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S08_CopperJoint.R
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S08_CopperJoint.R
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#### Joint analysis to understand copper redistribution in the immune cells after chelation ####
## author: Antonietta Salerno
## date: 13/04/2023
library("Seurat")
library("ggplot2")
library(RColorBrewer)
library(magick)
library(GetoptLong)
setwd("~/Library/CloudStorage/OneDrive-UNSW/TEPA_project")
source("TEPA_code/supportFunctions.R")
seuset_immune <- LoadSeuratRds("TEPA_results/S04_immuneDiff.Rds")
seuset_tumor <- LoadSeuratRds("TEPA_results/S05_seusetTumorClu.Rds")
seuset_full <- merge(seuset_immune, y = seuset_tumor,
add.cell.ids = c("immune", "tumor"),
project = "singleCell")
#seuset_full@assays$RNA@layers$scale.data <- scale(seuset_full@assays$RNA@layers$data, scale = TRUE)
seuset_full$celltypes <- ifelse(test = is.na(seuset_full$celltypes), yes = "Tumor", no = seuset_full$celltypes)
seuset_full$condition <- factor(seuset_full$condition,
levels=c("Control", "Treatment"))
[email protected]$celltypes <- factor([email protected]$celltypes,
levels=c("Cd4+ Naive T cells","Cd4+ Memory T cells",
"Cd8+ Naive-Memory T cells","Cd8+ NkT-like cells" ,
"Gamma-delta T cells","DN Regulatory T cells" ,
"Natural killer cells",
"B cells" , "Dendritic cells", "Macrophages",
"Basophils", "Eosinophils", "Neutrophils", "Tumor"
))
SaveSeuratRds(seuset_full, "TEPA_results/S08_seusetFull.Rds")
#### Create a signature of copper-related genes ####
copper_genes <- c("Sod1", "Sod2", "Gls","Sp1", "Atox1", "Mtf2","Pdha1", "Pdhb", "Lias", "Dld", "Dlat",
"Mt1", "Mt2", "Slc31a1", "Atp7a","Steap4",
"Atp7b", "Steap3",
"Sco1", "Cox11", "Commd1", "Mtf1","Fdx1")
# Search all isoforms of gene of interest
grep(pattern = "Sod1",
x = rownames(x = seuset_full@assays$RNA@data),
value = TRUE, ignore.case = TRUE)
#### Check differentially expressed genes in different cell types ####
seuset_full <- LoadSeuratRds("TEPA_results/S08_seusetFull.Rds")
clusters = levels(seuset_immune$celltypes)
df <- data.frame()
for (cluster in clusters){
file=paste0("TEPA_results/S03_immuneCond_DEA_",gsub(" |/", "_", cluster),".csv")
res <- read.csv(file, sep=",")
res$celltype <- cluster
df <- rbind(df, res)
}
file=paste0("TEPA_results/S05_tumorBulkDEA.csv")
res <- read.csv(file, sep=",")
res$celltype <- "Tumor"
df <- rbind(df, res)
df_copper <- df[df$X %in% copper_genes,]
df_copper <- df_copper %>% filter(p_val < 0.05)
View(df_copper)
png("TEPA_plots/S08_violinCopperGenesSign.png", h = 6000, w = 2000)
Idents(seuset_full) <- "celltypes"
VlnPlot(seuset_full, features = unique(df_copper$X),
assay = "RNA", split.by = "condition",
#size.x.use = 10, size.y.use = 10,
ncol = 1, pt.size = 0.000005)
dev.off()
#### Visualise expression of copper genes ####
### A - Dotplot to understand what most of the cells in the immune subset express ###
library(tidyverse)
library(ComplexHeatmap)
library(circlize)
save = "S08_complexDot_Copper_sign"
#png(paste0("TEPA_plots/",save,".png"), h = 5000, w = 6000, res = 400)
pdf(qq(paste0("TEPA_final_figures/",save,".pdf")), h = 15, w = 15)
sign_dotPlot(seuset_immune, copper_genes, immune=TRUE, cluster = FALSE,k=2, legend = FALSE) #check why it doesn't work
dev.off()
#### B - Create an heatmap to understand differences between immune cells and tumor cells as well as control and treatment ####
save = "S08_complexHeat_Copper_sign"
#png(paste0("TEPA_plots/",save,".png"), h = 5000, w = 6000, res = 400)
pdf(qq(paste0("TEPA_final_figures/",save,".pdf")), h = 15, w = 15)
sign_avgHeatMap(seuset_full, copper_genes, immune = FALSE,
cluster = FALSE, k = 1, legend = FALSE) #check why it doesn't work
dev.off()
# The expression values for each gene are scaled / standardized by subtracting the genes mean expression and dividing by its standard deviation.
# A value of -1 would imply it's one standard deviation below the mean expression for that gene.
### SINGLE-CELL ###
# order of annotations/colors are defined here
ordered_meta_data <- [email protected][order([email protected]$celltypes), ]
ordered_meta_data <- select(ordered_meta_data, c('condition','celltypes'))
annotation_colors <- list("celltypes" = cellt_col,
"condition" = cond_col)
ha = HeatmapAnnotation(df = as.data.frame(ordered_meta_data),
show_annotation_name = TRUE,
col = annotation_colors)
# Expression data
my_data <- seuset_full[copper_genes,]@[email protected]
my_data <- my_data[, rownames(ordered_meta_data)]
# Heatmap
png("TEPA_plots/S08_fullGenesCopper_complexHeat_SingleCell.png", h = 2000, w = 2500, res = 300)
col_fun = colorRamp2(c(-2, 0, 2), c("blue", "white", "red"))
h2 <- Heatmap(
my_data,
col = col_fun,
cluster_rows = TRUE,
heatmap_legend_param=list(title="z-score"),
cluster_columns = FALSE,
column_order = NULL,
show_row_dend = TRUE,
show_column_dend = FALSE,
show_row_names = TRUE,
show_column_names = FALSE,
use_raster = FALSE,
column_names_rot = 45,
#raster_device = c("png"),
bottom_annotation = NULL,
top_annotation = ha
)
dev.off()