Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Version_4_27 code #1

Open
wants to merge 1 commit into
base: main
Choose a base branch
from
Open

Version_4_27 code #1

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
Binary file added CRC_velocity.png
View file
Open in desktop
Loading
Sorry, something went wrong. Reload?
Sorry, we cannot display this file.
Sorry, this file is invalid so it cannot be displayed.
386 changes: 386 additions & 0 deletions Cell_Cell_interaction.r
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -0,0 +1,386 @@


library(Seurat)
dat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/CSOmap/LCBM_tumor_myeloid.RDS")
[email protected] <- factor(dat$celltype)
tmp <- AverageExpression(dat,assay="RNA")$RNA
tmp$M.T.FC <- tmp$MDM/tmp$tumor
tmp$T.M.FC <- tmp$tumor/tmp$MDM
# tmp <- tmp[order(tmp$M.T.FC,decreasing=T),]
tmp.f <- tmp[-which(is.infinite(tmp$T.M.FC)|is.infinite(tmp$M.T.FC)),]
MDM.h <- rownames(tmp.f)[which(tmp.f$M.T.FC>2)]
tumor.f <- rownames(tmp.f)[which(tmp.f$T.M.FC>100)]
# 2021-6-10
library(CellChat)
CellChatDB <- CellChatDB.human

info <- data.frame(ligand=sapply(strsplit(as.vector(CellChatDB$interaction$interaction_name_2)," - "),function(x){x[1]}),
receport=sapply(strsplit(as.vector(CellChatDB$interaction$interaction_name_2)," - "),function(x){x[2]}),stringsAsFactors=F)

for(i in 1:nrow(info)){
if(length(grep("\\)|\\(",info[i,2]))>0){
tmp <- gsub("\\(|\\)|^ ","",info[i,2])
pair <- strsplit(tmp,"\\+")[[1]]
for(j in 1:length(pair)){
value <- c(info[i,1],pair[j])
info <- rbind(info,value)
}
}
}
info.f <- info[-grep("\\+",info$receport),] # get gene pair
colnames(info.f) <- c("ligand","receptor")
saveRDS(info.f,file="/public/workspace/lily/Lung2Brain/Version5/CellChat/gene_pair.RDS")
# get gene expression

library(Seurat)
dat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/CSOmap/LCBM_tumor_myeloid.RDS")
[email protected] <- factor(dat$celltype)
tmp <- AverageExpression(dat,assay="RNA")$RNA
tmp$M.T <- tmp$MDM-tmp$tumor
tmp$T.M <- tmp$tumor-tmp$MDM
tmp$gene <- rownames(tmp)

# LUAD
dat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/CSOmap/LUAD_tumor_myeloid.RDS")
[email protected] <- factor(dat$celltype)
tmp <- AverageExpression(dat,assay="RNA")$RNA
tmp$M.T <- tmp$MDM-tmp$tumor
tmp$T.M <- tmp$tumor-tmp$MDM
tmp$gene <- rownames(tmp)


# GBM
dat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/CSOmap/GBM_tumor_myeloid.RDS")
[email protected] <- factor(dat$celltype)
tmp <- AverageExpression(dat,assay="RNA")$RNA
tmp$M.T <- tmp$MDM-tmp$GBM_tumor
tmp$T.M <- tmp$GBM_tumor-tmp$MDM
tmp$gene <- rownames(tmp)


genepair <- readRDS("/public/workspace/lily/Lung2Brain/Version5/CellChat/gene_pair.RDS")
# genepair$ligand <- gsub(" $","",genepair$ligand)
# genepair$receptor <- gsub("^ ","",genepair$receptor)
# saveRDS(genepair,file="/public/workspace/lily/Lung2Brain/Version5/CellChat/gene_pair.RDS")
tmp1 <- merge(tmp[,c("M.T","gene")],genepair,by.x="gene",by.y="ligand",all.y=T)
colnames(tmp1)[1] <- "ligand"
tmp2 <- merge(tmp[,c("T.M","gene")],tmp1,by.x="gene",by.y="receptor",all.y=T)
colnames(tmp2)[1] <- "receptor"
tmp2[which(tmp2$M.T>0.5&tmp2$T.M>0.5),]










###################################################################################################################
# 2021-6-11
#
library(Seurat)
dat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/CSOmap/LCBM_tumor_myeloid.RDS")
[email protected] <- factor(dat$celltype)
tmp <- AverageExpression(dat,assay="RNA")$RNA
tmp$M.T <- tmp$MDM-tmp$tumor
tmp$T.M <- tmp$tumor-tmp$MDM
tmp$gene <- rownames(tmp)

dat$class1 <- as.vector(dat$celltype)
dat$class1[which(dat$celltype%in%c("MG","Monocyte","tumor"))] <- "other"
[email protected] <- factor(dat$class1)
tmp1 <- AverageExpression(dat,assay="RNA")$RNA
tmp1$MDM.h <- tmp1$MDM -tmp1$other

dat$class2 <- as.vector(dat$celltype)
dat$class2[which(dat$celltype%in%c("MG","Monocyte","MDM"))] <- "other"
[email protected] <- factor(dat$class2)
tmp2 <- AverageExpression(dat,assay="RNA")$RNA
tmp2$tumor.h <- tmp2$tumor -tmp2$other

tmp <- cbind(tmp1$MDM.h,tmp2$tumor.h)
colnames(tmp) <- c("M.T","T.M")
rownames(tmp) <- rownames(tmp1)
tmp <- data.frame(tmp)
tmp$gene <- rownames(tmp)


genepair <- readRDS("/public/workspace/lily/Lung2Brain/Version5/CellChat/gene_pair.RDS")
# genepair$ligand <- gsub(" $","",genepair$ligand)
# genepair$receptor <- gsub("^ ","",genepair$receptor)
# saveRDS(genepair,file="/public/workspace/lily/Lung2Brain/Version5/CellChat/gene_pair.RDS")
tmp1 <- merge(tmp[,c("M.T","gene")],genepair,by.x="gene",by.y="receptor",all.y=T)
colnames(tmp1)[1] <- "receptor"
tmp2 <- merge(tmp[,c("T.M","gene")],tmp1,by.x="gene",by.y="ligand",all.y=T)
colnames(tmp2)[1] <- "ligand"

tmp2[which(tmp2$M.T>0.5&tmp2$T.M>0.5),]



tmp1 <- merge(tmp[,c("M.T","gene")],genepair,by.x="gene",by.y="ligand",all.y=T)
colnames(tmp1)[1] <- "ligand"
tmp2 <- merge(tmp[,c("T.M","gene")],tmp1,by.x="gene",by.y="receptor",all.y=T)
colnames(tmp2)[1] <- "receptor"
tmp2[which(tmp2$M.T>0.5&tmp2$T.M>0.5),]


# get gene pair names

tmp.log <- log2(tmp[,c("MDM","tumor")]+1)
tmp.log$group <- "no"
tmp.log$group[which(rownames(tmp.log)%in%c("SDC4","OSMR","EGFR","ITGA3","IL1R1","SDC1"))] <- "receptor"
tmp.log$group[which(rownames(tmp.log)%in%c("FN1","THBS1","OSM","IL1B","HBEGF"))] <- "ligand"
tmp.log$group <- factor(tmp.log$group,levels=c("receptor","ligand","no"))

tmp.log$gene <- ""
tmp.log$gene[which(rownames(tmp.log)%in%c("SDC4","OSMR","EGFR","ITGA3","IL1R1","SDC1"))] <- rownames(tmp.log)[which(rownames(tmp.log)%in%c("SDC4","OSMR","EGFR","ITGA3","IL1R1","SDC1"))]
tmp.log$gene[which(rownames(tmp.log)%in%c("FN1","THBS1","OSM","IL1B","HBEGF"))] <- rownames(tmp.log)[which(rownames(tmp.log)%in%c("FN1","THBS1","OSM","IL1B","HBEGF"))]


pdf("/public/workspace/lily/Lung2Brain/Version5/Myeloid/MDM2Tumor.interaction.pdf",useDingbats=F)
ggplot(tmp.log,aes(x=MDM,y=tumor,colour=group,group=group)) + geom_point() + theme_classic() +
scale_colour_manual(values=c("#007cc0","#ffb310","grey")) +
geom_text(aes(label = gene), size = 3)

dev.off()










#######################################################################################################################################
# 2021-6-17
# CellChat result check
#======================================================================================================================================
# 1. MDM2Tumor
library(Seurat)
library(CellChat)


cellchat1 <- readRDS("/public/workspace/lily/Lung2Brain/Version5/CellChat/LCBM.CellChat.RDS")
cellchat2 <- readRDS("/public/workspace/lily/Lung2Brain/Version5/CellChat/GBM.CellChat.RDS")
cellchat3 <- readRDS("/public/workspace/lily/Lung2Brain/Version5/CellChat/LUAD.CellChat.RDS")

# LCBM
tmp <- netVisual_bubble(cellchat1, sources.use = 3, targets.use = 4, remove.isolate = FALSE,return.data=T)$communication
tmp.res <- tmp[,c("prob","interaction_name_2")]
tmp.res$group <- "LCBM"
tmp1 <- tmp.res
# LUAD
tmp <- netVisual_bubble(cellchat3, sources.use = 1, targets.use = 4, remove.isolate = FALSE,return.data=T)$communication
tmp.res <- tmp[,c("prob","interaction_name_2")]
tmp.res$group <- "LUAD"
tmp3 <- tmp.res
# GBM
tmp <- netVisual_bubble(cellchat2, sources.use = 2, targets.use = 1, remove.isolate = FALSE,return.data=T)$communication
tmp.res <- tmp[,c("prob","interaction_name_2")]
tmp.res$group <- "GBM"
tmp2 <- tmp.res

# merge interaction data
tmp.f <- merge(tmp1,tmp2,all.x=T,all.y=T,by="interaction_name_2")
colnames(tmp.f)[2:5] <- c("prob.LCBM","group.LCBM","prob.GBM","group.GBM")
tmp.rs <- merge(tmp.f,tmp3,all.x=T,all.y=T,by="interaction_name_2")

sub.rs <- tmp.rs[,c("interaction_name_2","prob.LCBM","prob.GBM","prob")]
colnames(sub.rs)[4] <- "prob.LUAD"



tmp.lung <- sub.rs[which(sub.rs$prob.LUAD>0&sub.rs$prob.LCBM>0&is.na(sub.rs$prob.GBM)),]
rownames(tmp.lung) <- tmp.lung$interaction_name_2
tmp.lung <- tmp.lung[order(tmp.lung$prob.LCBM,decreasing=T),]
pdf("/public/workspace/lily/Lung2Brain/Version5/Myeloid/LCBM.LUAD.CellChat.MDM.tumor.pdf",useDingbats=F)
pheatmap::pheatmap(tmp.lung[,2:4],na_col="grey",cluster_rows=F,cluster_cols=F)
dev.off()

tmp.brain <- sub.rs[which(sub.rs$prob.GBM>0&sub.rs$prob.LCBM>0&is.na(sub.rs$prob.LUAD)),]
rownames(tmp.brain) <- tmp.brain$interaction_name_2
tmp.brain <- tmp.brain[order(tmp.brain$prob.LCBM,decreasing=T),]
pdf("/public/workspace/lily/Lung2Brain/Version5/Myeloid/LCBM.GBM.CellChat.MDM.tumor.pdf",useDingbats=F)
pheatmap::pheatmap(tmp.brain[,2:4],na_col="grey",cluster_rows=F,cluster_cols=F)
dev.off()







# 2021-7-6
# 2. Tumor2MDM
#####################################################################################################################################
library(Seurat)
library(CellChat)


cellchat1 <- readRDS("/public/workspace/lily/Lung2Brain/Version5/CellChat/LCBM.CellChat.RDS")
cellchat2 <- readRDS("/public/workspace/lily/Lung2Brain/Version5/CellChat/GBM.CellChat.RDS")
cellchat3 <- readRDS("/public/workspace/lily/Lung2Brain/Version5/CellChat/LUAD.CellChat.RDS")

# LCBM
tmp <- netVisual_bubble(cellchat1, sources.use = 4, targets.use = 1, remove.isolate = FALSE,return.data=T)$communication
tmp.res <- tmp[,c("prob","interaction_name_2")]
tmp.res$group <- "LCBM"
tmp1 <- tmp.res
# LUAD
tmp <- netVisual_bubble(cellchat3, sources.use = 4, targets.use = 1, remove.isolate = FALSE,return.data=T)$communication
tmp.res <- tmp[,c("prob","interaction_name_2")]
tmp.res$group <- "LUAD"
tmp3 <- tmp.res
# GBM
tmp <- netVisual_bubble(cellchat2, sources.use = 1, targets.use = 2, remove.isolate = FALSE,return.data=T)$communication
tmp.res <- tmp[,c("prob","interaction_name_2")]
tmp.res$group <- "GBM"
tmp2 <- tmp.res

# merge interaction data
tmp.f <- merge(tmp1,tmp2,all.x=T,all.y=T,by="interaction_name_2")
colnames(tmp.f)[2:5] <- c("prob.LCBM","group.LCBM","prob.GBM","group.GBM")
tmp.rs <- merge(tmp.f,tmp3,all.x=T,all.y=T,by="interaction_name_2")

sub.rs <- tmp.rs[,c("interaction_name_2","prob.LCBM","prob.GBM","prob")]
colnames(sub.rs)[4] <- "prob.LUAD"


tmp.lung <- sub.rs[which(sub.rs$prob.LUAD>0&sub.rs$prob.LCBM>0&is.na(sub.rs$prob.GBM)),]
rownames(tmp.lung) <- tmp.lung$interaction_name_2
tmp.lung <- tmp.lung[order(tmp.lung$prob.LCBM,decreasing=T),]
pdf("/public/workspace/lily/Lung2Brain/Version5/Myeloid/LCBM.LUAD.CellChat.Tumor.mdm.pdf",useDingbats=F)
pheatmap::pheatmap(tmp.lung[,2:4],na_col="grey",cluster_rows=F,cluster_cols=F)
dev.off()

tmp.brain <- sub.rs[which(sub.rs$prob.GBM>0&sub.rs$prob.LCBM>0&is.na(sub.rs$prob.LUAD)),]
rownames(tmp.brain) <- tmp.brain$interaction_name_2
tmp.brain <- tmp.brain[order(tmp.brain$prob.LCBM,decreasing=T),]
pdf("/public/workspace/lily/Lung2Brain/Version5/Myeloid/LCBM.GBM.CellChat.Tumor.mdm.pdf",useDingbats=F)
pheatmap::pheatmap(tmp.brain[,2:4],na_col="grey",cluster_rows=F,cluster_cols=F)
dev.off()













# 2021-7-6
# calculate LCBM samples Tumor with Myeloid cell cell communication
####################################################################################################################################
library(Seurat)
library(CellChat)
# make a data
dat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Myeloid/inte9_myeloid.RDS")
sub.dat <- subset(dat,cells=which(dat$type_group=="LCBM"))
tumor <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Fig4/Data/LCBM_sub.tumor.RDS")
tumor$celltype <- tumor$group
tmp.res <- merge(sub.dat,tumor)
sub.dat.f <- subset(tmp.res,cells=which(tmp.res$celltype%in%c("MDM","MG","Monocyte","group09","group16","group47")))
saveRDS(sub.dat.f,file="/public/workspace/lily/Lung2Brain/Version5/Myeloid/SubTumor/LCBM_subtumor_myeloid.RDS")

# by the way ,do cell chat analysis
dat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Myeloid/SubTumor/LCBM_subtumor_myeloid.RDS")
data.input <- GetAssayData(dat, assay = "RNA", slot = "data") # normalized data matrix
meta <- data.frame([email protected]) # create a dataframe of the cell labels
cellchat <- createCellChat(object = data.input, meta = meta, group.by = "celltype")
cellchat <- setIdent(cellchat, ident.use = "celltype") # set "labels" as default cell identity
levels(cellchat@idents)

CellChatDB <- CellChatDB.human
# use all DB
CellChatDB.use <- CellChatDB
# set the used database in the object
cellchat@DB <- CellChatDB.use
cellchat <- subsetData(cellchat) # This step is necessary even if using the whole database
future::plan("multiprocess", workers = 8) # do parallel

#####################################################################################################
cellchat <- identifyOverExpressedGenes(cellchat)
cellchat <- identifyOverExpressedInteractions(cellchat)
# project gene expression data onto PPI network (should use)
cellchat <- projectData(cellchat, PPI.human)

cellchat <- computeCommunProb(cellchat) # 1W cells 5min
# Filter out the cell-cell communication if there are only few number of cells in certain cell groups
cellchat <- filterCommunication(cellchat, min.cells = 10)
cellchat <- computeCommunProbPathway(cellchat)
# Calculate the aggregated cell-cell communication network
cellchat <- aggregateNet(cellchat)

saveRDS(cellchat,file="/public/workspace/lily/Lung2Brain/Version5/Myeloid/SubTumor/LCBM_subtumor_myeloid.cellchat.RDS")





#===================================================================================================================================
# check result
# 0. interaction number differneces
library(CellChat)
cellchat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Myeloid/SubTumor/LCBM_subtumor_myeloid.cellchat.RDS")
groupSize <- table(cellchat@idents)
netVisual_circle(cellchat@net$count, vertex.weight = groupSize, weight.scale = T, label.edge= F, title.name = "Number of interactions")

netVisual_bubble(cellchat, sources.use = 1, targets.use = 2, remove.isolate = FALSE,return.data=F)


cellchat@net$count -> tmp
tmp[1:3,1:3]
tmp[1:3,1:3] <- 0
tmp[4:6,4:6] <- 0

rownames(tmp) <- paste0(rownames(tmp),".source")
colnames(tmp) <- paste0(colnames(tmp),".target")
groups <- rep(c("group09","group16","group47","MDM","MG","Monocyte"),2)
names(groups) <- c(rownames(tmp),colnames(tmp))
cols <- c("#fbbc05","#fbbc05","#34a853","#34a853","#ea4335","#ea4335")
names(cols) <-c("Myeloid.source","Myeloid.target","T_cell.source","T_cell.target","Tumor_cell.source","Tumor_cell.target")

netVisual_chord_cell_internal(tmp,color.use=cols,sources.use=rownames(tmp),targets.use=colnames(tmp),group=groups,big.gap=20,small.gap=0)



# 1. tumor interaction with Myeloids
library(CellChat)
cellchat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Myeloid/SubTumor/LCBM_subtumor_myeloid.cellchat.RDS")
groupSize <- table(cellchat@idents)
tmp <- netVisual_bubble(cellchat, sources.use = 4, targets.use = 1:3, remove.isolate = FALSE,return.data=T)$communication
tmp.res <- tmp[,c("prob","source.target","interaction_name_2")]
rs <- reshape2::dcast(tmp.res,source.target~interaction_name_2,value.var="prob")
rs[is.na(rs)] <- 0
rownames(rs) <- rs[,1]
rs <- rs[,-1]
rs <- data.frame(t(rs))




















Loading