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Response_to_reviewers.Rmd
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---
title: "Answer reviewers comments"
author: "Mohamed Nadhir Djekidel"
date: "5/31/2019"
output: html_document
---
# load libraries
```{r}
require(Seurat)
require(ggsci)
require(pheatmap)
require(RColorBrewer)
require(xlsx)
require(circlize)
require(ComplexHeatmap)
```
# Use a different cutoff for the analysis of P21 Vs P60
```{r}
load("P21/P21_Adult_merged_DEG.RData")
P21_Adult_merged_DEG = lapply(P21_Adult_merged_DEG, function(x) {colnames(x)[1] = "Gene"; x})
```
```{r}
P21_Adult_merged_DEG_sig = list()
for(clus in names(P21_Adult_merged_DEG)){
print(clus)
fname = paste0("Compare_with_P21/DEG/All/",clus,".xlsx")
P21_Adult_merged_DEG_sig[[clus]] = read.xlsx(fname,sheetIndex = 1,as.data.frame = T)
colnames(P21_Adult_merged_DEG_sig[[clus]])[1] = "Gene"
P21_Adult_merged_DEG_sig[[clus]]$Cluster = clus
P21_Adult_merged_DEG_sig[[clus]]$Percent_diff = (P21_Adult_merged_DEG_sig[[clus]]$pct.1 - P21_Adult_merged_DEG_sig[[clus]]$pct.2)/P21_Adult_merged_DEG_sig[[clus]]$pct.1
}
```
# Write FC2 data
```{r}
P21_Adult_merged_DEG_sig_FC2 <- list()
dir.create("C:/Dropbox/PFC/Nature Communications/Second_submission/Data/P21_DEG",showWarnings = F)
for(clus in names(P21_Adult_merged_DEG)){
P21_Adult_merged_DEG_sig_FC2[[clus]] = subset(P21_Adult_merged_DEG_sig[[clus]], p_val_adj < 0.05 & abs(avg_logFC) > log(1.5) )
#fout = paste0("C:/Dropbox/PFC/Nature Communications/Second_submission/Data/P21_DEG/",clus,'_DEG.xlsx')
#write.xlsx(P21_Adult_merged_DEG_sig_FC2[[clus]], fout)
}
```
# Calculate the number of DEG per-cluster
```{r}
exc <- grep("Exc_",names(P21_Adult_merged_DEG_sig_FC2),value = T)
Inhib <- grep("Inhib_",names(P21_Adult_merged_DEG_sig_FC2),value = T)
NonNeuro <- c("NF Oligo","Oligo","OPC", "Astro","Endo","Microglia")
nbDEG <- matrix(0,nrow = length(P21_Adult_merged_DEG),ncol = 2)
rownames(nbDEG) <- names(P21_Adult_merged_DEG)
colnames(nbDEG) <- c("Up","Down")
for(cls in names(P21_Adult_merged_DEG)){
tmp = P21_Adult_merged_DEG_sig_FC2[[cls]]
nbUp = sum(tmp$avg_logFC >0)
nbDown = sum(tmp$avg_logFC <0)
nbDEG[cls,] <- c(nbUp,-nbDown)
}
nbDEG= as.data.frame(nbDEG)
nbDEG$cluster = rownames(nbDEG)
nbDEG$cutoff ="0.01"
nbDEG
```
```{r}
nbDEG_original <- matrix(0,nrow = length(P21_Adult_merged_DEG),ncol = 2)
rownames(nbDEG_original) <- names(P21_Adult_merged_DEG)
colnames(nbDEG_original) <- c("Up","Down")
for(cls in names(P21_Adult_merged_DEG)){
tmp = P21_Adult_merged_DEG_sig[[cls]]
nbUp = sum(tmp$avg_logFC >0)
nbDown = sum(tmp$avg_logFC <0)
nbDEG_original[cls,] <- c(nbUp,-nbDown)
}
nbDEG_original = as.data.frame(nbDEG_original)
nbDEG_original$cluster = rownames(nbDEG_original)
nbDEG_original$cutoff ="0.05"
nbDEG_original
```
```{r}
nbDEG_mlt = reshape2::melt(nbDEG)
nbDEG_original_mlt = reshape2::melt(nbDEG_original)
df = rbind(nbDEG_mlt, nbDEG_original_mlt)
df$value = abs(df$value)
df$cluster = factor(df$cluster,levels = c("Astro","Endo","Microglia","NF Oligo","Oligo", "OPC",
paste0("Exc_",1:13),
paste0("Inhib_",1:12)
)
)
df$type = "Non Neuro"
df$type[grep("Exc_",df$cluster)] = "Excitatory"
df$type[grep("Inhib_",df$cluster)] = "Inhibitory"
ggplot(df, aes(x=cluster , y=value+1, fill=cutoff)) + geom_bar(stat = "identity",position = position_dodge(),color="black") +
theme_bw() + facet_grid(type~variable) + scale_fill_aaas() + scale_y_log10() + coord_flip()
```
# nbDEG excitatory
```{r fig.height=5, fig.width=3}
ord = order(rowSums(abs(nbDEG[exc,])),decreasing = T)
H1 = Heatmap(nbDEG[exc[ord],1],cluster_rows = F,
col = colorRamp2(seq(0, max(nbDEG[exc,1]),length.out = 10), c("grey80",brewer.pal(9,"YlOrRd"))),
show_row_names = F,name="Up")
H2 = Heatmap(abs(nbDEG[exc[ord],2]),cluster_rows = F,
col = colorRamp2(seq(0, max(abs(nbDEG[exc,2])),length.out = 10), c("grey80", brewer.pal(9,"YlGnBu")) ),
name="Down"
)
pdf("C:/Dropbox/PFC/Nature Communications/Second_submission/RAW/Exc_FC1.5_Pval001_nbDEG.pdf",width = 3,height = 5)
H1+H2
dev.off()
H1+H2
```
# nbDEG Inhibitory
```{r fig.height=5, fig.width=3}
ord = order(rowSums(abs(nbDEG[Inhib,])),decreasing = T)
H1 = Heatmap(nbDEG[Inhib[ord],1],cluster_rows = F,
col = colorRamp2(seq(0, max(nbDEG[Inhib,1]),length.out = 10), c("grey80",brewer.pal(9,"YlOrRd"))),
show_row_names = F,name="Up")
H2 = Heatmap(abs(nbDEG[Inhib[ord],2]),cluster_rows = F,
col = colorRamp2(seq(0, max(abs(nbDEG[Inhib,2])),length.out = 10), c("grey80", brewer.pal(9,"YlGnBu"))),
name="Down"
)
pdf("C:/Dropbox/PFC/Nature Communications/Second_submission/RAW/Inhib_FC1.5_Pval001_nbDEG.pdf",width = 3,height = 5)
H1+H2
dev.off()
H1+H2
```
# nbDEG Non-neuronal
```{r fig.height=5, fig.width=3}
ord = order(rowSums(abs(nbDEG[NonNeuro,1:2])),decreasing = T)
H1 = Heatmap(nbDEG[NonNeuro[ord],1],cluster_rows = F,
col = colorRamp2(seq(0, max(nbDEG[NonNeuro,1]),length.out = 9), c(brewer.pal(9,"YlOrRd"))),
show_row_names = F,name="Up")
H2 = Heatmap(abs(nbDEG[NonNeuro[ord],2]),cluster_rows = F,show_row_names = T,
col = colorRamp2(seq(0, max(abs(nbDEG[NonNeuro,2])),length.out =9), c(brewer.pal(9,"YlGnBu"))),
name="Down"
)
pdf("C:/Dropbox/PFC/Nature Communications/Second_submission/RAW/NonNeuro_FC1.5_Pval005_nbDEG.pdf",width = 3,height = 5)
H1+H2
dev.off()
H1+H2
```
# Read IPA results
```{r}
FC2_IPA_results <- readIPAResults("C:/Dropbox/PFC/Nature Communications/Second_submission/Data/P21_DEG/IPA_results/",
pathway_pval = 1,
disease_pval = 1)
```
## load IPA pathways what were selected in our previous analysis
```{r}
P21_pathway_enrichment_mat_selected <- read.csv("Compare_with_P21/DEG/IPA_analysis/pathway_enrichment_matrix_SELECTED.csv",stringsAsFactors = F,row.names = 1)
colnames(P21_pathway_enrichment_mat_selected) <- c(1:13)
```
# Plot Pathways heatmap
```{r}
require(reshape2)
FC2_IPA_results_mat <- dcast(FC2_IPA_results$pathway,Pathway ~ cluster,value.var = "logPval",fill = 0)
rownames(FC2_IPA_results_mat) <- FC2_IPA_results_mat$Pathway
FC2_IPA_results_mat$Pathway = NULL
#FC2_IPA_results_mat = FC2_IPA_results_mat[rownames(FC2_IPA_results_mat) %in% rownames(P21_pathway_enrichment_mat_selected),]
FC2_IPA_results_mat = FC2_IPA_results_mat[rownames(FC2_IPA_results_mat) %in% selected_labels_order_selected,]
# Order them by cluster
FC2_IPA_results_mat = FC2_IPA_results_mat[,paste0("Exc_",1:13)]
labels_order <- c()
for(cls in colnames(FC2_IPA_results_mat)){
FC2_IPA_results_mat = FC2_IPA_results_mat[order(FC2_IPA_results_mat[,cls],decreasing = T),]
pos <- which(FC2_IPA_results_mat[,cls]>0)
labels_order <- c(labels_order, rownames(FC2_IPA_results_mat)[pos])
}
labels_order = unique(labels_order)
FC2_IPA_results_mat = data.matrix(FC2_IPA_results_mat)
q99 = quantile(FC2_IPA_results_mat,0.99)
FC2_IPA_results_mat[FC2_IPA_results_mat>q99]=q99
pathways_cols = colorRamp2(c(0, seq(-log10(0.05),q99,length.out = 11)), c("grey80", rev(brewer.pal(11,name = "Spectral")) ))
pathways_cols = colorRamp2(c(0, -log10(0.05),q99), c("grey80","grey80" , "red"))
col_ord = paste0("Exc_",1:13)
Heatmap(FC2_IPA_results_mat[selected_labels_order_selected,col_ord],
cluster_columns = F,
cluster_rows = F,
col = pathways_cols,
heatmap_legend_param = list(title="pvalue", at=c(0,1,-log10(0.05),2,3,4)))
pheatmap(FC2_IPA_results_mat[labels_order,],cluster_cols = F,cluster_rows = F,color = rev(brewer.pal(11,name = "Spectral")))
```
# Plot subsampling
```{r}
load("FinalObjects/PFC_Adult_P21_cca_final_sobj.RData")
load("Compare_with_P21/P21_map.result_all.RData")
P21_map.df = P21_map.result_all$map.df
P21_map.df$pred.cl = as.character(P21_map.df$pred.cl)
P21_map.df$Final_prediction = P21_map.df$pred.cl
P21_map.df$Final_prediction[P21_map.df$prob < 0.5] = "Unassigned"
[email protected]$Predicted_ident = "Unassigned"
pos <- which(!is.na([email protected]$L2_clusters))
[email protected]$Predicted_ident[pos] = as.character([email protected]$L2_clusters[pos])
[email protected][rownames(P21_map.df),]$Predicted_ident = as.character(P21_map.df$pred.cl)
pos <- which([email protected]$Predicted_ident != "Unassigned")
pos = [email protected][pos]
PFC_Adult_P21_cca_sobj = SubsetData(PFC_Adult_P21_cca_sobj,cells.use = pos,subset.raw = T)
# Merge non-neuronal
[email protected]$Predicted_ident_merged = [email protected]$Predicted_ident
[email protected]$Predicted_ident_merged[grep("Astro_",[email protected]$Predicted_ident_merged)] = "Astro"
[email protected]$Predicted_ident_merged[grep("Endo_",[email protected]$Predicted_ident_merged)] = "Endo"
[email protected]$Predicted_ident_merged[grep("Microglia_",[email protected]$Predicted_ident_merged)] = "Microglia"
[email protected]$Predicted_ident_merged[grep("NF Oligo_",[email protected]$Predicted_ident_merged)] = "NF Oligo"
[email protected]$Predicted_ident_merged[grep("Oligo_",[email protected]$Predicted_ident_merged)] = "Oligo"
[email protected]$Predicted_ident_merged[grep("OPC_",[email protected]$Predicted_ident_merged)] = "OPC"
[email protected]$Predicted_ident_merged[grep("OPC_",[email protected]$Predicted_ident_merged)] = "OPC"
```
## load Excitatory
```{r}
load("Excitatory/PFC_excitatory_sobj.RData")
PFC_excitatory_salie_sobj <- SubsetData(PFC_excitatory_sobj,subset.name = "treatment",accept.value = "Saline",subset.raw = T)
load("FinalObjects/PFC_Adult_P21_cca_sobj.RData")
PFC_Adult_P21_cca_Excitatory_sobj=SubsetData(PFC_Adult_P21_cca_sobj,subset.name = "Orignal_Ident",accept.value = "Excitatory",subset.raw = T)
PFC_Adult_P21_cca_Excitatory_sobj <- removeMitoandRiboGenes_raw(PFC_Adult_P21_cca_Excitatory_sobj)
PFC_Adult_P21_cca_Excitatory_sobj <- removeMitoandRiboGenes(PFC_Adult_P21_cca_Excitatory_sobj)
```
```{r}
Adult_cca_excitatory_sobj <- SubsetData(PFC_Adult_P21_cca_Excitatory_sobj, subset.name = "DevStage", accept.value = "Adult",subset.raw = T)
Adult_cca_excitatory_sobj <- SubsetData(Adult_cca_excitatory_sobj,cells.use = [email protected],subset.raw = T)
Adult_cca_excitatory_sobj <- removeMitoandRiboGenes_raw(Adult_cca_excitatory_sobj)
Adult_cca_excitatory_sobj <- removeMitoandRiboGenes(Adult_cca_excitatory_sobj)
pos <- match([email protected], [email protected])
[email protected]$Adult.cluster <- PFC_excitatory_salie_sobj@ident[pos]
TSNEPlot(Adult_cca_excitatory_sobj, group.by="Adult.cluster",do.label = T, colors.use = generateColors(28))
```
```{r}
P21_cca_excitatory_sobj <- SubsetData(PFC_Adult_P21_cca_Excitatory_sobj, subset.name = "DevStage", accept.value = "P21",subset.raw = T)
TSNEPlot(PFC_Adult_P21_cca_Excitatory_sobj)
```
# Try boostrapped correlation between samples
```{r}
require(scrattch.hicat)
g1= FastWhichCells(PFC_Adult_P21_cca_Excitatory_sobj, group.by= "DevStage", subset.value = "P21", invert = FALSE)
g2= FastWhichCells(PFC_Adult_P21_cca_Excitatory_sobj, group.by= "DevStage", subset.value = "P21", invert = TRUE)
var_genes = DimTopGenes(PFC_Adult_P21_cca_Excitatory_sobj,reduction.type = "cca",dim.use = 1:20,num.genes = 100)
P21.dat = PFC_Adult_P21_cca_Excitatory_sobj@data[,g1]
P60.dat = PFC_Adult_P21_cca_Excitatory_sobj@data[,g2]
P60.cl = [email protected][g2,]$L2_clusters
names(P60.cl) = g2
P21_map.result = map_sampling(P60.dat, P60.cl, P21.dat, markers = var_genes)
rm(P21.dat,P60.dat,P60.cl)
```
# Map the L2 identities
```{r}
P21_map.df = P21_map.result$map.df
P21_map.df$Final_Assignmet = as.character(P21_map.df$pred.cl)
P21_map.df$Final_Assignmet[P21_map.df$prob < 0.5] = "Unassigned"
[email protected][rownames(P21_map.df),]$L2_clusters = P21_map.df$Final_Assignmet
[email protected]$Predicted= "Adult"
[email protected][rownames(P21_map.df),]$Predicted = gsub("Exc_","", as.character(P21_map.df$Final_Assignmet))
[email protected]$Predicted = factor([email protected]$Predicted,
levels = c(as.character(1:26),"Unassigned", "Adult"))
TSNEPlot(PFC_Adult_P21_cca_Excitatory_sobj,group.by="Predicted",colors.use = c(generateColors(26),"black", "grey80"),do.label = T,label.size = 5,
order = c(as.character(1:26),"Unassigned", "Adult"))
```
# Map the L1 identities
```{r}
[email protected]$L1_clusters = "P21"
[email protected][g2,]$L1_clusters = [email protected][g2,]$L1_cluster
P60.cl.l1 = [email protected][g2,]$L1_clusters
names(P60.cl.l1) = g2
P21_map.L1.result = map_sampling(P60.dat, P60.cl.l1, P21.dat, markers = var_genes)
P21_map.L1.df = P21_map.L1.result$map.df
P21_map.L1.df$Final_Assignmet = as.character(P21_map.L1.df$pred.cl)
P21_map.L1.df$Final_Assignmet[P21_map.L1.df$prob < 0.5] = "Unassigned"
[email protected][rownames(P21_map.L1.df),]$L1_clusters = P21_map.L1.df$Final_Assignmet
[email protected]$Predicted_L1= "Adult"
[email protected][rownames(P21_map.L1.df),]$Predicted_L1 = gsub("Exc_","", as.character(P21_map.L1.df$Final_Assignmet))
[email protected]$Predicted_L1 = factor([email protected]$Predicted_L1,
levels = c(as.character(1:26),"Unassigned", "Adult") )
TSNEPlot(PFC_Adult_P21_cca_Excitatory_sobj,group.by="Predicted_L1",colors.use = c(generateColors(13),"black", "grey80"),do.label = T,label.size = 5,order = c(as.character(1:26),"Unassigned", "Adult"))
```
```{r fig.height=15, fig.width=8}
Obj1 = SubsetData(PFC_Adult_P21_cca_Excitatory_sobj,subset.name = "DevStage",accept.value = "Adult",subset.raw = T)
Obj1 = FindVariableGenes(Obj1)
Obj2 = SubsetData(PFC_Adult_P21_cca_Excitatory_sobj,subset.name = "DevStage",accept.value = "P21",subset.raw = T)
Obj2 = FindVariableGenes(Obj2)
hvg.p60 <- rownames(x = head(x = [email protected], n = 2000))
hvg.p21 <- rownames(x = head(x = [email protected], n = 2000))
hvg.union <- union(hvg.p60, hvg.p21)
PFC_Adult_P21_cca_Excitatory_sobj <- RunCCA(Obj1, Obj2,scale.data = TRUE,genes.use = hvg.union)
DimHeatmap(PFC_Adult_P21_cca_Excitatory_sobj,dim.use = 1:20,reduction.type = "cca",do.balanced = T,cells.use = 500)
```
```{r}
PFC_Adult_P21_cca_Excitatory_sobj = AlignSubspace(PFC_Adult_P21_cca_Excitatory_sobj,
reduction.type = "cca",
grouping.var = "DevStage",
dims.align = 1:20)
```
```{r}
p_PFC = MetageneBicorPlot(PFC_Adult_P21_cca_Excitatory_sobj, grouping.var = "DevStage",
dims.eval = 1:20, display.progress = TRUE,return.mat = T)
tmp =p_PFC %>% filter(bicor >= 0.2)
toUse = as.numeric( names(table(tmp$cc))[table(tmp$cc)==2])
toUse
PFC_Adult_P21_cca_Excitatory_sobj = RunTSNE(PFC_Adult_P21_cca_Excitatory_sobj,reduction.use = "cca.aligned",dims.use = 1:10)
TSNEPlot(PFC_Adult_P21_cca_Excitatory_sobj,group.by="Predicted_L1",
colors.use = c(generateColors(13),"black", "grey80"),
do.label = T,
label.size = 5,
order = c(as.character(1:13),"Unassigned", "Adult"))
```
```{r}
TSNEPlot(PFC_Adult_P21_cca_Excitatory_sobj,group.by="Predicted",colors.use = c(generateColors(26),"black", "grey80"),do.label = T,label.size = 5,
order = c(as.character(1:26),"Unassigned", "Adult"))
```
# GWAS analysis
Add ADHD to the results and regenerate related figures.
```{r}
require(readxl)
PFC_disease_GWAS <- list()
load("FinalObjects/PFC_disease_GWAS_genes.RData")
GWAS_excel = "GWAS/gwas_catalog_v1.0.2-associations_Diseases Split.xlsx"
for(sheet in excel_sheets(GWAS_excel)[-1]){
sheet_name <- gsub("\"","",sheet)
sheet_name <- gsub("\\s+","_",sheet_name)
print(sheet_name)
PFC_disease_GWAS[[sheet]] <- as.data.frame(read_excel(GWAS_excel, sheet = sheet))
}
```
```{r}
require(biomaRt)
PFC_disease_GWAS_genes <- list()
for(disease in "ADHD") {#names(PFC_disease_GWAS)){
tmp_genes <- unique(unlist(strsplit(PFC_disease_GWAS[[disease]]$`REPORTED GENE(S)`,split = ",")))
tmp_genes <- gsub("^\\s+","",tmp_genes)
tmp_genes <- setdiff(tmp_genes,c("intergenic","NR"))
tmp_genes_mouse <- convertHumanGeneList(tmp_genes)
PFC_disease_GWAS_genes[[disease]] = tmp_genes_mouse
}
```
```{r}
load("FinalObjects/all_cell_inDD_saline_sobj.RData")
load("GWAS/PFC_disease_GWAS_genes_score_ae.RData")
all_cell_inDD_saline_sobj <- SubsetData(all_cell_inDD_sobj,subset.name = "treatment",
accept.value = "Saline",subset.raw = T)
all_cell_inDD_saline_sobj <- SetIdent(all_cell_inDD_saline_sobj, ident.use = [email protected]$L1_clusters)
rs = apply(all_cell_inDD_saline_sobj@data,1,max)
all_cell_inDD_saline_sobj@data = all_cell_inDD_saline_sobj@data[rs>0,]
AE_expression = RobustAverageExpression(all_cell_inDD_saline_sobj)
all_cells_AE_detection <- AverageDetectionRate(all_cell_inDD_saline_sobj,thresh.min = 0)
PFC_disease_GWAS_genes$`Drug Abuse` =NULL
PFC_disease_GWAS_genes_association2 = list()
PFC_disease_GWAS_genes_association2[["e-5"]] = getDiseaseAssociation(PFC_disease_GWAS_genes[1],
all_cell_inDD_saline_sobj,
groups = levels(all_cell_inDD_saline_sobj@ident),
gpval = 1e-5,
lpval = 0.05,
perpval = 1e-6,
all_mrks = NULL,
AE_expression = AE_expression,
AE_detection = all_cells_AE_detection)
PFC_disease_GWAS_expressed_classes2 <- list()
for(disease in "ADHD"){
print(disease)
PFC_disease_GWAS_expressed_classes2[["e-5"]][[disease]]= getExpressedClasses2(PFC_disease_GWAS_genes_association2[["e-5"]][[disease]])
}
```
```{r}
load("FinalObjects/PFC_disease_GWAS_genes_association.RData")
load("FinalObjects/PFC_disease_GWAS_expressed_classes.RData")
PFC_disease_GWAS_genes_association[["e-5"]][['ADHD']] = PFC_disease_GWAS_genes_association2[["e-5"]]$ADHD
PFC_disease_GWAS_expressed_classes[["e-5"]][['ADHD']] = PFC_disease_GWAS_expressed_classes2[["e-5"]]$ADHD
```
# Get Stats of the cluster specific genes
```{r}
Total_stats = matrix(0, nrow=length(PFC_disease_GWAS_expressed_classes[["e-5"]]),
ncol=length(levels(all_cell_inDD_saline_sobj@ident)))
rownames(Total_stats) = names(PFC_disease_GWAS_expressed_classes[["e-5"]])
colnames(Total_stats) = levels(all_cell_inDD_saline_sobj@ident)
for(disease in names(PFC_disease_GWAS_expressed_classes[["e-5"]])){
print(disease)
tmp = subset(PFC_disease_GWAS_expressed_classes[["e-5"]][[disease]], category %in% c("cellType-specific","cluster-Specific") )
cs = colSums(PFC_disease_GWAS_genes_association[["e-5"]][[disease]][tmp$gene,])
cs =cs/nrow(PFC_disease_GWAS_genes_association[["e-5"]][[disease]][tmp$gene,])
cs = cs/max(cs)
Total_stats[disease,]=cs
}
PFC_disease_GWAS_expressed_classes$`e-5`$BEHAVIOR=NULL
PFC_disease_GWAS_expressed_classes$`e-5`$Parkinson=NULL
PFC_disease_GWAS_expressed_classes$`e-5`$`AdolescenceChildhood aggressive`=NULL
PFC_disease_GWAS_expressed_classes$`e-5`$Nicotine=NULL
PFC_disease_GWAS_expressed_classes$`e-5`$`Tourett Syndrome`=NULL
PFC_disease_GWAS_expressed_classes$`e-5`$`Cocaine Dependence`=NULL
tokeep = setdiff(rownames(Total_stats), c("BEHAVIOR","Parkinson","AdolescenceChildhood aggressive","Nicotine","Tourett Syndrome","Cocaine Dependence"))
Total_stats =Total_stats[tokeep,]
rownames(Total_stats) = gsub("Obsessive Compulsive Disorder","OCD",rownames(Total_stats))
rownames(Total_stats) = gsub("Autism-ADHD_but other too","Autism",rownames(Total_stats))
rownames(Total_stats) = gsub("Alcohol","Alcoholism",rownames(Total_stats))
rownames(Total_stats) = gsub("Alzheimer and Dementia","Alzheimer & Dementia",rownames(Total_stats))
#Total_stats = apply(Total_stats,1,function(x) x/max(x))
require(circlize)
q99=quantile(Total_stats,0.99)
col_fun = colorRamp2(seq(0,q99,length.out = 100), colorRampPalette(rev(brewer.pal(n = 11, name ="Spectral")))(100))
factors = gsub("_\\d+","",colnames(Total_stats))
factors = c("AA",factors)
mat_list = list()
ph =pheatmap(Total_stats)
for(f in unique(factors)[-1]){
pos <- which(factors==f)-1
mat_list[[f]] = Total_stats[ph$tree_row$order,pos]
}
Total_stats = Total_stats[ph$tree_row$order,]
gaps = gsub("_\\d+","",colnames(Total_stats))
gaps = factor(gaps)
gaps = diff(as.numeric(gaps))
gaps = which(gaps==1)
Total_stats2 = Total_stats
Total_stats2[Total_stats2>q99]=q99
pheatmap(t(Total_stats2),cluster_rows = F,gaps_row = gaps,color = colorRampPalette(rev(brewer.pal(n = 11, name ="Spectral")))(100))
# Global Distrbution of GWAS candicates accross diff. cell-types
circos.par(cell.padding = c(0, 0, 0, 0), gap.degree = 5,"start.degree" = 90)
circos.initialize(factors, xlim = cbind(rep(0, 9), c(12,table(factors)[-1]) ))
circos.track(factors = sort(unique(factors)), y = runif(9),bg.border = NA,track.height=0.05,
panel.fun = function(x, y) {
if(CELL_META$sector.index != "AA"){
circos.text(CELL_META$xcenter, CELL_META$cell.ylim[2],
CELL_META$sector.index)
}
#circos.axis(labels.cex = 0.6)
})
circos.track(ylim = c(0, nrow(Total_stats)), bg.border = NA, track.height =0.4,panel.fun = function(x, y) {
#browser()
print(CELL_META$sector.index)
sector.index = CELL_META$sector.index
if(sector.index != "AA"){
if(sector.index == "Astro"){
circos.yaxis(side = "left",
at = nrow(Total_stats):1-0.5,
labels = rownames(Total_stats),sector.index = sector.index,labels.cex = 1.2)
}
m = mat_list[[sector.index]]
col_mat = col_fun(m)
nr = nrow(m)
nc = ncol(m)
for(i in 1:nr) {
circos.rect(1:nc - 1, rep(nr - i, nc),
1:nc, rep(nr - i + 1, nc),
border = "black", col = col_mat[i, ])
}
}
})
for(f in sort(unique(factors))){
if(f != "AA"){
#browser()
pos = which(factors==f)-1
lbl = strsplit(colnames(Total_stats)[pos],split = "_")
lbl = sapply(lbl, function(x) x[2])
lbl = as.numeric(lbl)
# circos.axis(sector.index = f, h = "bottom", direction = "inside",
# labels = as.character(lbl),
# major.at = lbl-0.5,
# minor.ticks=0,
# labels.facing = "reverse.clockwise",
# labels.pos.adjust=F)
}
}
```
```{r}
enrichment_stats <- data.frame()
for(disease in names(PFC_disease_GWAS_expressed_classes[["e-5"]])){
tbl = table(PFC_disease_GWAS_expressed_classes[["e-5"]][[disease]]$category)
tbl = as.data.frame(tbl)
tbl$disease=disease
tbl$percent = tbl$Freq/sum(tbl$Freq)
colnames(tbl) = c("type","Freq","disease","percent")
enrichment_stats= rbind(enrichment_stats, tbl)
}
ggplot(enrichment_stats,aes(x=disease, y=percent, fill=type)) + geom_bar(stat = "identity") + theme_bw() + scale_fill_jco()
```
# Comparison between PL and ILA
## Plot Cholfin et al Data
```{r fig.height=8, fig.width=8}
Cholfin_markers <- c("Lmo4","Nt3","Lmo3","Cad6","RzrB","Ngn2", # Fig1
"Fgf8","Fgf18","Fgf17","Fgf15","Spry1","Spry2",# Fig2, Fig3
"Sp8","Erm","Pea3","Er81","Emx2" #Fig5
)
Cholfin_markers = intersect(Cholfin_markers,rownames(PFC_excitatory_sobj@data))
FeaturePlot(PFC_excitatory_sobj,Cholfin_markers, cols.use = c("grey80","red"),pt.size = 0.3)
```
We use Allen Brain to find the list of genes that show differential staining between the PL and ILA and try to use them
to see if they can be used to segregate cells.
```{r}
PFC_excitatory_salie_sobj = FindVariableGenes(PFC_excitatory_salie_sobj,x.low.cutoff = 0.25,y.cutoff = 1,x.high.cutoff = 20)
vargenes = rownames(subset([email protected], gene.dispersion.scaled >= 0.5 & gene.mean >=0.1))
rs = apply(PFC_excitatory_salie_sobj@data[vargenes,],1,function(x) sum(x>0))
rs = rs/length([email protected])
vargenes = names(rs[rs>0.1])
Avg_expr <- AverageExpression(PFC_excitatory_salie_sobj)
Avg_expr[Avg_expr<1] = 0
Avg_expr[Avg_expr>=1] = 1
Avg_pct <- AverageDetectionRate(PFC_excitatory_salie_sobj)
Avg_pct[Avg_pct<0.4] = 0
Avg_pct[Avg_pct>=0.4] = 1
expressed = rowSums(Avg_expr * Avg_pct)
expressed = expressed[expressed>0]
PL_ILA_layer_cmp <- list()
PL_ILA_layer_cmp_filtered = list()
for(layer in c(1,2,5,"6a","all")){
fname = paste0("Data/PL_Vs_ILA/PL_Vs_ILA_",layer,".csv")
PL_ILA_layer_cmp[[layer]] = read.csv(fname)
PL_ILA_layer_cmp_filtered[[layer]] = subset(PL_ILA_layer_cmp[[layer]], abs(log2(fold.change)) >4 &
gene.symbol %in% vargenes)
}
sapply(PL_ILA_layer_cmp_filtered, nrow)
```
```{r fig.height=10, fig.width=10}
FeaturePlot(PFC_excitatory_salie_sobj,unique(as.character(PL_ILA_layer_cmp_filtered$`1`$gene.symbol)),cols.use = c("grey80","red"))
```
```{r fig.height=10, fig.width=10}
FeaturePlot(PFC_excitatory_salie_sobj,unique(as.character(PL_ILA_layer_cmp_filtered$`2`$gene.symbol)),cols.use = c("grey80","red"))
```
# Cluster cells according to some validated markers
```{r fig.height=10, fig.width=10}
PL_markers <- c("Fam84b","Fezf2","Dkkl1","Ngb")
# PFC_excitatory_sobj2 = PFC_excitatory_sobj
#
# [email protected] = PL_markers
#
# PFC_excitatory_sobj2 = RunPCA(PFC_excitatory_sobj2)
#
# PFC_excitatory_sobj2 = ProjectPCA(PFC_excitatory_sobj2)fChd
#
#
# PFC_excitatory_sobj2 = RunTSNE(PFC_excitatory_sobj2,genes.use = PL_markers,check_duplicates=FALSE,dims.use = 1:4,perplexity =50)
PFC_excitatory_sobj = AddModuleScore(PFC_excitatory_sobj,genes.list = list(PL= PL_markers))
[email protected]$Location = ifelse([email protected]$Cluster1 >0, "PL", "IL")
# PFC_excitatory_sobj2 = SetIdent(PFC_excitatory_sobj2,ident.use = [email protected]$Location)
#
# FeaturePlot(PFC_excitatory_sobj2,PL_markers)
```
```{r}
TSNEPlot(PFC_excitatory_sobj,do.label = T,group.by="Location")
```
```{r}
#[email protected]$Location = ""
PL_sobj = SubsetData(PFC_excitatory_sobj,subset.name = "Location",accept.value = "PL",subset.raw = T)
IL_sobj = SubsetData(PFC_excitatory_sobj,subset.name = "Location",accept.value = "IL",subset.raw = T)
P1 = TSNEPlot(PL_sobj,do.label = F,group.by="Location", do.return=T,colors.use = "#1B9E77") + ggtitle("Prelimbic cells") +
theme(plot.title = element_text(hjust = 0.5))
P2 = TSNEPlot(IL_sobj,do.label = F,group.by="Location", do.return=T,colors.use = "#7570B3") + ggtitle("Infralimbic cells") +
theme(plot.title = element_text(hjust = 0.5))
gridExtra::grid.arrange(P1,P2,ncol=2)
```
```{r}
#PL_IL_markers <- FindMarkers(PFC_excitatory_sobj2,ident.1 = "PL")
IL_PL_by_clus <- list()
for(clus in levels([email protected]$L1_cluster)){
print(paste0("**** ", clus, " ****") )
clus_sobj <- SubsetData(PFC_excitatory_sobj,subset.name = "L1_cluster",accept.value = clus,subset.raw = T)
clus_sobj = SetIdent(clus_sobj,ident.use = [email protected]$Location)
IL_PL_by_clus[[clus]] <- FindMarkers(clus_sobj,ident.1 = "PL")
}
IL_PL_by_clus_sig = lapply(IL_PL_by_clus, function(x) {
x$pct.diff = abs(x$pct.1 - x$pct.2)/pmax(x$pct.1, x$pct.2)
x = subset(x, p_val_adj < 0.05 & abs(avg_logFC) >log(1.5) & pct.diff>0.3)
x
})
nbPL_IL_DEG <- data.frame(clusters = names(IL_PL_by_clus_sig),
nbDEG = sapply(IL_PL_by_clus_sig, nrow))
ggplot(nbPL_IL_DEG, aes(x=clusters, y=nbDEG)) + geom_bar(stat = "identity",width = 0.6) + theme_bw()
```
# Do differential gene expression of
```{r}
for()
```
# Check if there are any differential CHD8 genes
```{r}
Chd8_targets <- read.xlsx("C:/Dropbox/PFC/Nature Communications/Second_submission/Data/Phillip_et_al_2018/TableS2-S4.xlsx",
sheetName = "S3")
Chd8_targets = Chd8_targets[!duplicated(as.character(Chd8_targets$Gene.Name)),]
rownames(Chd8_targets) = as.character(Chd8_targets$Gene.Name)
Chd8_DEG_per_celType <- list()
for(clus in names(P21_Adult_merged_DEG)){
tmp = subset(P21_Adult_merged_DEG[[clus]], Gene %in% as.character(Chd8_targets$Gene.Name))
tmp = subset(tmp, p_val_adj < 0.05)
if(nrow(tmp)>0){
tmp$log2FC_in_P5 = Chd8_targets[as.character(tmp$Gene),]$log2FoldChange
tmp$Direction = ifelse(tmp$avg_logFC >=0, "Up","Down")
}
Chd8_DEG_per_celType[[clus]] = tmp
}
nbChd8_DEG_targets = sapply(Chd8_DEG_per_celType, function(x) table(x$Direction))
nbChd8_DEG_targets = do.call("rbind",nbChd8_DEG_targets)
nbChd8_DEG_targets = as.data.frame(nbChd8_DEG_targets)
nbChd8_DEG_targets$Cluster = rownames(nbChd8_DEG_targets)
nbChd8_DEG_targets = reshape2::melt(nbChd8_DEG_targets)
ggplot(nbChd8_DEG_targets, aes(x=Cluster, y=value, fill=variable)) + geom_bar(stat = "identity") + theme_bw() + scale_fill_aaas()
```
## Try ChIP-seq data from GSE99331
### Read data and convert it to GREAT format
```{r}
Chd8_peaks <- read.table("Data/Gompers_et_al/GSE99331_RAW/GSM2644272_Chd8_brain_WT_1_trim-vs.Input.no_model_peaks.narrowPeak.gz",
stringsAsFactors = F)
chd8_peaks_GREAT_format = Chd8_peaks[,1:3]
chd8_peaks_GREAT_format$peak = paste0("Peak",1:nrow(Chd8_peaks))
write.table(chd8_peaks_GREAT_format,file = "Data/Gompers_et_al/GSE99331_RAW/Chd8_GREAT.bed",sep = "\t",col.names = F,row.names = F,quote = F)
Chd8_target_genes <- read.table("Data/Gompers_et_al/GSE99331_RAW/Chd8_target_genes_GREAT_results.txt",
sep="\t",
stringsAsFactors = F)
# Get the list of genes from Yuta et al, 2017
yuta_peaks <- read.csv("Data/Yuta_et_al/ChipseqData.csv",stringsAsFactors = F)
colnames(yuta_peaks)[1] = "GeneSymbol"
yuta_peaks$Adult[yuta_peaks$Adult == "-"] =0
yuta_peaks$E14.5[yuta_peaks$E14.5 == "-"] =0
yuta_peaks$Adult = as.numeric(yuta_peaks$Adult)
yuta_peaks$E14.5 = as.numeric(yuta_peaks$E14.5)
ovp = subset(yuta_peaks, Adult>0 & E14.5>0)
E14_specific = subset(yuta_peaks, Adult==0 & E14.5>0)
Adult_specific = subset(yuta_peaks, Adult>0 & E14.5==0)
chd8_adult_targets <- c(ovp$GeneSymbol, E14_specific$GeneSymbol, Adult_specific$GeneSymbol) #yuta_peaks$GeneSymbol[yuta_peaks$Adult > 0]
Chd8_DEG_per_celType <- list()
for(clus in names(P21_Adult_merged_DEG)){
tmp = subset(P21_Adult_merged_DEG[[clus]], Gene %in% union(as.character(Chd8_target_genes$V1), chd8_adult_targets ))
tmp = subset(tmp, p_val_adj < 0.05 & abs(avg_logFC) >= log(1.5))
if(nrow(tmp)>0){
tmp$Direction = ifelse(tmp$avg_logFC >=0, "Up","Down")
tmp$cluster = clus
}
Chd8_DEG_per_celType[[clus]] = tmp
}
Chd8_DEG_per_celType_df <- do.call("rbind",Chd8_DEG_per_celType)
write.csv(Chd8_DEG_per_celType_df, file="Data/Yuta_et_al/Chd8_DEG_targets.csv")
nbChd8_DEG_targets = t(sapply(Chd8_DEG_per_celType, function(x) data.frame(Up = sum(x$Direction=="Up"), down= sum(x$Direction == "Down")) ))
#nbChd8_DEG_targets = do.call("rbind",nbChd8_DEG_targets)
nbChd8_DEG_targets = as.data.frame(nbChd8_DEG_targets)
nbChd8_DEG_targets$Up = unlist(nbChd8_DEG_targets$Up)
nbChd8_DEG_targets$down = unlist(nbChd8_DEG_targets$down)
nbChd8_DEG_targets$Cluster = rownames(nbChd8_DEG_targets)
nbChd8_DEG_targets = reshape2::melt(nbChd8_DEG_targets,id.vars = "Cluster")
nbChd8_DEG_targets2 = nbChd8_DEG_targets[grep("Exc",nbChd8_DEG_targets$Cluster),]
nbChd8_DEG_targets2$Cluster = factor(nbChd8_DEG_targets2$Cluster, levels = paste0("Exc_",1:13))
nbChd8_DEG_targets2 = subset(nbChd8_DEG_targets2, Cluster %in% paste0("Exc_",c(1:4,6:8,10,12:13)))
ggplot(nbChd8_DEG_targets2, aes(x=Cluster, y=value)) + geom_bar(stat = "identity",width=0.8) + theme_bw() + scale_fill_aaas()
```
# Check the diffential genes in Cartpt+ excitatory neurons