---
title: "Microglia & Macrophages"
author: "Luise A. Seeker"
date: "03/09/2021"
output: html_document
---
```{r}
library(Seurat)
library(here)
library(ggsci)
library(dplyr)
library(future)
library(scclusteval)
library(clustree)
library(tidyr)
library(gridExtra)
library(EnhancedVolcano)
```
```{r, echo = F}
mypal <- pal_npg("nrc", alpha = 0.7)(10)
mypal2 <-pal_tron("legacy", alpha = 0.7)(7)
mypal3 <- pal_lancet("lanonc", alpha = 0.7)(9)
mypal4 <- pal_simpsons(palette = c("springfield"), alpha = 0.7)(16)
mypal5 <- pal_rickandmorty(palette = c("schwifty"), alpha = 0.7)(6)
mypal6 <- pal_futurama(palette = c("planetexpress"), alpha = 0.7)(5)
mypal7 <- pal_startrek(palette = c("uniform"), alpha = 0.7)(5)
mycoloursP<- c(mypal, mypal2, mypal3, mypal4, mypal5, mypal6, mypal7)
```
```{r}
e <- 40000 * 1024^2
options(future.globals.maxSize = e)
```
```{r}
seur_comb <- readRDS(here("data",
"single_nuc_data",
"all_cell_types",
"srt_anno_01.RDS"))
```
```{r}
DimPlot(seur_comb, cols = mycoloursP, label = TRUE) + NoLegend()
```
```{r}
micro_srt <- subset(seur_comb, idents = c("Microglia-Macrophages_1",
"Microglia-Macrophages_2"))
```
```{r}
micro_srt <- FindVariableFeatures(micro_srt,
selection.method = "vst",
nfeatures = 2000)
all_genes <- rownames(micro_srt)
micro_srt <- ScaleData(micro_srt, features= all_genes)
micro_srt <- RunPCA(micro_srt, features = VariableFeatures(object = micro_srt))
ElbowPlot(micro_srt)
```
```{r}
micro_srt <- FindNeighbors(micro_srt, dims = 1:10)
# test different resolutions for clustering
micro_srt <- FindClusters(micro_srt, resolution = 0.7)
# non-linear reduction
micro_srt <- RunUMAP(micro_srt, dims = 1:10)
DimPlot(micro_srt, cols = mycoloursP[18:40], label = TRUE) + NoLegend()
```
```{r}
micro_srt <- FindClusters(micro_srt, resolution = c(0.01, 0.04, 0.05,
seq(from = 0.1, to = 1.5, by = 0.1)))
#Generate DimPlot of all tested clustering resolutions in metadata
# requires gtools library and Seurat
plot_list_func <- function(seur_obj,
col_pattern,
plot_cols,
clust_lab = TRUE,
label_size = 8,
num_col = 4,
save_dir = getwd(),
width=7,
height=5){
extr_res_col <- grep(pattern = col_pattern, names(seur_obj@meta.data))
res_names <- names(seur_obj@meta.data[extr_res_col])
# gtools function, sorts gene_names alphanumeric:
res_names <- gtools::mixedsort(res_names)
plot_l <-list()
for(i in 1: length(res_names)){
pdf(paste0(save_dir, "/",
res_names[i], "_umap.pdf"), width=width, height=height)
dim_plot <- DimPlot(seur_obj,
reduction = "umap",
cols= plot_cols,
group.by = res_names[i],
label = clust_lab,
label.size = label_size) + NoLegend()
print(dim_plot)
dev.off()
print(dim_plot)
}
}
save_dir_micro <- here("outs",
"microglia_macrophages",
"cluster_resolutions")
dir.create(save_dir_micro, recursive = TRUE)
plot_list_func(seur_obj = micro_srt,
col_pattern = "RNA_snn_res.",
plot_cols = mycoloursP[18:40],
save_dir = save_dir_micro)
```
```{r}
DimPlot(micro_srt,
cols = mycoloursP[18:40],
label = TRUE,
group.by = "RNA_snn_res.0.4",
split.by = "Tissue") + NoLegend()
```
```{r}
DimPlot(micro_srt,
cols = mycoloursP[18:40],
label = TRUE,
group.by = "RNA_snn_res.0.4",
split.by = "AgeGroup") + NoLegend()
```
```{r}
DimPlot(micro_srt,
cols = mycoloursP[18:40],
label = TRUE,
group.by = "RNA_snn_res.0.4",
split.by = "gender") + NoLegend()
```
```{r}
pdf(here(save_dir_micro, "microglia_clustree.pdf"),
paper="a4", width=8, height=11.5)
clustree(
micro_srt,
prefix = paste0("RNA", "_snn_res."),
exprs = c("data", "counts", "scale.data"),
assay = NULL
)
dev.off()
```
# Deciding for the a clustering resolution
I looked for variable genes between all markers and pairwise at reslution
0.4, filtered the markers and plotted them in a heat map. I did have a look at
the cluster QC below at that resolution as well which looked fine. I believe
that clusters 0,1 and 2 are not sufficiently different to remain separated.
Therefore I will merge them below and then repeat looking for cluster markers
overall and pairwise and present the cluster QC for the new clustering.
```{r}
Idents(micro_srt) <- "RNA_snn_res.0.4"
micro_srt <- RenameIdents(
micro_srt,
"0" = "MI_1",
"1" = "MI_1",
"2" = "MI_1",
"3" = "MI_2",
"4" = "MI_3",
"5" = "MI_4",
"6" = "BAM",
"7" = "MI_5"
)
# Plot result
DimPlot(micro_srt,label = TRUE, repel=TRUE)
DimPlot(micro_srt,label = TRUE, repel=TRUE, cols = mycoloursP[18:40],
label.size = 4.5)
# Save result
micro_srt$microglia_clu <- Idents(micro_srt)
```
Find markers for all interesting resolutions
```{r}
int_res_all_mark <- function(seur_obj,
int_cols,
only_pos = TRUE,
min_pct = 0.25,
logfc_threshold = 0.25,
fil_pct_1 = 0.25,
fil_pct_2 = 0.6,
avg_log = 1.2,
save_dir = getwd(),
test_use = "MAST"
){
for(i in 1:length(int_cols)){
Idents(seur_obj) <- int_cols[i]
all_mark <- FindAllMarkers(seur_obj,
only.pos = only_pos,
min.pct = min_pct,
logfc.threshold = logfc_threshold,
test.use = test_use)
fil_mark<- subset(all_mark,
all_mark$pct.1 > fil_pct_1 &
all_mark$pct.2 < fil_pct_2 )
write.csv(all_mark, paste(save_dir, "/all_mark", int_cols[i], ".csv", sep = "" ))
write.csv(fil_mark, paste(save_dir, "/fil_mark", int_cols[i], ".csv", sep = "" ))
}
}
```
```{r}
save_dir_micro <- here("outs",
"microglia_macrophages",
"cluster_marker_lists")
```
```{r, eval = FALSE}
dir.create(save_dir_micro, recursive = TRUE)
int_res_all_mark(micro_srt,
int_cols = c("RNA_snn_res.0.05",
"RNA_snn_res.0.1",
"RNA_snn_res.0.2",
"RNA_snn_res.0.3",
"RNA_snn_res.0.4",
"RNA_snn_res.0.7",
"microglia_clu"),
save_dir = save_dir_micro)
```
# Pairwise cluster comparison for markers
```{r}
Idents(micro_srt) <- "microglia_clu"
clust_id_list_2 <- list(list("Microglia_1", "Microglia_2"), list("Microglia_2", "Microglia_1"),
list("Microglia_1", "Microglia_3"), list("Microglia_3", "Microglia_1"),
list("Microglia_1", "Microglia_4"), list("Microglia_4", "Microglia_1"),
list("Microglia_1", "BAM"), list("BAM", "Microglia_1"),
list("Microglia_4", "BAM"), list("BAM", "Microglia_4"),
list("Microglia_3", "Microglia_5"), list("Microglia_5", "Microglia_3"),
list("Microglia_2", "Microglia_3"), list("Microglia_3", "Microglia_2"))
pairwise_mark <- function(seur_obj,
int_cols,
clust_id_list,
only_pos = TRUE,
min_pct = 0.25,
logfc_threshold = 0.25,
fil_pct_1 = 0.25,
fil_pct_2 = 0.1,
save_dir = getwd(),
test_use = "MAST"){
for(k in 1:length(int_cols)){
Idents(seur_obj) <- int_cols[k]
for(i in 1: length(clust_id_list)){
clust_mark <- FindMarkers(seur_obj,
ident.1 = clust_id_list[[i]][[1]],
ident.2 = clust_id_list[[i]][[2]],
min.pct = min_pct,
test.use = test_use)
clust_mark$cluster <- clust_id_list[[i]][[1]]
clust_mark$comp_to_clust <- clust_id_list[[i]][[2]]
write.csv(clust_mark,
paste(save_dir,
"/",
int_cols[k],
"_",
clust_id_list[[i]][[1]],
"_",
clust_id_list[[i]][[2]],
".csv", sep = "" ))
}
}
}
save_dir_micro_pw <- here("outs",
"microglia_macrophages",
"pairwise_cluster_marker_lists")
```
```{r, eval = FALSE}
dir.create(save_dir_micro_pw, recursive = TRUE)
pairwise_mark(micro_srt,
int_cols = "microglia_clu",
save_dir = save_dir_micro_pw,
clust_id_list = clust_id_list_2)
```
Read in data for plotting differentially expressed genes
```{r}
gen_mark_list <-function(file_dir = getwd(),
avg_log = 1.2,
pct_1 = 0.25,
pct_2 = 0.6,
pairwise = FALSE
){
temp = list.files(path = file_dir,
pattern="*.csv")
myfiles = lapply(paste(file_dir, temp, sep = "/"), read.csv)
for(i in 1:length(myfiles)){
dat <- myfiles[[i]]
av_log_fil <- subset(dat, dat$avg_log2FC > avg_log &
dat$pct.1 > pct_1 &
dat$pct.2 < pct_2)
if(pairwise == TRUE){
top10 <- av_log_fil %>% top_n(10, avg_log2FC)
top10$gene <- top10$X
}else{
av_log_fil$cluster <- as.character(av_log_fil$cluster)
top10 <- av_log_fil %>% group_by(cluster) %>%
top_n(10, avg_log2FC)
}
if(i ==1){
fil_genes <- top10
}else{
fil_genes <- rbind(fil_genes, top10)
}
fil_genes <- fil_genes[!duplicated(fil_genes$gene),]
}
return(fil_genes)
}
fil_genes <- gen_mark_list(file_dir = save_dir_micro)
fil_genes_pw <- gen_mark_list(file_dir = save_dir_micro_pw,
pairwise = TRUE)
int_genes <- c(fil_genes$gene, fil_genes_pw$gene)
int_genes <- unique(int_genes)
```
```{r, fig.height = 10, fig.width=6}
# every cell name
Idents(micro_srt) <- "microglia_clu"
cluster_averages <- AverageExpression(micro_srt,
group.by = "microglia_clu",
return.seurat = TRUE)
cluster_averages@meta.data$cluster <- levels(as.factor(micro_srt@meta.data$microglia_clu))
hm_av <- DoHeatmap(object = cluster_averages,
features = int_genes,
label = TRUE,
group.by = "cluster",
group.colors = mycoloursP[6:40],
draw.lines = F)
hm_av
```
# Plot cluster markers
```{r, fig.width=8, fig.height=8}
FeaturePlot(micro_srt, features = c(
"DPP10",
"NRG3",
"RNF219-AS1",
"SORBS1",
"TRPM3",
"CTNND2",
"ABLIM1",
"CNTN1",
"LSAMP",
"DCLK2",
"PPP2R2B",
"TNIK",
"NTM",
"RORA",
"MAGI2"
),ncol = 4)
```
```{r, fig.height = 6, fig.width - 6}
FeaturePlot(micro_srt,
features =c(
"ACSL1",
"CXCR4",
"FTH1",
"GPR183",
"CTNNB1"
),
ncol = 3)
```
```{r, fig.width = 6, fig.height = 3}
FeaturePlot(micro_srt,
features =c(
"GPNMB",
"SCD"
),
ncol = 3)
```
```{r, fig.width = 6, fig.height = 6}
FeaturePlot(micro_srt,
features =c(
"RASGEF1C",
"AC008691.1",
"TLN2"
),
ncol = 2)
```
# Markers provided by Veronique Miron
To see if they are microglia vs border associated macrophages or monocytes,
I would look at:
P2ry12, P2ry13, Sall1, Hexb, Tmem119 - for microglia
Pf4, Lyve1, Cd38, Mrc1 - for border associated macrophages
S100a9, Ccr2 -monocytes
For function, I would look at:
Itgax, Igf1, Spp1 (thought to be markers of mouse white matter associated microglia, likely phagocytic)
Cd68, Trem2, Cx3cr1, Tlr4 (phagocytosis)
```{r, fig.width = 6, fig.height = 9}
FeaturePlot(micro_srt,
features =c(
"P2RY12",
"P2RY13",
"SALL1",
"HEXB",
"TMEM119"
),
ncol = 2)
```
```{r, fig.width = 6, fig.height = 6}
FeaturePlot(micro_srt,
features =c(
"PF4",
"LYVE1",
"CD38",
"MRC1"
),
ncol = 2)
```
```{r, eval = FALSE}
FeaturePlot(micro_srt,
features =c(
"S100A9",
"CCR2"
))
# Monocyte genes are not found in this dataset
```
For function, I would look at:
Itgax, Igf1, Spp1 (thought to be markers of mouse white matter associated
microglia, likely phagocytic)
Cd68, Trem2, Cx3cr1, Tlr4 (phagocytosis)
```{r, fig.width = 9, fig.height = 9}
FeaturePlot(micro_srt,
features =c(
"ITGAX",
"IGF1",
"SPP1",
"CD68",
"TREM2",
"CX3CR1",
"TLR4"
),
ncol = 3)
#TLR4 is not on our dataset
```
# Find tissue markers
```{r}
Idents(micro_srt) <- "Tissue"
tissue_dir <- here("outs",
"microglia_macrophages",
"tissue_marker_list_mm")
```
```{r, eval = FALSE}
tissue_markers <- FindAllMarkers(micro_srt,
only.pos = TRUE,
min.pct = 0.25,
logfc.threshold = 0.25)
dir.create(tissue_dir)
write.csv(tissue_markers, here(tissue_dir,
"mm_tissue_marker.csv"))
```
```{r}
tissue_markers <- read.csv(here(tissue_dir,
"mm_tissue_marker.csv"))
tissue_markers
```
```{r}
sign_tissue <- subset(tissue_markers, tissue_markers$p_val_adj < 0.05)
top_tissue <- sign_tissue %>%
group_by(cluster) %>%
top_n(n = 20, wt = avg_log2FC)
p1 <- DotPlot(micro_srt, features = unique(top_tissue$gene), group.by = "Tissue") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
p1
```
```{r}
FeaturePlot(micro_srt, features = "HIF1A",
split.by = "Tissue")
```
# Find age markers
```{r}
Idents(micro_srt) <- "AgeGroup"
age_dir <- here("outs",
"microglia_macrophages",
"age_marker_list_mm")
```
```{r, eval = FALSE}
age_markers <- FindAllMarkers(micro_srt,
only.pos = TRUE,
min.pct = 0.25,
logfc.threshold = 0.25)
dir.create(age_dir)
write.csv(age_markers, here(age_dir,
"mm_age_marker.csv"))
```
```{r}
age_markers <- read.csv(here(age_dir, "mm_age_marker.csv"))
age_markers
```
```{r}
sing_age_m <- subset(age_markers, age_markers$p_val_adj < 0.05)
top_age <- sing_age_m %>%
group_by(cluster) %>%
top_n(n = 20, wt = avg_log2FC)
p2 <- DotPlot(micro_srt, features = unique(top_age$gene), group.by = "AgeGroup") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
p2
```
## More in young:
```{r, fig.width = 6, fig.height = 9}
FeaturePlot(micro_srt, features = c("IPCEF1",
"RTTN",
"CYFIP1"
),
split.by = "AgeGroup")
```
## More in old:
```{r, fig.width = 6, fig.height = 15}
FeaturePlot(micro_srt, features = c("HLA-DRB1",
"GLDN",
"CSGALNACT1",
"PADI2",
"SRGN",
"P4HA1"),
split.by = "AgeGroup")
```
# Find sex markers
```{r}
Idents(micro_srt) <- "gender"
sex_dir <- here("outs",
"microglia_macrophages",
"sex_marker_list_mm")
```
```{r, eval = FALSE}
sex_markers <- FindAllMarkers(micro_srt,
only.pos = TRUE,
min.pct = 0.25,
logfc.threshold = 0.25)
sex_markers
dir.create(sex_dir)
write.csv(sex_markers, here(sex_dir, "mm_sex_marker.csv"))
```
```{r}
sex_markers <- read.csv(here(sex_dir, "mm_sex_marker.csv"))
sex_markers
```
```{r}
sig_sex <- subset(sex_markers, sex_markers$p_val_adj < 0.05)
top_sex <- sig_sex %>%
group_by(cluster) %>%
top_n(n = 20, wt = avg_log2FC)
p3 <- DotPlot(micro_srt, features = unique(top_sex$gene), group.by = "gender") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
p3
```
```{r, fig.width = 8, fig.height = 10}
grid.arrange( p1, p2, p3, ncol = 1)
```
## More in women
```{r, fig.width = 6, fig.height = 18}
FeaturePlot(micro_srt, features = c("XIST",
"IFI44L",
"HLA-DRB5",
"RPS4X",
"APOE"
),
split.by = "gender")
# encoded on X-chromosome
```
## More in men
```{r, fig.width = 6, fig.height = 24}
FeaturePlot(micro_srt, features = c("LINC00278",
"UTY",
"USP9Y",
"NLGN4Y",
"TTTY14",
"HSPA1A",
"DUSP1",
"HSPH1"
),
split.by = "gender")
# first 5 are encoded on Y-chromosome
```
# Find age*sex markers
```{r}
Idents(micro_srt) <- "ageSex"
age_sex_dir <- here("outs",
"microglia_macrophages",
"age_sex_marker_list_mm")
```
```{r, eval = FALSE}
age_sex_markers <- FindAllMarkers(micro_srt,
only.pos = TRUE,
min.pct = 0.25,
logfc.threshold = 0.25)
dir.create(age_sex_dir)
write.csv(age_sex_markers, here(age_sex_dir, "mm_age_sex_marker.csv"))
```
```{r}
age_sex_markers <- read.csv(here(age_sex_dir, "mm_age_sex_marker.csv"))
age_sex_markers
```
```{r}
sig_age_sex <- subset(age_sex_markers, age_sex_markers$p_val_adj < 0.05)
top_age_sex <- sig_age_sex %>%
group_by(cluster) %>%
top_n(n = 20, wt = avg_log2FC)
micro_srt$ageSex <- factor(micro_srt$ageSex, levels = c("Old men",
"Old women",
"Young men",
"Young women"))
top_age_sex <- top_age_sex %>%
arrange(factor(top_age_sex$cluster, levels = c("Old men",
"Old women",
"Young men",
"Young women")))
Idents(micro_srt) <- "ageSex"
p4 <- DotPlot(micro_srt, features = unique(top_age_sex$gene), group.by = "ageSex") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
p4
```
# Microglia activation genes
```{r, fig.width = 6, fig.height = 24}
FeaturePlot(micro_srt, features = c("SPP1",
"CD74",
"FTL",
"APOE",
"FTH1",
"CST3",
"RPL29",
"APOC1"
),
split.by = "AgeGroup")
```
Of the genes above APOE has been shown to be more expressed in female aged mice
than in male aged mice (https://pubmed.ncbi.nlm.nih.gov/30082275/) and
in females in mouse models of AD
(reviewed by https://nnjournal.net/article/view/3357#B113).
https://www.sciencedirect.com/science/article/pii/S2211124720311785 this paper
claims single nucleus RNA seq data is not as well equipped as single cell
RNA seq data to detect genes of microglial activation. They compared human
single cell and single nucleus data. Their study was based on only a few samples
and processing two different isolations in paralell may have caused waiting times
that need to be consiederd. The authors used a reference genome that included
introns for the genome alignment of nucleus data which should have allowed
for the inclusion of unspliced data.
# Literature
```{r, fig.width = 6, fig.height = 18}
FeaturePlot(micro_srt, features = c("HLA-A",
"HLA-B",
"SHANK3",
"FXYD1",
"AQP1",
"TIMP3",
#"AKT1S1",
#"TREM1",
#"S100A9",
#"CXCL2",
"NFKB1"),
split.by = "gender")
```
```{r}
FeaturePlot(micro_srt, features = c("PTPRC", #CD45
"CD68",
"ITGAM" #CD11B
), split.by = "Tissue")
```
Activation markers
```{r, fig.width = 9, fig.height = 51}
FeaturePlot(micro_srt, features = c("FCGR3A", #CD16)
"FCGR2A", #CD32
"CD40",
"CD86",
"HLA-DMA",
"HLA-DMB",
"HLA-DOA",
"HLA-DOB",
"HLA-DPA1",
"HLA-DPB1",
"HLA-DQA1",
#"HLA-DQA2", not expresses
"HLA-DQB1",
#"HLA-DQB2", not expresses
"HLA-DRA",
"HLA-DRB1",
#"HLA-DRB3", not expresses
#"HLA-DRB4", not expresses
"HLA-DRB5",
"CD163",
"MRC1" #CD206
), split.by = "Tissue")
```
# Cluster markers together
```{r, fig.width = 8, fig.height = 2}
FeaturePlot(micro_srt, features = c("DPP10", "MAGI2"), order = T,
min.cutoff = "q1",
max.cutoff = "q99", blend = T,
cols = c("darkblue", "green", "magenta"),
blend.threshold = 0) &NoAxes()
```
```{r, fig.width = 8, fig.height = 2}
FeaturePlot(micro_srt, features = c("GPNMB", "RASGEF1C"), order = T,
min.cutoff = "q1",
max.cutoff = "q99", blend = T,
cols = c("darkblue", "green", "magenta"),
blend.threshold = 0) &NoAxes()
```
```{r, fig.width = 8, fig.height = 2}
FeaturePlot(micro_srt, features = c("LYVE1", "HIF1A"), order = T,
min.cutoff = "q1",
max.cutoff = "q99", blend = T,
cols = c("darkblue", "green", "magenta"),
blend.threshold = 0) &NoAxes()
```
# Cluster QC based on resolution 0.4
# Cluster QC
#### Individuals per cluster
How many individuals contribute to each cluster?
```{r indiv-per-cluster}
# count how many cells there are in each group and cluster
sum_caseNO_cluster <- table(micro_srt$microglia_clu, micro_srt$caseNO)
# For each cluster (on the rows) sum of individuals that do have cells on that cluster
rowSums(sum_caseNO_cluster > 0)
```
Nothing stands out
#### Percentage of cells that come from each individual
Some of the clusters might be mainly from one person, even though other subjects
do have some cells that cluster with it. To address this question we calculate
for each cluster the proportion of cells that come from each caseNO.
```{r prop-per-cluster}
# calculate the proportions, for each cluster (margin 1 for rows)
prop_caseNO_table <- prop.table(sum_caseNO_cluster, margin = 1)
# change the format to be a data.frame, this also expands to long formatting
prop_caseNO <- as.data.frame(prop_caseNO_table)
colnames(prop_caseNO) <- c("cluster", "caseNO", "proportion")
```
Flag the clusters where one of the individuals covers more than 40% of the
cluster. The expected would be around `1/20= 5%`
```{r}
prop_caseNO[prop_caseNO$proportion > 0.4, ]
```
#### Minimum threshold of 2% contribution to count individuals
Another interesting variable is the number of individuals that contribute to
more than a certain threshold (15%) to each cluster
```{r min-pct}
# Calculuate for each cluster the number of individuals that fulfill the condition of contributing more than a 15%
num_individuals_gt_30pt <- rowSums(prop_caseNO_table > 0.30)
# Sort the clusters by ascending order of number of individuals that contribute more than 2%
sort(num_individuals_gt_30pt)
#And a general overview of the data
summary(num_individuals_gt_30pt)
# Save the ones that are formed by less than 8 individuals that fulfill the condition
clusters_fail <- rownames(prop_caseNO_table)[which(num_individuals_gt_30pt < 8)]
clusters_fail
```
Plot the proportions for caseNo
```{r prop-plot}
# plot a barplot
ggplot(data = prop_caseNO, aes(x = cluster, y = proportion, fill = caseNO)) +
geom_bar(stat = "identity") + theme_classic() + scale_fill_manual(values=mycoloursP[1:20])
```
It is also worth keeping in mind the size of the cluster, there are in ascending
order (0 is the biggest cluster and 10 the smallest).
#### Samples instead of individuals
The proportions are again calculated but taking into consideration the samples
instead of the individuals
```{r}
# count how many cells there are in each group and cluster
sum_caseNOtissue_cluster <- table(micro_srt$microglia_clu, micro_srt$process_number)
# calculate the proportions, for each cluster (margin 1 for rows)
prop_caseNOtissue_table <- prop.table(sum_caseNOtissue_cluster, margin = 1)
# change the format to be a data.frame, this also expands to long formatting
prop_caseNOtissue <- as.data.frame(prop_caseNOtissue_table)
colnames(prop_caseNOtissue) <- c("cluster", "process_number", "proportion")
prop_caseNOtissue[prop_caseNOtissue$proportion > 0.3, ]
```
## Conclusion of cluster QC
I think the cluster QC looks fine.
# Compositional differences with age, sex and regions
Some general distributions about the data. We started with equal number of
sex/age/tissues but because we deleted samples these are not equal any more.
Also the number of cells from each one might differ
```{r general}
# Number of samples per ageSex
colSums(table(micro_srt$process_number, micro_srt$ageSex)>0)
# Number of samples per tissue
colSums(table(micro_srt$process_number, micro_srt$Tissue)>0)
# Both things combined (there might be another way of doing this, but it works)#
colSums(table(micro_srt$caseNO, micro_srt$ageSex, micro_srt$Tissue)>0)
# Number of nuclei per sexage group
table(micro_srt$ageSex)
#number of nuclei per tissue
table(micro_srt$Tissue)
#number of nuclei per age group
table(micro_srt$AgeGroup)
#number of nuclei per sex group
table(micro_srt$gender)
# both things combined
table(micro_srt$ageSex, micro_srt$Tissue)
```
## Age and Sex Grouping
Separate by both things in 4 plots. The plots are corrected by number of cells
per sexage group first (looking at the distribution of each group across
clusters) and then corrected for the number of cells per cluster (to visualize
the small and big clusters equally).
```{r, fig.width=8, fig.height=8}
# Sex
# DimPlot(nad_ol, split.by = "ageSex", group.by = "Tissue", ncol = 5)
DimPlot(micro_srt, split.by = "ageSex", group.by = "microglia_clu", ncol = 2,
cols = mycoloursP, pt.size = 2, label = TRUE, label.size = 6)
```
Calculate proportion clusters for each AgeSex
```{r}
# count how many cells there are in each group and cluster
sum_ageSex_cluster <- table(micro_srt$microglia_clu, micro_srt$ageSex)
# Calculate the proportions for each group:
# allows to normalise the groups and give the same weight to all groups,
# even though they might have different cell numbers (margin 2 for cols)
prop_ageSex_table_2 <- prop.table(sum_ageSex_cluster, margin = 2)
# calculate the proportions, for each cluster,
# allows to visualize on a scale from 0 to 1 (margin 1 for rows)
prop_ageSex_table_2_1 <- prop.table(prop_ageSex_table_2, margin = 1)
# change the format to be a data.frame, this also expands to long formatting
prop_ageSex <- as.data.frame(prop_ageSex_table_2)
colnames(prop_ageSex) <- c("cluster", "ageSex", "proportion")
level_list <- c("Young women", "Old women", "Young men", "Old men")
prop_ageSex$ageSex <- factor(prop_ageSex$ageSex, levels = level_list)
# plot a barplot proportion
ggplot(data = prop_ageSex, aes(x = cluster, y = proportion, fill = ageSex)) +
geom_bar(stat = "identity") + theme_classic() + scale_fill_manual(values=mycoloursP[1:20]) +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) +
ylab("Normalised counts")
ggplot(data = prop_ageSex, aes(x = cluster, y = proportion, fill = ageSex)) +
geom_bar(position = "fill", stat = "identity") + theme_classic() + scale_fill_manual(values=mycoloursP[1:20]) +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) +
ylab("Normalised counts")
```
Plot the same for Sex and Age separate
```{r}
# separate the sex and age
prop_ageSex_sep <- separate(prop_ageSex,
col = ageSex,
into = c("age", "sex"),
sep = " ")
# plot a barplot for the sex
ggplot(data = prop_ageSex_sep, aes(x = cluster, y = proportion, fill = sex)) +
geom_bar(stat = "identity") + theme_classic() + scale_fill_manual(values=mycoloursP[10:20]) +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
ggplot(data = prop_ageSex_sep, aes(x = cluster, y = proportion, fill = sex)) +
geom_bar(position = "fill", stat = "identity") + theme_classic() + scale_fill_manual(values=mycoloursP[10:20]) +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
# And for the age
ggplot(data = prop_ageSex_sep, aes(x = cluster, y = proportion, fill = age)) +
geom_bar(stat = "identity") + theme_classic() + scale_fill_manual(values=mycoloursP[15:20]) +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
ggplot(data = prop_ageSex_sep, aes(x = cluster, y = proportion, fill = age)) +
geom_bar(position = "fill", stat = "identity") + theme_classic() + scale_fill_manual(values=mycoloursP[15:20]) +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
```
## Tissue
split the clustering by the original tissues
```{r, fig.width=8, fig.height=8}
Idents(micro_srt) <- "microglia_clu"
umap_clusters <- DimPlot(micro_srt, split.by = "Tissue", ncol = 2,
cols = mycoloursP, pt.size = 2, label = T) +NoLegend()
umap_clusters
```
Calculate proportion clusters for each Tissue
```{r}
# count how many cells there are in each group and cluster
sum_tissue_cluster <- table(micro_srt$microglia_clu, micro_srt$Tissue)
keep <- rowSums(sum_tissue_cluster) > 0
sum_tissue_cluster <- sum_tissue_cluster[keep,]
# Calculate the proportions for each group:
# allows to normalise the groups and give the same weight to all groups,
# even though they might have different cell numbers (margin 2 for cols)
prop_tissue_table_2 <- prop.table(sum_tissue_cluster, margin = 2)
# calculate the proportions, for each cluster,
# allows to visualize on a scale from 0 to 1 (margin 1 for rows)
prop_tissue_table_2_1 <- prop.table(prop_tissue_table_2, margin = 1)
# change the format to be a data.frame, this also expands to long formatting
prop_tissue <- as.data.frame(prop_tissue_table_2)
colnames(prop_tissue) <- c("cluster", "Tissue", "proportion")
#plot
ggplot(data = prop_tissue, aes(x = cluster, y = proportion, fill = Tissue)) +
geom_bar(stat = "identity") + theme_classic() +
scale_fill_manual(values=mycoloursP[24:40]) +
ylab("Normalised counts")
```
```{r}
ggplot(data = prop_tissue, aes(x = cluster, y = proportion, fill = Tissue)) +
geom_bar(position = "fill", stat = "identity") + theme_classic() +
scale_fill_manual(values=mycoloursP[24:40])
```
### tables shown with numbers
To better understand the proportions I show the different steps with tables
```{r}
sum_ageSex_cluster
(prop_ageSex_table_2)*100
prop_ageSex_table_2_1*100
```
# Conclusion
There are interesting differences in microglia with tissue region age and sex.
To do:
- Differential gene expression for age and sex separately for each tissue
- find best cluster marker genes from heat map to plot
- compare results to literature
https://link.springer.com/article/10.1186/s12974-020-01774-9
https://www.nature.com/articles/s41467-018-02926-5
https://www.sciencedirect.com/science/article/pii/S1471491419301030?casa_token=AAmJyjUSOvcAAAAA:Lq0kJTLkIua8YIHmcfBL31XhyLM3KD8WS2zj6e6V2umfeSbbU6sSxD55qIBMtLRwlQOQbFj95Q
https://pubmed.ncbi.nlm.nih.gov/30082275/
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6024879/#!po=12.5000
https://nnjournal.net/article/view/3357#B113
https://www.sciencedirect.com/science/article/pii/S221112472030019X
https://jneuroinflammation.biomedcentral.com/articles/10.1186/s12974-021-02124-z
https://www.sciencedirect.com/science/article/pii/S1074761318304850
# Save dataset
```{r, eval = FALSE}
dir.create(here("data",
"single_nuc_data",
"microglia"))
saveRDS(micro_srt, here("data",
"single_nuc_data",
"microglia",
"HCA_microglia.RDS"))
```
Associated with neurodegeneration in mice (Krasemann et al., 2017)
upregulated:
```{r}
VlnPlot(micro_srt, features = "SPP1", split.by = "AgeGroup")
```
```{r}
VlnPlot(micro_srt, features = "ITGAX", split.by = "AgeGroup")
```
```{r}
VlnPlot(micro_srt, features = "AXL", split.by = "AgeGroup")
```
```{r}
VlnPlot(micro_srt, features = "LILRB4", split.by = "AgeGroup")
```
```{r}
VlnPlot(micro_srt, features = "CLEC7A", split.by = "AgeGroup")
```
```{r}
VlnPlot(micro_srt, features = "CCL2", split.by = "AgeGroup")
```
```{r}
VlnPlot(micro_srt, features = "CSF1", split.by = "AgeGroup")
```
```{r}
VlnPlot(micro_srt, features = "APOE", split.by = "AgeGroup")
```
Associated with neurodegeneration in mice (Krasemann et al., 2017)
downregulated:
```{r}
VlnPlot(micro_srt, features = "P2RY12", split.by = "AgeGroup")
```
```{r}
VlnPlot(micro_srt, features = "TMEM119", split.by = "AgeGroup")
```
```{r}
VlnPlot(micro_srt, features = "CSF1R", split.by = "AgeGroup")
```
```{r}
VlnPlot(micro_srt, features = "RHOB", split.by = "AgeGroup")
```
```{r}
VlnPlot(micro_srt, features = "CX3CR1", split.by = "AgeGroup")
```
```{r}
VlnPlot(micro_srt, features = "MEF2A", split.by = "AgeGroup")
```
```{r}
VlnPlot(micro_srt, features = "MAFB", split.by = "AgeGroup")
```
```{r}
VlnPlot(micro_srt, features = "SALL1", split.by = "AgeGroup")
```
Up in DAMS:
TREM2 independent
```{r}
VlnPlot(micro_srt, features = "TYROBP", split.by = "AgeGroup")
```
```{r}
VlnPlot(micro_srt, features = "APOE", split.by = "AgeGroup")
```
```{r}
VlnPlot(micro_srt, features = "B2M", split.by = "AgeGroup")
```
TREM2-dependent
```{r}
VlnPlot(micro_srt, features = "TREM2", split.by = "AgeGroup")
```
```{r}
VlnPlot(micro_srt, features = "SPP1", split.by = "AgeGroup")
```
```{r}
VlnPlot(micro_srt, features = "ITGAX", split.by = "AgeGroup")
```
```{r}
VlnPlot(micro_srt, features = "AXL", split.by = "AgeGroup")
```
```{r}
VlnPlot(micro_srt, features = "LILRB4", split.by = "AgeGroup")
```
```{r}
VlnPlot(micro_srt, features = "CLEC7A", split.by = "AgeGroup")
```
```{r}
VlnPlot(micro_srt, features = "CTSL", split.by = "AgeGroup")
```
```{r}
VlnPlot(micro_srt, features = "LPL", split.by = "AgeGroup")
```
```{r}
VlnPlot(micro_srt, features = "CD9", split.by = "AgeGroup")
```
```{r}
VlnPlot(micro_srt, features = "CSF1", split.by = "AgeGroup")
```
```{r}
VlnPlot(micro_srt, features = "CD68", split.by = "AgeGroup")
```
```{r}
VlnPlot(micro_srt, features = "LPL", split.by = "AgeGroup")
```
Markers of activation:
reviewed by Amor et al 2021
```{r}
VlnPlot(micro_srt, features = "AIF1", split.by = "AgeGroup")
```
```{r}
VlnPlot(micro_srt, features = "CX3CR1", split.by = "AgeGroup")
```
```{r}
VlnPlot(micro_srt, features = "CD40", split.by = "AgeGroup")
```
```{r}
VlnPlot(micro_srt, features = "CD163", split.by = "AgeGroup")
```
```{r}
#CD11B
VlnPlot(micro_srt, features = "ITGAM", split.by = "AgeGroup")
```
```{r}
#CD206
VlnPlot(micro_srt, features = "MRC1", split.by = "AgeGroup")
```
```{r}
#CD16
VlnPlot(micro_srt, features = "FCGR3A", split.by = "AgeGroup")
```
```{r}
#CD32
VlnPlot(micro_srt, features = "FCGR2A", split.by = "AgeGroup")
```
```{r}
VlnPlot(micro_srt, features = "HLA-DMA", split.by = "AgeGroup")
```
```{r}
VlnPlot(micro_srt, features = "HLA-DMB", split.by = "AgeGroup")
```
```{r}
VlnPlot(micro_srt, features = "HLA-DRA", split.by = "AgeGroup")
```
```{r}
VlnPlot(micro_srt, features = "HLA-DRB1", split.by = "AgeGroup")
```
```{r}
VlnPlot(micro_srt, features = "HLA-DRB5", split.by = "AgeGroup")
```
```{r}
VlnPlot(micro_srt, features = "HLA-DOA", split.by = "AgeGroup")
```
```{r}
VlnPlot(micro_srt, features = "HLA-DOB", split.by = "AgeGroup")
```
```{r}
VlnPlot(micro_srt, features = "HLA-DPA1", split.by = "AgeGroup")
```
```{r}
VlnPlot(micro_srt, features = "HLA-DPB1", split.by = "AgeGroup")
```
```{r}
VlnPlot(micro_srt, features = "HLA-DQA1", split.by = "AgeGroup")
```
Sex differences in
```{r}
VlnPlot(micro_srt, features = "RASGEF1B", split.by = "gender", group.by = "gender")+
scale_fill_manual(values=mycoloursP[10:40])
```
```{r}
VlnPlot(micro_srt, features = "RUNX1", split.by = "gender", group.by = "gender")+
scale_fill_manual(values=mycoloursP[10:40])
```
# Volcano plots
Below are vulcano plots of age sex and pairwise tissue and the corresponding vln
plots of the most interesting hits
# Sex
```{r, fig.height = 6, fig.width= 6}
sex_markers$male_log2 <- ifelse(sex_markers$cluster == "M",
paste(sex_markers$avg_log2FC),
paste(sex_markers$avg_log2FC * -1))
sex_markers$male_log2 <- as.numeric(paste(sex_markers$male_log2))
sex_markers$pval_plot <- ifelse(sex_markers$p_val_adj == 0,
paste(min(sex_markers$p_val_adj[sex_markers$p_val_adj >0]) *
0.01),
paste(sex_markers$p_val_adj))
sex_markers$pval_plot<- as.numeric(paste(sex_markers$pval_plot))
fil_genes <- subset(sex_markers, abs(sex_markers$avg_log2FC) > 0.5 &
sex_markers$p_val_adj < 0.0001)
fil_genes<- fil_genes$gene
EnhancedVolcano(sex_markers,
lab = sex_markers$gene,
x = 'male_log2',
y = 'pval_plot',
FCcutoff = 0.5,
title = "Male vs. female",
subtitle = "",
selectLab = fil_genes,
boxedLabels = TRUE,
pointSize = 4.0,
labSize = 6.0,
labCol = 'black',
#labFace = 'bold',
colAlpha = 4/5,
drawConnectors = TRUE,
widthConnectors = 1.0,
colConnectors = 'black')
EnhancedVolcano(sex_markers,
lab = sex_markers$gene,
x = 'male_log2',
y = 'pval_plot',
FCcutoff = 0.5,
title = "Male vs female",
subtitle = "",
#boxedLabels = TRUE,
#drawConnectors = TRUE,
#widthConnectors = 1.0,
#colConnectors = 'black'
)
```
```{r}
Idents(micro_srt) <- "gender"
sex_mark_p_n <- FindMarkers(micro_srt, ident.1 = "M",
ident.2 = "F",
only.pos = FALSE,
min.pct = 0.25, test.use = "MAST")
sex_mark_p_n$pval_plot <- ifelse(sex_mark_p_n$p_val_adj == 0,
paste(min(sex_mark_p_n$p_val_adj[sex_mark_p_n$p_val_adj >0]) *
0.01),
paste(sex_mark_p_n$p_val_adj))
sex_mark_p_n$pval_plot<- as.numeric(paste(sex_mark_p_n$pval_plot))
EnhancedVolcano(sex_mark_p_n,
lab = rownames(sex_mark_p_n),
x = 'avg_log2FC',
y = 'pval_plot',
xlim = c(-2.2, 2.5),
#ylim = c(0, 50),
FCcutoff = 0.25,
title = "Male vs. Female",
subtitle = "Microglia",
selectLab = c("XIST", "UTY", "TTTY14", "LINC00278", "NLGN4Y", "TTTY14", "USP9Y",
"IFI44L", "HSPA1A", "HSPA1B", "DUSP1"),
pointSize = 4.0,
labSize = 6.0,
labCol = 'black',
labFace = 'bold',
boxedLabels = FALSE,
colAlpha = 4/5,
drawConnectors = TRUE,
widthConnectors = 1.0,
colConnectors = 'black',
#parseLabels = TRUE,
col = mycoloursP[25:30],
legendPosition = 'bottom',
legendLabSize = 14,
legendIconSize = 2.0,
pCutoff = 0.05)
```
exclude gonosomal genes in plot
```{r}
EnhancedVolcano(sex_mark_p_n,
lab = rownames(sex_mark_p_n),
x = 'avg_log2FC',
y = 'pval_plot',
xlim = c(-1.5, 1.5),
ylim = c(0, 30),
FCcutoff = 0.25,
title = "Male vs. Female",
subtitle = "Microglia",
selectLab = c("IFI44L", "HSPA1A", "HSPA1B", "DUSP1", "KDM6A",
"HLA-DRB5", "HLA-DRB1", "OLR1", "HSP90AA1", "ZFP36L1",
"SCIN", "FKBP5", "IFI44", "KCNMA1", "UBC", "KCNMA1"),
pointSize = 4.0,
labSize = 6.0,
labCol = 'black',
labFace = 'bold',
boxedLabels = FALSE,
colAlpha = 4/5,
drawConnectors = TRUE,
widthConnectors = 1.0,
colConnectors = 'black',
#parseLabels = TRUE,
col = mycoloursP[25:30],
legendPosition = 'bottom',
legendLabSize = 14,
legendIconSize = 2.0,
pCutoff = 0.05)
```
Zoom in to lower p values
```{r}
EnhancedVolcano(sex_mark_p_n,
lab = rownames(sex_mark_p_n),
x = 'avg_log2FC',
y = 'pval_plot',
xlim = c(-2.2, 2.5),
ylim = c(0, 30),
FCcutoff = 0.25,
title = "Male vs. Female",
subtitle = "Microglia",
#selectLab = c("XIST", "UTY", "TTTY14", "LINC00278", "NLGN4Y", "TTTY14", "USP9Y",
# "IFI44L", "HSPA1A", "HSPA1B", "DUSP1"),
pointSize = 4.0,
labSize = 6.0,
labCol = 'black',
labFace = 'bold',
boxedLabels = FALSE,
colAlpha = 4/5,
drawConnectors = TRUE,
widthConnectors = 1.0,
colConnectors = 'black',
#parseLabels = TRUE,
col = mycoloursP[25:30],
legendPosition = 'bottom',
legendLabSize = 14,
legendIconSize = 2.0,
pCutoff = 0.05)
```
```{r}
Idents(micro_srt) <- "gender"
sex_mark <- FindMarkers(micro_srt, ident.1 = "M",
ident.2 = "F",
only.pos = FALSE,
min.pct = 0.25, test.use = "MAST")
sex_mark$pval_plot <- ifelse(sex_mark$p_val_adj == 0,
paste(min(sex_mark$p_val_adj[sex_mark$p_val_adj >0]) *
0.01),
paste(sex_mark$p_val_adj))
sex_mark$pval_plot<- as.numeric(paste(sex_mark$pval_plot))
fil_sex <- subset(sex_mark, sex_mark$p_val_adj < 0.05)
xsort <- fil_sex[order(fil_sex$avg_log2FC),]
top_x <- head(rownames(xsort))
bottom_x <- tail(rownames(xsort))
EnhancedVolcano(sex_mark,
lab = rownames(sex_mark),
x = 'avg_log2FC',
y = 'pval_plot',
xlim = c(-6, 5),
ylim = c(0, 300),
FCcutoff = 0.5,
title = "Male vs. Female",
subtitle = "Microglia",
selectLab = c(top_x, bottom_x),
pointSize = 4.0,
labSize = 6.0,
labCol = 'black',
labFace = 'bold',
boxedLabels = TRUE,
colAlpha = 4/5,
drawConnectors = TRUE,
widthConnectors = 1.0,
colConnectors = 'black',
#parseLabels = TRUE,
col = mycoloursP[14:50],
legendPosition = 'bottom',
legendLabSize = 14,
legendIconSize = 2.0) + coord_flip()
```
```{r}
VlnPlot(micro_srt, features = "IFI44L", group.by = "gender")+
scale_fill_manual(values=mycoloursP[10:40])
```
```{r}
VlnPlot(micro_srt, features = "HLA-DRB5", group.by = "gender")+
scale_fill_manual(values=mycoloursP[10:40])
```
```{r}
VlnPlot(micro_srt, features = "HSPA1A", group.by = "gender")+
scale_fill_manual(values=mycoloursP[10:40])
```
## Age
```{r, fig.height = 6, fig.width= 6}
age_markers$old_log2 <- ifelse(age_markers$cluster == "Old",
paste(age_markers$avg_log2FC),
paste(age_markers$avg_log2FC * -1))
age_markers$old_log2 <- as.numeric(paste(age_markers$old_log2))
age_markers$pval_plot <- ifelse(age_markers$p_val_adj == 0,
paste(min(age_markers$p_val_adj[age_markers$p_val_adj >0]) *
0.01),
paste(age_markers$p_val_adj))
age_markers$pval_plot<- as.numeric(paste(age_markers$pval_plot))
EnhancedVolcano(age_markers,
lab = age_markers$gene,
x = 'old_log2',
y = 'pval_plot',
FCcutoff = 0.5,
title = "Old vs. young",
subtitle = "")
```
```{r}
Idents(micro_srt) <- "AgeGroup"
age_mark <- FindMarkers(micro_srt, ident.1 = "Old",
ident.2 = "Young",
only.pos = FALSE,
min.pct = 0.25, test.use = "MAST")
age_mark$pval_plot <- ifelse(age_mark$p_val_adj == 0,
paste(min(age_mark$p_val_adj[age_mark$p_val_adj >0]) *
0.01),
paste(age_mark$p_val_adj))
age_mark$pval_plot<- as.numeric(paste(age_mark$pval_plot))
fil_age <- subset(age_mark, age_mark$p_val_adj < 0.05)
xsort <- fil_age[order(fil_age$avg_log2FC),]
top_x <- head(rownames(xsort))
bottom_x <- tail(rownames(xsort))
EnhancedVolcano(age_mark,
lab = rownames(age_mark),
x = 'avg_log2FC',
y = 'pval_plot',
#xlim = c(-6, 5),
#ylim = c(0, 300),
FCcutoff = 0.5,
title = "Old vs. Young",
subtitle = "Microglia",
selectLab = c(top_x[1:3], bottom_x, "NEAT1", "CSGALNACT1", "CX3CR1"),
pointSize = 4.0,
labSize = 6.0,
labCol = 'black',
labFace = 'bold',
boxedLabels = TRUE,
colAlpha = 4/5,
drawConnectors = TRUE,
widthConnectors = 1.0,
colConnectors = 'black',
#parseLabels = TRUE,
col = mycoloursP[14:50],
legendPosition = 'bottom',
legendLabSize = 14,
legendIconSize = 2.0) + coord_flip()
```
```{r}
Idents(micro_srt) <- "AgeGroup"
age_mark_p_n <- FindMarkers(micro_srt, ident.1 = "Old",
ident.2 = "Young",
only.pos = FALSE,
min.pct = 0.25, test.use = "MAST")
age_mark_p_n$pval_plot <- ifelse(age_mark_p_n$p_val_adj == 0,
paste(min(age_mark_p_n$p_val_adj[age_mark_p_n$p_val_adj >0]) *
0.01),
paste(age_mark_p_n$p_val_adj))
age_mark_p_n$pval_plot<- as.numeric(paste(age_mark_p_n$pval_plot))
EnhancedVolcano(age_mark_p_n,
lab = rownames(age_mark_p_n),
x = 'avg_log2FC',
y = 'pval_plot',
xlim = c(-2.2, 2.5),
#ylim = c(0, 50),
FCcutoff = 0.25,
title = "Old vs. Young",
subtitle = "Microglia",
selectLab = c("NEAT1", "IPCEF1", "HLA-C", "RTTN", "CYFIP1", "HLA_DRB1",
"CD74", "B2M", "GLDN", "DUSP1", "APOE", "CSGALNACT1", "P2RY12"),
pointSize = 4.0,
labSize = 6.0,
labCol = 'black',
labFace = 'bold',
boxedLabels = FALSE,
colAlpha = 4/5,
drawConnectors = TRUE,
widthConnectors = 1.0,
colConnectors = 'black',
#parseLabels = TRUE,
col = mycoloursP[25:30],
legendPosition = 'bottom',
legendLabSize = 14,
legendIconSize = 2.0,
pCutoff = 0.05)
```
```{r}
VlnPlot(micro_srt, features = "IPCEF1", group.by = "AgeGroup")
```
```{r}
VlnPlot(micro_srt, features = "RTTN", group.by = "AgeGroup")
```
```{r}
VlnPlot(micro_srt, features = "CYFIP1", group.by = "AgeGroup")
```
```{r}
VlnPlot(micro_srt, features = "NEAT1", group.by = "AgeGroup")
```
```{r}
VlnPlot(micro_srt, features = "CSGALNACT1", group.by = "AgeGroup")
```
```{r}
VlnPlot(micro_srt, features = "GLDN", group.by = "AgeGroup")
```
```{r}
VlnPlot(micro_srt, features = "HLA-C", group.by = "AgeGroup")
```
```{r}
VlnPlot(micro_srt, features = "DPYD", group.by = "AgeGroup")
```
```{r}
VlnPlot(micro_srt, features = "DUSP1", group.by = "AgeGroup")
```
```{r}
VlnPlot(micro_srt, features = "PADI2", group.by = "AgeGroup")
```
# Pairwise tissue comparison to plot
```{r, fig.height = 6, fig.width= 6}
#CB vs BA4
Idents(micro_srt) <- "Tissue"
cb_ba4_mark <- FindMarkers(micro_srt, ident.1 = "CB",
ident.2 = "BA4",
only.pos = FALSE,
min.pct = 0.25,
test.use = "MAST")
cb_ba4_mark$pval_plot <- ifelse(cb_ba4_mark$p_val_adj == 0,
paste(min(cb_ba4_mark$p_val_adj[cb_ba4_mark$p_val_adj >0]) *
0.01),
paste(cb_ba4_mark$p_val_adj))
cb_ba4_mark$pval_plot<- as.numeric(paste(cb_ba4_mark$pval_plot))
fil_cb_ba4 <- subset(cb_ba4_mark, cb_ba4_mark$pval_plot < 0.001)
xsort <- fil_cb_ba4[order(fil_cb_ba4$avg_log2FC),]
top_x <- head(rownames(xsort))
bottom_x <- tail(rownames(xsort))
EnhancedVolcano(cb_ba4_mark,
lab = rownames(cb_ba4_mark),
x = 'avg_log2FC',
y = 'pval_plot',
#xlim = c(-6, 5),
#ylim = c(0, 300),
FCcutoff = 0.5,
title = "CB vs. BA4",
subtitle = "Microglia",
selectLab = c( bottom_x, "DDX5","RHBDF2", "APOE"),
pointSize = 4.0,
labSize = 6.0,
labCol = 'black',
labFace = 'bold',
boxedLabels = TRUE,
colAlpha = 4/5,
drawConnectors = TRUE,
widthConnectors = 1.0,
colConnectors = 'black',
#parseLabels = TRUE,
col = mycoloursP[14:50],
legendPosition = 'bottom',
legendLabSize = 14,
legendIconSize = 2.0) + coord_flip()
EnhancedVolcano(cb_ba4_mark,
lab = rownames(cb_ba4_mark),
x = 'avg_log2FC',
y = 'pval_plot',
FCcutoff = 0.5,
title = "CB vs. BA4",
subtitle = "Microglia & macrophages")
cb_ba4 <- EnhancedVolcano(cb_ba4_mark,
lab = rownames(cb_ba4_mark),
x = 'avg_log2FC',
y = 'pval_plot',
#xlim = c(-6, 5),
#ylim = c(0, 300),
FCcutoff = 0.25,
title = "CB vs. BA4",
subtitle = "Microglia",
selectLab = c( bottom_x, "DDX5","RHBDF2", "APOE"),
pointSize = 4.0,
labSize = 6.0,
labCol = 'black',
labFace = 'bold',
boxedLabels = FALSE,
colAlpha = 4/5,
drawConnectors = TRUE,
widthConnectors = 1.0,
colConnectors = 'black',
#parseLabels = TRUE,
col = mycoloursP[25:30],
legendPosition = 'bottom',
legendLabSize = 14,
legendIconSize = 2.0,
pCutoff = 0.05)
```
```{r}
VlnPlot(micro_srt, features = "DDX5", group.by = "Tissue")
```
```{r}
VlnPlot(micro_srt, features = "FKBP5", group.by = "Tissue")
```
```{r}
VlnPlot(micro_srt, features = "DPYD", group.by = "Tissue")
```
```{r}
VlnPlot(micro_srt, features = "RPS27", group.by = "Tissue")
```
```{r}
VlnPlot(micro_srt, features = "SLC1A3", group.by = "Tissue")
```
```{r}
VlnPlot(micro_srt, features = "ADGRB3", group.by = "Tissue")
```
```{r}
VlnPlot(micro_srt, features = "NEAT1", group.by = "Tissue")
```
```{r, fig.height = 6, fig.width= 6}
#CSC vs BA4
csc_ba4_mark <- FindMarkers(micro_srt, ident.1 = "CSC",
ident.2 = "BA4",
only.pos = FALSE,
min.pct = 0.25, test.use = "MAST")
csc_ba4_mark$pval_plot <- ifelse(csc_ba4_mark$p_val_adj == 0,
paste(min(csc_ba4_mark$p_val_adj[csc_ba4_mark$p_val_adj >0]) *
0.01),
paste(csc_ba4_mark$p_val_adj))
csc_ba4_mark$pval_plot<- as.numeric(paste(csc_ba4_mark$pval_plot))
fil_csc_ba4 <- subset(csc_ba4_mark, csc_ba4_mark$pval_plot < 0.001)
xsort <- fil_csc_ba4[order(fil_csc_ba4$avg_log2FC),]
top_x <- head(rownames(xsort))
bottom_x <- tail(rownames(xsort))
EnhancedVolcano(csc_ba4_mark,
lab = rownames(csc_ba4_mark),
x = 'avg_log2FC',
y = 'pval_plot',
#xlim = c(-6, 5),
#ylim = c(0, 300),
FCcutoff = 0.5,
title = "CSC vs. BA4",
subtitle = "Microglia",
#selectLab = c( top_x, bottom_x),
selectLab = c( top_x[3:6], bottom_x[4:6], "B2M", "CD74", "HLA-DPA1",
"HLA-DRB1","HLA-DPB1", "HLA-DRA"),
pointSize = 4.0,
labSize = 6.0,
labCol = 'black',
labFace = 'bold',
boxedLabels = TRUE,
colAlpha = 4/5,
drawConnectors = TRUE,
widthConnectors = 1.0,
colConnectors = 'black',
#parseLabels = TRUE,
col = mycoloursP[14:50],
legendPosition = 'bottom',
legendLabSize = 14,
legendIconSize = 2.0) + coord_flip()
EnhancedVolcano(csc_ba4_mark,
lab = rownames(csc_ba4_mark),
x = 'avg_log2FC',
y = 'pval_plot',
FCcutoff = 0.5,
title = "CSC vs. BA4",
subtitle = "Microglia & macrophages")
csc_ba4 <- EnhancedVolcano(csc_ba4_mark,
lab = rownames(csc_ba4_mark),
x = 'avg_log2FC',
y = 'pval_plot',
#xlim = c(-6, 5),
#ylim = c(0, 300),
FCcutoff = 0.25,
title = "CSC vs. BA4",
subtitle = "Microglia",
selectLab = c("HIF1A", "GRID2", "HLA-DRA", "B2M", "APOE", "HLA-DRB1", "CD74",
"AC008691.1", "P2RY12"),
pointSize = 4.0,
labSize = 6.0,
labCol = 'black',
labFace = 'bold',
boxedLabels = FALSE,
colAlpha = 4/5,
drawConnectors = TRUE,
widthConnectors = 1.0,
colConnectors = 'black',
#parseLabels = TRUE,
col = mycoloursP[25:30],
legendPosition = 'bottom',
legendLabSize = 14,
legendIconSize = 2.0,
pCutoff = 0.05)
```
```{r}
VlnPlot(micro_srt, features = "P2RY12", group.by = "Tissue")+
scale_fill_manual(values=mycoloursP[24:40])
```
```{r}
VlnPlot(micro_srt, features = "ST6GALNAC3", group.by = "Tissue")+
scale_fill_manual(values=mycoloursP[24:40])
```
```{r}
VlnPlot(micro_srt, features = "LINC02642", group.by = "Tissue")+
scale_fill_manual(values=mycoloursP[24:40])
```
```{r}
VlnPlot(micro_srt, features = "TANC1", group.by = "Tissue")+
scale_fill_manual(values=mycoloursP[24:40])
```
```{r}
VlnPlot(micro_srt, features = "GRID2", group.by = "Tissue")+
scale_fill_manual(values=mycoloursP[24:40])
```
```{r}
VlnPlot(micro_srt, features = "HLA-DRA", group.by = "Tissue")+
scale_fill_manual(values=mycoloursP[24:40])
```
```{r}
VlnPlot(micro_srt, features = "HLA-DRB1", group.by = "Tissue")+
scale_fill_manual(values=mycoloursP[24:40])
```
```{r}
VlnPlot(micro_srt, features = "CD74", group.by = "Tissue")+
scale_fill_manual(values=mycoloursP[24:40])
```
```{r}
VlnPlot(micro_srt, features = "HIF1A", group.by = "Tissue")+
scale_fill_manual(values=mycoloursP[24:40])
```
```{r}
VlnPlot(micro_srt, features = "MBP", group.by = "Tissue")+
scale_fill_manual(values=mycoloursP[24:40])
```
```{r}
VlnPlot(micro_srt, features = "ACSL1", group.by = "Tissue")+
scale_fill_manual(values=mycoloursP[24:40])
```
```{r, fig.height = 6, fig.width= 6}
#CSC vs CB
csc_cb_mark <- FindMarkers(micro_srt, ident.1 = "CSC",
ident.2 = "CB",
only.pos = FALSE,
min.pct = 0.25, test.use = "MAST")
csc_cb_mark$pval_plot <- ifelse(csc_cb_mark$p_val_adj == 0,
paste(min(csc_cb_mark$p_val_adj[csc_cb_mark$p_val_adj >0]) *
0.01),
paste(csc_cb_mark$p_val_adj))
csc_cb_mark$pval_plot<- as.numeric(paste(csc_cb_mark$pval_plot))
fil_csc_cb <- subset(csc_cb_mark, csc_cb_mark$pval_plot < 0.001)
xsort <- fil_csc_cb[order(fil_csc_cb$avg_log2FC),]
top_x <- head(rownames(xsort))
bottom_x <- tail(rownames(xsort))
EnhancedVolcano(csc_cb_mark,
lab = rownames(csc_cb_mark),
x = 'avg_log2FC',
y = 'pval_plot',
xlim = c(-6, 5),
ylim = c(0, 300),
FCcutoff = 0.5,
title = "CSC vs. CB",
subtitle = "Microglia",
selectLab = c( top_x, bottom_x, "PLP1", "KCNIP1", "GRID2", "PLXDC2" ),
pointSize = 4.0,
labSize = 6.0,
labCol = 'black',
labFace = 'bold',
boxedLabels = TRUE,
colAlpha = 4/5,
drawConnectors = TRUE,
widthConnectors = 1.0,
colConnectors = 'black',
#parseLabels = TRUE,
col = mycoloursP[14:50],
legendPosition = 'bottom',
legendLabSize = 14,
legendIconSize = 2.0) + coord_flip()
EnhancedVolcano(csc_cb_mark,
lab = rownames(csc_cb_mark),
x = 'avg_log2FC',
y = 'pval_plot',
FCcutoff = 0.5,
title = "CSC vs. CB",
subtitle = "Microglia & macrophages")
csc_cb <- EnhancedVolcano(csc_cb_mark,
lab = rownames(csc_cb_mark),
x = 'avg_log2FC',
y = 'pval_plot',
xlim = c(-1.5, 2),
#ylim = c(0, 300),
FCcutoff = 0.25,
title = "CSC vs. CB",
subtitle = "Microglia",
selectLab = c("HSPA1A", "HSPA1B", "KCNIP1", "IPCEF1", "GRID2", "HIF1A",
"CD74",
"TMEM119", "P2RY12", "HLA-DRA", "B2M", "APOE", "HLA-DRB1"),
pointSize = 4.0,
labSize = 6.0,
labCol = 'black',
labFace = 'bold',
boxedLabels = FALSE,
colAlpha = 4/5,
drawConnectors = TRUE,
widthConnectors = 1.0,
colConnectors = 'black',
#parseLabels = TRUE,
col = mycoloursP[25:30],
legendPosition = 'bottom',
legendLabSize = 14,
legendIconSize = 2.0,
pCutoff = 0.05)
```
```{r}
VlnPlot(micro_srt, features = "KCNIP1", group.by = "Tissue")+
scale_fill_manual(values=mycoloursP[24:40])
```
```{r}
VlnPlot(micro_srt, features = "IPCEF1", group.by = "Tissue")+
scale_fill_manual(values=mycoloursP[24:40])
```
```{r}
VlnPlot(micro_srt, features = "PLP1", group.by = "Tissue")+
scale_fill_manual(values=mycoloursP[24:40])
```
```{r}
VlnPlot(micro_srt, features = "H3F3B", group.by = "Tissue")+
scale_fill_manual(values=mycoloursP[24:40])
```
```{r}
VlnPlot(micro_srt, features = "UBC", group.by = "Tissue")+
scale_fill_manual(values=mycoloursP[24:40])
```
```{r}
genes <- c("CD74",
"P2RY12",
#MI_5
"NRG3",
"RNF219-AS1",
"SORBS1",
"CTNND2",
"NTM",
"MAGI2",
"SCD",
"PLP1",
"NRG3",
#MI_3
"PCDH9",
"EDIL3",
"PTPRD",
#BAM
"F13A1",
"CD163",
"LYVE1",
"MRC1",
"PID1",
#MI_4
"HIF1A",
"GNA13",
"RGS1",
"RANBP2",
"FAM110B",
"ACSL1",
"CXCR4",
#MI_1
"P2RY12",
"RASGEF1C",
"AC008691.1",
"TLN2",
#MI_2
"GPNMB",
"MITF",
"APOC1",
"CPM",
# Immune
"HLA-A",
"PTPRC",
#homeostatic
"P2RY13",
"CX3CR1",
"TMEM119",
# activation
"CD68",
"TREM2" )
invert_genes <- rev(genes)
micro_srt$microglia_clu <- factor(micro_srt$microglia_clu, levels = c("Microglia_2",
"Microglia_1",
"Microglia_4",
"BAM",
"Microglia_3",
"Microglia_5"))
Idents(micro_srt) <- "microglia_clu"
DotPlot(micro_srt, features = unique(genes))+
theme(axis.text.x = element_text(angle = 45, hjust=0.9))
DotPlot(micro_srt, features = unique(invert_genes))+
theme(axis.text.x=element_text(angle=90,hjust=0.9,vjust=0.2)) + coord_flip()
```
```{r}
FeaturePlot(micro_srt, features = c("CX3CR1", "TREM2"), order = T,
min.cutoff = "q1",
max.cutoff = "q99", blend = T,
cols = c("darkblue", "green", "magenta"),
blend.threshold = 0) &NoAxes()
```
```{r}
FeaturePlot(micro_srt, features = c("CD163", "ACSL1"), order = T,
min.cutoff = "q1",
max.cutoff = "q99", blend = T,
cols = c("darkblue", "green", "magenta"),
blend.threshold = 0) &NoAxes()
```
```{r}
FeaturePlot(micro_srt, features = c("CD163", "FAM110B"), order = T,
min.cutoff = "q1",
max.cutoff = "q99", blend = T,
cols = c("darkblue", "green", "magenta"),
blend.threshold = 0) &NoAxes()
```
```{r}
FeaturePlot(micro_srt, features = c("HIF1A", "TLN2"), order = T,
min.cutoff = "q1",
max.cutoff = "q99", blend = T,
cols = c("darkblue", "green", "magenta"),
blend.threshold = 0) &NoAxes()
```
Compare to GPNMB positive microglia in https://www.biorxiv.org/content/10.1101/2022.05.18.492475v1.full
```{r}
FeaturePlot(micro_srt, features = "GPNMB")
FeaturePlot(micro_srt, features = "LGALS3")
FeaturePlot(micro_srt, features = "PLIN2")
FeaturePlot(micro_srt, features = "SOAT1")
FeaturePlot(micro_srt, features = "ABCA1")
```
```{r}
FeaturePlot(micro_srt, features = "GPNMB", split.by = "AgeGroup")
FeaturePlot(micro_srt, features = "CDKN1A", split.by = "AgeGroup")
FeaturePlot(micro_srt, features = "CDKN2A", split.by = "AgeGroup")
```
# Session Info
```{r}
sessionInfo()
```