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Last updated: 2022-12-01

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Knit directory: SingleCellMR/

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html	7389e87	Alexander Haglund	2022-12-01	Build site.
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Rmd	38a3835	Alexander Haglund	2022-11-18	wflow_publish(c("analysis/index.Rmd", "analysis/figures.Rmd"))

---

libraries

library(ggplot2)
library(viridis)
library(ggsci)
library(dplyr)
library(cowplot)
library(grid)
library(tidyr)
suppressMessages(library(reshape))
color_pal=ggsci::pal_nejm("default")(8)
colorvec<-c(Astrocytes=color_pal[1],
           Endothelial=color_pal[2],
           Excitatory=color_pal[3],
           Inhibitory=color_pal[4],Microglia=color_pal[5],
           ODC=color_pal[6],OPC=color_pal[7],Pericytes=color_pal[8])

FIGURE 1

coming soon

FIGURE 2

Figure 2a

coloc<-read.table("data/COLOC_MR_RESULTS/2022-10-25_FULL_COLOC_RES.txt")
x<-coloc[coloc$GWAS %in% "AD",]



genes<-x[x$PP.H4.abf>0.5,]$gene
x<-x[x$gene %in% genes,]
x$gene<-factor(x$gene,levels=unique(x$gene))

g<-ggplot(x,aes(x=celltype,y=gene,fill=PP.H4.abf))
g<-g+geom_tile(aes(fill=round(PP.H4.abf,2)),colour="black")+
geom_text(aes(label = round(PP.H4.abf, 2)),size=5*0.36,family="Helvetica")+
scale_fill_viridis()+
theme_classic()+
scale_y_discrete(limits=rev,expand = c(0, 0))+
scale_x_discrete(expand = c(0, 0),position="top")+
xlab("Alzheimer's Disease")
g

Past versions of unnamed-chunk-2-1.png

Version	Author	Date
8f071f3	Alexander Haglund	2022-11-18

Figure 2b

coming soon

Figure 2c

coming soon

Figure 2d

coloc_results<-read.table("data/COLOC_MR_RESULTS/2022-10-25_FULL_COLOC_RES.txt")
fig_dir<-"FIGURES/Figure_2/"

coloc_results<-coloc_results[coloc_results$PP.H4.abf>0.5,]


g<-ggplot(coloc_results,aes(y=GWAS))+geom_bar(color="black",fill="#166FA2")+
geom_text(aes(label=..count..),stat="count",hjust=-0.8,size=5/(14/5))+
labs(x="Number of colocalisations")+
theme_classic()+scale_x_continuous(limits=c(0,120),expand = c(0, 0))+
theme(text=element_text(family="Helvetica",face="bold"),
                                             axis.text.x=element_text(size=5,face="bold"),
                                            axis.text.y=element_text(size=5),
     axis.title.x=element_text(size=5),axis.title.y=element_text(size=5))
g

Past versions of unnamed-chunk-3-1.png

Version	Author	Date
3f6c35b	Alexander Haglund	2022-12-01

Figure 2e

coloc<-read.table("data/COLOC_MR_RESULTS/2022-10-25_FULL_COLOC_RES.txt")
##rename SCV caudate for spacing
coloc_results$GWAS<-sapply(coloc_results$GWAS,function(x){
    if(x=="SCV.CAUDATE"){
        x<-"SCV"}
    return(x)})

##filter to keep coloc hits
coloc_results_filtered<-coloc_results[coloc_results$PP.H4.abf>0.5,]


gwas_list<-unique(coloc_results_filtered$GWAS)
reslist<-list()

##count number of cell types per trait
for(i in 1:length(gwas_list)){
    tmp<-coloc_results_filtered %>% filter(GWAS==gwas_list[i])
    freq_table<-as.data.frame(table(tmp$celltype))
    tmp$celltype_freq<-freq_table[match(tmp$celltype,freq_table$Var1),]$Freq

    #scale by total number of colocs (otherwise larger gwases will have really large points)
    # tmp$celltype_freq<-tmp$celltype_freq/nrow(tmp)
    reslist[[i]]<-tmp
    }
coloc_results_filtered<-as.data.frame(do.call(rbind,reslist))

color_pal=ggsci::pal_nejm("default")(8)
g<-ggplot(coloc_results_filtered,aes(y=GWAS,x=celltype,size=celltype_freq,fill=celltype))+
geom_point(pch=21)+
scale_size(range=c(1,3))+
# geom_text(aes(label=celltype_freq),size=5/(14/5),vjust=-1.1)+
theme_classic()+
scale_fill_manual(values=color_pal)+
theme(text=element_text(family="Helvetica",face="bold"),
                                             axis.text.x=element_text(size=5,angle=45),
                                            axis.text.y=element_text(size=5),
     axis.title.x=element_text(size=5),axis.title.y=element_text(size=5),
      legend.text=element_text(size=5),legend.title=element_text(size=5),
      legend.spacing.y = unit(0.05, 'cm'),legend.position = "none")
g

Past versions of unnamed-chunk-4-1.png

Version	Author	Date
4fcf3ea	Alexander Haglund	2022-12-01
8f071f3	Alexander Haglund	2022-11-18

FIGURE 3

The code presented here is in step 5 (Epigenetic Intersection) of the downstream results section. The basic syntax is a geom_tile() function in ggplot with colours matching cell-types. More in data/MARKDOWN/helper_funcs.r

This is only for Fig 3.a - Fig 3.c and Fig 3.d were made using the UCSC track browser.

indir<-"data/EXT_DATASETS//RESULTS/"
fig_dir<-"FIGURES/Figure_3/"

g1<-readRDS(paste0(indir,"oligo_intersect_ggobject.rds"))
g2<-readRDS(paste0(indir,"Excneuron_intersect_ggobject.rds"))
g3<-readRDS(paste0(indir,"Inneuron_intersect_ggobject.rds"))
g4<-readRDS(paste0(indir,"microglia_intersect_ggobject.rds"))

g1<-g1+theme(legend.position = "none")
g2<-g2+theme(legend.position = "none")
g3<-g3+theme(legend.position = "none")
g4<-g4+theme(legend.position = "none")

Oligo

g1

Past versions of unnamed-chunk-6-1.png

Version	Author	Date
4fcf3ea	Alexander Haglund	2022-12-01
8f071f3	Alexander Haglund	2022-11-18

ExcNeur

g2

Past versions of unnamed-chunk-7-1.png

Version	Author	Date
4fcf3ea	Alexander Haglund	2022-12-01
8f071f3	Alexander Haglund	2022-11-18

InNeur

g3

Past versions of unnamed-chunk-8-1.png

Version	Author	Date
4fcf3ea	Alexander Haglund	2022-12-01

Microglia

g4

Past versions of unnamed-chunk-9-1.png

Version	Author	Date
4fcf3ea	Alexander Haglund	2022-12-01

FIGURE 4

Figure 4a

bind data together

#bind data together

pqtl<-read.table("data/TABLES/pQTL_table.txt")
stitch<-read.table("data/TABLES//stitch_table.txt")
dgidb<-read.table("data/TABLES/dgidb_table.txt")
opentargets<-read.table("data/TABLES/OpenTargets_table.txt")
coloc<-read.table("data/COLOC_MR_RESULTS//2022-10-25_FULL_COLOC_RES.txt")
full<-read.table("data/COLOC_MR_RESULTS//2022-10-25_FULL_MR_RES.txt")
full<-full[full$IVW<0.05,]
full$trait_gene<-paste0(full$GWAS,"_",full$gene)
full$IVW_dir<-sapply(full$IVW_beta,function(x){
    if(x>0){
        return("positive")
        }else{
        return("negative")
        }
    })
direction_vector<-vector()
for(i in 1:nrow(full)){
    tmp<-full[full$trait_gene %in% full$trait_gene[i],]
    betas<-tmp$IVW_beta
    
    if(all(betas>0)==TRUE){
    dir<-"positive"
    } else if(all(betas<0)==TRUE){
    dir<-"negative"
    }else{
        dir<-"N/A"
        }
    direction_vector<-c(direction_vector,dir)    
        }
full$IVW_dir<-direction_vector    


coloc$trait_gene_ct<-paste0(coloc$GWAS,"_",coloc$gene,"_",coloc$celltype)
trait_gene_ct<-paste0(pqtl$GWAS,"_",pqtl$gene,"_",pqtl$celltype)
coloc<-coloc[match(trait_gene_ct,coloc$trait_gene_ct),]

plot_df<-data.frame(gene_trait=paste0(pqtl$GWAS,".",pqtl$gene),
                    pQTL=pqtl$pQTL_hit,
                    STITCH=stitch$STITCH_intersect,
                    DGidb=dgidb$DGIDB_intersect,
                    OpenTargets=opentargets$OpenTargets_disease_hit,
                   coloc=coloc$PP.H4.abf,
                   celltype=pqtl$celltype,
                   IVW_dir=full$IVW_dir)

prep plots

##intersection plot
plot_df1<-plot_df[,c("gene_trait","pQTL","STITCH","DGidb","OpenTargets")]
melt_df<-melt(plot_df1,id=c("gene_trait"))
ggplot_mainplot1<-ggplot(melt_df,aes(x=variable,y=gene_trait,fill=value))+geom_tile(color="black")+
scale_fill_manual(values=c("#c8c8c8","#A6CEE3","#1F78B4"))+scale_x_discrete(expand=c(0,0))

##direction
ivw_dir<-plot_df[,c("gene_trait","IVW_dir")]
ivw_dir$trait<-"gwas"
ivw_dir1<-ivw_dir
ggplot_ivwdir1<-ggplot(ivw_dir1,aes(y=gene_trait,x=trait,fill=IVW_dir,group=IVW_dir))+geom_tile(colour="black")+
scale_fill_manual(values = c("#c8c8c8","#F8766D","#00BFC4"),guide = guide_legend(reverse = TRUE))+
theme_classic()

##celltypes

celltype_bar<-plot_df[,c("gene_trait","celltype")]
celltype_bar1<-celltype_bar
test<-celltype_bar1 %>% 
count(gene_trait,celltype,name="count") %>% 
complete(gene_trait,celltype)
newvec<-vector()
for(i in 1:nrow(test)){
    if(is.na(test$count[i])){
        newvec<-c(newvec,NA)
        }else{
        newvec<-c(newvec,test$celltype[i])
        }
    }
test$new<-newvec
# melt_df<-melt(celltype_bar1,id=c("gene_trait"))
# df2 = celltype_bar1 %>% complete(gene_trait,celltype)
ggplot_celltype1<-ggplot(test,aes(x=celltype,y=gene_trait,fill=new))+geom_tile(color="black")+
scale_fill_manual(values=colorvec,na.value="white")

plot

ggplot_mainplot1<-ggplot_mainplot1+theme_classic()+
theme(axis.text.x=element_text(size=5,face="bold",angle=45,vjust=0.8,hjust=0.8,family="Helvetica"),
      axis.text.y=element_text(size=5,face="bold",vjust=0.8,family="Helvetica"),axis.title.y=element_blank(),
      axis.line.y=element_blank(),axis.line.x=element_blank(),
      axis.title.x=element_blank(),legend.key.size=unit(0.7,"cm"),
      legend.title=element_blank(),
      legend.text=element_text(size=5),
      panel.background = element_rect(fill='transparent'),
      plot.background = element_rect(fill='transparent', color=NA),
      panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
      legend.background = element_rect(fill='transparent'),
      legend.key.height= unit(0.3, 'cm'),
      legend.key.width= unit(0.2, 'cm'))#

ggplot_ivwdir1<-ggplot_ivwdir1+theme_classic()+
theme(axis.text.x=element_blank(),
      axis.text.y=element_blank(),axis.title.y=element_blank(),axis.ticks.y=element_blank(),
      axis.ticks.x=element_blank(),
      axis.line.y=element_blank(),axis.line.x=element_blank(),
      axis.title.x=element_blank(),legend.key.size=unit(0.7,"cm"),
      legend.text=element_text(size=5), legend.title=element_text(size=5),
      panel.background = element_rect(fill='transparent'),
      plot.background = element_rect(fill='transparent', color=NA),
      legend.background = element_rect(fill='transparent'),
      legend.key.height= unit(0.3, 'cm'),
      legend.key.width= unit(0.2, 'cm'))#


ggplot_celltype1<-ggplot_celltype1+theme_classic()+
theme(axis.text.x=element_text(size=5,face="bold",angle=45,vjust=0.8,hjust=0.8,family="Helvetica"),
      axis.text.y=element_blank(),axis.title.y=element_blank(),axis.ticks.y=element_blank(),
      axis.line.y=element_blank(),axis.line.x=element_blank(),
      axis.title.x=element_blank(),legend.key.size=unit(0.7,"cm"),
      legend.title=element_blank(),
      legend.text=element_text(size=5),
      panel.background = element_rect(fill='transparent'),
      plot.background = element_rect(fill='transparent', color=NA),
      legend.background = element_rect(fill='transparent'),
      legend.key.height= unit(0.3, 'cm'),
      legend.key.width= unit(0.2, 'cm'))#

ggplot_mainplot1_legend<-get_legend(ggplot_mainplot1)
celltype_legend<-get_legend(ggplot_celltype1)
ivw_dir<-get_legend(ggplot_ivwdir1)


ggplot_ivwdir1<-ggplot_ivwdir1+theme(plot.margin = unit(c(0,0,0,0), "cm"),legend.position = "none")
ggplot_mainplot1<-ggplot_mainplot1+theme(plot.margin = unit(c(0,0,0,0), "cm"),legend.position = "none")
ggplot_celltype1<-ggplot_celltype1+theme(plot.margin = unit(c(0,0,0,0), "cm"),legend.position="none")

g<-plot_grid(ggplot_mainplot1,ggplot_celltype1,ggplot_ivwdir1, align = "h", ncol =3, rel_widths = c(0.008,0.005,0.001))
g

Past versions of unnamed-chunk-12-1.png

Version	Author	Date
3c08408	Alexander Haglund	2022-12-01
4fcf3ea	Alexander Haglund	2022-12-01
8f071f3	Alexander Haglund	2022-11-18

Figure 4b

suppressMessages(library(UpSetR))
library(dplyr)
suppressMessages(library(ComplexUpset))
library(tidyr)

color_pal=ggsci::pal_nejm("default")(8)
colorvec<-c(Astrocytes=color_pal[1],
           Endothelial=color_pal[2],
           Excitatory=color_pal[3],
           Inhibitory=color_pal[4],Microglia=color_pal[5],
           ODC=color_pal[6],OPC=color_pal[7],Pericytes=color_pal[8])
full<-read.table("data/COLOC_MR_RESULTS//2022-10-25_FULL_MR_RES.txt")
gwas<-unique(full$GWAS)
genes<-unique(full$gene)

resdf<-data.frame(gene=genes)
for(i in 1:length(gwas)){
    tmp_genes<-full[full$GWAS==gwas[i],]$gene
    resdf<-cbind(resdf,genes %in% tmp_genes)
}
rownames(resdf)<-resdf$gene
resdf$gene<-NULL
colnames(resdf)<-gwas

plot<-upset(resdf,gwas,width_ratio=0.1,height_ratio = 0.4,set_size=FALSE,
      themes=upset_default_themes(text=element_text(size=6,family="Helvetica")),
      base_annotations=list(
          'Intersection size'=intersection_size(color="#000000",size=0.25,
              mapping=aes(fill="bars_color"),
            text=list(size=5/(14/5),family="Helvetica"))
            +scale_fill_manual(values=c("bars_color"="#1F78B4"),guide="none")
          +theme(plot.background=element_blank(),
          panel.grid.major = element_blank(), 
          panel.grid.minor = element_blank())),
      stripes="white",matrix=(
        intersection_matrix(
            geom=geom_point(size=0.5),
            segment=geom_segment(size=0.3)
            ))
      )

plot

Past versions of unnamed-chunk-13-1.png

Version	Author	Date
43e9b1f	Alexander Haglund	2022-12-01
3c08408	Alexander Haglund	2022-12-01
4fcf3ea	Alexander Haglund	2022-12-01
8f071f3	Alexander Haglund	2022-11-18

SUPPLEMENTARY FIGURES

Suppl. Fig. 1

Suppl. Fig. 1a

metadata<-readRDS("data/METADATA/Final_Seurat_129samples_15May2022_metadata.rds")

##this sample was excluded earlier
metadata<-metadata[!metadata$Sample_ID %in% "O141",]

samples<-unique(metadata$Sample_ID)
df<-data.frame(samples=samples,new_id=1:length(samples))
metadata$new_id<-paste0("Sample ",as.character(df[match(metadata$Sample_ID,df$samples),]$new_id))
metadata$new_id<-factor(metadata$new_id, levels =unique(metadata$new_id))

tmp<-metadata
color_panel<-ggsci::pal_nejm("default")(8)
p<-ggplot(tmp,aes(x=new_id,fill=CellType))+geom_bar(color="black")+
scale_fill_manual(values=color_panel)+
scale_y_continuous(expand=c(0,0))+
theme_classic()+theme(axis.text.x=element_text(size=5,face="bold",angle=90),
                      plot.title = element_text(size=5,face="bold"),
                                  axis.text.y=element_text(size=5,face="bold"),
                                  axis.title.y=element_blank(),
                                 axis.title.x=element_blank(),
                                                 legend.key.size=unit(0.7,"cm"),
                      legend.position="none",

                                                                legend.text=element_text(size=15),panel.background = element_rect(fill='transparent'), #transparent panel bg
    plot.background = element_rect(fill='transparent', color=NA), #transparent plot bg
    panel.grid.major = element_blank(), #remove major gridlines
    panel.grid.minor = element_blank(), #remove minor gridlines
    legend.background = element_rect(fill='transparent'), #transparent legend bg
    legend.box.background = element_rect(fill='transparent'))+labs(title="Cell type distribution per indiviudal")
p

Past versions of unnamed-chunk-14-1.png

Version	Author	Date
3c46f4b	Alexander Haglund	2022-12-01
016f567	Alexander Haglund	2022-12-01
43e9b1f	Alexander Haglund	2022-12-01
3c08408	Alexander Haglund	2022-12-01
4fcf3ea	Alexander Haglund	2022-12-01
8f071f3	Alexander Haglund	2022-11-18

Suppl. Fig. 1b

library(ggrepel)
metadata<-readRDS("data/METADATA/Final_Seurat_129samples_15May2022_metadata.rds")
metadata<-metadata[!metadata$Sample_ID %in% "O141",]
df<-as.data.frame(table(metadata$CellType))
df$celltype<-df$Var1
library(dplyr)
df2 <- df %>% 
  mutate(csum = rev(cumsum(rev(Freq))), 
         pos =Freq/2 + lead(csum, 1),
         pos = if_else(is.na(pos), Freq/2, pos))

p<-ggplot(df, aes(x="",y=Freq,fill=celltype)) +
  geom_bar(stat="identity",colour="black",width=1)+
geom_label_repel(data = df2,
                   aes(y = pos, label = Freq),
                   size = 5/(14/5), nudge_x = 1, show.legend = FALSE)+
theme_classic()+theme(axis.text.x=element_blank(),
                      axis.line=element_blank(),
                      plot.title = element_text(size=7,face="bold"),
                                  axis.text.y=element_blank(),
                                  axis.title.y=element_blank(),
                                 axis.title.x=element_blank(),
                                                 legend.key.size=unit(0.7,"cm"),
                                                 legend.position="none",
                                                                legend.text=element_text(size=5),panel.background = element_rect(fill='transparent'), #transparent panel bg
    plot.background = element_rect(fill='transparent', color=NA), #transparent plot bg
    panel.grid.major = element_blank(), #remove major gridlines
    panel.grid.minor = element_blank(), #remove minor gridlines
    legend.background = element_rect(fill='transparent'), #transparent legend bg
    legend.box.background = element_rect(fill='transparent'))+labs(title="Total number of cells per cell type")+
ggsci::scale_fill_nejm()+ coord_polar("y", start=0)
p

Past versions of unnamed-chunk-15-1.png

Version	Author	Date
8f21a16	Alexander Haglund	2022-12-01
016f567	Alexander Haglund	2022-12-01
43e9b1f	Alexander Haglund	2022-12-01
3c08408	Alexander Haglund	2022-12-01
4fcf3ea	Alexander Haglund	2022-12-01
8f071f3	Alexander Haglund	2022-11-18

Suppl. Fig. 2

res<-read.table("data/EXT_DATASETS/METABRAIN//metabrain_replication.txt")
res$celltype<-c("Astrocytes","Endothelial","Excitatory","Inhibitory","Microglia","Oligo","OPC","Pericytes")
color_plane_1=ggsci::pal_nejm("default")(8)
colnames(res)<-c("total","replicated","percentage","totalinboth","celltype")
g<-ggplot()+
geom_point(res,mapping=aes(x=total,y=percentage,fill=celltype),size=2,shape=21)+
scale_fill_manual(values=c("Astrocytes"=color_plane_1[1],
"Endothelial"=color_plane_1[2],
"Excitatory"=color_plane_1[3],
"Inhibitory"=color_plane_1[4],
"Microglia"=color_plane_1[5],
"Oligo"=color_plane_1[6],
"OPC"=color_plane_1[7],
"Pericytes"=color_plane_1[8]))+
ylab("Percentage replication in metabrain")+
xlab("Total SNP gene pairs at 5% FDR\nalso assessed in metabrain")+
scale_y_continuous(breaks = scales::pretty_breaks(n = 10),limits=c(0,1),labels = function(x) paste0(x*100, "%"))+
                   scale_x_continuous(breaks = scales::pretty_breaks(n = 7))+
                  theme_classic()+theme(axis.text.x=element_text(size=5,family="Helvetica",angle=45,vjust=0.6,face="bold"),
                                  axis.text.y=element_text(size=5,family="Helvetica",face="bold"),
                                  axis.title.y=element_text(size=6,family="Helvetica",margin=margin(r=20),face="bold"),
                                 axis.title.x=element_text(size=6,family="Helvetica",margin=margin(t=20),face="bold"),
                                                 legend.key.size=unit(0.25,"cm"),
                                                 # legend.position="none",
                                                                legend.text=element_text(size=5),legend.title=element_blank())
g

Past versions of unnamed-chunk-16-1.png

Version	Author	Date
dd4ab87	Alexander Haglund	2022-12-01
fd90855	Alexander Haglund	2022-12-01
43e9b1f	Alexander Haglund	2022-12-01
3c08408	Alexander Haglund	2022-12-01
4fcf3ea	Alexander Haglund	2022-12-01

Suppl. Fig. 3

coloc_df<-read.table("data/COLOC_MR_RESULTS//2022-10-25_FULL_COLOC_RES.txt")
coloc_list<-split(coloc_df,coloc_df$GWAS)

heatmap_list<-list()
size_vector<-vector()
for(i in 1:length(coloc_list)){
    
    x<-coloc_list[[i]]
    if(length(x[x$PP.H4.abf>0.5,]$gene)==0){
       heatmap_list[[i]]<-0
        size_vector<-c(size_vector,0)
    }else{
    genes<-x[x$PP.H4.abf>0.5,]$gene
    x<-x[x$gene %in% genes,]
    x$Gene<-factor(x$gene,levels=unique(x$gene))
    g<-ggplot(x,aes(x=celltype,y=Gene,fill=PP.H4.abf))
    h1<-g+geom_tile(aes(fill=round(PP.H4.abf,2)),colour="black")+geom_text(aes(label = round(PP.H4.abf, 2)),size=5*0.36,family="Helvetica")+
        scale_fill_viridis(limits=c(0,1))+
    theme_classic()+scale_y_discrete(limits=rev)+scale_x_discrete(position="top")+xlab(x$GWAS[1])+
        theme(axis.text.x=element_text(size=5,family="Helvetica",face="bold"),
        axis.text.y=element_text(size=5,family="Helvetica"),
        axis.title=element_text(size=7,family="Helvetica"),
              legend.position="none")
        
   heatmap_list[[i]]<-h1
        
    size_vector<-c(size_vector,length(unique(genes)))
        }
    }
names(heatmap_list)<-unique(coloc_df$GWAS)
options(warn=-1)
for(i in 1:length(heatmap_list)){
    if(class(heatmap_list[[i]])!="numeric"){
      print(heatmap_list[[i]])
    }
}

Past versions of unnamed-chunk-17-1.png

Version	Author	Date
7389e87	Alexander Haglund	2022-12-01
bb50a67	Alexander Haglund	2022-12-01
10dd725	Alexander Haglund	2022-12-01
3c46f4b	Alexander Haglund	2022-12-01
fd90855	Alexander Haglund	2022-12-01
016f567	Alexander Haglund	2022-12-01
43e9b1f	Alexander Haglund	2022-12-01
3c08408	Alexander Haglund	2022-12-01
4fcf3ea	Alexander Haglund	2022-12-01

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Version	Author	Date
7389e87	Alexander Haglund	2022-12-01
bb50a67	Alexander Haglund	2022-12-01
10dd725	Alexander Haglund	2022-12-01

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Version	Author	Date
7389e87	Alexander Haglund	2022-12-01
bb50a67	Alexander Haglund	2022-12-01
10dd725	Alexander Haglund	2022-12-01

Past versions of unnamed-chunk-17-4.png

Version	Author	Date
7389e87	Alexander Haglund	2022-12-01
bb50a67	Alexander Haglund	2022-12-01
10dd725	Alexander Haglund	2022-12-01

Past versions of unnamed-chunk-17-5.png

Version	Author	Date
7389e87	Alexander Haglund	2022-12-01
bb50a67	Alexander Haglund	2022-12-01
10dd725	Alexander Haglund	2022-12-01

Past versions of unnamed-chunk-17-6.png

Version	Author	Date
7389e87	Alexander Haglund	2022-12-01
bb50a67	Alexander Haglund	2022-12-01
10dd725	Alexander Haglund	2022-12-01

Past versions of unnamed-chunk-17-7.png

Version	Author	Date
7389e87	Alexander Haglund	2022-12-01
bb50a67	Alexander Haglund	2022-12-01
10dd725	Alexander Haglund	2022-12-01

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Version	Author	Date
7389e87	Alexander Haglund	2022-12-01
bb50a67	Alexander Haglund	2022-12-01
10dd725	Alexander Haglund	2022-12-01

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Version	Author	Date
7389e87	Alexander Haglund	2022-12-01
bb50a67	Alexander Haglund	2022-12-01
10dd725	Alexander Haglund	2022-12-01

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Version	Author	Date
7389e87	Alexander Haglund	2022-12-01
bb50a67	Alexander Haglund	2022-12-01
10dd725	Alexander Haglund	2022-12-01

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Version	Author	Date
7389e87	Alexander Haglund	2022-12-01
bb50a67	Alexander Haglund	2022-12-01
10dd725	Alexander Haglund	2022-12-01

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Version	Author	Date
7389e87	Alexander Haglund	2022-12-01
bb50a67	Alexander Haglund	2022-12-01
10dd725	Alexander Haglund	2022-12-01

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Version	Author	Date
7389e87	Alexander Haglund	2022-12-01
bb50a67	Alexander Haglund	2022-12-01
10dd725	Alexander Haglund	2022-12-01

Past versions of unnamed-chunk-17-14.png

Version	Author	Date
7389e87	Alexander Haglund	2022-12-01
bb50a67	Alexander Haglund	2022-12-01
10dd725	Alexander Haglund	2022-12-01

Past versions of unnamed-chunk-17-15.png

Version	Author	Date
7389e87	Alexander Haglund	2022-12-01
bb50a67	Alexander Haglund	2022-12-01
10dd725	Alexander Haglund	2022-12-01

Past versions of unnamed-chunk-17-16.png

Version	Author	Date
7389e87	Alexander Haglund	2022-12-01
bb50a67	Alexander Haglund	2022-12-01
10dd725	Alexander Haglund	2022-12-01

Suppl. Fig. 4

mateqtlouts<-readRDS("data/eQTL_RESULTS//mateqtlouts_0.2FDR.rds")

mateqtlouts<-lapply(mateqtlouts,function(x){
  x$fstat=x$t.stat^2
  return(x)})

scientific_10 <- function(x) {
  parse(text=gsub("e", ".00 %*% 10^", scales::scientific_format(digits = 3)(x)))
}

library(ggplot2)
for(i in 1:length(mateqtlouts)){
    
    tmp<-mateqtlouts[[i]]
    title<-names(mateqtlouts[i])
    title<-gsub("_agg_cpm","",title)
    if(title=="Oligo"){
        title<-"ODC"
        }else if(title=="Endo"){
        title<-"Endothelial"
        }else if(title=="Per"){
        title<-"Pericytes"
        }else if(title=="Astro"){
        title<-"Astrocytes"
        }

    tmp<-tmp[tmp$FDR<0.05,]
    g<-ggplot(data=tmp,aes(x=fstat))+
    geom_line(aes(y=FDR,color="FDR"),size=0.5)+
    geom_line(aes(y=p.value,color="pvalue"),size=0.5)+
    scale_color_manual(values = c("FDR"="red", "pvalue"="blue"))+
    scale_y_continuous(trans="log10",breaks=c(5e-2,5e-5,5e-10,5e-20),labels=scientific_10)+
    theme_classic()+ggtitle(title)+
    xlab("F-statistic")+
    geom_vline(xintercept = 10,linetype="dashed",color="black")+theme(text = element_text(family="Helvetica",size=5),axis.title.y=element_blank(),
                                                                      axis.text.y=element_text(family="Helvetica",size=6),
                                                                      axis.text.x=element_text(family="Helvetica",size=6),
                                                                     legend.title=element_text(family="Helvetica",size=7),
                                                                      legend.text=element_text(family="Helvetica",size=6))
    print(g)
}

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Version	Author	Date
10dd725	Alexander Haglund	2022-12-01
8f21a16	Alexander Haglund	2022-12-01
3c46f4b	Alexander Haglund	2022-12-01

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10dd725	Alexander Haglund	2022-12-01

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10dd725	Alexander Haglund	2022-12-01

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10dd725	Alexander Haglund	2022-12-01

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10dd725	Alexander Haglund	2022-12-01

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10dd725	Alexander Haglund	2022-12-01

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Version	Author	Date
10dd725	Alexander Haglund	2022-12-01

Past versions of unnamed-chunk-18-8.png

Version	Author	Date
10dd725	Alexander Haglund	2022-12-01

Suppl. Fig. 5

Suppl. Fig. 5a

full<-read.table("data/COLOC_MR_RESULTS//2022-10-25_FULL_MR_RES.txt")
full$celltype_gene<-paste0(full$celltype,".",full$gene)
scz<-full[full$GWAS=="SCZ",]
iq<-full[full$GWAS=="IQ",]
common<-intersect(iq$celltype_gene,scz$celltype_gene)

#remove ct/gene combinations in other traits - we are only interested in the SCZ/IQ overlap
filtered<-full[full$celltype_gene %in% common,]
filtered<-filtered[!filtered$GWAS %in% c("SCZ","IQ"),]
to_exclude<-unique(filtered$celltype_gene)
common<-common[!common %in% to_exclude]



#filter both
scz<-scz[match(common,scz$celltype_gene),]
iq<-iq[match(common,iq$celltype_gene),]
df<-data.frame(celltype_gene=common,IQ=iq$IVW_beta,SCZ=scz$IVW_beta)


##now plot
ylims=c(-0.2,0.2)
xlims=c(-0.7,0.7)

g<-ggplot(df,aes(x=SCZ,y=IQ,fill=celltype_gene))+
geom_point(shape=21,size=2)+
scale_y_continuous(limits=ylims)+
scale_x_continuous(limits=xlims)+
geom_vline(xintercept =0,linetype="dashed",size=0.3)+
geom_hline(yintercept =0,linetype="dashed",size=0.3)+theme_classic()+
theme(text=element_text(size=5,family="Helvetica"),legend.title=element_blank(),
      axis.text.y=element_text(size=5,family="Helvetica"),axis.text.x=element_text(size=5,family="Helvetica"),
      legend.text = element_text(family="Helvetica",size=5,face="bold"),legend.spacing.y = unit(-1, 'cm'))+
xlab("SCZ - MR beta")+ylab("IQ - MR beta")+
guides(fill = guide_legend(byrow = TRUE))
g

Past versions of unnamed-chunk-19-1.png

Version	Author	Date
0f7883c	Alexander Haglund	2022-12-01

Suppl. Fig. 5b

full<-read.table("data/COLOC_MR_RESULTS//2022-10-25_FULL_MR_RES.txt")
full$celltype_gene<-paste0(full$celltype,".",full$gene)
scz<-full[full$GWAS=="SCZ",]
neur<-full[full$GWAS=="NEUR",]
common<-intersect(neur$celltype_gene,scz$celltype_gene)

filtered<-full[full$celltype_gene %in% common,]
filtered<-filtered[!filtered$GWAS %in% c("SCZ","NEUR"),]
to_exclude<-unique(filtered$celltype_gene)
common<-common[!common %in% to_exclude]


#filter both
scz<-scz[match(common,scz$celltype_gene),]
neur<-neur[match(common,neur$celltype_gene),]
df<-data.frame(celltype_gene=common,NEUR=neur$IVW_beta,SCZ=scz$IVW_beta)


##now plot
ylims=c(-0.2,0.2)
xlims=c(-0.8,0.8)

g<-ggplot(df,aes(x=SCZ,y=NEUR,fill=celltype_gene))+
geom_point(shape=21,size=2)+
scale_y_continuous(limits=ylims)+
scale_x_continuous(limits=xlims)+
geom_vline(xintercept =0,linetype="dashed",size=0.3)+
geom_hline(yintercept =0,linetype="dashed",size=0.3)+theme_classic()+
theme(text=element_text(size=5,family="Helvetica"),legend.title=element_blank(),
      axis.text.y=element_text(size=5,family="Helvetica"),axis.text.x=element_text(size=5,family="Helvetica"),
      legend.text = element_text(family="Helvetica",size=5,face="bold"),legend.spacing.y = unit(-1, 'cm'))+
xlab("SCZ - MR beta")+ylab("NEUR - MR beta")+guides(fill = guide_legend(byrow = TRUE))
g

Past versions of unnamed-chunk-20-1.png

Version	Author	Date
0f7883c	Alexander Haglund	2022-12-01

Suppl. Fig. 5c

full<-read.table("data/COLOC_MR_RESULTS//2022-10-25_FULL_MR_RES.txt")
full<-full[full$IVW<0.05,]
tmp<-full
df<-data.frame()
for(i in 1:length(unique(tmp$GWAS))){
    gwas<-unique(tmp$GWAS)[i]
    tmp_df<-tmp[grep(gwas,tmp$GWAS),]
    cellvec<-as.vector(table(tmp_df$celltype))
    df<-rbind(df,data.frame(gwas=gwas,celltype=names(table(tmp_df$celltype)),occurrence=cellvec))
}

g<-ggplot(data=df,aes(y=occurrence,x=gwas,fill=celltype))+geom_bar(stat="identity",colour="black")
g<-g+geom_text(aes(label=occurrence),family="Helvetica",size=5*0.36, position = position_stack(vjust = 0.5))+
scale_fill_manual(values=color_pal)+theme_classic()+
scale_y_continuous(expand = c(0, 0))+
theme(axis.text.x=element_text(size=5,face="bold",angle=45,vjust=0.01),
                                  axis.text.y=element_text(size=5,face="bold"),
                                  axis.title.y=element_text(size=7,face="bold",margin=margin(r=20)),
                                 axis.title.x=element_text(size=7,face="bold",margin=margin(t=20)),
                                                 legend.key.size=unit(0.7,"cm"),
                                                                legend.text=element_text(size=5),
     title=element_text(size=7,face="bold"),
panel.background = element_rect(fill='transparent'), #transparent panel bg
    plot.background = element_rect(fill='transparent', color=NA), #transparent plot bg
    panel.grid.major = element_blank(), #remove major gridlines
    panel.grid.minor = element_blank(), #remove minor gridlines
    legend.background = element_rect(fill='transparent'), #transparent legend bg
    legend.box.background = element_rect(fill='transparent'))#transparent legend panel
g<-g+theme(text=element_text(family="Helvetica"),
         axis.title.x=element_blank(),
             axis.title.y=element_blank(), axis.text.x=element_text(size=5,angle=45),
         legend.position = "none")+scale_y_continuous(expand = c(0, 0))

Scale for 'y' is already present. Adding another scale for 'y', which will
replace the existing scale.

g

Past versions of unnamed-chunk-21-1.png

Version	Author	Date
0f7883c	Alexander Haglund	2022-12-01

Suppl. Fig. 5d

full<-read.table("data/COLOC_MR_RESULTS//2022-10-25_FULL_MR_RES.txt")
full<-full[full$IVW<0.05,]
full$trait_gene<-paste0(full$GWAS,"_",full$gene)
tmp<-full[full$GWAS=="AD",]
tmp<-tmp[order(tmp$celltype),]
tmp$gene<-factor(tmp$gene,levels=unique(tmp$gene))
color_pal=ggsci::pal_nejm("default")(8)
colorvec<-c(Astrocytes=color_pal[1],
           Endothelial=color_pal[2],
           Excitatory=color_pal[3],
           Inhibitory=color_pal[4],Microglia=color_pal[5],
           ODC=color_pal[6],OPC=color_pal[7],Pericytes=color_pal[8])

g<-ggplot(tmp,aes(y=gene,x=IVW_beta,fill=celltype))+
geom_point(colour="black",shape=21,stroke=0.3,size=2)+
scale_fill_manual(values = colorvec)+
theme_classic()+
geom_vline(xintercept=0,linetype="dashed",size=0.3)+scale_x_continuous(limits=c(-0.6,0.6))
g2<-g+
theme(text=element_text(family="Helvetica",size=5),legend.text = element_text(family="Helvetica",size=5,face="bold"),
      legend.title=element_blank(),
      legend.spacing.y = unit(-1, 'cm'),axis.text.y=element_text(family="Helvetica",size=5,face="bold"))+guides(fill = guide_legend(byrow = TRUE))
g2

Past versions of unnamed-chunk-22-1.png

Version	Author	Date
0f7883c	Alexander Haglund	2022-12-01

Suppl. Fig. 6

full<-read.table("data/COLOC_MR_RESULTS//2022-10-25_FULL_MR_RES.txt")
full<-full[full$IVW<0.05,]
full$trait_gene<-paste0(full$GWAS,"_",full$gene)

tmp<-full[full$GWAS=="AD",]
tmp<-tmp[order(tmp$celltype),]
tmp$gene<-factor(tmp$gene,levels=unique(tmp$gene))
color_pal=ggsci::pal_nejm("default")(8)
colorvec<-c(Astrocytes=color_pal[1],
           Endothelial=color_pal[2],
           Excitatory=color_pal[3],
           Inhibitory=color_pal[4],Microglia=color_pal[5],
           ODC=color_pal[6],OPC=color_pal[7],Pericytes=color_pal[8])


for(i in 1:length(unique(full$GWAS))){
    gwas<-unique(full$GWAS)[i]
    tmp<-full[full$GWAS %in% gwas,]
    len<-nrow(tmp)
    fig_height=3/20*len
    lims=max(abs(tmp$IVW_beta))+0.1
    
    g<-ggplot(tmp,aes(y=gene,x=IVW_beta,fill=celltype))+
    geom_point(colour="black",shape=21,stroke=0.3)+
    scale_fill_manual(values = colorvec)+
    theme_classic()+
    geom_vline(xintercept=0,linetype="dotted")+scale_x_continuous(limits=c(lims*-1,lims))
    g<-g+
    theme(text=element_text(size=5,family="Helvetica"),legend.title=element_blank(),
      axis.text.y=element_text(size=5,family="Helvetica"),axis.text.x=element_text(size=5,family="Helvetica"),
      legend.text = element_text(family="Helvetica",size=5,face="bold"))+ggtitle(gwas)
   print(g)
}

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Version	Author	Date
dbf14b0	Alexander Haglund	2022-12-01

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dbf14b0	Alexander Haglund	2022-12-01

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dbf14b0	Alexander Haglund	2022-12-01

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dbf14b0	Alexander Haglund	2022-12-01

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dbf14b0	Alexander Haglund	2022-12-01

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dbf14b0	Alexander Haglund	2022-12-01

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dbf14b0	Alexander Haglund	2022-12-01

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dbf14b0	Alexander Haglund	2022-12-01

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dbf14b0	Alexander Haglund	2022-12-01

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dbf14b0	Alexander Haglund	2022-12-01

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dbf14b0	Alexander Haglund	2022-12-01

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dbf14b0	Alexander Haglund	2022-12-01

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dbf14b0	Alexander Haglund	2022-12-01

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dbf14b0	Alexander Haglund	2022-12-01

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dbf14b0	Alexander Haglund	2022-12-01

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Version	Author	Date
dbf14b0	Alexander Haglund	2022-12-01

Session information

sessionInfo()

R version 4.0.5 (2021-03-31)
Platform: x86_64-conda-linux-gnu (64-bit)
Running under: Ubuntu 18.04.6 LTS

Matrix products: default
BLAS/LAPACK: /home/ah3918/anaconda3/envs/ODIN3/lib/libopenblasp-r0.3.12.so

locale:
 [1] LC_CTYPE=en_GB.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_GB.UTF-8        LC_COLLATE=en_GB.UTF-8    
 [5] LC_MONETARY=en_GB.UTF-8    LC_MESSAGES=en_GB.UTF-8   
 [7] LC_PAPER=en_GB.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] grid      stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
 [1] ggrepel_0.9.1      ComplexUpset_1.3.3 UpSetR_1.4.0       reshape_0.8.9     
 [5] tidyr_1.2.1        cowplot_1.1.1      dplyr_1.0.9        ggsci_2.9         
 [9] viridis_0.6.2      viridisLite_0.4.1  ggplot2_3.3.6      workflowr_1.7.0   

loaded via a namespace (and not attached):
 [1] tidyselect_1.1.2 xfun_0.32        bslib_0.4.0      purrr_0.3.4     
 [5] colorspace_2.0-3 vctrs_0.5.0      generics_0.1.3   htmltools_0.5.3 
 [9] yaml_2.3.5       utf8_1.2.2       rlang_1.0.6      jquerylib_0.1.4 
[13] later_1.2.0      pillar_1.8.1     glue_1.6.2       withr_2.5.0     
[17] DBI_1.1.3        plyr_1.8.7       lifecycle_1.0.3  stringr_1.4.0   
[21] munsell_0.5.0    gtable_0.3.1     evaluate_0.16    labeling_0.4.2  
[25] knitr_1.39       callr_3.7.1      fastmap_1.1.0    httpuv_1.6.5    
[29] ps_1.7.1         fansi_1.0.3      highr_0.9        Rcpp_1.0.9      
[33] promises_1.2.0.1 scales_1.2.1     cachem_1.0.6     jsonlite_1.8.0  
[37] farver_2.1.1     fs_1.5.2         gridExtra_2.3    digest_0.6.30   
[41] stringi_1.7.8    processx_3.7.0   getPass_0.2-2    rprojroot_2.0.3 
[45] cli_3.4.1        tools_4.0.5      magrittr_2.0.3   sass_0.4.2      
[49] patchwork_1.1.2  tibble_3.1.8     whisker_0.4      pkgconfig_2.0.3 
[53] ellipsis_0.3.2   assertthat_0.2.1 rmarkdown_2.15   httr_1.4.3      
[57] rstudioapi_0.13  R6_2.5.1         git2r_0.30.1     compiler_4.0.5