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Alyssa Salazar
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RenalTumor
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[40a513]: / RNA-seq / AnalysisPipeline / 6.4.Visualizing.network.R

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#' @description: Visualized network view

#Combine R package---RCy3 (V2.10.2) and Cytoscape (V 3.8.2)

#Since cytoscape cannot be started on the server, it is currently being visualized locally



############################################## ########All interactions############################

####Step 1: Get network data

#91Server

#Since network.txt cannot distinguish the directional interaction, it is based on your own processing

library(dplyr)

library(tidyverse)

library(ggpubr)

setwd("/data/active_data/lzl/RenalTumor-20200713/DataAnalysis-20210803/scRNA/6.CrossTalk/CellPhoneDB")

count_network <- readRDS("All.interaction.number.rds") # mean>=1 & pvalue < 0.05

node.info <- count_network[,1:2]



mypvals <- readRDS("mypvals.usr.rds")

mymeans <- readRDS("mymeans.usr.rds")

mymeans <- mymeans[,-c(1,3:10,12)]

mypvals <- mypvals[,-c(1,3:10,12)]

mymeans %>% reshape2::melt(id.vars = c("interacting_pair", "ligand")) -> meansdf

colnames(meansdf)<- c("interacting_pair","ligand","CC","means")

mypvals %>% reshape2::melt(id.vars = c("interacting_pair", "ligand"))-> pvalsdf

colnames(pvalsdf)<- c("interacting_pair","ligand","CC","pvals")

pvalsdf$joinlab<- paste0(pvalsdf$interacting_pair, "_", pvalsdf$CC)

meansdf$joinlab<- paste0(meansdf$interacting_pair, "_", meansdf$CC)

pldf <- merge(pvalsdf,meansdf,by = "joinlab")

pldf <- pldf[,-c(6:8)]

pldf.pro <- pldf%>% filter(means>=1 & pvals<0.05) # 13421head

pldf.pro[,4] <- as.character(pldf.pro[,4])



#Adjust the way of interaction --- ligand-receptor

pldf.pro <- apply(pldf.pro, 1, function(x){

    if(x[3] == "Receptor"){



        pairs <- unlist(strsplit(x = x[2], split = "_"))

        x[2] <- paste0(pairs[2], "_", pairs[1])



        interactions <- unlist(strsplit(x = x[4], split = "\\|"))

        x[4] <- paste0(interactions[2], "|", interactions[1])



        x[1] <- paste0(x[2], "|", x[4])



        x[3] <- "Ligand"

    }

    return(x)

})

pldf.pro <- as.data.frame(t(pldf.pro))



edge.num <- as.data.frame(table(as.character(pldf.pro$CC.x)))

edge.info <- apply(edge.num, 1, function(x){

    inter <- unlist(strsplit(x[1], "\\|"))

    return(c(inter, x[2]))

})

edge.info <- as.data.frame(t(edge.info)) #189

colnames(edge.info) <- c("Source", "Target", "Number")

#Replace CD8+ T-IEG back to CD8+ T-Exhausted IEG

edge.info$Source <- gsub("CD8\\+ T-IEG", "CD8\\+ T-Exhausted IEG", edge.info$Source)

edge.info$Target <- gsub("CD8\\+ T-IEG", "CD8\\+ T-Exhausted IEG", edge.info$Target)

node.info$cellType <- gsub("CD8\\+ T-IEG", "CD8\\+ T-Exhausted IEG", node.info$cellType)



##Building network information

class.type <- rep("Lymphoid", nrow(node.info))

class.type[c(1, 8, 13:16, 19)] <- "Myeloid"

class.type[10] <- "Tumor"

class.type[c(4, 6, 12)] <- "Other"

nodes <- data.frame(id = node.info[,1],

                    group = class.type, # categorical strings

                    score = as.integer(node.info[,2]), # integers

                    stringsAsFactors=FALSE)

edges <- data.frame(source = edge.info$Source,

                    target = edge.info$Target,

                    weight = as.numeric(edge.info$Number), # numeric

                    stringsAsFactors=FALSE)

write.table(nodes, file = "nodes.txt", sep = "\t", row.names = F, col.names = T, quote = F)

write.table(edges, file = "edges.txt", sep = "\t", row.names = F, col.names = T, quote = F)





library(RCy3)

cytoscapePing()

cytoscapeVersionInfo()

setwd("D:/work/Renal Tumor/Result/DataAnalysis-2021.8.3/scRNA/6.CrossTalk/CellPhoneDB")

nodes <- read.table("nodes.txt", header = T, stringsAsFactors = F, sep = "\t")

edges <- read.table("edges.txt", header = T, stringsAsFactors = F, sep = "\t")



tumor.colors <- rep(0, nrow(edges))

#tumor- target

idx <- which(edges$target=="Tumor")

tumor.colors[idx] <- 2

#tumor-source

idx <- which(edges$source=="Tumor")

tumor.colors[idx] <- 1

edges$tumor <- tumor.colors



#Standardize the points to a size of 1-10

library(vegan)

nodes$weight <- round(nodes[,3]/100)

createNetworkFromDataFrames(nodes = nodes,edges = edges, title="cross-talk", collection="M1")



style.name = "myStyle"

defaults <- list(NODE_SHAPE="ELLIPSE",

                 EDGE_TRANSPARENCY=120)

nodeLabels <- mapVisualProperty('node label','id','p')

nodesizes <- mapVisualProperty("node size","weight","p")

#node.border.colors <- mapVisualProperty("node border paint","group","d",c("Lymphoid","Myeloid", "CancerCell"), c("#34A047","#00B3F1", "#EF7F48"))

#nodeFills <- mapVisualProperty("node fill color","group","d",c("Lymphoid","Myeloid", "CancerCell"), c("#34A047","#00B3F1", "#EF7F48"))

edgeWidth <- mapVisualProperty("edge width","weight","p")

createVisualStyle(style.name, defaults, list(nodeLabels, nodesizes, edgeWidth))



#pal_npg("nrc")(4)

# [1] "#E64B35FF" "#4DBBD5FF" "#00A087FF" "#3C5488FF" "#F39B7FFF" "#8491B4FF"

library(ggplot2)

library(ggsci)



node.colors <- nodes$group

node.colors <- gsub("Lymphoid", "#00A087", node.colors)

node.colors <- gsub("Myeloid", "#4DBBD5", node.colors)

node.colors <- gsub("Tumor", "#E64B35", node.colors)

node.colors <- gsub("Other", "#3C5488", node.colors)

setNodeColorBypass(node.names = nodes$id, new.colors = node.colors)

setNodeBorderColorMapping(table.column = 'group', table.column.values = c("Lymphoid","Myeloid", "Tumor", "Other"), colors = c("#00A087","#4DBBD5", "#E64B35", "#3C5488"), mapping.type = 'd', style.name = style.name)

setEdgeTargetArrowShapeDefault(new.shape = "ARROW", style.name = style.name)

setEdgeColorMapping(table.column = c('tumor'), table.column.values = c(0,1,2), colors = c("#CCCCCC", "#F15F30", "#1e90ff"), mapping.type = 'd', style.name = style.name)

setVisualStyle(style.name)





##########################################################Tumor interactions#############################

library(dplyr)

library(tidyverse)

library(ggpubr)

setwd("/data/active_data/lzl/RenalTumor-20200713/DataAnalysis-20210803/scRNA/6.CrossTalk/CellPhoneDB")

count_network <- readRDS("Tumor/tumor.interaction.number.rds") # mean>=1 & pvalue < 0.05

node.info <- count_network[,1:2]



mypvals <- readRDS("mypvals.usr.rds")

mymeans <- readRDS("mymeans.usr.rds")

mymeans <- mymeans[,-c(1,3:10,12)]

mypvals <- mypvals[,-c(1,3:10,12)]

mymeans %>% dplyr::select("interacting_pair", "ligand", starts_with("Tumor"), ends_with("Tumor")) %>% reshape2::melt() -> meansdf

colnames(meansdf)<- c("interacting_pair","ligand","CC","means")

mypvals %>% dplyr::select("interacting_pair", "ligand", starts_with("Tumor"), ends_with("Tumor")) %>% reshape2::melt()-> pvalsdf

colnames(pvalsdf)<- c("interacting_pair", "ligand", "CC","pvals")

pvalsdf$joinlab<- paste0(pvalsdf$interacting_pair, "_", pvalsdf$CC)

meansdf$joinlab<- paste0(meansdf$interacting_pair, "_", meansdf$CC)

pldf <- merge(pvalsdf,meansdf,by = "joinlab")

pldf <- pldf[,-c(6:8)]

pldf.pro <- pldf%>% filter(means>=1 & pvals<0.05)

pldf.pro[,4] <- as.character(pldf.pro[,4])



pldf.pro <- apply(pldf.pro, 1, function(x){

    if(x[3] == "Receptor"){

        pairs <- unlist(strsplit(x = x[2], split = "_"))

        x[2] <- paste0(pairs[2], "_", pairs[1])



        interactions <- unlist(strsplit(x = x[4], split = "\\|"))

        x[4] <- paste0(interactions[2], "|", interactions[1])



        #调整joinlab

        x[1] <- paste0(x[2], "|", x[4])



        x[3] <- "Ligand"

    }

    return(x)

})

pldf.pro <- as.data.frame(t(pldf.pro))

edge.num <- as.data.frame(table(as.character(pldf.pro$CC.x)))

edge.info <- apply(edge.num, 1, function(x){

    inter <- unlist(strsplit(x[1], "\\|"))

    return(c(inter, x[2]))

})

edge.info <- as.data.frame(t(edge.info)) #189

colnames(edge.info) <- c("Source", "Target", "Number")

edge.info$Source <- gsub("CD8\\+ T-IEG", "CD8\\+ T-Exhausted IEG", edge.info$Source)

edge.info$Target <- gsub("CD8\\+ T-IEG", "CD8\\+ T-Exhausted IEG", edge.info$Target)

node.info$cellType <- gsub("CD8\\+ T-IEG", "CD8\\+ T-Exhausted IEG", node.info$cellType)



class.type <- rep("Lymphoid", nrow(node.info))

class.type[c(7,10,12:13,16:18)] <- "Myeloid"

class.type[20] <- "Tumor"

class.type[c(8:9, 11)] <- "Other"

nodes <- data.frame(id = node.info[,1],

                    group = class.type, # categorical strings

                    score = as.integer(node.info[,2]), # integers

                    stringsAsFactors=FALSE)

edges <- data.frame(source = edge.info$Source,

                    target = edge.info$Target,

                    weight = as.numeric(edge.info$Number), # numeric

                    stringsAsFactors=FALSE)

write.table(nodes, file = "Tumor/nodes.txt", sep = "\t", row.names = F, col.names = T, quote = F)

write.table(edges, file = "Tumor/edges.txt", sep = "\t", row.names = F, col.names = T, quote = F)



library(RCy3)

cytoscapePing()

cytoscapeVersionInfo()

setwd("D:/work/Renal Tumor/Result/DataAnalysis-2021.8.3/scRNA/6.CrossTalk/CellPhoneDB/Tumor")

nodes <- read.table("nodes.txt", header = T, stringsAsFactors = F, sep = "\t")

edges <- read.table("edges.txt", header = T, stringsAsFactors = F, sep = "\t")



tumor.colors <- rep(0, nrow(edges))

#tumor- target

idx <- which(edges$target=="Tumor")

tumor.colors[idx] <- 2

#tumor-source

idx <- which(edges$source=="Tumor")

tumor.colors[idx] <- 1

edges$tumor <- tumor.colors



library(vegan)

nodes$weight <- round(nodes[,3]/10)

idx <- which(nodes$id == "Tumor")

nodes$weight[idx] <- 15

createNetworkFromDataFrames(nodes = nodes,edges = edges, title="cross-talk", collection="M1")



style.name = "myStyle"

defaults <- list(NODE_SHAPE="ELLIPSE",

                 EDGE_TRANSPARENCY=120)

nodeLabels <- mapVisualProperty('node label','id','p')

nodesizes <- mapVisualProperty("node size","weight","p")

#node.border.colors <- mapVisualProperty("node border paint","group","d",c("Lymphoid","Myeloid", "CancerCell"), c("#34A047","#00B3F1", "#EF7F48"))

#nodeFills <- mapVisualProperty("node fill color","group","d",c("Lymphoid","Myeloid", "CancerCell"), c("#34A047","#00B3F1", "#EF7F48"))

edgeWidth <- mapVisualProperty("edge width","weight","p")

createVisualStyle(style.name, defaults, list(nodeLabels, nodesizes, edgeWidth))



library(ggplot2)

library(ggsci)



node.colors <- nodes$group

node.colors <- gsub("Lymphoid", "#00A087", node.colors)

node.colors <- gsub("Myeloid", "#4DBBD5", node.colors)

node.colors <- gsub("Tumor", "#E64B35", node.colors)

node.colors <- gsub("Other", "#3C5488", node.colors)

setNodeColorBypass(node.names = nodes$id, new.colors = node.colors)

setNodeBorderColorMapping(table.column = 'group', table.column.values = c("Lymphoid","Myeloid", "Tumor", "Other"), colors = c("#00A087","#4DBBD5", "#E64B35", "#3C5488"), mapping.type = 'd', style.name = style.name)

setEdgeTargetArrowShapeDefault(new.shape = "ARROW", style.name = style.name)

setEdgeColorMapping(table.column = c('tumor'), table.column.values = c(0,1,2), colors = c("#CCCCCC", "#F15F30", "#1e90ff"), mapping.type = 'd', style.name = style.name)

setVisualStyle(style.name)