZ10	15	3731	0.4020370
NeuralCells	GZ10	32	3731	0.8576789
Perivascular	GZ10	452	3731	12.1147146
resMacrophage	GZ10	380	3731	10.1849370
SMC	GZ10	71	3731	1.9029751
Tcell	GZ10	125	3731	3.3503082
Adipocytes	GZ11	0	3991	0.0000000
Cardiomyocyte	GZ11	299	3991	7.4918567
EndoEC	GZ11	33	3991	0.8268604
Endothelial	GZ11	775	3991	19.4186921
Fibroblast	GZ11	841	3991	21.0724129
infMacrophage	GZ11	275	3991	6.8905036
LEC	GZ11	11	3991	0.2756201
NeuralCells	GZ11	27	3991	0.6765222
Perivascular	GZ11	1084	3991	27.1611125
resMacrophage	GZ11	384	3991	9.6216487
SMC	GZ11	116	3991	2.9065397
Tcell	GZ11	146	3991	3.6582310
Adipocytes	GZ12	0	3818	0.0000000
Cardiomyocyte	GZ12	117	3818	3.0644316
EndoEC	GZ12	23	3818	0.6024096
Endothelial	GZ12	1123	3818	29.4133054
Fibroblast	GZ12	1110	3818	29.0728130
infMacrophage	GZ12	65	3818	1.7024620
LEC	GZ12	42	3818	1.1000524
NeuralCells	GZ12	22	3818	0.5762179
Perivascular	GZ12	965	3818	25.2750131
resMacrophage	GZ12	154	3818	4.0335254
SMC	GZ12	131	3818	3.4311158
Tcell	GZ12	66	3818	1.7286537
Adipocytes	GZ13	3	9882	0.0303582
Cardiomyocyte	GZ13	2207	9882	22.3335357
EndoEC	GZ13	174	9882	1.7607772
Endothelial	GZ13	2065	9882	20.8965796
Fibroblast	GZ13	2217	9882	22.4347298
infMacrophage	GZ13	288	9882	2.9143898
LEC	GZ13	113	9882	1.1434932
NeuralCells	GZ13	202	9882	2.0441206
Perivascular	GZ13	1516	9882	15.3410241
resMacrophage	GZ13	611	9882	6.1829589
SMC	GZ13	301	9882	3.0459421
Tcell	GZ13	185	9882	1.8720907
Adipocytes	GZ14	0	1268	0.0000000
Cardiomyocyte	GZ14	50	1268	3.9432177
EndoEC	GZ14	4	1268	0.3154574
Endothelial	GZ14	319	1268	25.1577287
Fibroblast	GZ14	316	1268	24.9211356
infMacrophage	GZ14	19	1268	1.4984227
LEC	GZ14	3	1268	0.2365931
NeuralCells	GZ14	10	1268	0.7886435
Perivascular	GZ14	347	1268	27.3659306
resMacrophage	GZ14	72	1268	5.6782334
SMC	GZ14	92	1268	7.2555205
Tcell	GZ14	36	1268	2.8391167
Adipocytes	GZ15	0	4439	0.0000000
Cardiomyocyte	GZ15	117	4439	2.6357288
EndoEC	GZ15	8	4439	0.1802208
Endothelial	GZ15	1057	4439	23.8116693
Fibroblast	GZ15	1095	4439	24.6677180
infMacrophage	GZ15	109	4439	2.4555080
LEC	GZ15	33	4439	0.7434107
NeuralCells	GZ15	77	4439	1.7346249
Perivascular	GZ15	1321	4439	29.7589547
resMacrophage	GZ15	223	4439	5.0236540
SMC	GZ15	214	4439	4.8209056
Tcell	GZ15	185	4439	4.1676053
Adipocytes	GZ16	0	436	0.0000000
Cardiomyocyte	GZ16	25	436	5.7339450
EndoEC	GZ16	14	436	3.2110092
Endothelial	GZ16	58	436	13.3027523
Fibroblast	GZ16	88	436	20.1834862
infMacrophage	GZ16	32	436	7.3394495
LEC	GZ16	1	436	0.2293578
NeuralCells	GZ16	11	436	2.5229358
Perivascular	GZ16	66	436	15.1376147
resMacrophage	GZ16	24	436	5.5045872
SMC	GZ16	16	436	3.6697248
Tcell	GZ16	101	436	23.1651376
Adipocytes	GZ17	0	1370	0.0000000
Cardiomyocyte	GZ17	71	1370	5.1824818
EndoEC	GZ17	3	1370	0.2189781
Endothelial	GZ17	190	1370	13.8686131
Fibroblast	GZ17	368	1370	26.8613139
infMacrophage	GZ17	106	1370	7.7372263
LEC	GZ17	2	1370	0.1459854
NeuralCells	GZ17	28	1370	2.0437956
Perivascular	GZ17	151	1370	11.0218978
resMacrophage	GZ17	225	1370	16.4233577
SMC	GZ17	39	1370	2.8467153
Tcell	GZ17	187	1370	13.6496350
Adipocytes	GZ18	0	2280	0.0000000
Cardiomyocyte	GZ18	328	2280	14.3859649
EndoEC	GZ18	133	2280	5.8333333
Endothelial	GZ18	503	2280	22.0614035
Fibroblast	GZ18	623	2280	27.3245614
infMacrophage	GZ18	18	2280	0.7894737
LEC	GZ18	14	2280	0.6140351
NeuralCells	GZ18	29	2280	1.2719298
Perivascular	GZ18	394	2280	17.2807018
resMacrophage	GZ18	75	2280	3.2894737
SMC	GZ18	124	2280	5.4385965
Tcell	GZ18	39	2280	1.7105263
Adipocytes	GZ2	1	1684	0.0593824
Cardiomyocyte	GZ2	108	1684	6.4133017
EndoEC	GZ2	93	1684	5.5225653
Endothelial	GZ2	326	1684	19.3586698
Fibroblast	GZ2	638	1684	37.8859857
infMacrophage	GZ2	29	1684	1.7220903
LEC	GZ2	37	1684	2.1971496
NeuralCells	GZ2	29	1684	1.7220903
Perivascular	GZ2	274	1684	16.2707838
resMacrophage	GZ2	56	1684	3.3254157
SMC	GZ2	49	1684	2.9097387
Tcell	GZ2	44	1684	2.6128266
Adipocytes	GZ20	0	2706	0.0000000
Cardiomyocyte	GZ20	46	2706	1.6999261
EndoEC	GZ20	159	2706	5.8758315
Endothelial	GZ20	1081	2706	39.9482631
Fibroblast	GZ20	622	2706	22.9859571
infMacrophage	GZ20	36	2706	1.3303769
LEC	GZ20	16	2706	0.5912786
NeuralCells	GZ20	20	2706	0.7390983
Perivascular	GZ20	374	2706	13.8211382
resMacrophage	GZ20	101	2706	3.7324464
SMC	GZ20	125	2706	4.6193644
Tcell	GZ20	126	2706	4.6563193
Adipocytes	GZ21	0	1442	0.0000000
Cardiomyocyte	GZ21	58	1442	4.0221914
EndoEC	GZ21	8	1442	0.5547850
Endothelial	GZ21	250	1442	17.3370319
Fibroblast	GZ21	99	1442	6.8654646
infMacrophage	GZ21	38	1442	2.6352288
LEC	GZ21	0	1442	0.0000000
NeuralCells	GZ21	15	1442	1.0402219
Perivascular	GZ21	724	1442	50.2080444
resMacrophage	GZ21	143	1442	9.9167822
SMC	GZ21	79	1442	5.4785021
Tcell	GZ21	28	1442	1.9417476
Adipocytes	GZ22	0	1998	0.0000000
Cardiomyocyte	GZ22	159	1998	7.9579580
EndoEC	GZ22	3	1998	0.1501502
Endothelial	GZ22	270	1998	13.5135135
Fibroblast	GZ22	314	1998	15.7157157
infMacrophage	GZ22	113	1998	5.6556557
LEC	GZ22	6	1998	0.3003003
NeuralCells	GZ22	30	1998	1.5015015
Perivascular	GZ22	645	1998	32.2822823
resMacrophage	GZ22	331	1998	16.5665666
SMC	GZ22	83	1998	4.1541542
Tcell	GZ22	44	1998	2.2022022
Adipocytes	GZ23	0	841	0.0000000
Cardiomyocyte	GZ23	54	841	6.4209275
EndoEC	GZ23	9	841	1.0701546
Endothelial	GZ23	211	841	25.0891795
Fibroblast	GZ23	68	841	8.0856124
infMacrophage	GZ23	31	841	3.6860880
LEC	GZ23	6	841	0.7134364
NeuralCells	GZ23	7	841	0.8323424
Perivascular	GZ23	299	841	35.5529132
resMacrophage	GZ23	91	841	10.8204518
SMC	GZ23	38	841	4.5184304
Tcell	GZ23	27	841	3.2104637
Adipocytes	GZ24	1	1480	0.0675676
Cardiomyocyte	GZ24	117	1480	7.9054054
EndoEC	GZ24	4	1480	0.2702703
Endothelial	GZ24	272	1480	18.3783784
Fibroblast	GZ24	268	1480	18.1081081
infMacrophage	GZ24	29	1480	1.9594595
LEC	GZ24	10	1480	0.6756757
NeuralCells	GZ24	10	1480	0.6756757
Perivascular	GZ24	586	1480	39.5945946
resMacrophage	GZ24	77	1480	5.2027027
SMC	GZ24	71	1480	4.7972973
Tcell	GZ24	35	1480	2.3648649
Adipocytes	GZ3	2	2396	0.0834725
Cardiomyocyte	GZ3	234	2396	9.7662771
EndoEC	GZ3	163	2396	6.8030050
Endothelial	GZ3	403	2396	16.8196995
Fibroblast	GZ3	812	2396	33.8898164
infMacrophage	GZ3	43	2396	1.7946578
LEC	GZ3	50	2396	2.0868114
NeuralCells	GZ3	35	2396	1.4607679
Perivascular	GZ3	432	2396	18.0300501
resMacrophage	GZ3	76	2396	3.1719533
SMC	GZ3	93	2396	3.8814691
Tcell	GZ3	53	2396	2.2120200
Adipocytes	GZ4	1	545	0.1834862
Cardiomyocyte	GZ4	45	545	8.2568807
EndoEC	GZ4	55	545	10.0917431
Endothelial	GZ4	125	545	22.9357798
Fibroblast	GZ4	135	545	24.7706422
infMacrophage	GZ4	22	545	4.0366972
LEC	GZ4	4	545	0.7339450
NeuralCells	GZ4	7	545	1.2844037
Perivascular	GZ4	68	545	12.4770642
resMacrophage	GZ4	32	545	5.8715596
SMC	GZ4	23	545	4.2201835
Tcell	GZ4	28	545	5.1376147
Adipocytes	GZ5	0	781	0.0000000
Cardiomyocyte	GZ5	39	781	4.9935980
EndoEC	GZ5	27	781	3.4571063
Endothelial	GZ5	217	781	27.7848912
Fibroblast	GZ5	261	781	33.4186940
infMacrophage	GZ5	11	781	1.4084507
LEC	GZ5	1	781	0.1280410
NeuralCells	GZ5	27	781	3.4571063
Perivascular	GZ5	118	781	15.1088348
resMacrophage	GZ5	20	781	2.5608195
SMC	GZ5	38	781	4.8655570
Tcell	GZ5	22	781	2.8169014
Adipocytes	GZ6	0	491	0.0000000
Cardiomyocyte	GZ6	12	491	2.4439919
EndoEC	GZ6	37	491	7.5356415
Endothelial	GZ6	118	491	24.0325866
Fibroblast	GZ6	150	491	30.5498982
infMacrophage	GZ6	19	491	3.8696538
LEC	GZ6	0	491	0.0000000
NeuralCells	GZ6	3	491	0.6109980
Perivascular	GZ6	55	491	11.2016293
resMacrophage	GZ6	44	491	8.9613035
SMC	GZ6	17	491	3.4623218
Tcell	GZ6	36	491	7.3319756
Adipocytes	GZ7	41	653	6.2787136
Cardiomyocyte	GZ7	88	653	13.4762634
EndoEC	GZ7	5	653	0.7656968
Endothelial	GZ7	121	653	18.5298622
Fibroblast	GZ7	220	653	33.6906585
infMacrophage	GZ7	5	653	0.7656968
LEC	GZ7	13	653	1.9908116
NeuralCells	GZ7	12	653	1.8376723
Perivascular	GZ7	68	653	10.4134763
resMacrophage	GZ7	13	653	1.9908116
SMC	GZ7	55	653	8.4226646
Tcell	GZ7	12	653	1.8376723
Adipocytes	GZ8	4	3921	0.1020148
Cardiomyocyte	GZ8	714	3921	18.2096404
EndoEC	GZ8	16	3921	0.4080592
Endothelial	GZ8	752	3921	19.1787809
Fibroblast	GZ8	1144	3921	29.1762306
infMacrophage	GZ8	120	3921	3.0604438
LEC	GZ8	27	3921	0.6885998
NeuralCells	GZ8	65	3921	1.6577404
Perivascular	GZ8	584	3921	14.8941597
resMacrophage	GZ8	240	3921	6.1208875
SMC	GZ8	160	3921	4.0805917
Tcell	GZ8	95	3921	2.4228513
Adipocytes	GZ9	2	4908	0.0407498
Cardiomyocyte	GZ9	650	4908	13.2436838
EndoEC	GZ9	349	4908	7.1108394
Endothelial	GZ9	657	4908	13.3863081
Fibroblast	GZ9	1466	4908	29.8696007
infMacrophage	GZ9	228	4908	4.6454768
LEC	GZ9	31	4908	0.6316218
NeuralCells	GZ9	32	4908	0.6519967
Perivascular	GZ9	864	4908	17.6039120
resMacrophage	GZ9	409	4908	8.3333333
SMC	GZ9	136	4908	2.7709861
Tcell	GZ9	84	4908	1.7114914
Adipocytes	SG29	0	1242	0.0000000
Cardiomyocyte	SG29	109	1242	8.7761675
EndoEC	SG29	19	1242	1.5297907
Endothelial	SG29	147	1242	11.8357488
Fibroblast	SG29	140	1242	11.2721417
infMacrophage	SG29	187	1242	15.0563607
LEC	SG29	2	1242	0.1610306
NeuralCells	SG29	15	1242	1.2077295
Perivascular	SG29	86	1242	6.9243156
resMacrophage	SG29	136	1242	10.9500805
SMC	SG29	18	1242	1.4492754
Tcell	SG29	383	1242	30.8373591
Adipocytes	SG31	0	1192	0.0000000
Cardiomyocyte	SG31	178	1192	14.9328859
EndoEC	SG31	51	1192	4.2785235
Endothelial	SG31	206	1192	17.2818792
Fibroblast	SG31	347	1192	29.1107383
infMacrophage	SG31	36	1192	3.0201342
LEC	SG31	2	1192	0.1677852
NeuralCells	SG31	23	1192	1.9295302
Perivascular	SG31	191	1192	16.0234899
resMacrophage	SG31	88	1192	7.3825503
SMC	SG31	43	1192	3.6073826
Tcell	SG31	27	1192	2.2651007
Adipocytes	SG32	5	1428	0.3501401
Cardiomyocyte	SG32	40	1428	2.8011204
EndoEC	SG32	29	1428	2.0308123
Endothelial	SG32	550	1428	38.5154062
Fibroblast	SG32	306	1428	21.4285714
infMacrophage	SG32	19	1428	1.3305322
LEC	SG32	14	1428	0.9803922
NeuralCells	SG32	28	1428	1.9607843
Perivascular	SG32	254	1428	17.7871148
resMacrophage	SG32	46	1428	3.2212885
SMC	SG32	67	1428	4.6918768
Tcell	SG32	70	1428	4.9019608
Adipocytes	SG33	8	6286	0.1272669
Cardiomyocyte	SG33	212	6286	3.3725740
EndoEC	SG33	36	6286	0.5727012
Endothelial	SG33	219	6286	3.4839325
Fibroblast	SG33	401	6286	6.3792555
infMacrophage	SG33	1270	6286	20.2036271
LEC	SG33	2	6286	0.0318167
NeuralCells	SG33	24	6286	0.3818008
Perivascular	SG33	142	6286	2.2589882
resMacrophage	SG33	898	6286	14.2857143
SMC	SG33	29	6286	0.4613427
Tcell	SG33	3045	6286	48.4409800
Adipocytes	SG34	0	620	0.0000000
Cardiomyocyte	SG34	173	620	27.9032258
EndoEC	SG34	12	620	1.9354839
Endothelial	SG34	92	620	14.8387097
Fibroblast	SG34	115	620	18.5483871
infMacrophage	SG34	10	620	1.6129032
LEC	SG34	1	620	0.1612903
NeuralCells	SG34	6	620	0.9677419
Perivascular	SG34	122	620	19.6774194
resMacrophage	SG34	41	620	6.6129032
SMC	SG34	23	620	3.7096774
Tcell	SG34	25	620	4.0322581
Adipocytes	SG35	0	2363	0.0000000
Cardiomyocyte	SG35	304	2363	12.8650021
EndoEC	SG35	30	2363	1.2695726
Endothelial	SG35	550	2363	23.2754972
Fibroblast	SG35	759	2363	32.1201862
infMacrophage	SG35	76	2363	3.2162505
LEC	SG35	18	2363	0.7617435
NeuralCells	SG35	29	2363	1.2272535
Perivascular	SG35	335	2363	14.1768938
resMacrophage	SG35	129	2363	5.4591621
SMC	SG35	61	2363	2.5814642
Tcell	SG35	72	2363	3.0469742

ordVec <- datFrac %>% dplyr::filter(label=="Tcell") %>% 
  arrange(., percentage)

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

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

Version	Author	Date
3ac3709	mluetge	2022-10-13
8f79da0	mluetge	2022-10-13
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
Myocarditis	32300
Perimyocarditis	1192

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

	HH	InfCardiomyopathy	Myocarditis	Perimyocarditis
Adipocytes	71	5	53	0
Cardiomyocyte	5473	40	2169	178
EndoEC	644	29	892	51
Endothelial	6756	550	5861	206
Fibroblast	8510	306	7458	347
infMacrophage	1295	19	2154	36
LEC	261	14	239	2
NeuralCells	442	28	421	23
Perivascular	7719	254	4915	191
resMacrophage	2820	46	2302	88
SMC	1186	67	1203	43
Tcell	835	70	4633	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
Adipocytes	HH	71	36012	0.1971565
Cardiomyocyte	HH	5473	36012	15.1977119
EndoEC	HH	644	36012	1.7882928
Endothelial	HH	6756	36012	18.7604132
Fibroblast	HH	8510	36012	23.6310119
infMacrophage	HH	1295	36012	3.5960235
LEC	HH	261	36012	0.7247584
NeuralCells	HH	442	36012	1.2273687
Perivascular	HH	7719	36012	21.4345218
resMacrophage	HH	2820	36012	7.8307231
SMC	HH	1186	36012	3.2933467
Tcell	HH	835	36012	2.3186716
Adipocytes	InfCardiomyopathy	5	1428	0.3501401
Cardiomyocyte	InfCardiomyopathy	40	1428	2.8011204
EndoEC	InfCardiomyopathy	29	1428	2.0308123
Endothelial	InfCardiomyopathy	550	1428	38.5154062
Fibroblast	InfCardiomyopathy	306	1428	21.4285714
infMacrophage	InfCardiomyopathy	19	1428	1.3305322
LEC	InfCardiomyopathy	14	1428	0.9803922
NeuralCells	InfCardiomyopathy	28	1428	1.9607843
Perivascular	InfCardiomyopathy	254	1428	17.7871148
resMacrophage	InfCardiomyopathy	46	1428	3.2212885
SMC	InfCardiomyopathy	67	1428	4.6918768
Tcell	InfCardiomyopathy	70	1428	4.9019608
Adipocytes	Myocarditis	53	32300	0.1640867
Cardiomyocyte	Myocarditis	2169	32300	6.7151703
EndoEC	Myocarditis	892	32300	2.7616099
Endothelial	Myocarditis	5861	32300	18.1455108
Fibroblast	Myocarditis	7458	32300	23.0897833
infMacrophage	Myocarditis	2154	32300	6.6687307
LEC	Myocarditis	239	32300	0.7399381
NeuralCells	Myocarditis	421	32300	1.3034056
Perivascular	Myocarditis	4915	32300	15.2167183
resMacrophage	Myocarditis	2302	32300	7.1269350
SMC	Myocarditis	1203	32300	3.7244582
Tcell	Myocarditis	4633	32300	14.3436533
Adipocytes	Perimyocarditis	0	1192	0.0000000
Cardiomyocyte	Perimyocarditis	178	1192	14.9328859
EndoEC	Perimyocarditis	51	1192	4.2785235
Endothelial	Perimyocarditis	206	1192	17.2818792
Fibroblast	Perimyocarditis	347	1192	29.1107383
infMacrophage	Perimyocarditis	36	1192	3.0201342
LEC	Perimyocarditis	2	1192	0.1677852
NeuralCells	Perimyocarditis	23	1192	1.9295302
Perivascular	Perimyocarditis	191	1192	16.0234899
resMacrophage	Perimyocarditis	88	1192	7.3825503
SMC	Perimyocarditis	43	1192	3.6073826
Tcell	Perimyocarditis	27	1192	2.2651007

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

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

Version	Author	Date
3ac3709	mluetge	2022-10-13
8f79da0	mluetge	2022-10-13
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     24666     10254 

table(seurat$TcellGrp, seurat$ID)

           
             GZ1 GZ10 GZ11 GZ12 GZ13 GZ14 GZ15 GZ16 GZ17 GZ18  GZ2 GZ20 GZ21
  Ctrl         0 3731 3991 3818 9882    0    0    0    0    0    0    0 1442
  TcellHigh 2740    0    0    0    0    0 4439  436 1370    0    0 2706    0
  TcellLow     0    0    0    0    0 1268    0    0    0 2280 1684    0    0
           
            GZ22 GZ23 GZ24  GZ3  GZ4  GZ5  GZ6  GZ7  GZ8  GZ9 SG29 SG31 SG32
  Ctrl      1998  841 1480    0    0    0    0    0 3921 4908    0    0    0
  TcellHigh    0    0    0    0  545    0  491    0    0    0 1242    0 1428
  TcellLow     0    0    0 2396    0  781    0  653    0    0    0 1192    0
           
            SG33 SG34 SG35
  Ctrl         0    0    0
  TcellHigh 6286  620 2363
  TcellLow     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"

Version	Author	Date
3ac3709	mluetge	2022-10-13
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)

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

Version	Author	Date
3ac3709	mluetge	2022-10-13
8f79da0	mluetge	2022-10-13
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("")

Past versions of dotplot-1.png

Version	Author	Date
3ac3709	mluetge	2022-10-13
8f79da0	mluetge	2022-10-13
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("")

Past versions of dotplot-2.png

Version	Author	Date
3ac3709	mluetge	2022-10-13
e6d0c3c	mluetge	2022-09-26
cd902dd	mluetge	2022-09-12
141fae8	mluetge	2022-07-06

violin plots selected marker

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

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

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

Feature plots BMPs

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

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

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

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

average counts BMPs in Fibroblasts

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

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

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

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

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               ggbeeswarm_0.6.0       bitops_1.0-7          
 [37] spatstat.utils_2.3-1   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_2.3.5             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_4.1.16           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_1.14.2      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_1.8-40           
[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_3.1-0              cli_3.4.1             
[112] parallel_4.2.1         igraph_1.3.5           pkgconfig_2.0.3       
[115] plotly_4.10.0          spatstat.sparse_2.1-1  xml2_1.3.3            
[118] vipor_0.4.5            bslib_0.4.0            XVector_0.36.0        
[121] rvest_1.0.3            digest_0.6.29          RcppAnnoy_0.0.19      
[124] spatstat.data_2.2-0    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_3.1-159           jsonlite_1.8.2        
[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] "Wed Oct 19 17:31:47 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_0.6.3         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               ggbeeswarm_0.6.0       bitops_1.0-7          
 [37] spatstat.utils_2.3-1   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_2.3.5             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_4.1.16           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_1.14.2      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_1.8-40           
[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_3.1-0              cli_3.4.1             
[112] parallel_4.2.1         igraph_1.3.5           pkgconfig_2.0.3       
[115] plotly_4.10.0          spatstat.sparse_2.1-1  xml2_1.3.3            
[118] vipor_0.4.5            bslib_0.4.0            XVector_0.36.0        
[121] rvest_1.0.3            digest_0.6.29          RcppAnnoy_0.0.19      
[124] spatstat.data_2.2-0    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_3.1-159           jsonlite_1.8.2        
[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