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Commit e0a807d6 authored by Dimitrios Kyriakis's avatar Dimitrios Kyriakis
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Snakemake Pipeline

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Scripts/Snakemake/1.Preprocessing.R 0 → 100644
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options(future.globals.maxSize= 2122317824)
# py_config()
#library(ICSWrapper)
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)
set.seed(123)
tool="seurat"
project ="IPSCs_pink1"
dataset <- project
DATADIR<- "home/users/dkyriakis/PhD/Projects/Michi_Data/DATA/"
list_of_files <-c(
paste0(DATADIR,"DADA1_S1_DGE.txt"),
paste0(DATADIR,"DADA2_S2_DGE.txt"),
paste0(DATADIR,"DADA3_S3_DGE.txt"),
paste0(DATADIR,"DADA4_S4_DGE.txt"),
paste0(DATADIR,"DADA5_S1_DGE.txt"),
paste0(DATADIR,"DADA6_S2_DGE.txt"),
paste0(DATADIR,"DADA8_S4_DGE.txt"),
paste0(DATADIR,"DADD5_S2_DGE.txt"),
paste0(DATADIR,"DADD6_S3_DGE.txt"))
condition_names <- c(
"Control_D21",
"Control_D15",
"Control_D10",
"Control_D06",
"PINK1_D21",
"PINK1_D15",
"PINK1_D06",
"Control_IPSCs",
"PINK1_IPSCs")
organism<- "human"
dir.create("/home/users/dkyriakis/PhD/Projects/IPSCs_pink1/1.Preprocess")
setwd("/home/users/dkyriakis/PhD/Projects/IPSCs_pink1/1.Preprocess")
colormap_d<- c('#a6cee3','#1f78b4','#b2df8a','#33a02c','#fb9a99','#e31a1c','#fdbf6f','#ff7f00','#cab2d6','#6a3d9a','#ffff99','#b15928',"black","gray")
color_cond <- c(brewer.pal(8,"Dark2"),"black","gray","magenta4","seagreen4")[c(5,1,2,3,4,9,6,7,8)]
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)]
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)
# color_cells <-primary.colors(15, steps = 3, no.white = TRUE)
# ================================ SETTING UP ======================================== #
# Number of cells to use
imputation = FALSE
remove_mt=FALSE
remove_ribsomal=FALSE
n_cores=1
elbow = TRUE
SCT=TRUE
criteria_pass=3
min.cells <- 10
min.features <- 200
## ----read------------------------------------------------------------------------------------
# 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")
# debugonce(create_cds)
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("../")
## ----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$condition <- Idents(object = Seurat.combined)
Combined <- Seurat.combined
setwd("../")
## ----Clustering------------------------------------------------------------------------------
# ================================== Clustering =========================================
dir.create("Clusters")
setwd("Clusters")
# Combined <- ReduceDim(Combined,method="umap",project=project)$Combined
# debugonce(reduce_dim)
Combined <- ICSWrapper::reduce_dim(Combined,project=project,assay = "SCT")$Combined#,resolution=c(0.1))$Combined
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()
# ---------------------------------------------------------------------------------------
pdf("Combined_QC.pdf")
res<-ICSWrapper::scatter_gene(Combined,features = c("nCount_RNA","nFeature_RNA","percent.mito","percent.rb"),size=0.9)
print(res)
dev.off()
## ----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)
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")
ics_scanpy(Combined,features = category_genes_l,group.by = "condition",Rowv = NA,scale="c1")
setwd("../")
# --------------------------------------------------------------------------------------------------
save.image("IPSCs_PINK.RData")
saveRDS(Combined,"IPSCs_Combined.rds")
\ No newline at end of file
Scripts/Snakemake/2.1.Conserved_Markers.R 0 → 100644
View file @ e0a807d6
## ----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")
\ No newline at end of file
Scripts/Snakemake/2.2.Conserved_Markers_IPSCsAvg.R 0 → 100644
View file @ e0a807d6
## ----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$avg_FC) >0.1,]
dim(sub_markers2)
write.table(sub_markers2,"Conserved_all_alt.txt")
Scripts/Snakemake/2.3.Conserved_Markers_3Timepoints.R 0 → 100644
View file @ e0a807d6
## ----setup, include=FALSE--------------------------------------------------------------------
set.seed(123)
# library(reticulate)
options(future.globals.maxSize= 2122317824)
library(sctransform)
library(Seurat)
library(RColorBrewer)
library(tictoc)
library(crayon)
library(stringr)
library(Routliers)
library(jcolors)
library(cluster)
library(NMF)
library(ggplot2)
library(ggpubr)
library(cowplot)
colormap_d<- c('#a6cee3','#1f78b4','#b2df8a','#33a02c','#fb9a99','#e31a1c','#fdbf6f','#ff7f00','#cab2d6','#6a3d9a','#ffff99','#b15928',"black","gray")
color_cond <- c(brewer.pal(5,"Dark2"),"black","gray","magenta4","seagreen4",brewer.pal(9,"Set1")[-6])[c(5,1,2,3,4,9,6,7,8)]
olor_clust <- c(brewer.pal(12,"Paired")[-11],"black","gray","magenta4","seagreen4",brewer.pal(9,"Set1")[-6],brewer.pal(8,"Dark2"))
color_cells <- c(brewer.pal(9,"Set1")[-6],"goldenrod4","darkblue","seagreen4")
color_list <- list(condition=color_cond,Cluster=color_clust,Cell_Type=color_cells,State=color_clust)
dir.create("/home/users/dkyriakis/PhD/Projects/IPSCs_pink1/2.Differential_Epression/Conserved_Markers_3Timepoints")
setwd("/home/users/dkyriakis/PhD/Projects/IPSCs_pink1/2.Differential_Epression/Conserved_Markers_3Timepoints")
Combined <- readRDS("/home/users/dkyriakis/PhD/Projects/IPSCs_pink1/1.Preprocess/IPSCs_Combined.rds")
Conserved_M <- subset(Combined,subset= condition %in% c("Control_D06","Control_D15","Control_D21","PINK1_D06","PINK1_D15","PINK1_D21"))
DefaultAssay(Conserved_M) <- "RNA"
Conserved_M <-NormalizeData(Conserved_M)
Conserved_M <-ScaleData(Conserved_M)
Conserved_M$Timepoints <- as.vector(Conserved_M$condition)
Conserved_M$Timepoints[grep("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$avg_FC <- rowMeans(sub_markers[,c("Day06_avg_logFC","Day15_avg_logFC","Day21_avg_logFC")])
sub_markers2 <- sub_markers[abs(sub_markers$avg_FC) >0.1,]
dim(sub_markers2)
write.table(sub_markers2,"Conserved_3.txt")
Scripts/Snakemake/2.4.Pairwise.R 0 → 100644
View file @ e0a807d6
## ----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)
## ----Venn------------------------------------------------------------------------------------
library(VennDiagram)
library(EnhancedVolcano)
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)
dir.create("/home/users/dkyriakis/PhD/Projects/IPSCs_pink1/2.Differential_Epression/Pairwise")
setwd("/home/users/dkyriakis/PhD/Projects/IPSCs_pink1/2.Differential_Epression/Pairwise")
Combined <- readRDS("/home/users/dkyriakis/PhD/Projects/IPSCs_pink1/1.Preprocess/IPSCs_Combined.rds")
# =============================== PAIRWISE DF ===============================================
Combined$condition <- as.factor(Combined$condition)
Idents(Combined) <- as.factor(Combined$condition)
cl_combinations <- combn(levels(Combined$condition),2)
cl_combinations <- cl_combinations[,c(5,19,25,30)]
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")
setwd("../")
}
mclapply(c(1:dim(cl_combinations)[2]),FUN=pairwise_df,object=Combined,cl_combinations=cl_combinations,mc.cores=1)
setwd("../")
# ----------------------------------------------------------------------------------------------
Scripts/Snakemake/3.VennDiagrams.R 0 → 100644
View file @ e0a807d6
## ----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)
## ----Venn------------------------------------------------------------------------------------
library(VennDiagram)
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)
dir.create("/home/users/dkyriakis/PhD/Projects/IPSCs_pink1/3.Venn_Pairwise/")
setwd("/home/users/dkyriakis/PhD/Projects/IPSCs_pink1/3.Venn_Pairwise")
Combined <- readRDS("/home/users/dkyriakis/PhD/Projects/IPSCs_pink1/1.Preprocess/IPSCs_Combined.rds")
# ===================== OPEN FILES TAKE THE P.Adj- FC Genes
dirs_pairs <- list.dirs("/home/users/dkyriakis/PhD/Projects/IPSCs_pink1/2.Differential_Epression/Pairwise/",full.names = TRUE )[-1]
dirs_pairs <- grep('IPSC|D06.*D06|D15.*D15|D21.*D21',dirs_pairs,value = TRUE)
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$avg_logFC) >0.4,"gene"])
print(length(df_return_nt_cntrl[[iter]]))
print(length(df_return_nt_pink[[iter]]))
df_return_nt_all[[iter]] <- c(df_return_nt_cntrl[[iter]] ,df_return_nt_pink[[iter]])
}
# # ============= Intersect Common Genes
cntrl_intesect <- Reduce(intersect, df_return_nt_cntrl)
print(cntrl_intesect)
pink_intesect <- Reduce(intersect, df_return_nt_pink)
print(pink_intesect)
all_intesect <- Reduce(intersect, df_return_nt_all)
print(all_intesect)
pdf("All_venn_diagramm.pdf")
myCol <- brewer.pal(3, "Pastel2")
draw.triple.venn(area1 = 117, area2 = 110, area3 = 114, n12 = 23, n23 = 18, n13 = 10,
n123 = 7, category = c("Day06", "Day15", "Day21"),
fill = myCol)
dev.off()
# =========== Venn Diagrams DF genes
library(RColorBrewer)
library(VennDiagram)
myCol <- brewer.pal(4, "Pastel2")
pdf("Control_venn_diagramm.pdf")
day06 <- df_return_nt_cntrl[[1]]
day15 <- df_return_nt_cntrl[[2]]
day21 <- df_return_nt_cntrl[[3]]
IPSC <- df_return_nt_cntrl[[4]]
# Generate plot
v <- venn.diagram(list(Day06=day06, Day15=day15,Day21=day21,IPSC=IPSC),
fill = myCol,
alpha = c(0.5, 0.5, 0.5, 0.5), cat.cex = 1.5, cex=1.5,
filename=NULL)
# have a look at the default plot
grid.newpage()
grid.draw(v)
# have a look at the names in the plot object v
lapply(v, names)
# We are interested in the labels
lapply(v, function(i) i$label)
v[[14]]$label <- paste(intersect(intersect(intersect(day06, day15),day21),IPSC), collapse="\n")
# plot
grid.newpage()
grid.draw(v)
dev.off()
pdf("Pink1_venn_diagramm.pdf")
day06 <- df_return_nt_pink[[1]]
day15 <- df_return_nt_pink[[2]]
day21 <- df_return_nt_pink[[3]]
IPSC <- df_return_nt_pink[[4]]
# Generate plot
v <- venn.diagram(list(Day06=day06, Day15=day15,Day21=day21,IPSC=IPSC),
fill = myCol,
alpha = c(0.5, 0.5, 0.5,0.5), cat.cex = 1.5, cex=1.5,
filename=NULL)
# have a look at the default plot
grid.newpage()
grid.draw(v)
# have a look at the names in the plot object v
lapply(v, names)
# We are interested in the labels
lapply(v, function(i) i$label)
v[[14]]$label <- paste(intersect(intersect(intersect(day06, day15),day21),IPSC), collapse="\n")
# plot
grid.newpage()
grid.draw(v)
dev.off()
myCol <- brewer.pal(3, "Pastel2")
pdf("Summary_venn_diagramm.pdf")
day06 <- c(df_return_nt_cntrl[[1]],df_return_nt_pink[[1]])
day15 <- c(df_return_nt_cntrl[[2]],df_return_nt_pink[[2]])
day21 <- c(df_return_nt_cntrl[[3]],df_return_nt_pink[[3]])
# Generate plot
v <- venn.diagram(list(Day06=day06, Day15=day15,Day21=day21),
fill = myCol,
alpha = c(0.5, 0.5, 0.5), cat.cex = 1.5, cex=1.5,
filename=NULL)
# have a look at the default plot
grid.newpage()
grid.draw(v)
# have a look at the names in the plot object v
lapply(v, names)
# We are interested in the labels
lapply(v, function(i) i$label)
v[[11]]$label <- paste(intersect(intersect(day06, day15),day21), collapse="\n")
# plot
grid.newpage()
grid.draw(v)
dev.off()
\ No newline at end of file
Scripts/Snakemake/4.Network_Validation.R 0 → 100644
View file @ e0a807d6
## ----setup, include=FALSE--------------------------------------------------------------------
set.seed(123)
# library(reticulate)
options(future.globals.maxSize= 2122317824)
library(sctransform)
library(Seurat)
library(RColorBrewer)
library(tictoc)
library(crayon)
library(stringr)
library(Routliers)
library(jcolors)
library(cluster)
library(NMF)
library(ggplot2)
library(ggpubr)
library(cowplot)
library(STRINGdb)
library(igraph)
colormap_d<- c('#a6cee3','#1f78b4','#b2df8a','#33a02c','#fb9a99','#e31a1c','#fdbf6f','#ff7f00','#cab2d6','#6a3d9a','#ffff99','#b15928',"black","gray")
color_cond <- c(brewer.pal(5,"Dark2"),"black","gray","magenta4","seagreen4",brewer.pal(9,"Set1")[-6])[c(5,1,2,3,4,9,6,7,8)]
olor_clust <- c(brewer.pal(12,"Paired")[-11],"black","gray","magenta4","seagreen4",brewer.pal(9,"Set1")[-6],brewer.pal(8,"Dark2"))
color_cells <- c(brewer.pal(9,"Set1")[-6],"goldenrod4","darkblue","seagreen4")
color_list <- list(condition=color_cond,Cluster=color_clust,Cell_Type=color_cells,State=color_clust)
dir.create("/home/users/dkyriakis/PhD/Projects/IPSCs_pink1/4.Network_Validation/")
setwd("/home/users/dkyriakis/PhD/Projects/IPSCs_pink1/4.Network_Validation/")
Combined <- readRDS("/home/users/dkyriakis/PhD/Projects/IPSCs_pink1/1.Preprocess/IPSCs_Combined.rds")
# === READ DF GROUPS
g1 <- read.table("Conserved_all.txt")
g2 <- read.table("Conserved_all_alt.txt")
g3 <- read.table("Conserved_3.txt")
all_g <- rownames(Combined@assays$RNA@counts)
write.table(all_g,"All_genes.txt")
length(all_g)
all_df_g <- unique(c(rownames(g1),rownames(g2),rownames(g3)))
length(all_df_g)
rest <- all_g[!all_g%in%all_df_g]
mean_l <- c()
median_l <- c()
degree_l <- list()
num_nodes <- c()
num_inter <- c()
string_db = STRINGdb$new(version="10",species=9606)
string_plot_net <- function(i,mean_l,median_l,degree_l){
r_list1 <- sample(rest,292)
write.table(r_list1,paste0('Random_Genes',i,".txt"),row.names = F,col.names = F)
Genes <- string_db$mp(r_list1)
#string_db$plot_network(Genes)
inter_g <- string_db$get_interactions(Genes)
full.graph <- string_db$get_subnetwork(Genes)
degrees_f <- degree(full.graph)
mean_l <-c (mean_l,mean(degrees_f))
median_l <-c (median_l,median(degrees_f))
num_nodes <- c(num_nodes,length(degrees_f))
degree_l[[i]] <-degrees_f
num_inter <- c(num_inter,ecount(full.graph))
return(list("mean_l"=mean_l,"median_l"=median_l,"degree_l"=degree_l,"num_nodes"=num_nodes,"num_inter"=num_inter))
}
for(i in 1:50){
return_res <- string_plot_net(i,mean_l,median_l,degree_l)
mean_l <- return_res$mean_l
median_l <- return_res$median_l
num_nodes <- return_res$num_nodes
degree_l <- return_res$degree_l
num_inter <- return_res$num_inter
}
pdf("Ours.pdf")
write.table(all_df_g,paste0('all_df_g',51,".txt"),row.names = F,col.names = F)
string_db = STRINGdb$new(version="10",species=9606)
Genes <- string_db$mp(all_df_g)
string_db$plot_network(Genes)
full.graph <- string_db$get_subnetwork(Genes)
degrees_f <- degree(full.graph)
mean_l <-c (mean_l,mean(degrees_f))
median_l <-c (median_l,median(degrees_f))
num_nodes <- c(num_nodes,length(degrees_f))
num_inter <- c(num_inter,ecount(full.graph))
degree_l[[51]] <-degrees_f
dev.off()
rest_degree <- unlist(degree_l[1:50])
deg_degree <- unlist(degree_l[51])
df <- data.frame("degree"=unlist(degree_l))
length(deg_degree)
length(unlist(degree_l))
n_random <- length(unlist(degree_l)) - length(deg_degree)
df$ord <- "Random"
df$ord[n_random:length(unlist(degree_l))] <- "DEG"
rest_num_nodes <- unlist(num_nodes[1:50])
deg_num_nodes <- unlist(num_nodes[51])
rest_num_inter <- unlist(num_inter[1:50])
deg_num_inter <- unlist(num_inter[51])
d <- density(rest_num_nodes)
library(plyr)
mu <- ddply(df, "ord", summarise, grp.mean=mean(degree))
p1 <- df %>%
ggplot( aes(x=degree, fill=ord)) +
geom_density(alpha=0.8)+
geom_vline(data=mu, aes(xintercept=grp.mean, color=ord),size=2,
linetype="dashed")+theme_cowplot()+
theme(legend.position = "none") + ylab("Degree distribution") +xlab("Degree")
df <- data.frame("nodes"=rest_num_nodes)
df$ord <- "RANDOM"
p2 <- df %>%
ggplot( aes(x=nodes, color=ord)) +
geom_density(fill="#00BFC4", color="#00BFC4",alpha=0.8)+
geom_vline(xintercept = deg_num_nodes,color="#F8766D",linetype="dashed", size = 2)+theme_cowplot()+
theme(legend.position = "top") + ylab("Probability") + xlab("Number of nodes")
# annotate(geom="text", x=225, y=0.045, label="Random",color="#00BFC4",size=4)+
# annotate(geom="text", x=245, y=0.045, label="DEG",color="#F8766D",size=4)+
df <- data.frame("interactions"=rest_num_inter)
df$ord <- "RANDOM"
p6 <- df %>%
ggplot( aes(x=interactions, color=ord)) +
geom_density(fill="#00BFC4", color="#00BFC4",alpha=0.8)+
geom_vline(xintercept = deg_num_inter,color="#F8766D",linetype="dashed",size = 2)+
theme_cowplot()+
theme(legend.position = "top") + ylab("Probability") + xlab("Number of interactions") +xlim(c(400,1550))
#annotate(geom="text", x=840, y=0.003, label="Random",color="#00BFC4",size=4)+
#annotate(geom="text", x=1360, y=0.003, label="DEG",color="#F8766D",size=4)+
# #69b3a2
# #e9ecef
library(jsonlite)
library(purrr)
library(data.table)
dt_list <- map(degree_l, as.data.table)
dt <- rbindlist(dt_list, fill = TRUE, idcol = T)
colnames(dt)[2] <- "Degree"
dt$ord <- "RANDOM"
dt$ord[dt$.id==51]<- "DEG"
colnames(dt)[3] <- "Condition"
pdf("292.pdf")
plot(mean_l,median_l)
plot(mean_l,num_nodes)
plot(median_l,num_nodes)
dev.off()
p3 <- ggplot(dt, aes(x=.id, y=Degree,group=.id,fill=Condition)) +
geom_boxplot()+theme_cowplot()
# ggboxplot(dt, x = ".id", y = "V1",group=".id")+
# stat_compare_means() + # Global p-value
# stat_compare_means(ref.group = 51, label = "p.signif",
# label.y = c(22, 29))
p4 <- ggboxplot(dt, x = "Condition", y = "Degree",
fill = "Condition")+
stat_compare_means(comparisons = my_comparisons)+ # Add pairwise comparisons p-value
stat_compare_means(label.y = 50) + theme_cowplot() + ylab("Degree")
my_comparisons <- list( c("DEG","RANDOM"))
dt2 <- dt[order(Condition),]
p4 <- ggboxplot(dt2, x = "Condition", y = "Degree",
fill = "Condition")+
stat_compare_means(comparisons = my_comparisons, ref.group = "RANDOM") +
#annotate(geom="text", x="DEG", y=90, label="***",color="black",size=5)+theme_cowplot()+
theme(legend.position = "none") + ylab("Degree") +xlab("Condition")
p5 <- ggplot(dt, aes(x=Condition, y=Degree,group=Condition,fill=Condition)) +
geom_boxplot()+
stat_compare_means(method = "wilcox.test")+ # Add global p-value
stat_compare_means(label = "p.signif", method = "t.test",
ref.group = "RANDOM")+theme_cowplot()+
theme(legend.position = "top")
p12 <- ggarrange(plotlist=list(p1,p2),nrow = 1)
p45 <- ggarrange(plotlist=list(p4,p5),nrow = 1)
pdf("QC_292.pdf",height=12,width=8)
ggarrange(plotlist=list(p12,p3,p45),nrow = 3)
dev.off()
pdf("QC2_292.pdf",height=8,width=15)
p125 <- ggarrange(plotlist=list(p1,p2,p6,p4),ncol = 4)
ggarrange(plotlist=list(p125,p3),nrow = 2)
dev.off()
pdf("QC3_292.pdf",width=15,height=10)
ggarrange(plotlist=list(p2,p1,p6,p4),nrow = 2,ncol=2)
dev.off()
pdf("QC4_292.pdf",width=8,height=4)
p2
p1
p6
p4
dev.off()
Scripts/Snakemake/5.Correlation_Plots.R 0 → 100644
View file @ e0a807d6
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Scripts/Snakemake/Snakefile 0 → 100644
View file @ e0a807d6
rule all:
input:
"/home/users/dkyriakis/PhD/Projects/IPSCs_pink1/1.Preprocess/IPSCs_Combined.rds",
"/home/users/dkyriakis/PhD/Projects/IPSCs_pink1/2.Differential_Epression/Conserved_Markers_All/Conserved_all.txt",
"/home/users/dkyriakis/PhD/Projects/IPSCs_pink1/2.Differential_Epression/Conserved_Markers_IPSCsAvg/Conserved_all_alt.txt",
"/home/users/dkyriakis/PhD/Projects/IPSCs_pink1/2.Differential_Epression/Conserved_Markers_3Timepoints/Conserved_3.txt",
"/home/users/dkyriakis/PhD/Projects/IPSCs_pink1/2.Differential_Epression/Pairwise/Volcano.pdf",
"/home/users/dkyriakis/PhD/Projects/IPSCs_pink1/3.Venn_Pairwise/Summary_venn_diagramm.pdf",
"/home/users/dkyriakis/PhD/Projects/IPSCs_pink1/4.Network_Validation/QC4_292.pdf",
"/home/users/dkyriakis/PhD/Projects/IPSCs_pink1/5.Correlation_Net/Network_extented.pdf"
# ==================================================================== Preprocessing ======================================================================
rule Preprocess:
input:
"home/users/dkyriakis/PhD/Projects/Michi_Data/DATA/DADA1_S1_DGE.txt",
"home/users/dkyriakis/PhD/Projects/Michi_Data/DATA/DADA2_S2_DGE.txt",
"home/users/dkyriakis/PhD/Projects/Michi_Data/DATA/DADA3_S3_DGE.txt",
"home/users/dkyriakis/PhD/Projects/Michi_Data/DATA/DADA4_S4_DGE.txt",
"home/users/dkyriakis/PhD/Projects/Michi_Data/DATA/DADA5_S1_DGE.txt",
"home/users/dkyriakis/PhD/Projects/Michi_Data/DATA/DADA6_S2_DGE.txt",
"home/users/dkyriakis/PhD/Projects/Michi_Data/DATA/DADA8_S4_DGE.txt",
"home/users/dkyriakis/PhD/Projects/Michi_Data/DATA/DADD5_S2_DGE.txt",
"home/users/dkyriakis/PhD/Projects/Michi_Data/DATA/DADD6_S3_DGE.txt"
output:
"/home/users/dkyriakis/PhD/Projects/IPSCs_pink1/1.Preprocess/IPSCs_Combined.rds"
shell:
"Rscript 1.Preprocessing.R"
# ------------------------------------------------------------------------------------------------------------------------------------------------------
# ==================================================================== Conserved_Markers_All ======================================================================
rule Conserved_Markers_All:
input:
"/home/users/dkyriakis/PhD/Projects/IPSCs_pink1/1.Preprocess/IPSCs_Combined.rds"
output:
"/home/users/dkyriakis/PhD/Projects/IPSCs_pink1/2.Differential_Epression/Conserved_Markers_All/Conserved_all.txt"
shell:
"Rscript 2.1.Conserved_Markers.R"
# ------------------------------------------------------------------------------------------------------------------------------------------------------
# ==================================================================== Conserved_Markers_IPSCsAvg ======================================================================
rule Conserved_Markers_IPSCsAvg:
input:
"/home/users/dkyriakis/PhD/Projects/IPSCs_pink1/1.Preprocess/IPSCs_Combined.rds"
output:
"/home/users/dkyriakis/PhD/Projects/IPSCs_pink1/2.Differential_Epression/Conserved_Markers_IPSCsAvg/Conserved_all_alt.txt"
shell:
"Rscript 2.2.Conserved_Markers_IPSCsAvg.R"
# ------------------------------------------------------------------------------------------------------------------------------------------------------
# ==================================================================== Conserved_Markers_3Timepoints ======================================================================
rule Conserved_Markers_3Timepoints:
input:
"/home/users/dkyriakis/PhD/Projects/IPSCs_pink1/1.Preprocess/IPSCs_Combined.rds"
output:
"/home/users/dkyriakis/PhD/Projects/IPSCs_pink1/2.Differential_Epression/Conserved_Markers_3Timepoints/Conserved_3.txt"
shell:
"Rscript 2.3.Conserved_Markers_3Timepoints.R"
# ------------------------------------------------------------------------------------------------------------------------------------------------------
# ==================================================================== DF Pairwise ======================================================================
rule Pairwise:
input:
"/home/users/dkyriakis/PhD/Projects/IPSCs_pink1/1.Preprocess/IPSCs_Combined.rds"
output:
"/home/users/dkyriakis/PhD/Projects/IPSCs_pink1/2.Differential_Epression/Pairwise/Volcano.pdf",
"/home/users/dkyriakis/PhD/Projects/IPSCs_pink1/2.Differential_Epression/Pairwise/",
"/home/users/dkyriakis/PhD/Projects/IPSCs_pink1/2.Differential_Epression/Pairwise/",
"/home/users/dkyriakis/PhD/Projects/IPSCs_pink1/2.Differential_Epression/Pairwise/"
shell:
"Rscript 2.4.Pairwise.R"
# ------------------------------------------------------------------------------------------------------------------------------------------------------
# ==================================================================== Venn_diagramm ======================================================================
rule Venn_diagramm:
input:
"/home/users/dkyriakis/PhD/Projects/IPSCs_pink1/1.Preprocess/IPSCs_Combined.rds"
output:
"/home/users/dkyriakis/PhD/Projects/IPSCs_pink1/3.Venn_Pairwise/Summary_venn_diagramm.pdf"
shell:
"Rscript 3.VennDiagrams.R"
# ------------------------------------------------------------------------------------------------------------------------------------------------------
# ==================================================================== Correlation_Net ======================================================================
rule Correlation_Net:
input:
"/home/users/dkyriakis/PhD/Projects/IPSCs_pink1/2.Differential_Epression/Conserved_Markers_All/Conserved_all.txt",
"/home/users/dkyriakis/PhD/Projects/IPSCs_pink1/2.Differential_Epression/Conserved_Markers_IPSCsAvg/Conserved_all_alt.txt",
"/home/users/dkyriakis/PhD/Projects/IPSCs_pink1/2.Differential_Epression/Conserved_Markers_3Timepoints/Conserved_3.txt"
output:
"/home/users/dkyriakis/PhD/Projects/IPSCs_pink1/5.Correlation_Net/Network_extented.pdf"
shell:
"Rscript 5.Correlation_Plots.R"
# ------------------------------------------------------------------------------------------------------------------------------------------------------
# ==================================================================== Network_Validation ======================================================================
rule Network_Validation:
input:
"/home/users/dkyriakis/PhD/Projects/IPSCs_pink1/2.Differential_Epression/Conserved_Markers_All/Conserved_all.txt",
"/home/users/dkyriakis/PhD/Projects/IPSCs_pink1/2.Differential_Epression/Conserved_Markers_IPSCsAvg/Conserved_all_alt.txt",
"/home/users/dkyriakis/PhD/Projects/IPSCs_pink1/2.Differential_Epression/Conserved_Markers_3Timepoints/Conserved_3.txt"
output:
"/home/users/dkyriakis/PhD/Projects/IPSCs_pink1/4.Network_Validation/QC4_292.pdf"
shell:
"Rscript 4.Network_Validation.R"
# ------------------------------------------------------------------------------------------------------------------------------------------------------
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