assign labels
Mechthild Lütge
31 July 2022
Last updated: 2023-08-31
Checks: 6 1
Knit directory: humanCardiacFibroblasts/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| html | 61f9e3b | mluetge | 2022-12-12 | update assignment t cell grp |
| Rmd | 307a2ca | mluetge | 2022-12-12 | update pat ID SG29 |
| html | 307a2ca | mluetge | 2022-12-12 | update pat ID SG29 |
| Rmd | 66c2208 | mluetge | 2022-12-08 | compare T cell groups and project gene signatures |
| html | 66c2208 | mluetge | 2022-12-08 | compare T cell groups and project gene signatures |
| Rmd | 1e72380 | mluetge | 2022-12-02 | remove patients with diff diagnosis |
| html | 1e72380 | mluetge | 2022-12-02 | remove patients with diff diagnosis |
| Rmd | e469ce4 | mluetge | 2022-11-29 | add samples with Htrans |
| html | e469ce4 | mluetge | 2022-11-29 | add samples with Htrans |
load packages
suppressPackageStartupMessages({
library(SingleCellExperiment)
library(tidyverse)
library(Seurat)
library(magrittr)
library(dplyr)
library(purrr)
library(ggplot2)
library(here)
library(runSeurat3)
library(ggsci)
library(ggpubr)
library(pheatmap)
library(viridis)
library(sctransform)
})load data
basedir <- here()
seurat <- readRDS(file = paste0(basedir,
"/data/humanHeartsPlusGraz_intPatients_merged",
"_seurat.rds"))
Idents(seurat) <- seurat$integrated_snn_res.0.6
table(seurat$ID)
GZ1 GZ14 GZ15 GZ16 GZ17 GZ18 GZ2 GZ20 GZ3 GZ4 GZ5
2740 1268 4439 436 1370 2280 1684 2706 2396 545 781
GZ6 SG21 SG24 SG25 SG28 SG29_1 SG29_2 SG31 SG32 SG33 SG34
491 1536 2381 3131 1545 1254 1242 1192 1428 6286 620
SG35
2363 assign labels
seurat$label <- "other"
seurat$label[which(seurat$integrated_snn_res.0.6 %in% c("2","9"))] <- "Endothelial"
seurat$label[which(seurat$integrated_snn_res.0.6 %in% c("8"))] <- "EndoEC"
seurat$label[which(seurat$integrated_snn_res.0.6 %in% c("13"))] <- "LEC"
seurat$label[which(seurat$integrated_snn_res.0.6 %in% c("4", "11"))] <- "Tcell"
seurat$label[which(seurat$integrated_snn_res.0.6 %in% c("5"))] <- "Cardiomyocyte"
seurat$label[which(seurat$integrated_snn_res.0.6 %in% c("0", "3"))] <- "Fibroblast"
seurat$label[which(seurat$integrated_snn_res.0.6 %in% c("1"))] <- "Perivascular"
seurat$label[which(seurat$integrated_snn_res.0.6 %in% c("10"))] <- "SMC"
seurat$label[which(seurat$integrated_snn_res.0.6 %in% c("7"))] <- "resMacrophage"
seurat$label[which(seurat$integrated_snn_res.0.6 %in% c("6"))] <- "infMacrophage"
seurat$label[which(seurat$integrated_snn_res.0.6 %in% c("12"))] <- "NeuralCells"
seurat$label[which(seurat$integrated_snn_res.0.6 %in% c("14"))] <- "Adipocytes"color vectors
colPal <- c(pal_igv()(12),
pal_aaas()(10))[1:length(levels(seurat))]
colTec <- pal_jama()(length(unique(seurat$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("#c08b65", "#ba4e45", "#d4cc84", "#546f82", "#5c5cdf",
"#80396e", "#8d5639", "#779462", "#800000FF", "#d87c15",
"#FFA319FF", "#FF95A8FF")
names(colLab) <- c("EndoEC", "Tcell","resMacrophage", "Fibroblast",
"infMacrophage", "Perivascular","Cardiomyocyte",
"Endothelial","Adipocytes","NeuralCells","SMC","LEC")
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)vis data
clusters
DimPlot(seurat, reduction = "umap", cols=colPal)+
theme_bw() +
theme(axis.text = element_blank(), axis.ticks = element_blank(),
panel.grid.minor = element_blank()) +
xlab("UMAP1") +
ylab("UMAP2")
DimPlot(seurat, reduction = "umap", cols=colPal,
shuffle = T)+
theme_void()
label
DimPlot(seurat, reduction = "umap", group.by = "label", cols=colLab)+
theme_bw() +
theme(axis.text = element_blank(), axis.ticks = element_blank(),
panel.grid.minor = element_blank()) +
xlab("UMAP1") +
ylab("UMAP2")
DimPlot(seurat, reduction = "umap", group.by = "label", cols=colLab,
shuffle = T)+
theme_void()
technique
DimPlot(seurat, reduction = "umap", group.by = "technique", cols=colTec)+
theme_bw() +
theme(axis.text = element_blank(), axis.ticks = element_blank(),
panel.grid.minor = element_blank()) +
xlab("UMAP1") +
ylab("UMAP2")
Sample
DimPlot(seurat, reduction = "umap", group.by = "dataset", cols=colSmp)+
theme_bw() +
theme(axis.text = element_blank(), axis.ticks = element_blank(),
panel.grid.minor = element_blank()) +
xlab("UMAP1") +
ylab("UMAP2")
ID
DimPlot(seurat, reduction = "umap", group.by = "ID", cols=colID, shuffle = T)+
theme_bw() +
theme(axis.text = element_blank(), axis.ticks = element_blank(),
panel.grid.minor = element_blank()) +
xlab("UMAP1") +
ylab("UMAP2")
DimPlot(seurat, reduction = "umap", group.by = "ID", cols=colID,
shuffle = T)+
theme_void()
Origin
DimPlot(seurat, reduction = "umap", group.by = "origin", cols=colOrig,
shuffle = T)+
theme_bw() +
theme(axis.text = element_blank(), axis.ticks = element_blank(),
panel.grid.minor = element_blank()) +
xlab("UMAP1") +
ylab("UMAP2")
DimPlot(seurat, reduction = "umap", group.by = "origin", cols=colOrig,
shuffle = T)+
theme_void()
isolation
DimPlot(seurat, reduction = "umap", group.by = "isolation", cols=colIso)+
theme_bw() +
theme(axis.text = element_blank(), axis.ticks = element_blank(),
panel.grid.minor = element_blank()) +
xlab("UMAP1") +
ylab("UMAP2")
cond
DimPlot(seurat, reduction = "umap", group.by = "cond", cols=colCond)+
theme_bw() +
theme(axis.text = element_blank(), axis.ticks = element_blank(),
panel.grid.minor = element_blank()) +
xlab("UMAP1") +
ylab("UMAP2")
processing
DimPlot(seurat, reduction = "umap", group.by = "processing", cols=colProc)+
theme_bw() +
theme(axis.text = element_blank(), axis.ticks = element_blank(),
panel.grid.minor = element_blank()) +
xlab("UMAP1") +
ylab("UMAP2")
cnt tables
per patient
## total cells per patient
knitr::kable(table(seurat$ID))| Var1 | Freq |
|---|---|
| GZ1 | 2740 |
| GZ14 | 1268 |
| GZ15 | 4439 |
| GZ16 | 436 |
| GZ17 | 1370 |
| GZ18 | 2280 |
| GZ2 | 1684 |
| GZ20 | 2706 |
| GZ3 | 2396 |
| GZ4 | 545 |
| GZ5 | 781 |
| GZ6 | 491 |
| SG21 | 1536 |
| SG24 | 2381 |
| SG25 | 3131 |
| SG28 | 1545 |
| SG29_1 | 1254 |
| SG29_2 | 1242 |
| SG31 | 1192 |
| SG32 | 1428 |
| SG33 | 6286 |
| SG34 | 620 |
| SG35 | 2363 |
## celltype per patient counts
knitr::kable(table(seurat$label, seurat$ID))| GZ1 | GZ14 | GZ15 | GZ16 | GZ17 | GZ18 | GZ2 | GZ20 | GZ3 | GZ4 | GZ5 | GZ6 | SG21 | SG24 | SG25 | SG28 | SG29_1 | SG29_2 | SG31 | SG32 | SG33 | SG34 | SG35 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Adipocytes | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 2 | 1 | 0 | 0 | 1 | 3 | 49 | 3 | 1 | 5 | 0 | 5 | 8 | 0 | 0 |
| Cardiomyocyte | 207 | 50 | 116 | 25 | 71 | 329 | 106 | 46 | 238 | 44 | 39 | 12 | 96 | 298 | 396 | 73 | 78 | 100 | 178 | 40 | 212 | 174 | 304 |
| EndoEC | 184 | 43 | 172 | 23 | 32 | 211 | 139 | 349 | 215 | 69 | 42 | 59 | 50 | 108 | 116 | 82 | 67 | 57 | 67 | 92 | 96 | 23 | 91 |
| Endothelial | 280 | 279 | 912 | 50 | 161 | 426 | 297 | 890 | 349 | 117 | 203 | 95 | 177 | 377 | 535 | 471 | 363 | 77 | 190 | 485 | 160 | 82 | 486 |
| Fibroblast | 814 | 317 | 1123 | 130 | 374 | 627 | 661 | 653 | 847 | 142 | 264 | 155 | 614 | 869 | 844 | 322 | 271 | 163 | 357 | 320 | 414 | 117 | 778 |
| infMacrophage | 112 | 23 | 110 | 33 | 101 | 25 | 28 | 46 | 55 | 21 | 12 | 30 | 35 | 51 | 98 | 12 | 13 | 284 | 39 | 27 | 1407 | 13 | 74 |
| LEC | 6 | 0 | 0 | 0 | 0 | 6 | 6 | 2 | 30 | 0 | 0 | 0 | 1 | 9 | 0 | 1 | 15 | 0 | 0 | 4 | 0 | 0 | 0 |
| NeuralCells | 58 | 10 | 76 | 7 | 28 | 28 | 28 | 20 | 31 | 7 | 26 | 2 | 7 | 28 | 26 | 26 | 14 | 10 | 22 | 26 | 24 | 5 | 27 |
| Perivascular | 487 | 354 | 1348 | 20 | 147 | 387 | 270 | 352 | 407 | 62 | 122 | 53 | 318 | 286 | 460 | 432 | 269 | 41 | 183 | 241 | 135 | 121 | 328 |
| resMacrophage | 188 | 70 | 208 | 20 | 232 | 69 | 56 | 95 | 63 | 33 | 19 | 33 | 120 | 119 | 147 | 8 | 28 | 123 | 88 | 43 | 722 | 38 | 125 |
| SMC | 186 | 85 | 188 | 18 | 40 | 129 | 48 | 119 | 82 | 22 | 36 | 15 | 72 | 117 | 99 | 94 | 95 | 3 | 45 | 67 | 28 | 22 | 65 |
| Tcell | 218 | 37 | 186 | 110 | 184 | 43 | 44 | 134 | 77 | 27 | 18 | 37 | 45 | 116 | 361 | 21 | 40 | 379 | 23 | 78 | 3080 | 25 | 85 |
## celltype percentages per patient
datLab <- data.frame(table(seurat$label, seurat$ID))
colnames(datLab) <- c("label", "ID", "cnt")
datPat <- data.frame(table(seurat$ID))
colnames(datPat) <- c("ID", "total")
datFrac <- datLab %>% left_join(., datPat, by="ID") %>%
mutate(percentage = cnt*100/total)
knitr::kable(datFrac)| label | ID | cnt | total | percentage |
|---|---|---|---|---|
| Adipocytes | GZ1 | 0 | 2740 | 0.0000000 |
| Cardiomyocyte | GZ1 | 207 | 2740 | 7.5547445 |
| EndoEC | GZ1 | 184 | 2740 | 6.7153285 |
| Endothelial | GZ1 | 280 | 2740 | 10.2189781 |
| Fibroblast | GZ1 | 814 | 2740 | 29.7080292 |
| infMacrophage | GZ1 | 112 | 2740 | 4.0875912 |
| LEC | GZ1 | 6 | 2740 | 0.2189781 |
| NeuralCells | GZ1 | 58 | 2740 | 2.1167883 |
| Perivascular | GZ1 | 487 | 2740 | 17.7737226 |
| resMacrophage | GZ1 | 188 | 2740 | 6.8613139 |
| SMC | GZ1 | 186 | 2740 | 6.7883212 |
| Tcell | GZ1 | 218 | 2740 | 7.9562044 |
| Adipocytes | GZ14 | 0 | 1268 | 0.0000000 |
| Cardiomyocyte | GZ14 | 50 | 1268 | 3.9432177 |
| EndoEC | GZ14 | 43 | 1268 | 3.3911672 |
| Endothelial | GZ14 | 279 | 1268 | 22.0031546 |
| Fibroblast | GZ14 | 317 | 1268 | 25.0000000 |
| infMacrophage | GZ14 | 23 | 1268 | 1.8138801 |
| LEC | GZ14 | 0 | 1268 | 0.0000000 |
| NeuralCells | GZ14 | 10 | 1268 | 0.7886435 |
| Perivascular | GZ14 | 354 | 1268 | 27.9179811 |
| resMacrophage | GZ14 | 70 | 1268 | 5.5205047 |
| SMC | GZ14 | 85 | 1268 | 6.7034700 |
| Tcell | GZ14 | 37 | 1268 | 2.9179811 |
| Adipocytes | GZ15 | 0 | 4439 | 0.0000000 |
| Cardiomyocyte | GZ15 | 116 | 4439 | 2.6132012 |
| EndoEC | GZ15 | 172 | 4439 | 3.8747466 |
| Endothelial | GZ15 | 912 | 4439 | 20.5451678 |
| Fibroblast | GZ15 | 1123 | 4439 | 25.2984907 |
| infMacrophage | GZ15 | 110 | 4439 | 2.4780356 |
| LEC | GZ15 | 0 | 4439 | 0.0000000 |
| NeuralCells | GZ15 | 76 | 4439 | 1.7120973 |
| Perivascular | GZ15 | 1348 | 4439 | 30.3671998 |
| resMacrophage | GZ15 | 208 | 4439 | 4.6857400 |
| SMC | GZ15 | 188 | 4439 | 4.2351881 |
| Tcell | GZ15 | 186 | 4439 | 4.1901329 |
| Adipocytes | GZ16 | 0 | 436 | 0.0000000 |
| Cardiomyocyte | GZ16 | 25 | 436 | 5.7339450 |
| EndoEC | GZ16 | 23 | 436 | 5.2752294 |
| Endothelial | GZ16 | 50 | 436 | 11.4678899 |
| Fibroblast | GZ16 | 130 | 436 | 29.8165138 |
| infMacrophage | GZ16 | 33 | 436 | 7.5688073 |
| LEC | GZ16 | 0 | 436 | 0.0000000 |
| NeuralCells | GZ16 | 7 | 436 | 1.6055046 |
| Perivascular | GZ16 | 20 | 436 | 4.5871560 |
| resMacrophage | GZ16 | 20 | 436 | 4.5871560 |
| SMC | GZ16 | 18 | 436 | 4.1284404 |
| Tcell | GZ16 | 110 | 436 | 25.2293578 |
| Adipocytes | GZ17 | 0 | 1370 | 0.0000000 |
| Cardiomyocyte | GZ17 | 71 | 1370 | 5.1824818 |
| EndoEC | GZ17 | 32 | 1370 | 2.3357664 |
| Endothelial | GZ17 | 161 | 1370 | 11.7518248 |
| Fibroblast | GZ17 | 374 | 1370 | 27.2992701 |
| infMacrophage | GZ17 | 101 | 1370 | 7.3722628 |
| LEC | GZ17 | 0 | 1370 | 0.0000000 |
| NeuralCells | GZ17 | 28 | 1370 | 2.0437956 |
| Perivascular | GZ17 | 147 | 1370 | 10.7299270 |
| resMacrophage | GZ17 | 232 | 1370 | 16.9343066 |
| SMC | GZ17 | 40 | 1370 | 2.9197080 |
| Tcell | GZ17 | 184 | 1370 | 13.4306569 |
| Adipocytes | GZ18 | 0 | 2280 | 0.0000000 |
| Cardiomyocyte | GZ18 | 329 | 2280 | 14.4298246 |
| EndoEC | GZ18 | 211 | 2280 | 9.2543860 |
| Endothelial | GZ18 | 426 | 2280 | 18.6842105 |
| Fibroblast | GZ18 | 627 | 2280 | 27.5000000 |
| infMacrophage | GZ18 | 25 | 2280 | 1.0964912 |
| LEC | GZ18 | 6 | 2280 | 0.2631579 |
| NeuralCells | GZ18 | 28 | 2280 | 1.2280702 |
| Perivascular | GZ18 | 387 | 2280 | 16.9736842 |
| resMacrophage | GZ18 | 69 | 2280 | 3.0263158 |
| SMC | GZ18 | 129 | 2280 | 5.6578947 |
| Tcell | GZ18 | 43 | 2280 | 1.8859649 |
| Adipocytes | GZ2 | 1 | 1684 | 0.0593824 |
| Cardiomyocyte | GZ2 | 106 | 1684 | 6.2945368 |
| EndoEC | GZ2 | 139 | 1684 | 8.2541568 |
| Endothelial | GZ2 | 297 | 1684 | 17.6365796 |
| Fibroblast | GZ2 | 661 | 1684 | 39.2517815 |
| infMacrophage | GZ2 | 28 | 1684 | 1.6627078 |
| LEC | GZ2 | 6 | 1684 | 0.3562945 |
| NeuralCells | GZ2 | 28 | 1684 | 1.6627078 |
| Perivascular | GZ2 | 270 | 1684 | 16.0332542 |
| resMacrophage | GZ2 | 56 | 1684 | 3.3254157 |
| SMC | GZ2 | 48 | 1684 | 2.8503563 |
| Tcell | GZ2 | 44 | 1684 | 2.6128266 |
| Adipocytes | GZ20 | 0 | 2706 | 0.0000000 |
| Cardiomyocyte | GZ20 | 46 | 2706 | 1.6999261 |
| EndoEC | GZ20 | 349 | 2706 | 12.8972653 |
| Endothelial | GZ20 | 890 | 2706 | 32.8898744 |
| Fibroblast | GZ20 | 653 | 2706 | 24.1315595 |
| infMacrophage | GZ20 | 46 | 2706 | 1.6999261 |
| LEC | GZ20 | 2 | 2706 | 0.0739098 |
| NeuralCells | GZ20 | 20 | 2706 | 0.7390983 |
| Perivascular | GZ20 | 352 | 2706 | 13.0081301 |
| resMacrophage | GZ20 | 95 | 2706 | 3.5107169 |
| SMC | GZ20 | 119 | 2706 | 4.3976349 |
| Tcell | GZ20 | 134 | 2706 | 4.9519586 |
| Adipocytes | GZ3 | 2 | 2396 | 0.0834725 |
| Cardiomyocyte | GZ3 | 238 | 2396 | 9.9332220 |
| EndoEC | GZ3 | 215 | 2396 | 8.9732888 |
| Endothelial | GZ3 | 349 | 2396 | 14.5659432 |
| Fibroblast | GZ3 | 847 | 2396 | 35.3505843 |
| infMacrophage | GZ3 | 55 | 2396 | 2.2954925 |
| LEC | GZ3 | 30 | 2396 | 1.2520868 |
| NeuralCells | GZ3 | 31 | 2396 | 1.2938230 |
| Perivascular | GZ3 | 407 | 2396 | 16.9866444 |
| resMacrophage | GZ3 | 63 | 2396 | 2.6293823 |
| SMC | GZ3 | 82 | 2396 | 3.4223706 |
| Tcell | GZ3 | 77 | 2396 | 3.2136895 |
| Adipocytes | GZ4 | 1 | 545 | 0.1834862 |
| Cardiomyocyte | GZ4 | 44 | 545 | 8.0733945 |
| EndoEC | GZ4 | 69 | 545 | 12.6605505 |
| Endothelial | GZ4 | 117 | 545 | 21.4678899 |
| Fibroblast | GZ4 | 142 | 545 | 26.0550459 |
| infMacrophage | GZ4 | 21 | 545 | 3.8532110 |
| LEC | GZ4 | 0 | 545 | 0.0000000 |
| NeuralCells | GZ4 | 7 | 545 | 1.2844037 |
| Perivascular | GZ4 | 62 | 545 | 11.3761468 |
| resMacrophage | GZ4 | 33 | 545 | 6.0550459 |
| SMC | GZ4 | 22 | 545 | 4.0366972 |
| Tcell | GZ4 | 27 | 545 | 4.9541284 |
| Adipocytes | GZ5 | 0 | 781 | 0.0000000 |
| Cardiomyocyte | GZ5 | 39 | 781 | 4.9935980 |
| EndoEC | GZ5 | 42 | 781 | 5.3777209 |
| Endothelial | GZ5 | 203 | 781 | 25.9923175 |
| Fibroblast | GZ5 | 264 | 781 | 33.8028169 |
| infMacrophage | GZ5 | 12 | 781 | 1.5364917 |
| LEC | GZ5 | 0 | 781 | 0.0000000 |
| NeuralCells | GZ5 | 26 | 781 | 3.3290653 |
| Perivascular | GZ5 | 122 | 781 | 15.6209987 |
| resMacrophage | GZ5 | 19 | 781 | 2.4327785 |
| SMC | GZ5 | 36 | 781 | 4.6094750 |
| Tcell | GZ5 | 18 | 781 | 2.3047375 |
| Adipocytes | GZ6 | 0 | 491 | 0.0000000 |
| Cardiomyocyte | GZ6 | 12 | 491 | 2.4439919 |
| EndoEC | GZ6 | 59 | 491 | 12.0162933 |
| Endothelial | GZ6 | 95 | 491 | 19.3482688 |
| Fibroblast | GZ6 | 155 | 491 | 31.5682281 |
| infMacrophage | GZ6 | 30 | 491 | 6.1099796 |
| LEC | GZ6 | 0 | 491 | 0.0000000 |
| NeuralCells | GZ6 | 2 | 491 | 0.4073320 |
| Perivascular | GZ6 | 53 | 491 | 10.7942974 |
| resMacrophage | GZ6 | 33 | 491 | 6.7209776 |
| SMC | GZ6 | 15 | 491 | 3.0549898 |
| Tcell | GZ6 | 37 | 491 | 7.5356415 |
| Adipocytes | SG21 | 1 | 1536 | 0.0651042 |
| Cardiomyocyte | SG21 | 96 | 1536 | 6.2500000 |
| EndoEC | SG21 | 50 | 1536 | 3.2552083 |
| Endothelial | SG21 | 177 | 1536 | 11.5234375 |
| Fibroblast | SG21 | 614 | 1536 | 39.9739583 |
| infMacrophage | SG21 | 35 | 1536 | 2.2786458 |
| LEC | SG21 | 1 | 1536 | 0.0651042 |
| NeuralCells | SG21 | 7 | 1536 | 0.4557292 |
| Perivascular | SG21 | 318 | 1536 | 20.7031250 |
| resMacrophage | SG21 | 120 | 1536 | 7.8125000 |
| SMC | SG21 | 72 | 1536 | 4.6875000 |
| Tcell | SG21 | 45 | 1536 | 2.9296875 |
| Adipocytes | SG24 | 3 | 2381 | 0.1259975 |
| Cardiomyocyte | SG24 | 298 | 2381 | 12.5157497 |
| EndoEC | SG24 | 108 | 2381 | 4.5359093 |
| Endothelial | SG24 | 377 | 2381 | 15.8336833 |
| Fibroblast | SG24 | 869 | 2381 | 36.4972701 |
| infMacrophage | SG24 | 51 | 2381 | 2.1419572 |
| LEC | SG24 | 9 | 2381 | 0.3779924 |
| NeuralCells | SG24 | 28 | 2381 | 1.1759765 |
| Perivascular | SG24 | 286 | 2381 | 12.0117598 |
| resMacrophage | SG24 | 119 | 2381 | 4.9979000 |
| SMC | SG24 | 117 | 2381 | 4.9139017 |
| Tcell | SG24 | 116 | 2381 | 4.8719026 |
| Adipocytes | SG25 | 49 | 3131 | 1.5649952 |
| Cardiomyocyte | SG25 | 396 | 3131 | 12.6477164 |
| EndoEC | SG25 | 116 | 3131 | 3.7048866 |
| Endothelial | SG25 | 535 | 3131 | 17.0871926 |
| Fibroblast | SG25 | 844 | 3131 | 26.9562440 |
| infMacrophage | SG25 | 98 | 3131 | 3.1299904 |
| LEC | SG25 | 0 | 3131 | 0.0000000 |
| NeuralCells | SG25 | 26 | 3131 | 0.8304056 |
| Perivascular | SG25 | 460 | 3131 | 14.6917918 |
| resMacrophage | SG25 | 147 | 3131 | 4.6949856 |
| SMC | SG25 | 99 | 3131 | 3.1619291 |
| Tcell | SG25 | 361 | 3131 | 11.5298627 |
| Adipocytes | SG28 | 3 | 1545 | 0.1941748 |
| Cardiomyocyte | SG28 | 73 | 1545 | 4.7249191 |
| EndoEC | SG28 | 82 | 1545 | 5.3074434 |
| Endothelial | SG28 | 471 | 1545 | 30.4854369 |
| Fibroblast | SG28 | 322 | 1545 | 20.8414239 |
| infMacrophage | SG28 | 12 | 1545 | 0.7766990 |
| LEC | SG28 | 1 | 1545 | 0.0647249 |
| NeuralCells | SG28 | 26 | 1545 | 1.6828479 |
| Perivascular | SG28 | 432 | 1545 | 27.9611650 |
| resMacrophage | SG28 | 8 | 1545 | 0.5177994 |
| SMC | SG28 | 94 | 1545 | 6.0841424 |
| Tcell | SG28 | 21 | 1545 | 1.3592233 |
| Adipocytes | SG29_1 | 1 | 1254 | 0.0797448 |
| Cardiomyocyte | SG29_1 | 78 | 1254 | 6.2200957 |
| EndoEC | SG29_1 | 67 | 1254 | 5.3429027 |
| Endothelial | SG29_1 | 363 | 1254 | 28.9473684 |
| Fibroblast | SG29_1 | 271 | 1254 | 21.6108453 |
| infMacrophage | SG29_1 | 13 | 1254 | 1.0366826 |
| LEC | SG29_1 | 15 | 1254 | 1.1961722 |
| NeuralCells | SG29_1 | 14 | 1254 | 1.1164274 |
| Perivascular | SG29_1 | 269 | 1254 | 21.4513557 |
| resMacrophage | SG29_1 | 28 | 1254 | 2.2328549 |
| SMC | SG29_1 | 95 | 1254 | 7.5757576 |
| Tcell | SG29_1 | 40 | 1254 | 3.1897927 |
| Adipocytes | SG29_2 | 5 | 1242 | 0.4025765 |
| Cardiomyocyte | SG29_2 | 100 | 1242 | 8.0515298 |
| EndoEC | SG29_2 | 57 | 1242 | 4.5893720 |
| Endothelial | SG29_2 | 77 | 1242 | 6.1996779 |
| Fibroblast | SG29_2 | 163 | 1242 | 13.1239936 |
| infMacrophage | SG29_2 | 284 | 1242 | 22.8663446 |
| LEC | SG29_2 | 0 | 1242 | 0.0000000 |
| NeuralCells | SG29_2 | 10 | 1242 | 0.8051530 |
| Perivascular | SG29_2 | 41 | 1242 | 3.3011272 |
| resMacrophage | SG29_2 | 123 | 1242 | 9.9033816 |
| SMC | SG29_2 | 3 | 1242 | 0.2415459 |
| Tcell | SG29_2 | 379 | 1242 | 30.5152979 |
| Adipocytes | SG31 | 0 | 1192 | 0.0000000 |
| Cardiomyocyte | SG31 | 178 | 1192 | 14.9328859 |
| EndoEC | SG31 | 67 | 1192 | 5.6208054 |
| Endothelial | SG31 | 190 | 1192 | 15.9395973 |
| Fibroblast | SG31 | 357 | 1192 | 29.9496644 |
| infMacrophage | SG31 | 39 | 1192 | 3.2718121 |
| LEC | SG31 | 0 | 1192 | 0.0000000 |
| NeuralCells | SG31 | 22 | 1192 | 1.8456376 |
| Perivascular | SG31 | 183 | 1192 | 15.3523490 |
| resMacrophage | SG31 | 88 | 1192 | 7.3825503 |
| SMC | SG31 | 45 | 1192 | 3.7751678 |
| Tcell | SG31 | 23 | 1192 | 1.9295302 |
| Adipocytes | SG32 | 5 | 1428 | 0.3501401 |
| Cardiomyocyte | SG32 | 40 | 1428 | 2.8011204 |
| EndoEC | SG32 | 92 | 1428 | 6.4425770 |
| Endothelial | SG32 | 485 | 1428 | 33.9635854 |
| Fibroblast | SG32 | 320 | 1428 | 22.4089636 |
| infMacrophage | SG32 | 27 | 1428 | 1.8907563 |
| LEC | SG32 | 4 | 1428 | 0.2801120 |
| NeuralCells | SG32 | 26 | 1428 | 1.8207283 |
| Perivascular | SG32 | 241 | 1428 | 16.8767507 |
| resMacrophage | SG32 | 43 | 1428 | 3.0112045 |
| SMC | SG32 | 67 | 1428 | 4.6918768 |
| Tcell | SG32 | 78 | 1428 | 5.4621849 |
| Adipocytes | SG33 | 8 | 6286 | 0.1272669 |
| Cardiomyocyte | SG33 | 212 | 6286 | 3.3725740 |
| EndoEC | SG33 | 96 | 6286 | 1.5272033 |
| Endothelial | SG33 | 160 | 6286 | 2.5453388 |
| Fibroblast | SG33 | 414 | 6286 | 6.5860643 |
| infMacrophage | SG33 | 1407 | 6286 | 22.3830735 |
| LEC | SG33 | 0 | 6286 | 0.0000000 |
| NeuralCells | SG33 | 24 | 6286 | 0.3818008 |
| Perivascular | SG33 | 135 | 6286 | 2.1476297 |
| resMacrophage | SG33 | 722 | 6286 | 11.4858416 |
| SMC | SG33 | 28 | 6286 | 0.4454343 |
| Tcell | SG33 | 3080 | 6286 | 48.9977728 |
| Adipocytes | SG34 | 0 | 620 | 0.0000000 |
| Cardiomyocyte | SG34 | 174 | 620 | 28.0645161 |
| EndoEC | SG34 | 23 | 620 | 3.7096774 |
| Endothelial | SG34 | 82 | 620 | 13.2258065 |
| Fibroblast | SG34 | 117 | 620 | 18.8709677 |
| infMacrophage | SG34 | 13 | 620 | 2.0967742 |
| LEC | SG34 | 0 | 620 | 0.0000000 |
| NeuralCells | SG34 | 5 | 620 | 0.8064516 |
| Perivascular | SG34 | 121 | 620 | 19.5161290 |
| resMacrophage | SG34 | 38 | 620 | 6.1290323 |
| SMC | SG34 | 22 | 620 | 3.5483871 |
| Tcell | SG34 | 25 | 620 | 4.0322581 |
| Adipocytes | SG35 | 0 | 2363 | 0.0000000 |
| Cardiomyocyte | SG35 | 304 | 2363 | 12.8650021 |
| EndoEC | SG35 | 91 | 2363 | 3.8510368 |
| Endothelial | SG35 | 486 | 2363 | 20.5670758 |
| Fibroblast | SG35 | 778 | 2363 | 32.9242488 |
| infMacrophage | SG35 | 74 | 2363 | 3.1316124 |
| LEC | SG35 | 0 | 2363 | 0.0000000 |
| NeuralCells | SG35 | 27 | 2363 | 1.1426153 |
| Perivascular | SG35 | 328 | 2363 | 13.8806602 |
| resMacrophage | SG35 | 125 | 2363 | 5.2898857 |
| SMC | SG35 | 65 | 2363 | 2.7507406 |
| Tcell | SG35 | 85 | 2363 | 3.5971223 |
ordVec <- datFrac %>% dplyr::filter(label=="Tcell") %>%
arrange(., percentage)
ordBar <- c("Tcell","infMacrophage","resMacrophage","Fibroblast","Perivascular",
"SMC","Endothelial","EndoEC","LEC","Cardiomyocyte","Adipocytes",
"NeuralCells")
datFrac <-datFrac %>% mutate(labelFac=factor(label, levels = ordBar))
ggbarplot(datFrac, x="ID", y="percentage",
fill = "labelFac",
palette = colLab,
order= ordVec$ID) +
rotate_x_text(angle = 90)
## only Myocarditis patients
selMyo <- unique(seurat$ID[which(seurat$cond != "HH")])
datFracSel <- datFrac %>% filter(ID %in% selMyo)
ggbarplot(datFracSel, x="ID", y="percentage",
fill = "labelFac",
palette = colLab,
order= ordVec$ID) +
rotate_x_text(angle = 90)
assign groups based on T cell fraction
TcellGrp <- read_tsv(paste0(basedir, "/data/assignTcellGrp.txt"))
IDtoTcell <- data.frame(ID=seurat$ID) %>% left_join(., TcellGrp, by="ID")
seurat$TcellGrp <- IDtoTcell$TcellGrp
table(seurat$TcellGrp)
TcellHigh TcellInt TcellLow
15696 18132 10286 table(seurat$TcellGrp, seurat$ID)
GZ1 GZ14 GZ15 GZ16 GZ17 GZ18 GZ2 GZ20 GZ3 GZ4 GZ5 GZ6 SG21
TcellHigh 2740 0 0 436 1370 0 0 0 0 0 0 491 0
TcellInt 0 0 4439 0 0 0 0 2706 2396 545 0 0 0
TcellLow 0 1268 0 0 0 2280 1684 0 0 0 781 0 1536
SG24 SG25 SG28 SG29_1 SG29_2 SG31 SG32 SG33 SG34 SG35
TcellHigh 0 3131 0 0 1242 0 0 6286 0 0
TcellInt 2381 0 0 1254 0 0 1428 0 620 2363
TcellLow 0 0 1545 0 0 1192 0 0 0 0vis marker for celltype assignment
heatmap
genes <- data.frame(gene=rownames(seurat)) %>%
mutate(geneID=gsub("^.*\\.", "", gene))
selGenesAll <- read_tsv(file = paste0(basedir,
"/data/markerLabels.txt")) %>%
left_join(., genes, by = "geneID")
Idents(seurat) <- seurat$seurat_clusters
pOut <- avgHeatmap(seurat = seurat, selGenes = selGenesAll,
colVecIdent = colPal,
ordVec=levels(seurat),
gapVecR=NULL, gapVecC=NULL,cc=T,
cr=F, condCol=F)
Idents(seurat) <- seurat$label
pOut <- avgHeatmap(seurat = seurat, selGenes = selGenesAll,
colVecIdent = colLab,
ordVec=levels(seurat),
gapVecR=NULL, gapVecC=NULL,cc=T,
cr=F, condCol=F)
Dotplot
DotPlot(seurat, assay="RNA", features = selGenesAll$gene, scale =T,
cluster.idents = T) +
scale_color_viridis_c() +
coord_flip() +
theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
scale_x_discrete(breaks=selGenesAll$gene, labels=selGenesAll$geneID) +
xlab("") + ylab("")
Idents(seurat) <- seurat$seurat_clusters
DotPlot(seurat, assay="RNA", features = selGenesAll$gene, scale =T,
cluster.idents = T) +
scale_color_viridis_c() +
coord_flip() +
theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
scale_x_discrete(breaks=selGenesAll$gene, labels=selGenesAll$geneID) +
xlab("") + ylab("")
violin plots selected marker
genesDat <- data.frame(EnsID=rownames(seurat)) %>%
mutate(gene=gsub(".*\\.", "", EnsID))
selGenes <- data.frame(gene=c("CD2", "TNNT2", "PECAM1", "NRG1", "PROX1",
"PDGFRA", "RGS5", "MYH11", "C1QA", "NRXN1",
"PLIN1", "BMP4", "BMPR1A", "BMPR2")) %>%
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)
})
Feature plots BMPs split by TcellGrp
## plotting loop order=F
pList <- sapply(selGenes$EnsID, function(x){
p <- FeaturePlot(seurat, reduction = "umap",
features = x,
cols=c("lightgrey", "darkred"),
order = F,
split.by = "TcellGrp")+
theme(legend.position="right")
plot(p)
})
## plotting loop order=T
pList <- sapply(selGenes$EnsID, function(x){
p <- FeaturePlot(seurat, reduction = "umap",
features = x,
cols=c("lightgrey", "darkred"),
order = T,
split.by = "TcellGrp")+
theme(legend.position="right")
plot(p)
})
average counts BMPs in Fibroblasts
seuratSub <- subset(seurat, label=="Fibroblast")
## assay data
clusterAssigned <- as.data.frame(seuratSub$ID) %>%
dplyr::mutate(cell=rownames(.))
colnames(clusterAssigned)[1] <- "ident"
seuratDat <- GetAssayData(seuratSub)
## genes of interest
genes <- data.frame(gene=rownames(seuratSub)) %>%
mutate(geneID=gsub("^.*\\.", "", gene)) %>% filter(geneID %in% selGenes$gene)
## matrix with averaged cnts per ident
logNormExpres <- as.data.frame(t(as.matrix(
seuratDat[which(rownames(seuratDat) %in% genes$gene),])))
logNormExpres <- logNormExpres %>% dplyr::mutate(cell=rownames(.)) %>%
dplyr::left_join(.,clusterAssigned, by=c("cell")) %>%
dplyr::select(-cell) %>% dplyr::group_by(ident) %>%
dplyr::summarise_all(mean)
write.table(logNormExpres,
file=paste0(basedir, "/data/BmpCntsFibroblastsPerPatient_woHH.txt"),
row.names = F, col.names = T, sep = "\t", quote = F)save seurat object
saveRDS(seurat, file = paste0(basedir,
"/data/humanHeartsPlusGraz_intPatients_merged",
"labeled_woHH_seurat.rds"))session info
sessionInfo()R version 4.3.0 (2023-04-21)
Platform: x86_64-apple-darwin20 (64-bit)
Running under: macOS Ventura 13.4.1
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRlapack.dylib; LAPACK version 3.11.0
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
time zone: Europe/Berlin
tzcode source: internal
attached base packages:
[1] stats4 stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] sctransform_0.3.5 viridis_0.6.4
[3] viridisLite_0.4.2 pheatmap_1.0.12
[5] ggpubr_0.6.0 ggsci_3.0.0
[7] runSeurat3_0.1.0 here_1.0.1
[9] magrittr_2.0.3 SeuratObject_4.1.3
[11] Seurat_4.3.0.1 lubridate_1.9.2
[13] forcats_1.0.0 stringr_1.5.0
[15] dplyr_1.1.2 purrr_1.0.2
[17] readr_2.1.4 tidyr_1.3.0
[19] tibble_3.2.1 ggplot2_3.4.3
[21] tidyverse_2.0.0 SingleCellExperiment_1.22.0
[23] SummarizedExperiment_1.30.2 Biobase_2.60.0
[25] GenomicRanges_1.52.0 GenomeInfoDb_1.36.2
[27] IRanges_2.34.1 S4Vectors_0.38.1
[29] BiocGenerics_0.46.0 MatrixGenerics_1.12.3
[31] matrixStats_1.0.0
loaded via a namespace (and not attached):
[1] RcppAnnoy_0.0.21 splines_4.3.0 later_1.3.1
[4] bitops_1.0-7 polyclip_1.10-4 lifecycle_1.0.3
[7] rstatix_0.7.2 rprojroot_2.0.3 globals_0.16.2
[10] lattice_0.21-8 vroom_1.6.3 MASS_7.3-60
[13] backports_1.4.1 plotly_4.10.2 sass_0.4.7
[16] rmarkdown_2.24 jquerylib_0.1.4 yaml_2.3.7
[19] httpuv_1.6.11 sp_2.0-0 spatstat.sparse_3.0-2
[22] reticulate_1.31 cowplot_1.1.1 pbapply_1.7-2
[25] RColorBrewer_1.1-3 abind_1.4-5 zlibbioc_1.46.0
[28] Rtsne_0.16 RCurl_1.98-1.12 git2r_0.32.0
[31] GenomeInfoDbData_1.2.10 ggrepel_0.9.3 irlba_2.3.5.1
[34] listenv_0.9.0 spatstat.utils_3.0-3 goftest_1.2-3
[37] spatstat.random_3.1-5 fitdistrplus_1.1-11 parallelly_1.36.0
[40] leiden_0.4.3 codetools_0.2-19 DelayedArray_0.26.7
[43] tidyselect_1.2.0 farver_2.1.1 spatstat.explore_3.2-1
[46] jsonlite_1.8.7 ellipsis_0.3.2 progressr_0.14.0
[49] ggridges_0.5.4 survival_3.5-7 tools_4.3.0
[52] ica_1.0-3 Rcpp_1.0.11 glue_1.6.2
[55] gridExtra_2.3 xfun_0.40 withr_2.5.0
[58] fastmap_1.1.1 fansi_1.0.4 digest_0.6.33
[61] timechange_0.2.0 R6_2.5.1 mime_0.12
[64] colorspace_2.1-0 scattermore_1.2 tensor_1.5
[67] spatstat.data_3.0-1 utf8_1.2.3 generics_0.1.3
[70] data.table_1.14.8 httr_1.4.7 htmlwidgets_1.6.2
[73] S4Arrays_1.0.5 whisker_0.4.1 uwot_0.1.16
[76] pkgconfig_2.0.3 gtable_0.3.4 workflowr_1.7.1
[79] lmtest_0.9-40 XVector_0.40.0 htmltools_0.5.6
[82] carData_3.0-5 scales_1.2.1 png_0.1-8
[85] knitr_1.43 rstudioapi_0.15.0 tzdb_0.4.0
[88] reshape2_1.4.4 nlme_3.1-163 cachem_1.0.8
[91] zoo_1.8-12 KernSmooth_2.23-22 vipor_0.4.5
[94] parallel_4.3.0 miniUI_0.1.1.1 ggrastr_1.0.2
[97] pillar_1.9.0 grid_4.3.0 vctrs_0.6.3
[100] RANN_2.6.1 promises_1.2.1 car_3.1-2
[103] xtable_1.8-4 cluster_2.1.4 beeswarm_0.4.0
[106] evaluate_0.21 cli_3.6.1 compiler_4.3.0
[109] rlang_1.1.1 crayon_1.5.2 future.apply_1.11.0
[112] ggsignif_0.6.4 labeling_0.4.3 ggbeeswarm_0.7.2
[115] plyr_1.8.8 fs_1.6.3 stringi_1.7.12
[118] deldir_1.0-9 munsell_0.5.0 lazyeval_0.2.2
[121] spatstat.geom_3.2-4 Matrix_1.6-1 hms_1.1.3
[124] patchwork_1.1.3 bit64_4.0.5 future_1.33.0
[127] shiny_1.7.5 highr_0.10 ROCR_1.0-11
[130] igraph_1.5.1 broom_1.0.5 bslib_0.5.1
[133] bit_4.0.5 date()[1] "Thu Aug 31 09:37:56 2023"
sessionInfo()R version 4.3.0 (2023-04-21)
Platform: x86_64-apple-darwin20 (64-bit)
Running under: macOS Ventura 13.4.1
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRlapack.dylib; LAPACK version 3.11.0
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
time zone: Europe/Berlin
tzcode source: internal
attached base packages:
[1] stats4 stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] sctransform_0.3.5 viridis_0.6.4
[3] viridisLite_0.4.2 pheatmap_1.0.12
[5] ggpubr_0.6.0 ggsci_3.0.0
[7] runSeurat3_0.1.0 here_1.0.1
[9] magrittr_2.0.3 SeuratObject_4.1.3
[11] Seurat_4.3.0.1 lubridate_1.9.2
[13] forcats_1.0.0 stringr_1.5.0
[15] dplyr_1.1.2 purrr_1.0.2
[17] readr_2.1.4 tidyr_1.3.0
[19] tibble_3.2.1 ggplot2_3.4.3
[21] tidyverse_2.0.0 SingleCellExperiment_1.22.0
[23] SummarizedExperiment_1.30.2 Biobase_2.60.0
[25] GenomicRanges_1.52.0 GenomeInfoDb_1.36.2
[27] IRanges_2.34.1 S4Vectors_0.38.1
[29] BiocGenerics_0.46.0 MatrixGenerics_1.12.3
[31] matrixStats_1.0.0
loaded via a namespace (and not attached):
[1] RcppAnnoy_0.0.21 splines_4.3.0 later_1.3.1
[4] bitops_1.0-7 polyclip_1.10-4 lifecycle_1.0.3
[7] rstatix_0.7.2 rprojroot_2.0.3 globals_0.16.2
[10] lattice_0.21-8 vroom_1.6.3 MASS_7.3-60
[13] backports_1.4.1 plotly_4.10.2 sass_0.4.7
[16] rmarkdown_2.24 jquerylib_0.1.4 yaml_2.3.7
[19] httpuv_1.6.11 sp_2.0-0 spatstat.sparse_3.0-2
[22] reticulate_1.31 cowplot_1.1.1 pbapply_1.7-2
[25] RColorBrewer_1.1-3 abind_1.4-5 zlibbioc_1.46.0
[28] Rtsne_0.16 RCurl_1.98-1.12 git2r_0.32.0
[31] GenomeInfoDbData_1.2.10 ggrepel_0.9.3 irlba_2.3.5.1
[34] listenv_0.9.0 spatstat.utils_3.0-3 goftest_1.2-3
[37] spatstat.random_3.1-5 fitdistrplus_1.1-11 parallelly_1.36.0
[40] leiden_0.4.3 codetools_0.2-19 DelayedArray_0.26.7
[43] tidyselect_1.2.0 farver_2.1.1 spatstat.explore_3.2-1
[46] jsonlite_1.8.7 ellipsis_0.3.2 progressr_0.14.0
[49] ggridges_0.5.4 survival_3.5-7 tools_4.3.0
[52] ica_1.0-3 Rcpp_1.0.11 glue_1.6.2
[55] gridExtra_2.3 xfun_0.40 withr_2.5.0
[58] fastmap_1.1.1 fansi_1.0.4 digest_0.6.33
[61] timechange_0.2.0 R6_2.5.1 mime_0.12
[64] colorspace_2.1-0 scattermore_1.2 tensor_1.5
[67] spatstat.data_3.0-1 utf8_1.2.3 generics_0.1.3
[70] data.table_1.14.8 httr_1.4.7 htmlwidgets_1.6.2
[73] S4Arrays_1.0.5 whisker_0.4.1 uwot_0.1.16
[76] pkgconfig_2.0.3 gtable_0.3.4 workflowr_1.7.1
[79] lmtest_0.9-40 XVector_0.40.0 htmltools_0.5.6
[82] carData_3.0-5 scales_1.2.1 png_0.1-8
[85] knitr_1.43 rstudioapi_0.15.0 tzdb_0.4.0
[88] reshape2_1.4.4 nlme_3.1-163 cachem_1.0.8
[91] zoo_1.8-12 KernSmooth_2.23-22 vipor_0.4.5
[94] parallel_4.3.0 miniUI_0.1.1.1 ggrastr_1.0.2
[97] pillar_1.9.0 grid_4.3.0 vctrs_0.6.3
[100] RANN_2.6.1 promises_1.2.1 car_3.1-2
[103] xtable_1.8-4 cluster_2.1.4 beeswarm_0.4.0
[106] evaluate_0.21 cli_3.6.1 compiler_4.3.0
[109] rlang_1.1.1 crayon_1.5.2 future.apply_1.11.0
[112] ggsignif_0.6.4 labeling_0.4.3 ggbeeswarm_0.7.2
[115] plyr_1.8.8 fs_1.6.3 stringi_1.7.12
[118] deldir_1.0-9 munsell_0.5.0 lazyeval_0.2.2
[121] spatstat.geom_3.2-4 Matrix_1.6-1 hms_1.1.3
[124] patchwork_1.1.3 bit64_4.0.5 future_1.33.0
[127] shiny_1.7.5 highr_0.10 ROCR_1.0-11
[130] igraph_1.5.1 broom_1.0.5 bslib_0.5.1
[133] bit_4.0.5