update images

Paper_IPCS_genes.txt

+iPSC_identity	CMYX
+iPSC_identity	OCT4
+iPSC_identity	POU5F1
+iPSC_identity	SOX2
+iPSC_identity	KLF
+iPSC_identity	NANOG
+iPSC_identity	LIN28
+Mda_identity_stage1	OTX2
+Mda_identity_stage1	WNT1
+Mda_identity_stage1	EN1
+Mda_identity_stage1	LMX1B
+Mda_identity_stage2	LMX1A
+Mda_identity_stage2	FOXA2
+Mda_identity_stage3	NGN2
+Mda_identity_stage3	NEUROG
+Mda_identity_stage3	NR4A2
+Mda_identity_stage3	NURR1
+Mda_identity_stage3	PITX3
+Mda_identity_stage3	TH
+Mda_identity_stage4	VMAT2
+Mda_identity_stage4	SLC18A2
+Mda_identity_stage4	DAT
+Mda_identity_stage4	SLC6A3
+Mda_identity_stage4	ALDH1A1
+Mda_identity_stage4	GIRK2
+Mda_identity_stage4	KCNJ6
+Mda_identity_stage4	GDAP1L1
+Non_Mda	ALDH1A3
+Non_Mda	PAX6

README.md

@@ -13,11 +13,11 @@ https://3.basecamp.com/3652073/buckets/15964731/vaults/2493637291
 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.
 
 <!-- ![Figure1](Figures/Figure1.jpg) -->
-<img src="Figures/Figure1.jpg" width=50%>
+<img src="Figures/Fig1.tif" width=50%>
 
 **Figure 1:** Experimental design.
 
-![Figure2](Figures/Figure2.jpg)
+![Figure2](Figures/Fig2.tif)
 **Figure 2:** Generation and classification of iPS cell lines.
 **a)**. Immunocytochemistry. Staining for the iPSC markers Oct3/4 and TRA-180. DAPI was used to stain cell nuclei as a reference. 
 **b)**. Results of Scorecard analysis of iPSCs and embryonic bodies (EBs). 
@@ -25,16 +25,16 @@ iPSCs are expected to show high expression of self-renewal genes (Self-renew +)
 EBs are cells at an early stage of spontaneous differentiation. Scorecard analysis of EBs determines the iPSC cell line’s potential to differentiate into the three germ layers: ectoderm, mesoderm, and endoderm. EBs are expected to express few or no self-renewal genes (Self-renew -) and to show expression of some mesoderm, ectoderm and endoderm markers: Ecto +/-, Meso +/-, Endo +/-.
 
 
-![Figure3](Figures/Figure3.jpg)
+![Figure3](Figures/Fig3.tif)
 **Figure 3:** iPSC status, differentiation and classification of mDA neurons.
 **a)**. Heatmap of the top 15 differential expressed genes per time-point (adjusted p-value<0.01 and fold change >0.1) across the different time points of the control data that examined (IPSCs,Day06, Day15 and Day21).  **b)**. Expression of stemness markers: SOX2, MYC (c-Myc), POU5F1 (Oct4), and NANOG, and mDA-specific differentiation pathways in differentiating neurons (SC and qPCR): Otx2, EN1, Lmx1b, Lmx1a, and Foxa2. SOX2 directs the differentiation of iPSCs into neural progenitors and for maintains the properties of neural progenitor stem cells.
 
 
-![Figure4](Figures/Figure4.jpg)
+![Figure4](Figures/Fig4.tif)
 **Figure 4:** iPSC status, differentiation and classification of mDA neurons. **a)**. Expression of mDA-specific differentiation pathways in differentiating neurons (SC and qPCR): Otx2, EN1, Lmx1b, Lmx1a, and Foxa2 (also see Table 2). **b)**. Staining for DA marker TH, neuronal marker MAP2.
 
 
-![Figure5](Figures/Figure5.jpg)
+![Figure5](Figures/Fig5.tif)
 **Figure 5:** **a)** Heatmap of the common differential expressed genes (adjusted p-value<0.01 and fold change >0.1) across the different time points that examined (Day06, Day15 and Day21). Each column is a single cell, and each row is a single gene. The bar on the top shows the experimental origin of cells. **b)** Venn diagrams of the differential expressed genes across time points. **c)** Volcano plot for the pairwise differential expression analysis. For illustration purposes we used 0.6 fold change as threshold to annotate the genes with greater fold change and significant adjusted p-value (adjusted p-value<0.01).
 
 

Scripts/Create_Paper_Figures.R

-# ======================== LOAD =========================
-library("Seurat")
-library("tidyverse")
-library("viridis")
-library("ggpubr")
-library("gridExtra")
-library("VennDiagram")
-library("RColorBrewer")
-Combined<- readRDS("Seurat.rds")
-DefaultAssay(Combined) <- "SCT"
-Combined$Timepoints <- as.vector(Combined$condition)
-Combined$Timepoints[grep("IPSC",Combined$condition)] <-"iPSCs"
-Combined$Timepoints[grep("D06",Combined$condition)] <-"Day06"
-Combined$Timepoints[grep("D10",Combined$condition)] <-"Day10"
-Combined$Timepoints[grep("D15",Combined$condition)] <-"Day15"
-Combined$Timepoints[grep("D21",Combined$condition)] <-"Day21"
-Combined$Timepoints <- factor(Combined$Timepoints,c("iPSCs","Day06","Day10","Day15","Day21"))
-
-# -------------------------------------------------------
-
-
-# ======================= FIGURE 3 ==========================
-# === Figure a
-dir.create("DF_Conditions")
-setwd("DF_Conditions")
-DefaultAssay(Combined) <-"RNA"
-Combined <- NormalizeData(Combined)
-Combined <- ScaleData(Combined)
-Controls_DM <- subset(Combined,subset= Treatment !="PINK")
-DefaultAssay(Controls_DM) <- "RNA"
-Idents(Controls_DM)<-as.factor(Controls_DM$condition)
-pbmc.markers <- FindAllMarkers(object = Controls_DM,
-                            assay ="RNA",min.pct =0.1,
-                            logfc.threshold=0.1,
-                            only.pos = TRUE,
-                            test.use = "MAST",latent.vars = c("nCount_RNA"))
-
-pbmc.markers$gene <- rownames(pbmc.markers)
-qvalue <- p.adjust(pbmc.markers$p_val, method = "BH",n=dim(Controls_DM@assays$RNA@counts)[1])
-pbmc.markers$qvalue <- qvalue
-
-top <- pbmc.markers[pbmc.markers$p_val_adj<0.01 & abs(pbmc.markers$avg_logFC) >0.1,]
-to_heat <- top %>% group_by(cluster) %>% top_n(n = 15, wt = abs(avg_logFC)) 
-to_heat <- to_heat[order(to_heat$cluster),]
-Controls_DM$condition <- as.factor(Controls_DM$condition)
-ICSWrapper::annotated_heat(object=Controls_DM,
-                   row_annotation=c(1),
-                   gene_list=to_heat$gene,
-                   Rowv=NA,
-                   gene_list_name="DF_genes",
-                   title="Figure3a",
-                   ordering="Treatment",One_annot = TRUE,color_list = color_list)
- 
-
-# === Figure b
-Stemness_markers<- c("SOX2", "MYC", "POU5F1", "NANOG", "LIN28")
-differentiation_path<- c("OTX2", "LMX1B", "LMX1A", "FOXA2", "PTCH1", "FZD7")
-last <- c(Stemness_markers,differentiation_path)
-pdf("Figure3b.pdf",width= 8)
-DoHeatmap(Combined,last,group.by = "Timepoints", raster = FALSE)+ scale_fill_viridis(option="inferno")
-dev.off()
-# -----------------------------------------------------------
-
-
-
-
-
-
-# ======================= FIGURE 4 ==========================
-mDA <- c("TCF12", "ALCAM", "PITX2", "DDC", "ASCL1")
-mDA <- mDA[mDA %in% rownames(Combined@assays$RNA@counts)]
-p0 <- DimPlot(Combined,group.by = "condition",cols=color_cond)
-#FeaturePlot(Combined,features = c("TH","KCNJ6"),pt.size = 2,,order = TRUE,cols = c("lightgrey","#FDBB84","#EF6548","#D7301F","#B30000","#7F0000"))
-results <- ICSWrapper::scatter_gene(object = Combined,features = c("TH","KCNJ6"),ncol =1 ,nrow =2)+ theme_void()
-plot4 <- DoHeatmap(Combined,mDA,group.by = "Timepoints", raster = FALSE)+  
-scale_fill_viridis(option="inferno")
-plot5 <- grid.arrange(p0, results, widths=c(2/3, 1/2), ncol=2, nrow=1)
-pdf("Figure4.pdf",width=12,height=10)
-grid.arrange(plot5,plot4,ncol=1, nrow=2,clip=TRUE)
-dev.off()
-# -----------------------------------------------------------
-
-
-# ================== FIGURE 5 ==============================
-color_list_three <- color_list
-color_list_three$condition <- color_list_three$condition[-c(1,3,6)]
-dirs_pairs <- list.dirs("C:/Users/dimitrios.kyriakis/Desktop/PhD/Projects/Michi_Data/2020-04-17_seurat_elbow_TRUE_Mito-FALSE_Ribo-FALSE_SCT-TRUE_criteria_pass-3/DF_Pairwise",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) >0.1,"gene"])
-    df_return_nt_pink[[iter]] <- as.vector(pink_l1[pink_l1$p_val_adj<0.01 & abs(pink_l1$avg_logFC) >0.1,"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)
-
-
-Conserved_M <- subset(Combined,subset= Timepoints %in% c("Day06","Day15","Day21"))
-
-Conserved_M$condition<- factor(Conserved_M$condition)
-ICSWrapper::annotated_heat(object=Conserved_M,
-                   row_annotation=c(1),
-                   gene_list=c(cntrl_intesect,pink_intesect),
-                   Rowv=NA,
-                   gene_list_name="DF_genes",
-                   title="Figure5a",
-                   ordering="Treatment",One_annot = TRUE,color_list =  color_list_three)
-
-
-# ============================= Figure 5b
-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]])
-}
-cntrl_intesect <- Reduce(intersect, df_return_nt_cntrl)
-print(cntrl_intesect)
-pink_intesect <- Reduce(intersect, df_return_nt_pink)
-myCol <- brewer.pal(3, "Pastel2")
-# === COntrol
-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]])
-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)
-grid.newpage()
-grid.draw(v)
-lapply(v,  names)
-lapply(v, function(i) i$label)
-v[[11]]$label <- paste(intersect(intersect(day06, day15),day21), collapse="\n")  
-grid.newpage()
-grid.draw(v)
-dev.off()
-v1 <-v
-#------------
-
-# === PINK
-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]])
-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)
-grid.newpage()
-grid.draw(v)
-lapply(v,  names)
-lapply(v, function(i) i$label)
-
-v[[11]]$label <- paste(intersect(intersect(day06, day15),day21), collapse="\n")  
-grid.newpage()
-p1 <-grid.draw(v)
-dev.off()
-v2 <-v
-# ------------------ 
-
-# ===== Plot
-library(grid)
-library(gridBase)
-graphics.off()
-pdf("Figure5b.pdf")
-plot.new()
-vp1 <- viewport(x=0,y=0.5,width=0.5, height=0.5, just = c("left", "bottom"))
-vp2 <- viewport(x=0,y=0,width=0.5, height=0.5, just = c("left", "bottom"))
-upViewport()
-pushViewport(vp1)
-grid.draw(v1)
-par(new=TRUE, fig=gridFIG())
-
-upViewport()
-pushViewport(vp2)
-grid.draw(v2)
-par(new=TRUE, fig=gridFIG())
-dev.off()
-
-# -----------------------------------------------------------
-
-
-
-
-# ==================== Correlation Network ==================
+# ======================== LOAD =========================
+library("Seurat")
+library("tidyverse")
+library("viridis")
+library("ggpubr")
+library("gridExtra")
+library("VennDiagram")
+library("RColorBrewer")
+Combined<- readRDS("Seurat.rds")
+DefaultAssay(Combined) <- "SCT"
+Combined$Timepoints <- as.vector(Combined$condition)
+Combined$Timepoints[grep("IPSC",Combined$condition)] <-"iPSCs"
+Combined$Timepoints[grep("D06",Combined$condition)] <-"Day06"
+Combined$Timepoints[grep("D10",Combined$condition)] <-"Day10"
+Combined$Timepoints[grep("D15",Combined$condition)] <-"Day15"
+Combined$Timepoints[grep("D21",Combined$condition)] <-"Day21"
+Combined$Timepoints <- factor(Combined$Timepoints,c("iPSCs","Day06","Day10","Day15","Day21"))
+
+# -------------------------------------------------------
+
+
+# ======================= FIGURE 3 ==========================
+# === Figure a
+dir.create("DF_Conditions")
+setwd("DF_Conditions")
+DefaultAssay(Combined) <-"RNA"
+Combined <- NormalizeData(Combined)
+Combined <- ScaleData(Combined)
+Controls_DM <- subset(Combined,subset= Treatment !="PINK")
+DefaultAssay(Controls_DM) <- "RNA"
+Idents(Controls_DM)<-as.factor(Controls_DM$condition)
+pbmc.markers <- FindAllMarkers(object = Controls_DM,
+                            assay ="RNA",min.pct =0.1,
+                            logfc.threshold=0.1,
+                            only.pos = TRUE,
+                            test.use = "MAST",latent.vars = c("nCount_RNA"))
+
+pbmc.markers$gene <- rownames(pbmc.markers)
+qvalue <- p.adjust(pbmc.markers$p_val, method = "BH",n=dim(Controls_DM@assays$RNA@counts)[1])
+pbmc.markers$qvalue <- qvalue
+
+top <- pbmc.markers[pbmc.markers$p_val_adj<0.01 & abs(pbmc.markers$avg_logFC) >0.1,]
+to_heat <- top %>% group_by(cluster) %>% top_n(n = 15, wt = abs(avg_logFC)) 
+to_heat <- to_heat[order(to_heat$cluster),]
+Controls_DM$condition <- as.factor(Controls_DM$condition)
+ICSWrapper::annotated_heat(object=Controls_DM,
+                   row_annotation=c(1),
+                   gene_list=to_heat$gene,
+                   Rowv=NA,
+                   gene_list_name="DF_genes",
+                   title="Figure3a",
+                   ordering="Treatment",One_annot = TRUE,color_list = color_list)
+ 
+
+# === Figure b
+Stemness_markers<- c("SOX2", "MYC", "POU5F1", "NANOG", "LIN28")
+differentiation_path<- c("OTX2", "LMX1B", "LMX1A", "FOXA2", "PTCH1", "FZD7")
+last <- c(Stemness_markers,differentiation_path)
+Controls_DM <-ScaleData(Controls_DM,last)
+pdf("Figure3b.pdf",width= 8)
+DoHeatmap(Controls_DM,last,group.by = "Timepoints", raster = FALSE)+ scale_fill_viridis(option="inferno")
+dev.off()
+
+
+
+
+
+
+
+genes_f <- c(
+"SHH"
+"FGF8"
+"PAX2"
+"WNT1"
+"EN2"
+"MSX1"
+"FERD3L"
+"GLI1"
+"SNCA",
+"RELN",
+"CTNNB1",
+"VIP",
+"DMRTA2",
+"SOX2",
+"HES1",
+"HES5",
+"ADH1B",
+"CALB1",
+"EPHA5",
+"NTN1",
+"EBF1",
+'HES1',
+"NKX2-1",
+"SLIT1",
+"SLIT2",
+"ROBO1",
+"ROBO2",
+"SEMA3A")
+pdf("Figure3b2.pdf",width= 8)
+Controls_DM<-ScaleData(Controls_DM,genes_f)
+DoHeatmap(Controls_DM,genes_f,cells=Controls_DM$Timepoints!="Day10",group.by = "Timepoints", raster = FALSE)+ scale_fill_viridis(option="inferno")
+dev.off()
+
+
+
+# -----------------------------------------------------------
+
+mDA2 <- c(
+"PTCH1"	,
+"FZD7"	,
+"FABP7",
+"CTNNB1",
+"SHH"	,
+"SOX2"	,
+"FOXA2"	,
+"OTX2"	,
+"PBX1"	,
+"HES1"	,
+
+"NTN1",
+"SOX6"	,
+"DCX"	,
+"DDC"	,
+
+
+"TCF12"	,
+"ALCAM",
+
+"SLIT2"	,
+"LMO3",
+
+"LMX1A"	,
+"WNT5A"	,
+"RSPO2"	,
+"MSX1"	,
+"NEUROG2",
+"MSX1"	,
+"DMRTA2",	
+# "ROBO1"	,
+# "ROBO2"	,
+# "NTN1"	,
+# "SLIT1"	,
+# "OTX2"	,
+# "KCNH6" ,	
+"PITX2",
+"ASCL1",
+"PITX3"	,
+"NEUROD1",
+"CORIN",
+"SLIT1",
+"TH",
+"NR4A2"
+# "CORIN",
+# "NEUROD1",
+# "TUBB3",
+# "NR4A2"	
+)
+pdf("Figure3b2.pdf",width= 8)
+DefaultAssay(Controls) <- "RNA"
+Controls<-ScaleData(Controls,mDA2)
+DoHeatmap(Controls,mDA2,group.by = "Timepoints", raster = FALSE)+ scale_fill_viridis(option="inferno")
+dev.off()
+
+
+mDA21 <- c("FABP7"	,
+"SOX2"	,
+"FOXA2"	,
+"LMX1A"	,
+"OTX2"	,
+"WNT5A"	,
+"RSPO2"	,
+"MSX1"	,
+"CORIN"	,
+"ASCL1"	,
+	      "NEUROG2",
+	       "NEUROD1",
+	       "TUBB3",
+"DDC"	,
+"DCX"	,
+"NR4A2"	,
+"PBX1"	,
+"PITX3"	,
+"EN1"	,
+"TH"	,
+'BNC2'	,
+"SLC18A2"	,
+	      "SLC6A3",
+	       "CALB1",
+	       "LMO3",
+	      #"ALDH1A1",
+"SOX6"	,
+"SHH"	,
+"MSX1"	,
+"FERD3L",	
+"CTNNB1",	
+"DMRTA2",	
+"HES1"	,
+"SLIT2"	,
+"ROBO1"	,
+"ROBO2"	,
+"NTN1"	,
+"SLIT1"	,
+"OTX2"	,
+"PTCH1"	,
+"FZD7"	,
+"KCNH6" ,	
+"TCF12"	,
+"ALCAM",
+"PITX2"
+)
+Controls_DM 
+
+# ======================= FIGURE 4 ==========================
+mDA <- c("TCF12", "ALCAM", "PITX2", "DDC", "ASCL1")
+mDA <- mDA[mDA %in% rownames(Combined@assays$RNA@counts)]
+p0 <- DimPlot(Combined,group.by = "condition",cols=color_cond)
+#FeaturePlot(Combined,features = c("TH","KCNJ6"),pt.size = 2,,order = TRUE,cols = c("lightgrey","#FDBB84","#EF6548","#D7301F","#B30000","#7F0000"))
+results <- ICSWrapper::scatter_gene(object = Combined,features = c("TH","KCNJ6"),ncol =1 ,nrow =2)+ theme_void()
+plot4 <- DoHeatmap(Combined,mDA,group.by = "Timepoints", raster = FALSE)+  
+scale_fill_viridis(option="inferno")
+DefaultAssay(Combined) <- "SCT"
+Combined <- ScaleData(Combined,mDA2)
+plot4.1 <- DoHeatmap(Combined,mDA2,group.by = "Timepoints", raster = FALSE)+  
+scale_fill_viridis(option="inferno")
+DefaultAssay(Combined) <- "RNA"
+Combined <- ScaleData(Combined,mDA2)
+plot4.1  <- DoHeatmap(Combined,mDA2,group.by = "Timepoints", raster = FALSE)+  
+scale_fill_viridis(option="inferno")
+
+pdf("Figure4c.pdf",width=12,height=10)
+plot4.1 
+dev.off()
+
+
+plot5 <- grid.arrange(p0, results, widths=c(2/3, 1/2), ncol=2, nrow=1)
+
+plot5 <- ggarrange(plotlist=list(p0, results), widths=c(2/3, 1/2), ncol=2, nrow=1)
+
+pdf("Figure4.pdf",width=12,height=10)
+grid.arrange(plot5,plot4,ncol=1, nrow=2,clip=TRUE)
+dev.off()
+# -----------------------------------------------------------
+
+
+# ================== FIGURE 5 ==============================
+color_list_three <- color_list
+color_list_three$condition <- color_list_three$condition[-c(1,3,6)]
+dirs_pairs <- list.dirs("C:/Users/dimitrios.kyriakis/Desktop/PhD/Projects/Michi_Data/2020-04-17_seurat_elbow_TRUE_Mito-FALSE_Ribo-FALSE_SCT-TRUE_criteria_pass-3/DF_Pairwise",full.names = TRUE )[-1]
+dirs_pairs <- grep('IPSC|D06.*D06|D15.*D15|D21.*D21',dirs_pairs,value = TRUE)
+dirs_pairs <- dirs_pairs[-4]
+df_return_nt_cntrl <- list()
+df_return_nt_pink <- list()
+df_return_nt_all <- list()
+for (iter in 1:length(dirs_pairs)){
+    dirs_iter <- dirs_pairs[iter]
+    file <- paste0(dirs_iter ,"/", dir(dirs_iter, "*.tsv"))
+    l1 <- read.table(file,header=TRUE)
+    l1$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$avg_logFC) >0.1,"gene"])
+    df_return_nt_pink[[iter]] <- as.vector(pink_l1[pink_l1$p_val_adj<0.01 & abs(pink_l1$avg_logFC) >0.1,"gene"])
+    print(length(df_return_nt_cntrl[[iter]]))
+    print(length(df_return_nt_pink[[iter]]))
+    df_return_nt_all[[iter]] <- c(df_return_nt_cntrl[[iter]] ,df_return_nt_pink[[iter]])
+}
+
+# # ============= Intersect Common Genes
+cntrl_intesect <- Reduce(intersect, df_return_nt_cntrl)
+print(cntrl_intesect)
+pink_intesect <- Reduce(intersect, df_return_nt_pink)
+print(pink_intesect)
+length(cntrl_intesect)
+length(pink_intesect)
+
+
+Conserved_M <- subset(Combined,subset= Timepoints %in% c("Day06","Day15","Day21"))
+
+Conserved_M$condition<- factor(Conserved_M$condition)
+ICSWrapper::annotated_heat(object=Conserved_M,
+                   row_annotation=c(1),
+                   gene_list=c(cntrl_intesect,pink_intesect),
+                   Rowv=NA,
+                   gene_list_name="DF_genes",
+                   title="Figure5a",
+                   ordering="Treatment",One_annot = TRUE,color_list =  color_list_three)
+
+
+# ============================= Figure 5b
+df_return_nt_cntrl <- list()
+df_return_nt_pink <- list()
+df_return_nt_all <- list()
+for (iter in 1:length(dirs_pairs)){
+    dirs_iter <- dirs_pairs[iter]
+    file <- paste0(dirs_iter ,"/", dir(dirs_iter, "*.tsv"))
+    l1 <- read.table(file,header=TRUE)
+    l1$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$avg_logFC) >0.4,"gene"])
+    print(length(df_return_nt_cntrl[[iter]]))
+    print(length(df_return_nt_pink[[iter]]))
+    df_return_nt_all[[iter]] <- c(df_return_nt_cntrl[[iter]] ,df_return_nt_pink[[iter]])
+}
+cntrl_intesect <- Reduce(intersect, df_return_nt_cntrl)
+print(cntrl_intesect)
+pink_intesect <- Reduce(intersect, df_return_nt_pink)
+myCol <- brewer.pal(3, "Pastel2")
+# === COntrol
+pdf("Control_venn_diagramm.pdf")
+day06 <- c(df_return_nt_cntrl[[1]])
+day15 <- c(df_return_nt_cntrl[[2]])
+day21 <- c(df_return_nt_cntrl[[3]])
+v <- venn.diagram(list(Day06=day06, Day15=day15,Day21=day21),
+                  fill = myCol,
+                  alpha = c(0.5, 0.5, 0.5), cat.cex = 1.5, cex=1.5,
+                  filename=NULL)
+grid.newpage()
+grid.draw(v)
+lapply(v,  names)
+lapply(v, function(i) i$label)
+v[[11]]$label <- paste(intersect(intersect(day06, day15),day21), collapse="\n")  
+grid.newpage()
+grid.draw(v)
+dev.off()
+v1 <-v
+#------------
+
+# === PINK
+pdf("PINK_venn_diagramm.pdf")
+day06 <- c(df_return_nt_pink[[1]])
+day15 <- c(df_return_nt_pink[[2]])
+day21 <- c(df_return_nt_pink[[3]])
+v <- venn.diagram(list(Day06=day06, Day15=day15,Day21=day21),
+                  fill = myCol,
+                  alpha = c(0.5, 0.5, 0.5), cat.cex = 1.5, cex=1.5,
+                  filename=NULL)
+grid.newpage()
+grid.draw(v)
+lapply(v,  names)
+lapply(v, function(i) i$label)
+
+v[[11]]$label <- paste(intersect(intersect(day06, day15),day21), collapse="\n")  
+grid.newpage()
+p1 <-grid.draw(v)
+dev.off()
+v2 <-v
+# ------------------ 
+
+# ===== Plot
+library(grid)
+library(gridBase)
+graphics.off()
+pdf("Figure5b.pdf")
+plot.new()
+vp1 <- viewport(x=0,y=0.5,width=0.5, height=0.5, just = c("left", "bottom"))
+vp2 <- viewport(x=0,y=0,width=0.5, height=0.5, just = c("left", "bottom"))
+upViewport()
+pushViewport(vp1)
+grid.draw(v1)
+par(new=TRUE, fig=gridFIG())
+
+upViewport()
+pushViewport(vp2)
+grid.draw(v2)
+par(new=TRUE, fig=gridFIG())
+dev.off()
+
+# -----------------------------------------------------------
+
+
+
+
+# ==================== Correlation Network ==================
 # -----------------------------------------------------------
\ No newline at end of file

Scripts/Snakemake/2.1.Conserved_Markers.R

-## ----setup, include=FALSE--------------------------------------------------------------------
-set.seed(123)
-# library(reticulate)
-options(future.globals.maxSize= 2122317824)
-library(sctransform)
-library(Seurat)
-library(RColorBrewer)
-library(tictoc)
-library(crayon)
-library(stringr)
-library(Routliers)
-library(jcolors)
-library(cluster)
-library(NMF)
-library(ggplot2)
-library(ggpubr)
-library(cowplot)
-
-colormap_d<- c('#a6cee3','#1f78b4','#b2df8a','#33a02c','#fb9a99','#e31a1c','#fdbf6f','#ff7f00','#cab2d6','#6a3d9a','#ffff99','#b15928',"black","gray")
-color_cond  <- c(brewer.pal(5,"Dark2"),"black","gray","magenta4","seagreen4",brewer.pal(9,"Set1")[-6])[c(5,1,2,3,4,9,6,7,8)]
-olor_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)
-
-
-
-Combined <- readRDS("/home/users/dkyriakis/PhD/Projects/IPSCs_pink1/1.Preprocess/IPSCs_Combined.rds")
-
-dir.create("/home/users/dkyriakis/PhD/Projects/IPSCs_pink1/2.Differential_Epression/Conserved_Markers_All/")
-setwd("/home/users/dkyriakis/PhD/Projects/IPSCs_pink1/2.Differential_Epression/Conserved_Markers_All/")
-        
-Conserved_M <- subset(Combined,subset= condition !="Control_D10")
-DefaultAssay(Conserved_M) <- "RNA"
-Conserved_M <-NormalizeData(Conserved_M)
-Conserved_M <-ScaleData(Conserved_M)
-Conserved_M$Timepoints <- as.vector(Conserved_M$condition)
-Conserved_M$Timepoints[grep("IPSC",Conserved_M$condition)] <-"IPSCs"
-Conserved_M$Timepoints[grep("D06",Conserved_M$condition)] <-"Day06"
-Conserved_M$Timepoints[grep("D15",Conserved_M$condition)] <-"Day15"
-Conserved_M$Timepoints[grep("D21",Conserved_M$condition)] <-"Day21"
-Idents(Conserved_M) <- "Treatment"
-markers <- FindConservedMarkers(Conserved_M,ident.1 = "Control",ident.2 = "PINK",grouping.var = "Timepoints",test.use="MAST",latent.vars="nCount_RNA",logfc.threshold=0.0)
-
-
-index_fc <- c(sign(markers$IPSCs_avg_logFC)==sign(markers$Day06_avg_logFC) & sign(markers$Day06_avg_logFC)==sign(markers$Day15_avg_logFC) & sign(markers$Day15_avg_logFC)==sign(markers$Day21_avg_logFC))
-sub_markers <- markers[markers$max_pval < 0.01 & index_fc,]
-sub_markers$avg_FC <- rowMeans(sub_markers[,c("IPSCs_avg_logFC","Day06_avg_logFC","Day15_avg_logFC","Day21_avg_logFC")])
-sub_markers2 <- sub_markers[abs(sub_markers$avg_FC) >0.1,]
-dim(sub_markers2)
-write.table(sub_markers2,"Conserved_all.txt")
+## ----setup, include=FALSE--------------------------------------------------------------------
+set.seed(123)
+# library(reticulate)
+options(future.globals.maxSize= 2122317824)
+library(sctransform)
+library(Seurat)
+library(RColorBrewer)
+library(tictoc)
+library(crayon)
+library(stringr)
+library(Routliers)
+library(jcolors)
+library(cluster)
+library(NMF)
+library(ggplot2)
+library(ggpubr)
+library(cowplot)
+
+colormap_d<- c('#a6cee3','#1f78b4','#b2df8a','#33a02c','#fb9a99','#e31a1c','#fdbf6f','#ff7f00','#cab2d6','#6a3d9a','#ffff99','#b15928',"black","gray")
+color_cond  <- c(brewer.pal(5,"Dark2"),"black","gray","magenta4","seagreen4",brewer.pal(9,"Set1")[-6])[c(5,1,2,3,4,9,6,7,8)]
+olor_clust <- c(brewer.pal(12,"Paired")[-11],"black","gray","magenta4","seagreen4",brewer.pal(9,"Set1")[-6],brewer.pal(8,"Dark2"))
+color_cells <- c(brewer.pal(9,"Set1")[-6],"goldenrod4","darkblue","seagreen4")
+color_list <- list(condition=color_cond,Cluster=color_clust,Cell_Type=color_cells,State=color_clust)
+
+
+
+Combined <- readRDS("/home/users/dkyriakis/PhD/Projects/IPSCs_pink1/1.Preprocess/IPSCs_Combined.rds")
+
+dir.create("/home/users/dkyriakis/PhD/Projects/IPSCs_pink1/2.Differential_Epression/Conserved_Markers_All/")
+setwd("/home/users/dkyriakis/PhD/Projects/IPSCs_pink1/2.Differential_Epression/Conserved_Markers_All/")
+        
+
+
+
+Conserved_M <- subset(Combined,subset= condition !="Control_D10")
+DefaultAssay(Conserved_M) <- "RNA"
+Conserved_M <-NormalizeData(Conserved_M)
+Conserved_M <-ScaleData(Conserved_M)
+Conserved_M$Timepoints <- as.vector(Conserved_M$condition)
+Conserved_M$Timepoints[grep("IPSC",Conserved_M$condition)] <-"IPSCs"
+Conserved_M$Timepoints[grep("D06",Conserved_M$condition)] <-"Day06"
+Conserved_M$Timepoints[grep("D15",Conserved_M$condition)] <-"Day15"
+Conserved_M$Timepoints[grep("D21",Conserved_M$condition)] <-"Day21"
+Idents(Conserved_M) <- "Treatment"
+markers <- FindConservedMarkers(Conserved_M,ident.1 = "Control",ident.2 = "PINK",grouping.var = "Timepoints",test.use="MAST",latent.vars="nCount_RNA",logfc.threshold=0.0)
+
+
+index_fc <- c(sign(markers$IPSCs_avg_logFC)==sign(markers$Day06_avg_logFC) & sign(markers$Day06_avg_logFC)==sign(markers$Day15_avg_logFC) & sign(markers$Day15_avg_logFC)==sign(markers$Day21_avg_logFC))
+sub_markers <- markers[markers$max_pval < 0.01 & index_fc,]
+sub_markers$avg_FC <- rowMeans(sub_markers[,c("IPSCs_avg_logFC","Day06_avg_logFC","Day15_avg_logFC","Day21_avg_logFC")])
\ No newline at end of file
+sub_markers2 <- sub_markers[abs(sub_markers$avg_FC) >0.1,]
+dim(sub_markers2)
+# 151
+write.table(sub_markers2,"Conserved_all.txt")
+
+
+
+# ======================= Enrichment ==================================
+control_m <- rownames(sub_markers2[sub_markers2$avg_FC < 0,] )
+pink_m <- sub_markers2[sub_markers2$avg_FC > 0,])
+library(clusterProfiler)
+library(enrichplot)
+library(ReactomePA)
+library(org.Hs.eg.db)
+control_m_e <- bitr(control_m, fromType="SYMBOL", toType="ENTREZID", OrgDb="org.Hs.eg.db")
+pink_m_e <- bitr(pink_m, fromType="SYMBOL", toType="ENTREZID", OrgDb="org.Hs.eg.db")
+n_c <- length(control_m_e$ENTREZID)
+n_p <- length(pink_m_e$ENTREZID)
+c_d <- data.frame(ENTREZID = control_m_e$ENTREZID , Condition ="Control")
+p_d <- data.frame(ENTREZID = pink_m_e$ENTREZID , Condition ="PINK1")
+all_d <- rbind(c_d,p_d)
+mydf <- as.data.frame(all_d)
+mydf$ENTREZID <- as.vector(mydf$ENTREZID)
+mydf$ENTREZID <-as.numeric(mydf$ENTREZID )
+GOclusterplot <- compareCluster(ENTREZID~Condition,data=mydf, fun = "enrichGO", OrgDb = "org.Hs.eg.db")
+Keggclusterplot <- compareCluster(ENTREZID~Condition,data=mydf, fun = "enrichKEGG")
+Reactomeclusterplot <- compareCluster(ENTREZID~Condition,data=mydf, fun = "enrichPathway")
+
+
+
+
+pdf("GO_Enrichment_DF_Clusters.pdf",width=22,height=11)
+dotplot(GOclusterplot)
+dotplot(Keggclusterplot)
+dotplot(GOclusterplot)+ ggplot2::facet_grid(~Condition, scales = "free")
+dotplot(Keggclusterplot)+ ggplot2::facet_grid(~Condition, scales = "free")
+dotplot(Reactomeclusterplot)+ ggplot2::facet_grid(~Condition, scales = "free")
+dev.off()
+# --------------------------------------------------------------------
\ No newline at end of file

Scripts/Snakemake/2.2.Conserved_Markers_IPSCsAvg.R

-## ----setup, include=FALSE--------------------------------------------------------------------
-set.seed(123)
-# library(reticulate)
-options(future.globals.maxSize= 2122317824)
-library(sctransform)
-library(Seurat)
-library(RColorBrewer)
-library(tictoc)
-library(crayon)
-library(stringr)
-library(Routliers)
-library(jcolors)
-library(cluster)
-library(NMF)
-library(ggplot2)
-library(ggpubr)
-library(cowplot)
-
-colormap_d<- c('#a6cee3','#1f78b4','#b2df8a','#33a02c','#fb9a99','#e31a1c','#fdbf6f','#ff7f00','#cab2d6','#6a3d9a','#ffff99','#b15928',"black","gray")
-color_cond  <- c(brewer.pal(5,"Dark2"),"black","gray","magenta4","seagreen4",brewer.pal(9,"Set1")[-6])[c(5,1,2,3,4,9,6,7,8)]
-olor_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)
-
-
-
-Combined <- readRDS("/home/users/dkyriakis/PhD/Projects/IPSCs_pink1/1.Preprocess/IPSCs_Combined.rds")
-
-dir.create("/home/users/dkyriakis/PhD/Projects/IPSCs_pink1/2.Differential_Epression/Conserved_Markers_IPSCsAvg/")
-setwd("/home/users/dkyriakis/PhD/Projects/IPSCs_pink1/2.Differential_Epression/Conserved_Markers_IPSCsAvg/")
-
-
-
-
-Conserved_M <- subset(Combined,subset= condition !="Control_D10")
-DefaultAssay(Conserved_M) <- "RNA"
-Conserved_M <-NormalizeData(Conserved_M)
-Conserved_M <-ScaleData(Conserved_M)
-Conserved_M$Timepoints <- as.vector(Conserved_M$condition)
-Conserved_M$Timepoints[grep("IPSC",Conserved_M$condition)] <-"IPSCs"
-Conserved_M$Timepoints[grep("D06",Conserved_M$condition)] <-"Day06"
-Conserved_M$Timepoints[grep("D15",Conserved_M$condition)] <-"Day15"
-Conserved_M$Timepoints[grep("D21",Conserved_M$condition)] <-"Day21"
-Idents(Conserved_M) <- "Treatment"
-markers <- FindConservedMarkers(Conserved_M,ident.1 = "Control",ident.2 = "PINK",grouping.var = "Timepoints",test.use="MAST",latent.vars="nCount_RNA",logfc.threshold=0.0)
-
-index_fc <- c( sign(markers$Day06_avg_logFC)==sign(markers$Day15_avg_logFC) & sign(markers$Day15_avg_logFC)==sign(markers$Day21_avg_logFC))
-sub_markers <- markers[markers$max_pval < 0.01 & index_fc,]
-sub_markers$avg_FC <- rowMeans(sub_markers[,c("IPSCs_avg_logFC","Day06_avg_logFC","Day15_avg_logFC","Day21_avg_logFC")])
-sub_markers2 <- sub_markers[abs(sub_markers$avg_FC) >0.1,]
-dim(sub_markers2)
-write.table(sub_markers2,"Conserved_all_alt.txt")
+## ----setup, include=FALSE--------------------------------------------------------------------
+set.seed(123)
+# library(reticulate)
+options(future.globals.maxSize= 2122317824)
+library(sctransform)
+library(Seurat)
+library(RColorBrewer)
+library(tictoc)
+library(crayon)
+library(stringr)
+library(Routliers)
+library(jcolors)
+library(cluster)
+library(NMF)
+library(ggplot2)
+library(ggpubr)
+library(cowplot)
+
+colormap_d<- c('#a6cee3','#1f78b4','#b2df8a','#33a02c','#fb9a99','#e31a1c','#fdbf6f','#ff7f00','#cab2d6','#6a3d9a','#ffff99','#b15928',"black","gray")
+color_cond  <- c(brewer.pal(5,"Dark2"),"black","gray","magenta4","seagreen4",brewer.pal(9,"Set1")[-6])[c(5,1,2,3,4,9,6,7,8)]
+olor_clust <- c(brewer.pal(12,"Paired")[-11],"black","gray","magenta4","seagreen4",brewer.pal(9,"Set1")[-6],brewer.pal(8,"Dark2"))
+color_cells <- c(brewer.pal(9,"Set1")[-6],"goldenrod4","darkblue","seagreen4")
+color_list <- list(condition=color_cond,Cluster=color_clust,Cell_Type=color_cells,State=color_clust)
+
+
+
+Combined <- readRDS("/home/users/dkyriakis/PhD/Projects/IPSCs_pink1/1.Preprocess/IPSCs_Combined.rds")
+
+dir.create("/home/users/dkyriakis/PhD/Projects/IPSCs_pink1/2.Differential_Epression/Conserved_Markers_IPSCsAvg/")
+setwd("/home/users/dkyriakis/PhD/Projects/IPSCs_pink1/2.Differential_Epression/Conserved_Markers_IPSCsAvg/")
+
+
+
+
+Conserved_M <- subset(Combined,subset= condition !="Control_D10")
+DefaultAssay(Conserved_M) <- "RNA"
+Conserved_M <-NormalizeData(Conserved_M)
+Conserved_M <-ScaleData(Conserved_M)
+Conserved_M$Timepoints <- as.vector(Conserved_M$condition)
+Conserved_M$Timepoints[grep("IPSC",Conserved_M$condition)] <-"IPSCs"
+Conserved_M$Timepoints[grep("D06",Conserved_M$condition)] <-"Day06"
+Conserved_M$Timepoints[grep("D15",Conserved_M$condition)] <-"Day15"
+Conserved_M$Timepoints[grep("D21",Conserved_M$condition)] <-"Day21"
+Idents(Conserved_M) <- "Treatment"
+markers <- FindConservedMarkers(Conserved_M,ident.1 = "Control",ident.2 = "PINK",grouping.var = "Timepoints",test.use="MAST",latent.vars="nCount_RNA",logfc.threshold=0.0)
+
+index_fc <- c( sign(markers$Day06_avg_logFC)==sign(markers$Day15_avg_logFC) & sign(markers$Day15_avg_logFC)==sign(markers$Day21_avg_logFC))
+sub_markers <- markers[markers$max_pval < 0.01 & index_fc,]
+sub_markers$avg_FC <- rowMeans(sub_markers[,c("IPSCs_avg_logFC","Day06_avg_logFC","Day15_avg_logFC","Day21_avg_logFC")])
+sub_markers2 <- sub_markers[abs(sub_markers$avg_FC) >0.1,]
+dim(sub_markers2)
+#172
+write.table(sub_markers2,"Conserved_all_alt.txt")
+
+
+
+
+
+
+
+
+
+# ======================= Enrichment ==================================
+control_m <- rownames(sub_markers2[sub_markers2$avg_FC < 0,] )
+pink_m <-  rownames(sub_markers2[sub_markers2$avg_FC > 0,]))
+library(clusterProfiler)
+library(enrichplot)
+library(ReactomePA)
+library(org.Hs.eg.db)
+control_m_e <- bitr(control_m, fromType="SYMBOL", toType="ENTREZID", OrgDb="org.Hs.eg.db")
+pink_m_e <- bitr(pink_m, fromType="SYMBOL", toType="ENTREZID", OrgDb="org.Hs.eg.db")
+n_c <- length(control_m_e$ENTREZID)
+n_p <- length(pink_m_e$ENTREZID)
+c_d <- data.frame(ENTREZID = control_m_e$ENTREZID , Condition ="Control")
+p_d <- data.frame(ENTREZID = pink_m_e$ENTREZID , Condition ="PINK1")
+all_d <- rbind(c_d,p_d)
+mydf <- as.data.frame(all_d)
+mydf$ENTREZID <- as.vector(mydf$ENTREZID)
+mydf$ENTREZID <-as.numeric(mydf$ENTREZID )
+GOclusterplot <- compareCluster(ENTREZID~Condition,data=mydf, fun = "enrichGO", OrgDb = "org.Hs.eg.db")
+Keggclusterplot <- compareCluster(ENTREZID~Condition,data=mydf, fun = "enrichKEGG")
+Reactomeclusterplot <- compareCluster(ENTREZID~Condition,data=mydf, fun = "enrichPathway")
+
+
+
+
+pdf("GO_Enrichment_DF_Clusters.pdf",width=22,height=11)
+# dotplot(GOclusterplot)
+# dotplot(Keggclusterplot)
+# dotplot(GOclusterplot)+ ggplot2::facet_grid(~Condition, scales = "free")
+# dotplot(Keggclusterplot)+ ggplot2::facet_grid(~Condition, scales = "free")
+dotplot(Reactomeclusterplot)+ ggplot2::facet_grid(~Condition, scales = "free")
+dev.off()
+# --------------------------------------------------------------------
\ No newline at end of file