Merge samples
Mechthild Lütge
14 May 2020
Last updated: 2022-09-12
Checks: 6 1
Knit directory: humanCardiacFibroblasts/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| 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$seurat_clustersassign labels
seurat$label <- "other"
seurat$label[which(seurat$seurat_clusters %in% c("4","6","7"))] <- "Endothelial"
seurat$label[which(seurat$seurat_clusters %in% c("5"))] <- "Tcell"
seurat$label[which(seurat$seurat_clusters %in% c("2"))] <- "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 <- pal_igv()(length(levels(seurat)))
colTec <- pal_jama()(length(unique(seurat$technique)))
colSmp <- c(pal_uchicago()(8), pal_npg()(8), pal_aaas()(10))[1:length(unique(seurat$dataset))]
colCond <- pal_npg()(length(unique(seurat$cond)))
colID <- c(pal_jco()(10), pal_npg()(10), pal_jama()(10))[1:length(unique(seurat$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 |
|---|---|---|
| 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 |
|---|---|---|
| 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 |
|---|---|---|
| 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 |
|---|---|---|
| 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 |
|---|---|---|
| 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 |
|---|---|---|
| 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 |
|---|---|---|
| 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 |
|---|---|---|
| 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 |
|---|---|---|
| 141fae8 | mluetge | 2022-07-06 |
cnt tables
per patient
## total cells per patient
knitr::kable(table(seurat$ID))| Var1 | Freq |
|---|---|
| GZ1 | 2740 |
| GZ10 | 3731 |
| GZ11 | 3991 |
| GZ12 | 3818 |
| GZ13 | 9882 |
| GZ2 | 1684 |
| GZ3 | 2396 |
| GZ4 | 545 |
| GZ5 | 781 |
| GZ6 | 491 |
| GZ7 | 653 |
| GZ8 | 3921 |
| GZ9 | 4908 |
| SG29 | 1242 |
| SG30 | 236 |
| SG31 | 1192 |
| SG32 | 1428 |
| SG33 | 6286 |
| SG34 | 620 |
| SG35 | 2363 |
## celltype per patient counts
knitr::kable(table(seurat$label, seurat$ID))| GZ1 | GZ10 | GZ11 | GZ12 | GZ13 | GZ2 | GZ3 | GZ4 | GZ5 | GZ6 | GZ7 | GZ8 | GZ9 | SG29 | SG30 | SG31 | SG32 | SG33 | SG34 | SG35 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Cardiomyocyte | 208 | 1102 | 298 | 115 | 2204 | 108 | 234 | 44 | 38 | 12 | 88 | 711 | 650 | 108 | 47 | 178 | 40 | 211 | 173 | 304 |
| Endothelial | 448 | 404 | 803 | 1146 | 2236 | 430 | 577 | 180 | 243 | 154 | 134 | 775 | 1004 | 169 | 70 | 258 | 578 | 244 | 105 | 580 |
| Fibroblast | 760 | 994 | 838 | 1137 | 2286 | 642 | 830 | 139 | 261 | 154 | 227 | 1144 | 1490 | 153 | 41 | 352 | 313 | 399 | 116 | 767 |
| MonocyteMacrophage | 301 | 486 | 661 | 221 | 895 | 84 | 119 | 54 | 31 | 63 | 17 | 352 | 641 | 271 | 14 | 124 | 68 | 2147 | 50 | 206 |
| other | 65 | 101 | 28 | 29 | 228 | 35 | 65 | 8 | 27 | 4 | 46 | 82 | 54 | 14 | 4 | 22 | 35 | 32 | 6 | 30 |
| Perivascular | 679 | 502 | 1192 | 1083 | 1720 | 304 | 492 | 86 | 155 | 68 | 121 | 731 | 939 | 82 | 56 | 230 | 306 | 162 | 141 | 381 |
| Tcell | 279 | 142 | 171 | 87 | 313 | 81 | 79 | 34 | 26 | 36 | 20 | 126 | 130 | 445 | 4 | 28 | 88 | 3091 | 29 | 95 |
## celltype percentages per patient
datLab <- data.frame(table(seurat$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 | 448 | 2740 | 16.3503650 |
| Fibroblast | GZ1 | 760 | 2740 | 27.7372263 |
| MonocyteMacrophage | GZ1 | 301 | 2740 | 10.9854015 |
| other | GZ1 | 65 | 2740 | 2.3722628 |
| Perivascular | GZ1 | 679 | 2740 | 24.7810219 |
| Tcell | GZ1 | 279 | 2740 | 10.1824818 |
| Cardiomyocyte | GZ10 | 1102 | 3731 | 29.5363173 |
| Endothelial | GZ10 | 404 | 3731 | 10.8281962 |
| Fibroblast | GZ10 | 994 | 3731 | 26.6416510 |
| MonocyteMacrophage | GZ10 | 486 | 3731 | 13.0259984 |
| other | GZ10 | 101 | 3731 | 2.7070490 |
| Perivascular | GZ10 | 502 | 3731 | 13.4548378 |
| Tcell | GZ10 | 142 | 3731 | 3.8059501 |
| Cardiomyocyte | GZ11 | 298 | 3991 | 7.4668003 |
| Endothelial | GZ11 | 803 | 3991 | 20.1202706 |
| Fibroblast | GZ11 | 838 | 3991 | 20.9972438 |
| MonocyteMacrophage | GZ11 | 661 | 3991 | 16.5622651 |
| other | GZ11 | 28 | 3991 | 0.7015786 |
| Perivascular | GZ11 | 1192 | 3991 | 29.8672012 |
| Tcell | GZ11 | 171 | 3991 | 4.2846404 |
| Cardiomyocyte | GZ12 | 115 | 3818 | 3.0120482 |
| Endothelial | GZ12 | 1146 | 3818 | 30.0157150 |
| Fibroblast | GZ12 | 1137 | 3818 | 29.7799895 |
| MonocyteMacrophage | GZ12 | 221 | 3818 | 5.7883709 |
| other | GZ12 | 29 | 3818 | 0.7595600 |
| Perivascular | GZ12 | 1083 | 3818 | 28.3656365 |
| Tcell | GZ12 | 87 | 3818 | 2.2786799 |
| Cardiomyocyte | GZ13 | 2204 | 9882 | 22.3031775 |
| Endothelial | GZ13 | 2236 | 9882 | 22.6269986 |
| Fibroblast | GZ13 | 2286 | 9882 | 23.1329690 |
| MonocyteMacrophage | GZ13 | 895 | 9882 | 9.0568711 |
| other | GZ13 | 228 | 9882 | 2.3072253 |
| Perivascular | GZ13 | 1720 | 9882 | 17.4053835 |
| Tcell | GZ13 | 313 | 9882 | 3.1673750 |
| Cardiomyocyte | GZ2 | 108 | 1684 | 6.4133017 |
| Endothelial | GZ2 | 430 | 1684 | 25.5344418 |
| Fibroblast | GZ2 | 642 | 1684 | 38.1235154 |
| MonocyteMacrophage | GZ2 | 84 | 1684 | 4.9881235 |
| other | GZ2 | 35 | 1684 | 2.0783848 |
| Perivascular | GZ2 | 304 | 1684 | 18.0522565 |
| Tcell | GZ2 | 81 | 1684 | 4.8099762 |
| Cardiomyocyte | GZ3 | 234 | 2396 | 9.7662771 |
| Endothelial | GZ3 | 577 | 2396 | 24.0818030 |
| Fibroblast | GZ3 | 830 | 2396 | 34.6410684 |
| MonocyteMacrophage | GZ3 | 119 | 2396 | 4.9666110 |
| other | GZ3 | 65 | 2396 | 2.7128548 |
| Perivascular | GZ3 | 492 | 2396 | 20.5342237 |
| Tcell | GZ3 | 79 | 2396 | 3.2971619 |
| Cardiomyocyte | GZ4 | 44 | 545 | 8.0733945 |
| Endothelial | GZ4 | 180 | 545 | 33.0275229 |
| Fibroblast | GZ4 | 139 | 545 | 25.5045872 |
| MonocyteMacrophage | GZ4 | 54 | 545 | 9.9082569 |
| other | GZ4 | 8 | 545 | 1.4678899 |
| Perivascular | GZ4 | 86 | 545 | 15.7798165 |
| Tcell | GZ4 | 34 | 545 | 6.2385321 |
| Cardiomyocyte | GZ5 | 38 | 781 | 4.8655570 |
| Endothelial | GZ5 | 243 | 781 | 31.1139565 |
| Fibroblast | GZ5 | 261 | 781 | 33.4186940 |
| MonocyteMacrophage | GZ5 | 31 | 781 | 3.9692702 |
| other | GZ5 | 27 | 781 | 3.4571063 |
| Perivascular | GZ5 | 155 | 781 | 19.8463508 |
| Tcell | GZ5 | 26 | 781 | 3.3290653 |
| Cardiomyocyte | GZ6 | 12 | 491 | 2.4439919 |
| Endothelial | GZ6 | 154 | 491 | 31.3645621 |
| Fibroblast | GZ6 | 154 | 491 | 31.3645621 |
| MonocyteMacrophage | GZ6 | 63 | 491 | 12.8309572 |
| other | GZ6 | 4 | 491 | 0.8146640 |
| Perivascular | GZ6 | 68 | 491 | 13.8492872 |
| Tcell | GZ6 | 36 | 491 | 7.3319756 |
| Cardiomyocyte | GZ7 | 88 | 653 | 13.4762634 |
| Endothelial | GZ7 | 134 | 653 | 20.5206738 |
| Fibroblast | GZ7 | 227 | 653 | 34.7626340 |
| MonocyteMacrophage | GZ7 | 17 | 653 | 2.6033691 |
| other | GZ7 | 46 | 653 | 7.0444104 |
| Perivascular | GZ7 | 121 | 653 | 18.5298622 |
| Tcell | GZ7 | 20 | 653 | 3.0627871 |
| Cardiomyocyte | GZ8 | 711 | 3921 | 18.1331293 |
| Endothelial | GZ8 | 775 | 3921 | 19.7653660 |
| Fibroblast | GZ8 | 1144 | 3921 | 29.1762306 |
| MonocyteMacrophage | GZ8 | 352 | 3921 | 8.9773017 |
| other | GZ8 | 82 | 3921 | 2.0913032 |
| Perivascular | GZ8 | 731 | 3921 | 18.6432033 |
| Tcell | GZ8 | 126 | 3921 | 3.2134660 |
| Cardiomyocyte | GZ9 | 650 | 4908 | 13.2436838 |
| Endothelial | GZ9 | 1004 | 4908 | 20.4563977 |
| Fibroblast | GZ9 | 1490 | 4908 | 30.3585982 |
| MonocyteMacrophage | GZ9 | 641 | 4908 | 13.0603097 |
| other | GZ9 | 54 | 4908 | 1.1002445 |
| Perivascular | GZ9 | 939 | 4908 | 19.1320293 |
| Tcell | GZ9 | 130 | 4908 | 2.6487368 |
| Cardiomyocyte | SG29 | 108 | 1242 | 8.6956522 |
| Endothelial | SG29 | 169 | 1242 | 13.6070853 |
| Fibroblast | SG29 | 153 | 1242 | 12.3188406 |
| MonocyteMacrophage | SG29 | 271 | 1242 | 21.8196457 |
| other | SG29 | 14 | 1242 | 1.1272142 |
| Perivascular | SG29 | 82 | 1242 | 6.6022544 |
| Tcell | SG29 | 445 | 1242 | 35.8293076 |
| Cardiomyocyte | SG30 | 47 | 236 | 19.9152542 |
| Endothelial | SG30 | 70 | 236 | 29.6610169 |
| Fibroblast | SG30 | 41 | 236 | 17.3728814 |
| MonocyteMacrophage | SG30 | 14 | 236 | 5.9322034 |
| other | SG30 | 4 | 236 | 1.6949153 |
| Perivascular | SG30 | 56 | 236 | 23.7288136 |
| Tcell | SG30 | 4 | 236 | 1.6949153 |
| Cardiomyocyte | SG31 | 178 | 1192 | 14.9328859 |
| Endothelial | SG31 | 258 | 1192 | 21.6442953 |
| Fibroblast | SG31 | 352 | 1192 | 29.5302013 |
| MonocyteMacrophage | SG31 | 124 | 1192 | 10.4026846 |
| other | SG31 | 22 | 1192 | 1.8456376 |
| Perivascular | SG31 | 230 | 1192 | 19.2953020 |
| Tcell | SG31 | 28 | 1192 | 2.3489933 |
| Cardiomyocyte | SG32 | 40 | 1428 | 2.8011204 |
| Endothelial | SG32 | 578 | 1428 | 40.4761905 |
| Fibroblast | SG32 | 313 | 1428 | 21.9187675 |
| MonocyteMacrophage | SG32 | 68 | 1428 | 4.7619048 |
| other | SG32 | 35 | 1428 | 2.4509804 |
| Perivascular | SG32 | 306 | 1428 | 21.4285714 |
| Tcell | SG32 | 88 | 1428 | 6.1624650 |
| Cardiomyocyte | SG33 | 211 | 6286 | 3.3566656 |
| Endothelial | SG33 | 244 | 6286 | 3.8816417 |
| Fibroblast | SG33 | 399 | 6286 | 6.3474388 |
| MonocyteMacrophage | SG33 | 2147 | 6286 | 34.1552657 |
| other | SG33 | 32 | 6286 | 0.5090678 |
| Perivascular | SG33 | 162 | 6286 | 2.5771556 |
| Tcell | SG33 | 3091 | 6286 | 49.1727649 |
| Cardiomyocyte | SG34 | 173 | 620 | 27.9032258 |
| Endothelial | SG34 | 105 | 620 | 16.9354839 |
| Fibroblast | SG34 | 116 | 620 | 18.7096774 |
| MonocyteMacrophage | SG34 | 50 | 620 | 8.0645161 |
| other | SG34 | 6 | 620 | 0.9677419 |
| Perivascular | SG34 | 141 | 620 | 22.7419355 |
| Tcell | SG34 | 29 | 620 | 4.6774194 |
| Cardiomyocyte | SG35 | 304 | 2363 | 12.8650021 |
| Endothelial | SG35 | 580 | 2363 | 24.5450698 |
| Fibroblast | SG35 | 767 | 2363 | 32.4587389 |
| MonocyteMacrophage | SG35 | 206 | 2363 | 8.7177317 |
| other | SG35 | 30 | 2363 | 1.2695726 |
| Perivascular | SG35 | 381 | 2363 | 16.1235717 |
| Tcell | SG35 | 95 | 2363 | 4.0203132 |
ggbarplot(datFrac, x="ID", y="percentage",
fill = "label",
palette = colLab) +
rotate_x_text(angle = 90)
| Version | Author | Date |
|---|---|---|
| 141fae8 | mluetge | 2022-07-06 |
per cond
## total cells per cond
knitr::kable(table(seurat$cond))| Var1 | Freq |
|---|---|
| HH | 30251 |
| InfCardiomyopathy | 1428 |
| inflamation | 236 |
| Myocarditis | 19801 |
| Perimyocarditis | 1192 |
## celltype per cond counts
knitr::kable(table(seurat$label, seurat$cond))| HH | InfCardiomyopathy | inflamation | Myocarditis | Perimyocarditis | |
|---|---|---|---|---|---|
| Cardiomyocyte | 5080 | 40 | 47 | 1528 | 178 |
| Endothelial | 6368 | 578 | 70 | 3264 | 258 |
| Fibroblast | 7889 | 313 | 41 | 4448 | 352 |
| MonocyteMacrophage | 3256 | 68 | 14 | 3343 | 124 |
| other | 522 | 35 | 4 | 332 | 22 |
| Perivascular | 6167 | 306 | 56 | 2671 | 230 |
| Tcell | 969 | 88 | 4 | 4215 | 28 |
## celltype percentages per cond
datLab <- data.frame(table(seurat$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 | 5080 | 30251 | 16.792833 |
| Endothelial | HH | 6368 | 30251 | 21.050544 |
| Fibroblast | HH | 7889 | 30251 | 26.078477 |
| MonocyteMacrophage | HH | 3256 | 30251 | 10.763281 |
| other | HH | 522 | 30251 | 1.725563 |
| Perivascular | HH | 6167 | 30251 | 20.386103 |
| Tcell | HH | 969 | 30251 | 3.203200 |
| Cardiomyocyte | InfCardiomyopathy | 40 | 1428 | 2.801120 |
| Endothelial | InfCardiomyopathy | 578 | 1428 | 40.476191 |
| Fibroblast | InfCardiomyopathy | 313 | 1428 | 21.918768 |
| MonocyteMacrophage | InfCardiomyopathy | 68 | 1428 | 4.761905 |
| other | InfCardiomyopathy | 35 | 1428 | 2.450980 |
| Perivascular | InfCardiomyopathy | 306 | 1428 | 21.428571 |
| Tcell | InfCardiomyopathy | 88 | 1428 | 6.162465 |
| Cardiomyocyte | inflamation | 47 | 236 | 19.915254 |
| Endothelial | inflamation | 70 | 236 | 29.661017 |
| Fibroblast | inflamation | 41 | 236 | 17.372881 |
| MonocyteMacrophage | inflamation | 14 | 236 | 5.932203 |
| other | inflamation | 4 | 236 | 1.694915 |
| Perivascular | inflamation | 56 | 236 | 23.728814 |
| Tcell | inflamation | 4 | 236 | 1.694915 |
| Cardiomyocyte | Myocarditis | 1528 | 19801 | 7.716782 |
| Endothelial | Myocarditis | 3264 | 19801 | 16.484016 |
| Fibroblast | Myocarditis | 4448 | 19801 | 22.463512 |
| MonocyteMacrophage | Myocarditis | 3343 | 19801 | 16.882986 |
| other | Myocarditis | 332 | 19801 | 1.676683 |
| Perivascular | Myocarditis | 2671 | 19801 | 13.489218 |
| Tcell | Myocarditis | 4215 | 19801 | 21.286804 |
| Cardiomyocyte | Perimyocarditis | 178 | 1192 | 14.932886 |
| Endothelial | Perimyocarditis | 258 | 1192 | 21.644295 |
| Fibroblast | Perimyocarditis | 352 | 1192 | 29.530201 |
| MonocyteMacrophage | Perimyocarditis | 124 | 1192 | 10.402685 |
| other | Perimyocarditis | 22 | 1192 | 1.845638 |
| Perivascular | Perimyocarditis | 230 | 1192 | 19.295302 |
| Tcell | Perimyocarditis | 28 | 1192 | 2.348993 |
ggbarplot(datFrac, x="cond", y="percentage",
fill = "label",
palette = colLab) +
rotate_x_text(angle = 90)
| Version | Author | Date |
|---|---|---|
| 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$seurat_clusters
pOut <- avgHeatmap(seurat = seurat, selGenes = selGenesAll,
colVecIdent = colPal,
ordVec=levels(seurat),
gapVecR=NULL, gapVecC=NULL,cc=T,
cr=F, condCol=F)
| Version | Author | Date |
|---|---|---|
| 141fae8 | mluetge | 2022-07-06 |
Idents(seurat) <- seurat$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 |
|---|---|---|
| 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$geneID) +
xlab("") + ylab("")
| Version | Author | Date |
|---|---|---|
| 141fae8 | mluetge | 2022-07-06 |
Idents(seurat) <- seurat$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 |
|---|---|---|
| 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_1.2.0
[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_1.32.3
[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_1.6.5 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_3.3.0 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_1.0.1 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 12 13:18:27 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_1.2.0
[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_1.32.3
[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_1.6.5 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_3.3.0 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_1.0.1 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