PLX5622 Diet Mice scRNAseq
All Clusters Pass 1 Annotation
Kennedi Todd
11/02/2023
Setup
Experiment notes
16 scRNAseq samples of meningeal endothelial cells were sent for sequencing. In this experiment we treated E3 and E4, male and female mice (12-month-old), with a control diet or a PLX5622 diet (to ablate MHC-II high myeloid cells in the meninges), and we intend to see the effects on the transcriptome of BECs and LECs. This will then be correlated with the effects on the morphology of the meningeal lymphatics and with our very interesting behavior data. There are 8 groups and 2 biological replicates per groups (each replicate consisting of cells pooled from two animals). We will be submitting ~5000 cells for sequencing at 50k read pairs per cell.
Working directory
knitr::opts_knit$set(root.dir = ".")Libraries
# load packages
library(Seurat, # Idents()
lib.loc = "/home/mayo/m214960/R/x86_64-pc-linux-gnu-library/4.2")
library(dplyr) # ungroup()
library(ggtree) # BuildClusterTree()
library(gridExtra) # grid.arrange()
library(gtools) # smartbind()
library(parallel) # detectCores()
library(plotly) # plot_ly()
library(ShinyCell) # createConfig()
library(tidyr) # %>%
# work in parallel
options(mc.cores = detectCores() - 1)Variables and functions
# variables
sample_order <- c("E3CF1","E3CF2","E3CM1","E3CM2","E3PF1","E3PF2","E3PM1","E3PM2",
"E4CF1","E4CF2","E4CM1","E4CM2","E4PF1","E4PF2","E4PM1","E4PM2")
sample_colors <- c("#9e0909","#f75959","#f0b402","#f5d67a",
"#fbff0a","#fafaa7","#1d8c0e","#63e851",
"#0271f0","#9cc3f0","#6c39f7","#c589fa",
"#ed2af7","#f5c1f7","#755410","#b5aa82")
group_colors <- c("#f75959","#f0b402",
"#fbff0a","#63e851",
"#0271f0","#c589fa",
"#ed2af7","#b5aa82")
group2_colors <- c("#f75959","#fbff0a","#63e851","#0271f0")
isoform_order <- c("E4","E3")
isoform_colors <- c("darkgray","cornflowerblue")
sex_order <- c("Male","Female")
sex_colors <- c("green","purple")
diet_order <- c("control","PLX5622")
diet_color <- c("coral","cyan")
# single cell functions
source("../../../functions/Kennedi_single_cell_functions.R")
# save function
saveToPDF <- function(...) {
d = dev.copy(pdf,...)
dev.off(d)
}Read data
mouse.unannotated <- readRDS("../../rObjects/pass1_unannotated.rds")
Idents(mouse.unannotated) <- "seurat_clusters"
DefaultAssay(mouse.unannotated) <- "RNA"
mouse.unannotated <- NormalizeData(mouse.unannotated)
mouse.unannotated## An object of class Seurat
## 45085 features across 42378 samples within 2 assays
## Active assay: RNA (23566 features, 0 variable features)
## 1 other assay present: SCT
## 2 dimensional reductions calculated: pca, umapUnannotated QC
UMAP
cluster_colors <- c("black","gray40","gray","red1","blue","magenta1","darkorange2",
"darkorange4","yellow1","yellow4","yellow2","green","lightgreen",
"chartreuse1","Aquamarine","cyan","SteelBlue","red4","forestgreen",
"purple1","purple4","orange","plum1","salmon","tan","chocolate4")
u1 <- DimPlot(object = mouse.unannotated,
reduction = "umap",
shuffle = TRUE,
repel = TRUE,
cols = cluster_colors,
label = TRUE)
u1
u2 <- DimPlot(object = mouse.unannotated,
reduction = "umap",
shuffle = TRUE,
repel = TRUE,
dims = c(2,3),
cols = cluster_colors,
label = TRUE)
u2
Feature plots
# UMAP percent.mt
FeaturePlot(mouse.unannotated,
reduction = "umap",
features = "percent.mt") +
scale_colour_gradientn(colours = c("blue","lightblue","yellow","orange","red"))
# UMAP percent.ribo
FeaturePlot(mouse.unannotated,
reduction = "umap",
features = "percent.ribo.protein") +
scale_colour_gradientn(colours = c("blue","lightblue","yellow","orange","red"))
# UMAP percent.hb
FeaturePlot(mouse.unannotated,
reduction = "umap",
features = "percent.hb") +
scale_colour_gradientn(colours = c("blue","lightblue","yellow","orange","red"))
# UMAP nCount
FeaturePlot(mouse.unannotated,
reduction = "umap",
features = "nCount_RNA") +
scale_colour_gradientn(colours = c("blue","lightblue","yellow","orange","red"))
# UMAP nFeature
FeaturePlot(mouse.unannotated,
reduction = "umap",
features = "nFeature_RNA") +
scale_colour_gradientn(colours = c("blue","lightblue","yellow","orange","red"))
# UMAP cell.complexity
FeaturePlot(mouse.unannotated,
reduction = "umap",
features = "cell.complexity") +
scale_colour_gradientn(colours = c("blue","lightblue","yellow","orange","red"))
# UMAP Ttr expression
FeaturePlot(mouse.unannotated,
reduction = "umap",
features = "Ttr") +
scale_colour_gradientn(colours = c("blue","lightblue","yellow","orange","red"))
Violins
VlnPlot(mouse.unannotated,
features = "nCount_RNA",
split.by = "seurat_clusters")## The default behaviour of split.by has changed.
## Separate violin plots are now plotted side-by-side.
## To restore the old behaviour of a single split violin,
## set split.plot = TRUE.
##
## This message will be shown once per session.
VlnPlot(mouse.unannotated,
features = "nFeature_RNA",
split.by = "seurat_clusters")
Cells per cluster
# Cells per sample per cluster
sample_ncells <- FetchData(mouse.unannotated,
vars = c("ident", "sample")) %>%
dplyr::count(ident,sample) %>%
tidyr::spread(ident, n)
sample_ncells## sample 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
## 1 E3CF1 581 285 163 116 178 167 40 63 87 62 44 37 57 45 50 33 32
## 2 E3CF2 389 201 143 115 118 96 45 68 63 48 37 31 43 29 33 30 26
## 3 E3CM1 720 531 382 273 235 126 103 444 187 144 75 142 123 105 43 79 28
## 4 E3CM2 334 149 162 162 116 63 43 97 72 44 36 40 52 31 14 38 14
## 5 E3PF1 111 175 262 136 230 49 23 63 39 66 8 24 39 19 10 60 9
## 6 E3PF2 91 164 159 86 204 45 37 31 40 30 9 72 26 23 19 28 14
## 7 E3PM1 72 163 152 293 139 38 26 110 62 53 41 34 15 28 16 39 29
## 8 E3PM2 96 413 290 433 166 60 68 140 88 83 60 74 22 51 20 40 39
## 9 E4CF1 668 243 193 67 203 283 51 68 51 28 11 45 49 33 30 37 6
## 10 E4CF2 774 352 319 60 252 294 59 96 87 33 25 53 89 70 53 67 20
## 11 E4CM1 420 369 519 440 118 198 74 150 124 230 29 84 48 47 21 41 47
## 12 E4CM2 256 100 180 248 79 64 38 42 46 111 56 27 25 10 16 33 46
## 13 E4PF1 423 312 445 255 401 451 1175 327 445 67 215 175 227 193 247 90 228
## 14 E4PF2 812 1207 716 644 726 139 294 216 298 224 424 225 229 154 236 125 162
## 15 E4PM1 159 335 372 580 163 50 27 99 93 207 98 62 37 36 49 49 108
## 16 E4PM2 92 268 243 183 121 38 33 76 50 70 34 50 17 27 8 48 17
## 17 18 19 20 21 22 23
## 1 18 30 7 10 15 1 NA
## 2 18 16 13 9 8 NA NA
## 3 176 54 50 28 12 6 NA
## 4 56 20 20 6 7 NA NA
## 5 22 26 6 12 6 1 NA
## 6 19 28 11 4 5 NA NA
## 7 57 10 21 8 10 3 NA
## 8 121 17 16 24 14 4 NA
## 9 14 32 10 11 19 2 NA
## 10 20 13 15 29 21 1 NA
## 11 28 20 16 18 15 3 NA
## 12 3 5 4 8 6 6 NA
## 13 63 196 58 15 15 4 39
## 14 22 105 62 92 68 11 NA
## 15 1 23 23 12 5 12 NA
## 16 NA 27 15 20 13 6 NA# Cells per isoform per cluster
isoform_ncells <- FetchData(mouse.unannotated,
vars = c("ident", "isoform")) %>%
dplyr::count(ident,isoform) %>%
tidyr::spread(ident, n)
isoform_ncells## isoform 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
## 1 E4 3604 3186 2987 2477 2063 1517 1751 1074 1194 970 892 721 721 570 660
## 2 E3 2394 2081 1713 1614 1386 644 385 1016 638 530 310 454 377 331 205
## 15 16 17 18 19 20 21 22 23
## 1 490 634 151 421 203 205 162 45 39
## 2 347 191 487 201 144 101 77 15 NA# Cells per sex per cluster
sex_ncells <- FetchData(mouse.unannotated,
vars = c("ident", "sex")) %>%
dplyr::count(ident,sex) %>%
tidyr::spread(ident, n)
sex_ncells## sex 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
## 1 Male 2149 2328 2300 2612 1137 637 412 1158 722 942 429 513 339 335 187
## 2 Female 3849 2939 2400 1479 2312 1524 1724 932 1110 558 773 662 759 566 678
## 15 16 17 18 19 20 21 22 23
## 1 367 328 442 176 165 124 82 40 NA
## 2 470 497 196 446 182 182 157 20 39# Cells per diet per cluster
diet_ncells <- FetchData(mouse.unannotated,
vars = c("ident", "diet")) %>%
dplyr::count(ident, diet) %>%
tidyr::spread(ident, n)
diet_ncells## diet 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
## 1 control 4142 2230 2061 1481 1299 1291 453 1028 717 700 313 459 486 370 260
## 2 PLX5622 1856 3037 2639 2610 2150 870 1683 1062 1115 800 889 716 612 531 605
## 15 16 17 18 19 20 21 22 23
## 1 358 219 333 190 135 119 103 19 NA
## 2 479 606 305 432 212 187 136 41 39Gene histogram
# User params
goi <- "Malat1"
threshold <- 1
# Subset data
log2.threshold <- log2(threshold + 0.01)
counts.df <- FetchData(mouse.unannotated, vars = goi)
colnames(counts.df) <- "counts"
log2.counts.df <- log2(counts.df + 0.01)
# Histogram
title <- paste0(goi, "\nnCount_RNA > ", threshold)
hist1 <- ggplot(counts.df, aes(x = counts)) +
geom_histogram(bins = 100, fill = "gray", color = "black") +
labs(title = title, x=NULL, y=NULL) +
xlab(paste0(goi, " nCount_RNA")) + ylab("# of Samples") + theme_bw() +
geom_vline(xintercept = threshold, col = "blue") +
annotate("rect",
xmin = -Inf,
xmax = threshold,
ymin = 0,
ymax=Inf,
alpha=0.2,
fill="chocolate4") +
annotate("rect",
xmin = threshold,
xmax = Inf,
ymin = 0,
ymax=Inf,
alpha=0.2,
fill="deepskyblue")
# Histogram log transformed
hist2 <- ggplot(log2.counts.df, aes(x = counts)) +
geom_histogram(bins = 100, fill = "gray", color = "black") +
labs(title = title, x=NULL, y=NULL) +
xlab(paste0("Log2(",goi, " nCount_RNA)")) + ylab("# of Samples") + theme_bw() +
geom_vline(xintercept = log2.threshold, col = "blue") +
annotate("rect",
xmin = -Inf,
xmax = log2.threshold,
ymin = 0,
ymax=Inf,
alpha=0.2,
fill="chocolate4") +
annotate("rect",
xmin = log2.threshold,
xmax = Inf,
ymin = 0,
ymax=Inf,
alpha=0.2,
fill="deepskyblue")
# plot
plots1 <- list(hist1,hist2)
layout1 <- rbind(c(1),c(2))
grid1 <- grid.arrange(grobs = plots1, layout_matrix = layout1)
Percent gene
# user define variable
goi <- "Malat1"
# Extract counts data
DefaultAssay(mouse.unannotated) <- "RNA"
Idents(mouse.unannotated) <- "seurat_clusters"
geneData <- FetchData(mouse.unannotated,
vars = goi,
slot = "counts")
geneData <- geneData > 1
table(geneData)## geneData
## FALSE TRUE
## 3134 39244mouse.unannotated$Expression <- geneData
FetchData(mouse.unannotated,
vars = c("ident", "Expression")) %>%
dplyr::count(ident, Expression) %>%
tidyr::spread(ident, n)## Expression 0 1 2 3 4 5 6 7 8 9 10 11 12
## 1 FALSE 39 5 15 6 9 1304 480 436 90 NA 15 104 25
## 2 TRUE 5959 5262 4685 4085 3440 857 1656 1654 1742 1500 1187 1071 1073
## 13 14 15 16 17 18 19 20 21 22 23
## 1 27 26 1 155 243 7 18 97 29 NA 3
## 2 874 839 836 670 395 615 329 209 210 60 36# Plot
mouse.unannotated@meta.data %>%
group_by(seurat_clusters, Expression) %>%
dplyr::count() %>%
group_by(seurat_clusters) %>%
dplyr::mutate(percent = 100*n/sum(n)) %>%
ungroup() %>%
ggplot(aes(x=seurat_clusters,y=percent, fill=Expression)) +
geom_col() +
ggtitle(paste0("Percentage of cells with > 1 counts for ", goi)) +
theme(axis.text.x = element_text(angle = 90))
Cluster tree
- Cluster trees are helpful in deciding what clusters to merge.
mouse.unannotated <- BuildClusterTree(object = mouse.unannotated,
dims = 1:15,
reorder = FALSE,
reorder.numeric = FALSE)
tree <- mouse.unannotated@tools$BuildClusterTree
tree$tip.label <- paste0(tree$tip.label)
ggtree::ggtree(tree, aes(x, y)) +
scale_y_reverse() +
ggtree::geom_tree() +
ggtree::theme_tree() +
ggtree::geom_tiplab(offset = 1) +
ggtree::geom_tippoint(color = cluster_colors[1:length(tree$tip.label)], shape = 16, size = 5) +
coord_cartesian(clip = 'off') +
theme(plot.margin = unit(c(0,2.5,0,0), 'cm'))
Potential Markers
B-cells / Plasma cells
- Cd19: expressed in B-cells and follicular dendritic cells
- Fcrla: a B-cell specific protein in mice
- Cd79a & Cd79b: together form BCR complex
- Sdc1: Plasma cells
Idents(mouse.unannotated) <- "seurat_clusters"
VlnPlot(mouse.unannotated,
features = "Cd19",
cols = cluster_colors,
split.by = "seurat_clusters")
VlnPlot(mouse.unannotated,
features = "Fcrla",
cols = cluster_colors,
split.by = "seurat_clusters")
VlnPlot(mouse.unannotated,
features = "Cd79a",
cols = cluster_colors,
split.by = "seurat_clusters")
VlnPlot(mouse.unannotated,
features = "Cd79b",
cols = cluster_colors,
split.by = "seurat_clusters")
VlnPlot(mouse.unannotated,
features = "Sdc1",
cols = cluster_colors,
split.by = "seurat_clusters")
T-cells
- Trac/Cd3d/Cd3e/Cd3g are components of the TCR
VlnPlot(mouse.unannotated,
features = "Trac",
cols = cluster_colors,
split.by = "seurat_clusters")
VlnPlot(mouse.unannotated,
features = "Cd3e",
cols = cluster_colors,
split.by = "seurat_clusters")
VlnPlot(mouse.unannotated,
features = "Cd8a",
cols = cluster_colors,
split.by = "seurat_clusters")
VlnPlot(mouse.unannotated,
features = "Cd4",
cols = cluster_colors,
split.by = "seurat_clusters")
Endothelial cells
VlnPlot(mouse.unannotated,
features = "Ly6c1",
cols = cluster_colors,
split.by = "seurat_clusters")
VlnPlot(mouse.unannotated,
features = "Flt1",
cols = cluster_colors,
split.by = "seurat_clusters")
Fibroblasts
VlnPlot(mouse.unannotated,
features = "Col1a1",
cols = cluster_colors,
split.by = "seurat_clusters")
VlnPlot(mouse.unannotated,
features = "Col1a2",
cols = cluster_colors,
split.by = "seurat_clusters")
Mast cells
VlnPlot(mouse.unannotated,
features = "Fcer1a",
cols = cluster_colors,
split.by = "seurat_clusters")
VlnPlot(mouse.unannotated,
features = "Kit", # aka Cd117
cols = cluster_colors,
split.by = "seurat_clusters")
Macrophage
VlnPlot(mouse.unannotated,
features = "C1qa",
cols = cluster_colors,
split.by = "seurat_clusters")
VlnPlot(mouse.unannotated,
features = "C1qb",
cols = cluster_colors,
split.by = "seurat_clusters")
VlnPlot(mouse.unannotated,
features = "Mki67",
cols = cluster_colors,
split.by = "seurat_clusters")
VlnPlot(mouse.unannotated,
features = "Mki67",
cols = cluster_colors,
split.by = "seurat_clusters")
VlnPlot(mouse.unannotated,
features = "Il7r",
cols = cluster_colors,
split.by = "seurat_clusters")
Monocytes/DCs
- Cd11c/Itgax: Monocytes & DCs
VlnPlot(mouse.unannotated,
features = "Itgax", # Cd11c
cols = cluster_colors,
split.by = "seurat_clusters")
VlnPlot(mouse.unannotated,
features = "Cd209a",
cols = cluster_colors,
split.by = "seurat_clusters")
Neutrophils
VlnPlot(mouse.unannotated,
features = "Ly6g",
cols = cluster_colors,
split.by = "seurat_clusters")
VlnPlot(mouse.unannotated,
features = "Retnlg",
cols = cluster_colors,
split.by = "seurat_clusters")
Pericytes & SMCs
VlnPlot(mouse.unannotated,
features = "Acta2",
cols = cluster_colors,
split.by = "seurat_clusters")
VlnPlot(mouse.unannotated,
features = "Myl9",
cols = cluster_colors,
split.by = "seurat_clusters")
VlnPlot(mouse.unannotated,
features = "Rgs5",
cols = cluster_colors,
split.by = "seurat_clusters")
Schwann cells
VlnPlot(mouse.unannotated,
features = "Cdh19",
cols = cluster_colors,
split.by = "seurat_clusters")
VlnPlot(mouse.unannotated,
features = "Mpz",
cols = cluster_colors,
split.by = "seurat_clusters")
Sandro’s markers
Idents(mouse.unannotated) <- "seurat_clusters"
goi1 <- c("Cd3e","Trbc1","Cd4","Cd8a","Foxp3","Tbx21","Gata3","Thy1",
"Cd19","Ms4a1","Cd27","Ighg1","Ptprc","Ly6g","Itgam","Aif1","Klrb1")
goi2 <- c("Adgre1","Ms4a3","Ly6c2","Mrc1","Lyz2","Cd74","Cd83","Cd14","H2-Aa",
"H2-Ab1","Sirpa","Xcr1","Siglech","Itgax","Kit","Mcpt4")
goi3 <- c("Pdgfra","Col1a1","Lum","Pdgfrb","Rgs5","Cspg4","Acta2","Tagln",
"Pecam1","Cd34","Plvap","Stmn2","Slc38a5","Vwf","Mfsd2a","Cldn5")
goi4 <- c("Prox1","Flt4","Pdpn","Lyve1","Sox10","Mbp","Fgf13","Kcnab2","Tubb3",
"Slc17a6","Shank2","Erbb4","Park7","Kif5b","Slc4a1","Hmbs")
goi5 <- c("Pecam1","Flt4","Itgam","Mrc1","Cd3e","Gata3","Cd19","Ly6g","Pdgfrb",
"Kit","Col1a1","Pmp22","Hmbs")
v1 <- VlnPlot(mouse.unannotated,
features = goi1,
cols = cluster_colors,
split.by = "seurat_clusters",
flip = TRUE,
stack = TRUE)## Warning in FetchData.Seurat(object = object, vars = features, slot = slot): The
## following requested variables were not found: Ighg1v1
v2 <- VlnPlot(mouse.unannotated,
features = goi2,
cols = cluster_colors,
split.by = "seurat_clusters",
flip = TRUE,
stack = TRUE)
v2
v3 <- VlnPlot(mouse.unannotated,
features = goi3,
cols = cluster_colors,
split.by = "seurat_clusters",
flip = TRUE,
stack = TRUE)
v3
v4 <- VlnPlot(mouse.unannotated,
features = goi4,
cols = cluster_colors,
split.by = "seurat_clusters",
flip = TRUE,
stack = TRUE)
v4
v5 <- VlnPlot(mouse.unannotated,
features = goi5,
cols = cluster_colors,
split.by = "seurat_clusters",
flip = TRUE,
stack = TRUE)
v5
Automatically detect markers
- This is submitted in a separate script (03a_find_markers.R and 03b_submit_job.sh) due the high amount of memory needed and time taken to run
# Find markers for each cluster
# Compares single cluster vs all other clusters
# Default is logfc.threshold = 0.25, min.pct = 0.5
Idents(mouse.unannotated) <- "seurat_clusters"
all.markers <- FindAllMarkers(object = mouse.unannotated,
assay = "RNA",
test.use = "MAST",
verbose = TRUE)
# add column
all.markers$delta_pct <- abs(all.markers$pct.1 - all.markers$pct.2)
# rename columns and rows
rownames(all.markers) <- 1:nrow(all.markers)
all.markers <- all.markers[,c(6,7,1,5,2:4,8)]
colnames(all.markers)[c(6,7)] <- c("pct_1","pct_2")
# save
saveRDS(all.markers, "../../rObjects/pass1_unannotated_cluster_markers.rds")# read object
all.markers <- readRDS("../../rObjects/pass1_unannotated_cluster_markers.rds")
# more stringent filtering
all.markers <- all.markers[all.markers$p_val_adj < 0.01,]
# compare
table(all.markers$cluster)##
## 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
## 3075 3096 3216 3493 3245 3815 2614 3191 2699 2827 5230 3157 2456 2801 2365 3164
## 16 17 18 19 20 21 22 23
## 3842 4104 2620 2340 3624 3031 954 1877# subset
cluster0 <- all.markers[all.markers$cluster == 0,]
cluster1 <- all.markers[all.markers$cluster == 1,]
cluster2 <- all.markers[all.markers$cluster == 2,]
cluster3 <- all.markers[all.markers$cluster == 3,]
cluster4 <- all.markers[all.markers$cluster == 4,]
cluster5 <- all.markers[all.markers$cluster == 5,]
cluster6 <- all.markers[all.markers$cluster == 6,]
cluster7 <- all.markers[all.markers$cluster == 7,]
cluster8 <- all.markers[all.markers$cluster == 8,]
cluster9 <- all.markers[all.markers$cluster == 9,]
cluster10 <- all.markers[all.markers$cluster == 10,]
cluster11 <- all.markers[all.markers$cluster == 11,]
cluster12 <- all.markers[all.markers$cluster == 12,]
cluster13 <- all.markers[all.markers$cluster == 13,]
cluster14 <- all.markers[all.markers$cluster == 14,]
cluster15 <- all.markers[all.markers$cluster == 15,]
cluster16 <- all.markers[all.markers$cluster == 16,]
cluster17 <- all.markers[all.markers$cluster == 17,]
cluster18 <- all.markers[all.markers$cluster == 18,]
cluster19 <- all.markers[all.markers$cluster == 19,]
cluster20 <- all.markers[all.markers$cluster == 20,]
cluster21 <- all.markers[all.markers$cluster == 21,]
cluster22 <- all.markers[all.markers$cluster == 22,]
cluster23 <- all.markers[all.markers$cluster == 23,]
# save
write.table(all.markers,
"../../results/all_clusters_pass1/markers/unannotated_cluster_markers_pvaladj_0.01.tsv",
sep = "\t", quote = FALSE, row.names = FALSE)Cluster Annotations
Cluster 0: Macrophages
- Mrc1, C1qb
VlnPlot(mouse.unannotated,
features = cluster0$gene[1:20],
cols = cluster_colors,
stack = TRUE,
flip = TRUE,
split.by = "seurat_clusters")
Cluster 1: Endothelial
- Flt1
VlnPlot(mouse.unannotated,
features = cluster1$gene[1:20],
cols = cluster_colors,
stack = TRUE,
flip = TRUE,
split.by = "seurat_clusters")
Cluster 2: T-cells
- Cd3e
# no markers
# cluster tree groups with cluster 6
VlnPlot(mouse.unannotated,
features = "Flt1",
split.by = "seurat_clusters",
cols = cluster_colors)
Cluster 3: B-cells
- Cd19
VlnPlot(mouse.unannotated,
features = cluster3$gene[1:20],
cols = cluster_colors,
stack = TRUE,
flip = TRUE,
split.by = "seurat_clusters")
Cluster 4: Innate lymphoid cells
- Gata3, Il7r
VlnPlot(mouse.unannotated,
features = cluster4$gene[1:20],
cols = cluster_colors,
stack = TRUE,
flip = TRUE,
split.by = "seurat_clusters")
Cluster 5: Macrophages
- C1qb
Cluster 6: Endothelial
- Flt1
VlnPlot(mouse.unannotated,
features = cluster6$gene[1:20],
cols = cluster_colors,
stack = TRUE,
flip = TRUE,
split.by = "seurat_clusters")
Cluster 7: Fibroblasts
- Col1a1
VlnPlot(mouse.unannotated,
features = cluster7$gene[1:20],
cols = cluster_colors,
stack = TRUE,
flip = TRUE,
split.by = "seurat_clusters")
Cluster 8: Endothelial
- Flt1
VlnPlot(mouse.unannotated,
features = cluster8$gene[1:20],
cols = cluster_colors,
stack = TRUE,
flip = TRUE,
split.by = "seurat_clusters")
Cluster 9: B-cells
-Cd19
VlnPlot(mouse.unannotated,
features = cluster9$gene[1:20],
cols = cluster_colors,
stack = TRUE,
flip = TRUE,
split.by = "seurat_clusters")
Cluster 10: Neutrophils
- Retnlg, Ly6g
VlnPlot(mouse.unannotated,
features = cluster10$gene[1:20],
cols = cluster_colors,
stack = TRUE,
flip = TRUE,
split.by = "seurat_clusters")
Cluster 11: Pericytes & SMCs
- Rgs5
VlnPlot(mouse.unannotated,
features = cluster11$gene[1:20],
cols = cluster_colors,
stack = TRUE,
flip = TRUE,
split.by = "seurat_clusters")
Cluster 12: Monocytes/DCs
- Itgax (Cd11c), Cd209a
VlnPlot(mouse.unannotated,
features = cluster12$gene[1:20],
cols = cluster_colors,
stack = TRUE,
flip = TRUE,
split.by = "seurat_clusters")
Cluster 13: Endothelial
- Flt1
VlnPlot(mouse.unannotated,
features = cluster13$gene[1:20],
cols = cluster_colors,
stack = TRUE,
flip = TRUE,
split.by = "seurat_clusters")
Cluster 14:
- Siglech
VlnPlot(mouse.unannotated,
features = cluster14$gene[1:20],
cols = cluster_colors,
stack = TRUE,
flip = TRUE,
split.by = "seurat_clusters")
Cluster 15: T-cells
- Cd3e
VlnPlot(mouse.unannotated,
features = cluster15$gene[1:20],
cols = cluster_colors,
stack = TRUE,
flip = TRUE,
split.by = "seurat_clusters")
Cluster 16: B-cells / Mki67 high?
-Cd19, Mki67
VlnPlot(mouse.unannotated,
features = cluster16$gene[1:20],
cols = cluster_colors,
stack = TRUE,
flip = TRUE,
split.by = "seurat_clusters")
Cluster 17:
VlnPlot(mouse.unannotated,
features = cluster17$gene[1:20],
cols = cluster_colors,
stack = TRUE,
flip = TRUE,
split.by = "seurat_clusters")
Cluster 18: Mast cells
- Kit
VlnPlot(mouse.unannotated,
features = cluster18$gene[1:20],
cols = cluster_colors,
stack = TRUE,
flip = TRUE,
split.by = "seurat_clusters")
Cluster 19:
- Thy1, Mrc1, Cldn5
- Prox1, Flt4, Lyve1
VlnPlot(mouse.unannotated,
features = cluster19$gene[1:20],
cols = cluster_colors,
stack = TRUE,
flip = TRUE,
split.by = "seurat_clusters")
Cluster 20:
- Cldn5, Stmn2, Sox10, Mbp
VlnPlot(mouse.unannotated,
features = cluster20$gene[1:20],
cols = cluster_colors,
stack = TRUE,
flip = TRUE,
split.by = "seurat_clusters")
Cluster 21: Scwann cells?
- Mpz, Sox10, Mbp
VlnPlot(mouse.unannotated,
features = cluster21$gene[1:20],
cols = cluster_colors,
stack = TRUE,
flip = TRUE,
split.by = "seurat_clusters")
Cluster 22:
- Itgam
VlnPlot(mouse.unannotated,
features = cluster22$gene[1:20],
cols = cluster_colors,
stack = TRUE,
flip = TRUE,
split.by = "seurat_clusters")
Cluster 23:
- Vwf
VlnPlot(mouse.unannotated,
features = cluster23$gene[1:20],
cols = cluster_colors,
stack = TRUE,
flip = TRUE,
split.by = "seurat_clusters")
Merge cluster names
# Rename all identities
mouse.annotated <- RenameIdents(object = mouse.unannotated,
"0" = "Macrophages", # Mrc1
"1" = "Endothelial", # Flt1
"2" = "T-cells", #
"3" = "B-cells", #
"4" = "Innate Lymphoid Cells", # Gata3
"5" = "Macrophages", #
"6" = "Endothelial", #
"7" = "Fibroblasts", #
"8" = "Endothelial", #
"9" = "B-cells", #
"10" = "Neutrophils", #
"11" = "Pericytes & SMCs", #
"12" = "Monocytes and Dendritic cells",
"13" = "Endothelial", #
"14" = "",
"15" = "T-cells", #
"16" = "B-cells", #
"17" = "", #
"18" = "Mast cells", #
"19" = "", #
"20" = "", #
"21" = "Schwann cells", #
"22" = "", #
"23" = "") #
mouse.annotated$merged_clusters <- Idents(mouse.annotated)# set colors
merged_colors <- c("darkred","firebrick1","gold","darkolivegreen2","darkgreen",
"cyan","cornflowerblue","blue","darkorchid1","deeppink",
"plum1","burlywood3","azure4", "black")
# umap
umap <- DimPlot(object = mouse.annotated,
reduction = "umap",
repel = TRUE,
group.by = "merged_clusters",
cols = merged_colors)
umapMarkers
v <- VlnPlot(mouse.annotated,
features = c("C1qb","Flt1","Col1a2","Cd19","Itgax","Fgr","Retnlg","Cd3e",
"Il7r","Rgs5","Kit","Mpz","Sdc1"),
split.by = "merged_clusters",
flip = TRUE,
stack = TRUE,
cols = merged_colors)
vv
path <- "../../results/all_clusters_pass1/markers/cluster_markers_violin"
saveToPDF(paste0(path, ".pdf"), width = 10, height = 6)
dev.off()Shiny App
shiny.obj <- mouse.unannotated
shiny.obj@assays$RNA@var.features <-
shiny.obj@assays$SCT@var.features
metadata <- shiny.obj@meta.data
metadata <- metadata[,c(27,1:26)]
shiny.obj@meta.data <- metadata
shiny.obj@assays$SCT@meta.features <- metadata
shiny.obj@assays$RNA@meta.features <- metadata# make shiny folder
DefaultAssay(shiny.obj) <- "RNA"
Idents(shiny.obj) <- shiny.obj$seurat_clusters
sc.config <- createConfig(shiny.obj)
setwd("../../results/all_clusters_pass1/")
makeShinyApp(shiny.obj, sc.config, gene.mapping = TRUE,
shiny.title = "All Clusters Pass 1") manual edits
setwd("../../results/all_clusters_pass1/shinyApp")
sc1conf <- readRDS("sc1conf.rds")
sc1conf[1,4] <- "red|gold|blue"
sc1conf[2,4] <- "red|gold|blue"
sc1conf[3,4] <- "red|gold|green|blue"
sc1conf[4,4] <- "red|gold|green|blue"
saveRDS(sc1conf, "sc1conf.rds")