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03c_genetic_association_threshold_EWCE_DLPFC.R
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03c_genetic_association_threshold_EWCE_DLPFC.R
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library("EWCE")
library("biomaRt")
library("ggplot2")
library("grid")
library("dplyr")
set.seed(1234)
setwd("/Users/melis/Documents/GitHub/LR_project")
load("C:/Users/melis/Documents/GitHub/LR_project/processed_data/03c-EWCE/ctd_cellType_layer.rda") #Load CTD data
n = 10000
#################------------------- 0.7 threshold
data = read.csv("processed_data/03-LR_network_visualisation/louvain_largest_cluster_0.7.csv", row.names = 1)
list = data$X0
#Load target gene list
full_results <- EWCE::bootstrap_enrichment_test(sct_data = ctd,
sctSpecies = "human",
genelistSpecies = "human",
hits = list,
mtc_method = "fdr",
reps = n, #Bootstrap repeats set to 10000
annotLevel = 1, #Annotation level where 1= major cell types, 2= higher resolution
geneSizeControl = TRUE) #Control for GC content and gene length
thisResult1 = data.frame(full_results$results)
thisResult1$MajorCellType = unlist(lapply(strsplit(as.character(thisResult1$CellType),'_'), `[[`, 1))
thisResult1$testList = '0.7 threshold'
FinalResult1 = thisResult1
plot_list = EWCE::ewce_plot(total_res = full_results$results, #Write plot data with BH correction p vals
mtc_method = "fdr",
ctd = ctd)
plot_list$withDendro+theme(text = element_text(size = 12))+theme(plot.margin=grid::unit(c(0.5,0.5,0.5,0.5), "in"))
ggsave(
filename="plots/03-LR_network_visualisation/03c-specificity_major_network_DLPFC/annotation1_thr07.png",
plot = last_plot(),
device ="png",
scale = 1,
width = 7,
height = 7,
units = c("in"),
dpi = 300
)
write.csv(data.frame(full_results$results), "processed_data/03-LR_network_visualisation/03c-specificity_major_network_DLPFC/annotation1_thr07.csv", row.names=TRUE, quote=FALSE)
rm(full_results)
rm(plot_list)
full_results <- EWCE::bootstrap_enrichment_test(sct_data = ctd,
sctSpecies = "human",
genelistSpecies = "human",
hits = list,
mtc_method = "fdr",
reps = n, #Bootstrap repeats set to 10000
annotLevel = 2, #Annotation level where 1= major cell types, 2= higher resolution
geneSizeControl = TRUE) #Control for GC content and gene length
thisResult = data.frame(full_results$results)
thisResult$MajorCellType = unlist(lapply(strsplit(as.character(thisResult$CellType),'_'), `[[`, 1))
thisResult$testList = '0.7 threshold'
FinalResult = thisResult
plot_list = EWCE::ewce_plot(total_res = full_results$results, #Write plot data with BH correction p vals
mtc_method = "fdr",
ctd = ctd)
plot_list$withDendro+ theme(text = element_text(size = 12))+theme(plot.margin=grid::unit(c(0.5,0.5,0.5,0.5), "in"))
ggsave(
filename="plots/03-LR_network_visualisation/03c-specificity_major_network_DLPFC/annotation2_thr07.png",
plot = last_plot(),
device ="png",
scale = 1,
width = 20,
height = 7,
units = c("in"),
dpi = 300
)
write.csv(data.frame(full_results$results), "processed_data/03-LR_network_visualisation/03c-specificity_major_network/annotation2_thr07.csv", row.names=TRUE, quote=FALSE)
rm(full_results)
rm(plot_list)
#################------------------- Threshold 0.4
#Load target gene list
data = read.csv("processed_data/03-LR_network_visualisation/louvain_largest_cluster_0.4.csv", row.names = 1)
list = data$X0
full_results <- EWCE::bootstrap_enrichment_test(sct_data = ctd,
sctSpecies = "human",
genelistSpecies = "human",
hits = list,
mtc_method = "fdr",
reps = n, #Bootstrap repeats set to 10000
annotLevel = 1, #Annotation level where 1= major cell types, 2= higher resolution
geneSizeControl = TRUE) #Control for GC content and gene length
thisResult1 = data.frame(full_results$results)
thisResult1$MajorCellType = unlist(lapply(strsplit(as.character(thisResult1$CellType),'_'), `[[`, 1))
thisResult1$testList = '0.4 threshold'
FinalResult1 = rbind(FinalResult1,thisResult1)
plot_list = EWCE::ewce_plot(total_res = full_results$results, #Write plot data with BH correction p vals
mtc_method = "fdr",
ctd = ctd)
plot_list$withDendro+ theme(text = element_text(size = 12))+theme(plot.margin=grid::unit(c(0.5,0.5,0.5,0.5), "in"))
ggsave(
filename="plots/03-LR_network_visualisation/03c-specificity_major_network_DLPFC/annotation1_thr04.png",
plot = last_plot(),
device ="png",
scale = 1,
width = 7,
height = 7,
units = c("in"),
dpi = 300
)
write.csv(data.frame(full_results$results), "processed_data/03-LR_network_visualisation/03c-specificity_major_network_DLPFC/annotation1_thr04.csv", row.names=TRUE, quote=FALSE)
rm(full_results)
rm(plot_list)
full_results <- EWCE::bootstrap_enrichment_test(sct_data = ctd,
sctSpecies = "human",
genelistSpecies = "human",
hits = list,
mtc_method = "fdr",
reps = n, #Bootstrap repeats set to 10000
annotLevel = 2, #Annotation level where 1= major cell types, 2= higher resolution
geneSizeControl = TRUE) #Control for GC content and gene length
thisResult = data.frame(full_results$results)
thisResult$MajorCellType = unlist(lapply(strsplit(as.character(thisResult$CellType),'_'), `[[`, 1))
thisResult$testList = '0.4 threshold'
FinalResult = rbind(FinalResult,thisResult)
plot_list = EWCE::ewce_plot(total_res = full_results$results, #Write plot data with BH correction p vals
mtc_method = "fdr",
ctd = ctd)
plot_list$withDendro+ theme(text = element_text(size = 12))+theme(plot.margin=grid::unit(c(0.5,0.5,0.5,0.5), "in"))
ggsave(
filename="plots/03-LR_network_visualisation/03c-specificity_major_network_DLPFC/annotation2_thr04.png",
plot = last_plot(),
device ="png",
scale = 1,
width = 20,
height = 7,
units = c("in"),
dpi = 300
)
write.csv(data.frame(full_results$results), "processed_data/03-LR_network_visualisation/03c-specificity_major_network_DLPFC/annotation2_thr04.csv", row.names=TRUE, quote=FALSE)
rm(full_results)
rm(plot_list)
#################------------------- Threshold 0.1
#Load target gene list
data = read.csv("processed_data/03-LR_network_visualisation/louvain_largest_cluster_0.55.csv", row.names = 1)
list = data$X0
full_results <- EWCE::bootstrap_enrichment_test(sct_data = ctd,
sctSpecies = "human",
genelistSpecies = "human",
hits = list,
mtc_method = "fdr",
reps = 50000, #Bootstrap repeats set to 10000
annotLevel = 1, #Annotation level where 1= major cell types, 2= higher resolution
geneSizeControl = TRUE) #Control for GC content and gene length
thisResult1 = data.frame(full_results$results)
thisResult1$MajorCellType = unlist(lapply(strsplit(as.character(thisResult1$CellType),'_'), `[[`, 1))
thisResult1$testList = '0.55 threshold'
FinalResult1 = rbind(FinalResult1,thisResult1)
plot_list = EWCE::ewce_plot(total_res = full_results$results, #Write plot data with BH correction p vals
mtc_method = "fdr",
ctd = ctd)
plot_list$withDendro+ theme(text = element_text(size = 12))+theme(plot.margin=grid::unit(c(0.5,0.5,0.5,0.5), "in"))
ggsave(
filename="plots/03-LR_network_visualisation/03c-specificity_major_network_DLPFC/annotation1_thr055.png",
plot = last_plot(),
device ="png",
scale = 1,
width = 7,
height = 7,
units = c("in"),
dpi = 300
)
write.csv(data.frame(full_results$results), "processed_data/03-LR_network_visualisation/03c-specificity_major_network_DLPFC/annotation1_thr055.csv", row.names=TRUE, quote=FALSE)
rm(full_results)
rm(plot_list)
full_results <- EWCE::bootstrap_enrichment_test(sct_data = ctd,
sctSpecies = "human",
genelistSpecies = "human",
hits = list,
mtc_method = "fdr",
reps = n, #Bootstrap repeats set to 10000
annotLevel = 2, #Annotation level where 1= major cell types, 2= higher resolution
geneSizeControl = TRUE) #Control for GC content and gene length
thisResult = data.frame(full_results$results)
thisResult$MajorCellType = unlist(lapply(strsplit(as.character(thisResult$CellType),'_'), `[[`, 1))
thisResult$testList = '0.55 threshold'
FinalResult = rbind(FinalResult,thisResult)
plot_list = EWCE::ewce_plot(total_res = full_results$results, #Write plot data with BH correction p vals
mtc_method = "fdr",
ctd = ctd)
plot_list$withDendro+ theme(text = element_text(size = 12))+theme(plot.margin=grid::unit(c(0.5,0.5,0.5,0.5), "in"))
ggsave(
filename="plots/03-LR_network_visualisation/03c-specificity_major_network_DLPFC/annotation2_thr055.png",
plot = last_plot(),
device ="png",
scale = 1,
width = 20,
height = 7,
units = c("in"),
dpi = 300
)
#write.csv(data.frame(full_results$results), "processed_data/03-LR_network_visualisation/03c-specificity_major_network/annotation2_thr01.csv", row.names=TRUE, quote=FALSE)
#rm(full_results)
#rm(plot_list)
############-------------------------------- FINAL SAVE and PLOT
write.csv((.packages()), "processed_data/03-LR_network_visualisation/03c-specificity_major_network_DLPFC/loaded_R_packages.R")
write.csv(FinalResult1, "processed_data/03-LR_network_visualisation/03c-specificity_major_network_DLPFC/final_results_table_annot1.csv")
sig1 = FinalResult1[FinalResult1$q>0.05,]
FinalResult1 = read.csv("processed_data/03-LR_network_visualisation/03c-specificity_major_network_DLPFC/final_results_table_annot1.csv", row.names = 1)
FinalResult1 %>%
mutate(CellType = factor(CellType,c("Astro","Micro","OPC","Oligo","EndoMural",
"Inhib","Excit_L6",
"Excit_L5_6","Excit_L5",
"Excit_L4","Excit_L3_4_5","Excit_L3","Excit_L2_3"))) %>%
ggplot() +
geom_point(aes(x = -log10(q), y = CellType,color = MajorCellType, size = fold_change, alpha = q<0.05))+
scale_alpha_discrete(range=c(0.4,1))+
facet_grid(cols = vars(testList))+
theme(text = element_text(size = 12))+ theme(plot.margin=grid::unit(c(0.5,0.5,0.5,0.5), "in"))+
geom_vline(xintercept=1.3010, linetype="dashed", color = "darkgray") +
xlab("-log10(q)") +
ylab("Cell type") +
#coord_flip() + # this is to make the graph landscape
theme_light() +
viridis::scale_fill_viridis() + ## to make the colour scheme color-blind safe, the parameter option="A" or option="B",etc will change palette
guides(fill = guide_legend(title = "Major Cell Type")) +
theme(axis.line = element_line(colour = "black"),
axis.text = element_text(colour = "black", size = "10"),
axis.text.x = element_text(colour = "black", size = "10", angle=90, hjust=1),
axis.title = element_text(colour = "black", size = "10"),
strip.text = element_text(colour = "black", size = "10"),
legend.text = element_text(colour = "black", size = "10"),
plot.caption = element_text(colour = "black", size = "10"),
plot.title = element_text(colour = "black", size = "10"),
legend.title = element_text(colour = "black", size = "10"),
legend.position = "bottom",
## This is to plot the two legends in two rows
legend.box="vertical")
ggsave(
filename="plots/03-LR_network_visualisation/03c-specificity_major_network_DLPFC/annotation1_multiple_thr.png",
plot = last_plot(),
device ="png",
scale = 1,
width = 8,
height = 7,
units = c("in"),
dpi = 300
)
sig = FinalResult[FinalResult$q<0.05,]
write.csv(FinalResult, "processed_data/03-LR_network_visualisation/03c-specificity_major_network_DLPFC/annotation1_multiple_thr.csv")
ggplot(FinalResult) +
geom_point(aes(x = -log10(q), y = CellType, color = MajorCellType, size = fold_change, alpha = q<0.05))+
scale_alpha_discrete(range=c(0.4,1))+
facet_grid(rows = vars(testList))+
theme(text = element_text(size = 12))+ theme(plot.margin=grid::unit(c(0.5,0.5,0.5,0.5), "in"))+
geom_vline(xintercept=1.3010, linetype="dashed", color = "darkgray") +
xlab("-log10(q)") +
ylab("Cell type") +
coord_flip() + # this is to make the graph landscape
theme_light() +
viridis::scale_fill_viridis() + ## to make the colour scheme color-blind safe, the parameter option="A" or option="B",etc will change palette
guides(fill = guide_legend(title = "Major Cell Type")) +
theme(axis.line = element_line(colour = "black"),
axis.text = element_text(colour = "black", size = "10"),
axis.text.x = element_text(colour = "black", size = "10", angle=270, hjust=1),
axis.title = element_text(colour = "black", size = "10"),
strip.text = element_text(colour = "black", size = "10"),
legend.text = element_text(colour = "black", size = "10"),
plot.caption = element_text(colour = "black", size = "10"),
plot.title = element_text(colour = "black", size = "10"),
legend.title = element_text(colour = "black", size = "10"),
legend.position = "top",
## This is to plot the two legends in two rows
legend.box="vertical")
ggsave(
filename="plots/03-LR_network_visualisation/03c-specificity_major_network_DLPFC/annotation2_multiple_thr.png",
plot = last_plot(),
device ="png",
scale = 1,
width = 8,
height = 7,
units = c("in"),
dpi = 300
)