# PINK1 shows LRRK2, Parkin, and SNCA as part of the Parkinson’s network.


# Abstract

Parkinson’s disease (PD) is the second most prevalent neurodegenerative disorder, yet there is no treatment that can prevent or slow its progression. The mechanisms leading to PD pathology are not well understood, but we can gain insight by studying mutations known to cause PD. We used iPSCs carrying a homozygous mutation (ILE368ASN) within the PINK1 (PARK6) gene to generate midbrain dopaminergic (mDA) neurons, the primary targets of PD. Pairwise comparison between three independent pairs of a PINK1 and a control cell line, using single cell RNA sequencing, identified 151 genes consistently dysregulated at three different timepoints of dopaminergic differentiation. Upon examination, many of these genes formed a network which not only includes genes directly interacting with PINK1-related pathways like Parkin, but also genes that link to several additional PD-related pathways, including LRRK2, DJ-1 and α-synuclein. This suggests that pathology resulting from other PD mutations converges on a common PD network.




# Libraries 
```{r libraries, include=FALSE}
library(reticulate)
use_python("C:/Users/dimitrios.kyriakis/AppData/Local/Continuum/anaconda3/envs/iscwrapper/python.exe", required = TRUE)
options(future.globals.maxSize= 2122317824)
library(sctransform)
library(Seurat)
ibrary( RColorBrewer)
library(tictoc)
library(crayon)
library(stringr)
library(Routliers)
library(jcolors)
library(cluster)
library(garnett)
library(NMF)
library(ggplot2)
library(ggpubr)
library(cowplot)
set.seed(123)
```

# Setting Up  
```{r setup, include=FALSE}
# ================================ SETTING UP ======================================== #
tool="seurat"
project ="Michi_Data"
dataset <- project
Data_select <- ICSWrapper::data_selection(project)
WORKDIR <- Data_select$WORKDIR
list_of_files <- Data_select$list_of_files
condition_names <- Data_select$condition_names
condition_names <- condition_names[c(1,2,3,4,5,6,8,28,29)]
list_of_files <- list_of_files[c(1,2,3,4,5,6,8,28,29)]
organism<- Data_select$organism
file<- Data_select$file
data_10x<- Data_select$data_10x
setwd(Data_select$WORKDIR)
color_cond <- c( "magenta4", "#007A87",brewer.pal(6,"Dark2")[-1],"#FF5A5F","black")
color_clust <- c(brewer.pal(12,"Paired")[-11],"black","gray","magenta4","seagreen4",brewer.pal(9,"Set1")[-6],brewer.pal(8,"Dark2"))
color_cells <- c(brewer.pal(9,"Set1")[-6],"goldenrod4","darkblue","seagreen4")
color_list <- list(condition=color_cond,Cluster=color_clust,Cell_Type=color_cells,State=color_clust)

# Number of cells to use
imputation = FALSE
remove_mt=FALSE
remove_ribsomal=FALSE
n_cores=4
elbow = TRUE
SCT=TRUE
criteria_pass=3
min.cells <- 10
min.features <- 200
```

## Preprocessing

The identification of the low quality cells was done separately in each data set. In order to select only the highest quality data, we sorted the cells by the cumulative gene expression. A subset of cells with the highest cumulative expression was considered for the analysis [1]. 
Additional to this filtering, we defined cells as low-quality, based on three criteria for each cell. The number of the genes that expressed is more than 200 and 2 median-absolute- deviations (MADs) above the median, the total number of counts is 2 MADs above or below the median and the percentage of counts to mitochondrial genes is 1.5 median-absolute- deviations (MADs) above the median. Cells failing at least one criteria were considered as low quality cells and filtered out from further analysis. Similar to the cell filtering, we filtered out the low quality genes that been expressed in less than 10 cells in the data. 


```{r readfiles}
# options(future.globals.maxSize= 2122317824)
# ==============================================================================================
# ================================ Setup the Seurat objects ====================================
# ==============================================================================================
# ======== Perform an integrated analysis ====
NewDir <- paste0(Sys.Date(),"_",tool,"_elbow_",elbow,"_Mito-",remove_mt,"_Ribo-",remove_ribsomal,"_SCT-",SCT,"_criteria_pass-",criteria_pass)
dir.create(NewDir)
setwd(NewDir)
dir.create("QC")
setwd("QC")

Return_fun <- ICSWrapper::create_cds2(list_of_files=list_of_files,
                                      condition_names=condition_names,
                                      min.features =min.features,min.cells=min.cells,
                                      remove_mt=remove_mt,data_10x=data_10x,
                                      elbow = elbow,tool=tool,n_cores=1,SCT=SCT,
                                      criteria_pass = criteria_pass,vars.to.regress=c("nCount_RNA"))

Combined  <- Return_fun$Combined
Data_List <- Return_fun$Data_List
setwd("../")
```



## Data Intergration

The integration of the filtered matrices of the different datasets was performed using scTransform [2] on a Seurat object [3] based on the treatment. The final gene expression matrix which used for the downstream analysis, consist of 4495 cells and 39194 genes. Principal component analysis (PCA) was computed using the 5000 most variable genes on the integrated data. 

```{r remapping}
dir.create("Aligned_Cond_RegPhase")
setwd("Aligned_Cond_RegPhase")
# ================================== ALLIGN CONDITIONS =========================================
DefaultAssay(Combined) <- "RNA"

Combined$condition <- factor(as.factor(Combined$condition), levels = c("Control_IPSCs", "Control_D06"  ,"Control_D10",   "Control_D15",   "Control_D21",
"PINK1_IPSCs","PINK1_D06",     "PINK1_D15",     "PINK1_D21"))

Combined$Treatment <-as.vector(Combined$condition)
Combined$Treatment[grep("Control",Combined$Treatment)] <- "Control"
Combined$Treatment[grep("PINK",Combined$Treatment)] <- "PINK"
pink.list <-SplitObject(Combined,split.by = "Treatment")

for (i in 1:length(pink.list)) {
    pink.list[[i]] <- SCTransform(pink.list[[i]], verbose = FALSE,vars.to.regress=c("G2M.Score","S.Score"))
}
 # doi: https://doi.org/10.1101/576827
int.features <- SelectIntegrationFeatures(object.list = pink.list, nfeatures = 3000)
pink.list <- PrepSCTIntegration(object.list = pink.list, anchor.features = int.features,
                                     verbose = FALSE)
int.anchors <- FindIntegrationAnchors(object.list = pink.list, normalization.method = "SCT",
                                            anchor.features = int.features, verbose = FALSE)
Seurat.combined <- IntegrateData(anchorset = int.anchors, normalization.method = "SCT",
                                      verbose = FALSE)
DefaultAssay(object = Seurat.combined) <- "integrated"
#Seurat.combined$condition <- Idents(object = Seurat.combined)

Combined <- Seurat.combined

setwd("../")
```





## Clustering

The clustering of data was performed using Louvain clustering. The resolution of the clustering was selected based on the best silhouette score of the different resolutions [4].

```{r Clustering}
# ================================== Clustering =========================================
dir.create("Clusters")
setwd("Clusters")


# Combined <- ReduceDim(Combined,method="umap",project=project)$Combined
# debugonce(reduce_dim)
Combined <- ICSWrapper::reduce_dim(Combined,project=project,assay = "SCT")$Combined#,resolution=c(0.1))$Combined

Combined$condition <- factor(as.factor(Combined$condition), levels = c("Control_IPSCs", "Control_D06"  ,"Control_D10",   "Control_D15",   "Control_D21",
"PINK1_IPSCs","PINK1_D06",     "PINK1_D15",     "PINK1_D21"))


pdf(paste(Sys.Date(),project,"tsne","projection.pdf",sep="_"))
ICSWrapper::plot_cells(Combined,target="condition",leg_pos="right",save=FALSE,ncol=1,color_list = color_list)
ICSWrapper::plot_cells(Combined,target="Cluster",leg_pos="right",save=FALSE,ncol=1,color_list = color_list)
dev.off()

ICSWrapper::plot_nFeatures(Combined,title="",save=TRUE,tiff=FALSE,reduce="t-SNE",p3D=FALSE)
ICSWrapper::plot_tot_mRNA(Combined,title="",save=TRUE,tiff=FALSE,reduce="t-SNE",p3D=FALSE)


if(tolower(tool)=="seurat" & elbow){
    p3 <- DimPlot(object = Combined, reduction = "umap", group.by = "condition",cols = color_cond)
    p4 <- DimPlot(object = Combined, reduction = "umap", label = TRUE,cols = color_clust)
    pdf(paste(Sys.Date(),project,"umap","Seurat.pdf",sep="_"))
    print(p3)
    print(p4)
    dev.off()
}
setwd("../")
saveRDS(Combined,paste0("Clustered_",NewDir,".rds"))

pdf(paste(Sys.Date(),project,"_projection_Aligned_Treatment.pdf",sep="_"))
ICSWrapper::plot_cells(Combined,target="condition",leg_pos="right",save=FALSE,ncol=1,reduction="umap",color_list = color_list)
ICSWrapper::plot_cells(Combined,target="Cluster",leg_pos="right",save=FALSE,ncol=1,reduction="umap",color_list = color_list)
ICSWrapper::plot_cells(Combined,target="Phase",leg_pos="right",save=FALSE,ncol=1,reduction="umap",color_list = color_list)
ICSWrapper::plot_cells(Combined,target="condition",leg_pos="right",save=FALSE,ncol=1,reduction="tsne",color_list = color_list)
ICSWrapper::plot_cells(Combined,target="Cluster",leg_pos="right",save=FALSE,ncol=1,reduction="tsne",color_list = color_list)
ICSWrapper::plot_cells(Combined,target="Phase",leg_pos="right",save=FALSE,ncol=1,reduction="tsne",color_list = color_list)
dev.off()
# ---------------------------------------------------------------------------------------

res<-ICSWrapper::scatter_gene(Combined,features = c("nCount_RNA","nFeature_RNA","percent.mito","percent.rb"),size=0.9)

pdf("Combined_QC.pdf")
print(res)
dev.off()


Return_fun <- NULL
Seurat.combined <- NULL
pink.list <- NULL
#save.image("IPSCs_PINK.RData")



```



## iPSCs Differentiation

A short list of manually curated markers was used in order to validate the different stages of the differentiation process. 


```{r  Developmental_Markers}
# ================================== Developmental Stages =========================================
dir.create("Developmental_Markers")
setwd("Developmental_Markers")
DefaultAssay(Combined) <- "RNA"
file <- paste0(WORKDIR,"/Gene_Lists/Paper_IPCS_genes.txt")

genes_state <-read.table(file)


# debugonce(cell_type_assignment)

pdf("Cell_Assignment_Plots.pdf")
res <- cell_type_assignment(object=Combined,tab_name = "Identity",group_by="Cluster",file,assign=TRUE,color_list = color_clust)


Combined$Identity <- as.vector(Combined$Cluster)
for (level in levels(Combined$Cluster)){
    Combined$Identity[as.vector(Combined$Cluster) == as.numeric(level)] <- res$radar$Identity[as.numeric(level)]
}
Combined$Identity <- as.factor(Combined$Identity)
DimPlot(Combined,group.by = c("Identity","Cluster"))
dev.off()



for(category in levels(as.factor(genes_state$V1))){
  category_genes <- toupper(as.vector(genes_state[genes_state$V1==category,2]))
  category_genes_l <- category_genes[category_genes%in%rownames(Combined)]
  Combined <- AddModuleScore(Combined,features = list(category_genes_l),name = category)

  pdf(paste0(category,"_umap_projection_condition_regPhase.pdf"),width = 8,height = 8)
  res <- ICSWrapper::scatter_gene(Combined,features = category_genes_l,ncol = 2,nrow = 2,size=1.1)
  plot(res)
  dev.off()
  
}
# early_genes <- toupper(as.vector(genes_state[genes_state$V1=="Early",2]))
# mid_genes <- toupper(as.vector(genes_state[genes_state$V1=="Mid",2]))
# late_genes <- toupper(as.vector(genes_state[genes_state$V1=="Late",2]))
# early_genes_l <- early_genes[early_genes%in%rownames(Combined)]
# mid_genes_l <- mid_genes[mid_genes%in%rownames(Combined)]
# late_genes_l <- late_genes[late_genes%in%rownames(Combined)]
# 
# Combined <- AddModuleScore(Combined,features = list(early_genes_l),name = "Early")
# Combined <- AddModuleScore(Combined,features = list(mid_genes_l),name = "Mid")
# Combined <- AddModuleScore(Combined,features = list(late_genes_l),name = "Late")
# 
# pdf("Early_umap_projection_condition_regPhase.pdf",width = 8,height = 8)
# res <- ICSWrapper::scatter_gene(Combined,features = early_genes_l,ncol = 2,nrow = 2,size=1.1)
# ggarrange(plotlist=res,ncol = 2,nrow = 2)
# dev.off()
# pdf("Mid_umap_projection_condition_regPhase.pdf",width = 8,height = 8)
# res <- ICSWrapper::scatter_gene(Combined,features = mid_genes_l,ncol = 2,nrow = 2,size=1.1)
# ggarrange(plotlist=res,ncol = 2,nrow = 2)
# dev.off()
# pdf("Late_umap_projection_condition_regPhase.pdf",width = 8,height = 8)
# res <- ICSWrapper::scatter_gene(Combined,features = late_genes_l,ncol = 2,nrow = 2,size=1.1)
# ggarrange(plotlist=res,ncol = 2,nrow = 2)
# dev.off()

features <- c("iPSC_identity1","Mda_identity_stage11", "Mda_identity_stage21","Mda_identity_stage31","Mda_identity_stage41", "Non.Mda1")    

pdf("Development_umap_projection_condition_regPhase.pdf",width = 12,height = 8)
res <- ICSWrapper::scatter_gene(Combined,features = features,ncol = 3,nrow = 2,size=1.1)
print(ggarrange(plotlist=res,ncol = 3,nrow = 2))
dev.off()


scan_dim <- function (object, group.by = "Cell_Type", features, assay = "RNA", 
    method = "heat", organism = "human", cellheight = 20, 
    cellwidth = 20, width = 10) 
{
    title <- paste0(Sys.Date(), "_", group.by)
    require(NMF)
    require(ggplot2)
    require(dplyr)
    require(viridis)
    require(Seurat)
    graphics.off()
    scaled_data <- t(as.matrix(object@assays[[assay]]@counts)[features, 
        ])
    df <- as.data.frame(scaled_data)
    if (organism != "human") {
        colnames(df) <- unlist(lapply(tolower(colnames(df)), 
            ICSWrapper::simpleCap))
        features <- unlist(lapply(tolower(colnames(df)), ICSWrapper::simpleCap))
    }
    df[[group.by]] <- object[[group.by]]
    heat_cl <- aggregate(df[, 1:dim(df)[2] - 1], list(df[, dim(df)[2]])[[1]], 
        mean)
    row.names(heat_cl) <- heat_cl[[group.by]]
    heat_cl[[group.by]] <- NULL
    print("The heatmap created with c1 scale")
    aheatmap(heat_cl, color = viridis(1000), scale = "column", 
        distfun = "correlation", cellwidth = cellwidth,Rowv = NA, 
        cellheight = cellheight, border_color = "gray", 
        filename = paste0(title, "_Mean_heat_extra.pdf"),width=10,height=8)
    library(reshape2)
    id <- df[[group.by]]
    df[[group.by]] <- NULL
    df <- cbind(id = id, df)
    melted_df <- melt(df)
    violin_df <- melted_df
    library(ggplot2)
    print("The Violin created with log1p counts")
    pdf(paste0(title, "_Violin_extra_log.pdf"), width = 20)
    plot(ggplot(violin_df, aes(x = variable, y = log1p(value), 
        fill = get(group.by))) + geom_violin(scale = "width", 
        width = 0.7) + facet_grid(get(group.by) ~ ., switch = "y", 
        space = "free") + cowplot::theme_cowplot() + theme(axis.text.x = element_text(angle = 45, 
        hjust = 1), strip.text.y = element_text(angle = 180), 
        strip.placement = "outside", strip.background = element_rect(colour = "white", 
            fill = "white")) + scale_x_discrete(limits = features) + 
        NoLegend() + ylab("") + xlab("") + scale_fill_manual(values = color_list[[group.by]], 
        name = group.by, na.value = "gray"))
    dev.off()
    pdf(paste0(title, "_Jitter_extra_log.pdf"), width = 20)
    plot(ggplot(violin_df, aes(x = variable, y = log1p(value), 
        fill = get(group.by))) + geom_jitter(aes(color = get(group.by))) + 
        scale_fill_manual(values = color_list[[group.by]], name = group.by, 
            na.value = "gray") + facet_grid(get(group.by) ~ 
        ., switch = "y", space = "free") + cowplot::theme_cowplot() + 
        theme(axis.text.x = element_text(angle = 45, hjust = 1), 
            strip.text.y = element_text(angle = 180), strip.placement = "outside", 
            strip.background = element_rect(colour = "white", 
                fill = "white")) + scale_x_discrete(limits = features) + 
        NoLegend() + ylab("") + xlab(""))
    dev.off()
}

Combined <- ScaleData(Combined,rownames(Combined))
category_genes <- toupper(as.vector(genes_state[,2]))
category_genes_l <- category_genes[category_genes%in%rownames(Combined)]
ICSWrapper::annotated_heat(Combined,row_annotation = c(1),gene_list = category_genes_l,ordering = "condition",title="Development_Markers",color_list = color_list)
ics_scanpy(Combined,features = category_genes_l,group.by = "condition",Rowv = NA,scale="c1")
setwd("../")
# --------------------------------------------------------------------------------------------------
```


# Pairwise Differential Expression


```{r Pairwise DF}
# =============================== PAIRWISE DF ===============================================
dir.create("DF_Pairwise_PAPER")
setwd("DF_Pairwise_PAPER")
library(EnhancedVolcano)
Combined$condition <- as.factor(Combined$condition)
Idents(Combined) <- as.factor(Combined$condition)
cl_combinations <- combn(levels(Combined$condition),2)
cl_combinations <- cl_combinations[,c(5,13,25,30)]
DefaultAssay(Combined) <- "RNA"
Combined <- NormalizeData(Combined)
Combined <- ScaleData(Combined,rownames(Combined@assays$RNA@counts))

library(parallel)
pairwise_df <- function (comb,object,cl_combinations){
    DefaultAssay(object) <- "RNA"

    # for(comb in 1:dim(cl_combinations)[2]){
    title <- paste(cl_combinations[,comb],collapse = "_")
    dir.create(title)
    setwd(title)
    target <- "condition"
    idents <- as.vector(cl_combinations[,comb])
    ident.1 <- idents[1]
    print(ident.1)

    ident.2 <- idents[2]

    pbmc.markers <- FindMarkers(object = object,
                                    ident.1 = ident.1,
                                    ident.2 =ident.2,
                                   assay ="RNA",min.pct =0.1,
                                   logfc.threshold=0.0,
                                   only.pos = FALSE,
                                   test.use = "MAST",latent.vars = c("nCount_RNA"))
    pbmc.markers$gene <- rownames(pbmc.markers)
    qvalue <- p.adjust(pbmc.markers$p_val, method = "BH",n=dim(object@assays$RNA@counts)[1])
    pbmc.markers$qvalue <- qvalue

    top <- pbmc.markers[pbmc.markers$p_val_adj<0.05,]

    to_fc <- top[order(abs(top$avg_logFC),decreasing = TRUE),]
    to_fc_gene <- rownames(to_fc)[1:50]
    #top10 <- top %>% top_n(n = 50, wt = abs(avg_logFC))
    #top10_genes<- rownames(top10)

    temp <- object[,object$condition%in%c(ident.1,ident.2)]
    temp$condition <- as.factor(as.vector(temp$condition))

    # debugonce(annotated_heat)
    pdf("Volcano.pdf")
    plot(EnhancedVolcano(pbmc.markers,
                    lab = pbmc.markers$gene,
                    x = 'avg_logFC',
                    y = 'p_val_adj',subtitle = paste(ident.1,"vs",ident.2,"(FCcutoff=0.6)"),
                    xlim = c(-2, 2),FCcutoff = 0.6))
    dev.off()

    ICSWrapper::annotated_heat(object=temp,
                   row_annotation=c(1),
                   gene_list=to_fc_gene,
                   Rowv=TRUE,
                   gene_list_name="DF_genes",
                   title=title,
                   ordering="condition",One_annot = TRUE)

    DefaultAssay(temp) <- "integrated"
    write.table(pbmc.markers, file = paste0(Sys.Date(),"_TO_EXP_each_",target,"_",title,".tsv"),row.names=FALSE, na="", sep="\t")


    # test <- split(top$gene,top$cluster)
    #
    # x=compareCluster(test, fun='enrichGO', OrgDb='org.Hs.eg.db',keyType="SYMBOL",pAdjustMethod = "BH",
    #                  pvalueCutoff  = 0.05,ont="BP")
    # pdf(paste0(Sys.Date(),"_enrichGO_BP_",target,"_",title,".pdf"),width=12,height=10)
    # plot(dotplot(x, showCategory=5, includeAll=FALSE))
    # dev.off()
    #
    # x=compareCluster(test, fun='enrichGO', OrgDb='org.Hs.eg.db',keyType="SYMBOL",pAdjustMethod = "BH",
    #                  pvalueCutoff  = 0.05,ont="MF")
    # pdf(paste0(Sys.Date(),"_enrichGO_MF_",target,"_",title,".pdf"),width=12,height=10)
    # plot(dotplot(x, showCategory=5, includeAll=FALSE))

    setwd("../")
}

mclapply(c(1:dim(cl_combinations)[2]),FUN=pairwise_df,object=Combined,cl_combinations=cl_combinations,mc.cores=1)


dirs_pairs <- list.dirs("C:/Users/dimitrios.kyriakis/Desktop/PhD/Projects/Michi_Data/DF_Pairwise_Networks/DF_Pairwise_PAPER",full.names = TRUE )[-1]
dirs_pairs <- grep('IPSC|D06.*D06|D15.*D15|D21.*D21',dirs_pairs,value = TRUE)
dirs_pairs <- dirs_pairs[-4]
df_return_nt_cntrl <- list()
df_return_nt_pink <- list()
df_return_nt_all <- list()


for (iter in 1:length(dirs_pairs)){
    dirs_iter <- dirs_pairs[iter]
    file <- paste0(dirs_iter ,"/", dir(dirs_iter, "*.tsv"))
    l1 <- read.table(file,header=TRUE)
    l1$cluster <- l1$avg_logFC
    l1$cluster[ l1$avg_logFC<0] <- "PINK"
    l1$cluster[ l1$avg_logFC>0] <- "Control"
    ctrl_l1 <- l1[grep("Control",l1$cluster),]
    pink_l1 <- l1[grep("PINK",l1$cluster),]
	all_l1 <-  l1
    df_return_nt_cntrl[[iter]] <- as.vector(ctrl_l1[ctrl_l1$p_val_adj<0.01 & abs(ctrl_l1$avg_logFC) >0.4,"gene"])
    df_return_nt_pink[[iter]] <- as.vector(pink_l1[pink_l1$p_val_adj<0.01 & abs(pink_l1$avg_logFC) >0.4,"gene"])
    print(length(df_return_nt_cntrl[[iter]]))
    print(length(df_return_nt_pink[[iter]]))
    df_return_nt_all[[iter]] <- c(df_return_nt_cntrl[[iter]] ,df_return_nt_pink[[iter]])
}

# # ============= Intersect Common Genes
cntrl_intesect <- Reduce(intersect, df_return_nt_cntrl)
print(cntrl_intesect)
pink_intesect <- Reduce(intersect, df_return_nt_pink)
print(pink_intesect)

length(cntrl_intesect)
length(pink_intesect)

pdf("Control_venn_diagramm.pdf")
day06 <- c(df_return_nt_cntrl[[1]])
day15 <- c(df_return_nt_cntrl[[2]])
day21 <- c(df_return_nt_cntrl[[3]])

# Generate plot
v <- venn.diagram(list(Day06=day06, Day15=day15,Day21=day21),
                  fill = myCol,
                  alpha = c(0.5, 0.5, 0.5), cat.cex = 1.5, cex=1.5,
                  filename=NULL)
# have a look at the default plot
grid.newpage()
grid.draw(v)
# have a look at the names in the plot object v
lapply(v,  names)
# We are interested in the labels
lapply(v, function(i) i$label)

v[[11]]$label <- paste(intersect(intersect(day06, day15),day21), collapse="\n")  
# plot  
grid.newpage()
grid.draw(v)
dev.off()

pdf("PINK_venn_diagramm.pdf")
day06 <- c(df_return_nt_pink[[1]])
day15 <- c(df_return_nt_pink[[2]])
day21 <- c(df_return_nt_pink[[3]])

# Generate plot
v <- venn.diagram(list(Day06=day06, Day15=day15,Day21=day21),
                  fill = myCol,
                  alpha = c(0.5, 0.5, 0.5), cat.cex = 1.5, cex=1.5,
                  filename=NULL)
# have a look at the default plot
grid.newpage()
grid.draw(v)
# have a look at the names in the plot object v
lapply(v,  names)
# We are interested in the labels
lapply(v, function(i) i$label)

v[[11]]$label <- paste(intersect(intersect(day06, day15),day21), collapse="\n")  
# plot  
grid.newpage()
grid.draw(v)
dev.off()


setwd("../")
# ----------------------------------------------------------------------------------------------

```