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plot.clust.R
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plot.clust.R
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source("R/modelfns.R")
source("R/descend.R")
source("R/clustplots.R")
source("R/plotfns.R")
library(leiden)
library(optparse)
library(igraph)
library(dirfns)
library(purrr)
library(umap)
dot.stat <- function(y,dat,...){
require(ggplot2)
ggplot(dat,
aes_string(x=names(dat)[1],
y=y,...))+geom_point()
}
dot.col <- function(y,dat,cols='',id='group',...){
require(ggplot2)
dat <- do.call(cbind,append(list(dat),setNames(list(cols),id)))
dot.stat(y,dat,col=id,...)
}
dir.f <- function(f,file.arg='filename'){
require(dirfns)
function(...,filename='',ext='',path='.',append.date=T){
out <- mkdate(filename,ext=ext,path=path,append.date=append.date)
arglist <- append(list(...),setNames(out,file.arg))
do.call(f,arglist)
}
}
dir.plot <- function(out,outfn = dir.pdf){
require(dirfns)
function(f,...){
outfn(out)
tryCatch(f(...),finally=dev.off())
}
}
arrange.stats <- function(plots,filename,ncols=3,...){
require(ggpubr)
nrows <- ceiling(length(plots)/ncols)
dat <- lapply(plots,'[[','data')
# rho <- sapply(dat,
# function(x) cor(x[,1],
# x[,2],
# method='spearman'))
# title <- paste('Spearman correlation =',
# as.character(rho))
plots <- ggarrange(plotlist=plots,
labels=names(plots),
ncol=3,nrow=nrows)
dir.f(ggexport)(plots,
filename=paste0(filename,'.pdf'),
...)
}
plot.knn <- function(dat,g){
require(igraph)
dists <- get.edgelist(g)
f <- function(d,col) apply(d,1,
function(x) lines(dat[x,],
col=col))
# dir.pdf(out)
plot(NULL, xlim=range(dat[,1]),
ylim=range(dat[,2]),
xlab=colnames(dat)[1],
ylab=colnames(dat)[2])
f(dists, col=rgb(0,0,0,0.1))
return(dists)
}
plot.edge <- function(dat,g,clusts=NULL){
dists <- plot.knn(dat,g)
if(length(clusts>0)){
clustn <- unique(clusts)
clustn <- clustn[order(clustn)]
clustsel <- lapply(clustn,
purrr::compose(which,
partial(`==`,clusts)))
clustl <- lapply(clustsel,
function(x){
dists[do.call(`&`, as.data.frame(apply(dists, 2,`%in%`,x))),]
})
cols <- rainbow(length(clustsel))
mapply(f,clustl,cols)
if(length(clustl)>1){
legend(
'bottomleft',
as.character(clustn),
col=cols,
lty=1,
)
}
# dev.off()
}
}
plot.pt <- function(dat,g,clusts=NULL,pch=19,cex=0.8,...){
require(purrr)
f <- function(sel,col) points(dat[sel,], col=col,pch=pch,cex=cex,...)
dists <- plot.knn(dat,g)
clustn <- unique(clusts)
clustn <- clustn[order(clustn)]
clustsel <- lapply(clustn,
purrr::compose(which,
partial(`==`,clusts)))
cols <- rainbow(length(clustsel))
mapply(f,clustsel,cols)
legend(
'bottomleft',
as.character(clustn),
col=cols,
pch=pch,
)
}
statplot <- function(clusts,out){
plots <- lapply(names(clusts)[2:7],dot.stat,clusts)
arrange.stats(plots,paste0(out,'.optimization'))
}
cutoff <- function(g,genes){
edges <- get.edgelist(g)
edges <- cbind(edges,E(g)$weight)
edges <- edges[order(E(g)$weight,decreasing = T),]
f.cutoff <- function(genes,i){
genes <- setdiff(genes,edges[i,1:2])
if(length(genes)==0) return(edges[1:i,])
f.cutoff(genes,i+1)
}
e <- f.cutoff(genes,1)
res <- graph_from_edgelist(e[,1:2],F)
E(res)$weight <- as.numeric(e[,3])
return(res)
}
f.connected <- function(nodes,res){
connected <- sapply(nodes,intersect,res)
res <- connected[[which.max(sapply(connected,length))]]
nodes <- nodes[setdiff(names(nodes),res)]
if(length(nodes)==0) return(res)
f.connected(nodes,res)
}
write.dot <- function(g,filename,...){
write.graph(g,format='dot',file='tmp.dot')
out <- mkdate(filename,'dot')
system2('sed',paste("'s/name/label/' tmp.dot >",out))
system2('dot',paste('-Tsvg -O',out))
# V(g)$label <- names(V(g))
#
# clust <- sapply(names(V(g)),neighbors,graph=g)
# clust <- clust[order(sapply(clust,length),decreasing = F)]
#
# colfn <- col.abs(E(g)$weight)
# E(g)$color <- colfn(E(g)$weight)
# dir.f(write.graph,'file')(g,format='dot',filename=filename)
}
parser <- OptionParser()
parser <- add_option(parser, '--modeldir', action = 'store',
default = 'data/2024-05-03')
parser <- add_option(parser, '--clustdir', action = 'store',
default = '2024-05-08')
parser <- add_option(parser, '--groups', action = 'store',
default = 'data/groups.csv')
parser <- add_option(parser, '--pheno', action = 'store',
default = 'data/phenotype.csv')
opts <- parse_args(parser)
groups <- read.csv(opts$groups)
pheno <- read.csv(opts$pheno,row.names=1)
dat <- as.matrix(read.csv(paste0(opts$modeldir,"/dat.csv"),row.names=1))
model <- read.model(paste0(opts$modeldir,'/encode2.model'),dat)
model <- list()
encoded <- read.csv("data/2024-05-03/dense/L1_L2/E.csv")
dists <- as.matrix(dist(encoded))
model$dists <- dists
model$encoded <- encoded
model$k <- read.csv(paste0(opts$clustdir,'/k.csv'),row.names=1)
names(model$k)[1:2] <- c('k','ES')
clusts <- lrtab(opts$clustdir,read.csv,'leiden',row.names=1)
# which.k <- which.max(sapply(clusts,
# purrr::compose(max, '['), ,
# 'stat'))
# k <- as.numeric(sub('leiden.k','',names(clusts)[which.k]))
which.k <- which.max(model$k$ES)
k <- model$k$k[which.k]
#model$umap <- umap(encoded,n_neighbors=k)$layout
model$umap <- umap(encoded)$layout
colnames(model$umap) <- c("UMAP1","UMAP2")
knn <- get.knn(model$dists,k,'plus')
enrichCond(groups$Condition,
as.matrix(as_adjacency_matrix(knn)),
'knn.network.fr',layout.fruchterman.reingold)
enrichCond(groups$Condition,
as.matrix(as_adjacency_matrix(knn)),
'knn.network')
hyper <- get.hyper(knn, groups$Condition)
g <- poisGraph(hyper)
networkPois(g,'hyper.k',
colfn=colorRamp2(c(0,max(E(g)$weight)),
c('white','red')))
model$clusts <- clusts$leiden
names(model$clusts)[1:2] <- c('resolution','combined_score')
model$clusts <- model$clusts[model$clusts$nclust>1,]
# model$stats <- read.csv(paste0(opts$dir,'/stats.csv'),row.names=1)
# model$clusts <- clusts$clusts[,c(1,7:nrow(clusts$encoded)+7)]
mapply(statplot,clusts,sub('leiden.','',names(clusts)))
statplot(model$clusts,'leiden.k17',nrows=3,ncols=3)
dir.plot('knn')(plot.edge,model$umap,knn)
dir.f(ggexport)(dot.col('encoding2',
as.data.frame(model$encoded),
col=groups$Condition,'condition'),
filename='encoding.pdf')
plots <- lapply(names(model$clusts)[2:7],dot.stat,clusts$clusts)
es <- dot.stat('ES',model$k)
dir.f(ggexport)(ggarrange(plotlist=list(es),ncol=3,nrow=3),
filename="ES.pdf")
plots <- append(plots,list(es))
arrange.stats(plots,'optimization')
arrange.stats(lapply(names(model$stats)[-1],dot.stat,clusts$stats),'stats')
leidens <- t(model$clusts[,-1:-7])
sel <- sapply(model$clusts[,c(2:6)],which.max)
sel['recall'] <- which(model$clusts[,1]==max(model$clusts[model$clusts[,4]==max(model$clusts[,4]),1]))
leidens <- leidens[,sel]
colnames(leidens) <- names(model$clusts[2:6])
label <- paste('resolution =',model$clusts[sel,1])
ggdat <- cbind(as.data.frame(model$umap),sapply(as.data.frame(leidens),as.character),Depdc=as.character(groups$Condition=="Depdc"))
g <- ggplot(ggdat, aes_string(x='UMAP1', y='UMAP2'))
f <- function(fill) g +
geom_point(aes_string(color="Depdc", fill=fill),
shape=21) +
scale_color_manual(values=c("white","black"))
plots <- lapply(colnames(leidens),f)
dir.f(arrange.plots,'out')(plots, labs=label,
filename='leidens.depdc')
dists <- as.matrix(as_adjacency_matrix(knn))
row.names(dists) <- as.numeric(1:nrow(dists))
colnames(dists) <- as.numeric(1:nrow(dists))
dir.plot('knn.clust')(plot.pt,model$umap,knn,leidens[,1])
clustknn <- function(cond,clusts,...)
plot
clustcond <- function(cond,clusts=NULL,...){
dir.pdf(paste0('umap/',gsub("/","_",cond)))
plot.edge(model$umap,knn,clusts)
points(model$umap[groups$Condition==cond,],...)
dev.off()
}
sapply(unique(groups$Condition),clustcond,clusts=leidens[,1])
knncond <- function(cond,clusts=NULL,...){
dir.pdf(paste0('umap/',gsub("/","_",cond)))
plot.knn(model$umap,knn)
# points(model$umap,pch=1,col=1,cex=0.8)
points(model$umap[groups$Condition==cond,],pch=19,cex=0.8,col=1)
dev.off()
}
sapply(unique(groups$Condition),knncond)
dir.pdf('clust.depdc')
plot.edge(model$umap,knn,leidens[,1])
points(model$umap[groups$Condition=="Depdc",])
dev.off()
clustplots(model$encoded,
leidens[,1],
groups$Phenotype,
dists,'pheno')
clustplots(model$encoded,
leidens[,1],
NULL,
dists,legend.ncol=1,legend.cex=1)
pheno.sel <- sapply(compose(unique, unlist,
strsplit)(groups$Comment.detailed,
split=' / '),
grep,
groups$Comment.detailed)
pheno <- replicate(5,rep('WT',nrow(groups)))
colnames(pheno) <- c("TVC.division","TVC.migration",
"ATM.division","ATM.migration",
"other")
row.names(pheno) <- row.names(groups)
pheno[pheno.sel$`inhibited TVC division`,
"TVC.division"] <- "inhibited"
pheno[pheno.sel$`enhanced TVC division`,
"TVC.division"] <- "enhanced"
pheno[pheno.sel$`inhibited ATM division`,
"ATM.division"] <- "inhibited"
pheno[pheno.sel$`enhanced ATM division`,
"ATM.division"] <- "enhanced"
pheno[pheno.sel$`inhibited TVC migration`,
"TVC.migration"] <- "inhibited"
pheno[pheno.sel$`enhanced TVC migration`,
"TVC.migration"] <- "enhanced"
pheno[pheno.sel$`inhibited ATM migration`,
"ATM.migration"] <- "inhibited"
pheno[pheno.sel$`enhanced ATM migration`,
"ATM.migration"] <- "enhanced"
pheno[pheno.sel$`problem disposition TVC`,
"other"] <- "TVC disposition"
pheno[pheno.sel$`TVC cell alignment`,
"other"] <- "TVC alignment"
# pheno[pheno.sel$`problem disposition ATM`,
# "other"] <- "ATM disposition"
# pheno <- cbind(Condition=groups$Condition,pheno)
dir.f(clusthyper,'out')(groups[,"Condition",drop=F],leidens[,1])
dir.f(clusthyper,'out')(as.data.frame(pheno),leidens[,1],
filename='pheno')
res <- model$clusts[which.max(model$clusts[,2]),1]
g <- gene.network(knn,res,groups$Condition,mode='directed')
edgelist <- cbind(as.data.frame(get.edgelist(g)),E(g)$weight)
edgelist <- do.call(rbind,lapply(split(edgelist,edgelist[,1]),function(x) x[order(x[,3],decreasing=T)[1:min(nrow(x),5)],]))
reduced <- graph_from_edgelist(as.matrix(edgelist[,-3]),F)
write.dot(reduced,'gene_network')
graph.pdf('gene_network',reduced)
sel <- E(g)$weight>quantile(E(g)$weight,0.9)
E(reduced)$weight <- E(g)$weight[sel]
dir.f(networkPois,'out')(reduced,'gene_network',colfn=col.abs(E(reduced)$weight),title='modularity')
dir.f(write.graph,'file')(reduced,format='dot',filename='gene.network.dot')
V(g)$cluster <- leiden(g,resolution_parameter=1.05)
reduced <- cutoff(g,unique(groups$Condition))
write.dot(g,'gene.network.dot')