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GeneratePlots.R
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library(Matrix)
library(stringi)
library(parallel)
library(dendextend)
setwd('~/pancancer_fsm/')
ppi <- readRDS('data/inbiomap.hsa.symbol.rds')
univ <- colnames(ppi)
result.wd <- '~/pancancer_fsm/PANCANCER-GDC/fsm_results/'
### Read FSM Results ##
cl <- makeCluster(20)
fsm.res <- parSapply(cl,
list.files(path = result.wd,
pattern = 'names.txt',
full.names = TRUE,
recursive = TRUE,
include.dirs = TRUE),
simplify = FALSE,
function(fName){
require(stringi)
if(file.size(fName) > 0){
tryCatch({
local.fsg.data <- read.table(fName, header = FALSE, sep = '\t', stringsAsFactors = FALSE)
},
warning = function(c){
warning("Issue reading file")
return(NULL)
},
error = function(c){
return(NULL)
})
sample.name <- as.character(stri_match(fName, regex = 'TCGA-.{2}-.{4}-\\d{2}.{1}', mode = 'first'))
return(data.frame('sample.id' = rep(sample.name,
nrow(local.fsg.data)),
'fsg' = local.fsg.data,
stringsAsFactors = FALSE))
}else{
return(NULL)
}
})
stopCluster(cl)
fsm.res <- do.call('rbind',
lapply(fsm.res,
function(x){
return(x)
}))
colnames(fsm.res) <- c('sample.id', 'fsg')
rownames(fsm.res) <- 1:nrow(fsm.res)
temp <- split(fsm.res$fsg, fsm.res$sample.id)
## Phenotype Filtering (If some disease statest are of very low count)
pheno.data <- read.table('~/pancancer_fsm/data/gdc_pancancer/GDC-PANCAN.GDC_phenotype.tsv',
header = TRUE,
sep = '\t',
comment.char = '',
quote = '',
stringsAsFactors = FALSE)
pheno.data <- pheno.data[match(names(temp),table=pheno.data$sample),]
## Extract Genes per FSG ##
cl <- makeCluster(20)
clusterExport(cl, varlist = c('temp'))
fsm.genes.pp <- parSapply(cl, 1:length(temp), simplify = FALSE, function(x){
require(stringi)
local.s <- stri_match_all(temp[[x]], regex = "\\d\\:([^\\(]+?)\\]")
local.g <- lapply(local.s, function(k){
local.local.g <- paste(sort(unique(unlist(strsplit(k[,2],split = '.',fixed = TRUE)))), collapse = ',')
})
local.g <- unique(do.call('c', lapply(local.g, function(k){return(k)})))
return(data.frame('sample.id' = rep(names(temp)[x], length(local.g)),
'fsg' = local.g,
stringsAsFactors = FALSE))
})
stopCluster(cl)
fsm.genes.pp.ft <- do.call('rbind', lapply(fsm.genes.pp, function(x){return(x)}))
## Format as adjacency matrix
#sig.fsg <- fsm.genes.pp.ft$fsg[which(as.numeric(table(fsm.genes.pp.ft$fsg)) > 8)] ## Check ?? Might be to prioritize fsg based on frequency
# sig.fsg <- unique(fsm.genes.pp.ft$fsg)
# sig.pp <- unique(fsm.genes.pp.ft$sample.id)
# row.idx <- match(fsm.genes.pp.ft$fsg, table = sig.fsg)
# col.idx <- match(fsm.genes.pp.ft$sample.id, table = sig.pp)
# sig.fsg.pp.mat <- sparseMatrix(i = row.idx,
# j = col.idx,
# x = 1,
# dims = c(length(sig.fsg), length(sig.pp)),
# dimnames = list(sig.fsg, sig.pp),
# use.last.ij = TRUE)
# sig.fsg.pp.mat <- as.matrix(sig.fsg.pp.mat)
#
# require(fastcluster)
# require(parallelDist)
# require(pheatmap)
#
# hc.dist <- parDist(t(sig.fsg.pp.mat), method = 'manhattan', threads = 20)
# hr.dist <- parDist(sig.fsg.pp.mat, method = 'manhattan', threads = 20)
#
# hc <- fastcluster::hclust(hc.dist, method = 'average')
# hr <- fastcluster::hclust(hr.dist, method = 'average')
#
# ## Plot Heatmap ##
# pheatmap(sig.fsg.pp.mat[hr$order,hc$order],
# cluster_rows = FALSE,
# cluster_cols = FALSE,
# fontsize = 12,
# show_rownames = FALSE,
# show_colnames = FALSE,
# filename = '~/FsmPlots/PatientFSGClusterBIOGRID.pdf',
# width = 12,
# height = 10)
## Plot Dendrogram ##
colors.manual <- c('aliceblue','antiquewhite3','antiquewhite4',
'aquamarine', 'aquamarine3', 'aquamarine4',
'azure', 'azure4', 'beige',
'blueviolet', 'burlywood', 'burlywood4',
'cadetblue', 'cadetblue4', 'chartreuse',
'chartreuse4', 'chocolate', 'cornflowerblue',
'cyan', 'cyan4', 'darkgoldenrod1',
'darkorchid4', 'darkred','hotpink1',
'darksalmon', 'darkslategrey', 'greenyellow',
'lightpink4', 'seashell4', 'blue4',
'yellow', 'tan', 'slateblue4')
#pheno.data.ft <- data.frame('sample.id' = hc$labels,
# 'disease.id' = pheno.data$X_primary_disease[match(hc$labels, table = pheno.data$sample)],
# stringsAsFactors = FALSE)
# hc.res.dend <- as.dendrogram(hc)
# hc.res.dend %>%
# set("labels_col", colors.manual[as.numeric(as.factor(pheno.data.ft$disease.id[match(labels(hc.res.dend),
# table = pheno.data.ft$sample.id)]))]) %>%
# set("labels_cex", 0.5) %>%
# set("branches_lty", 3) %>%
# set("branches_lwd", 0.5) %>%
# plot(main = "FSG Level Clustering")
#
#
# hc.res.dend %>%
# set("labels_cex", 0.1) %>%
# plot(main = "BioGrid FSG Level Clustering")
## Graph Cluster ##
fsm.edge <- unique(do.call("c", lapply(stri_match_all(fsm.res$fsg,
regex = "\\d\\:([^\\(]+?)\\]"),
function(x){x[,2]})))
fsm.edge.ft <- do.call('rbind', strsplit(fsm.edge, split = '.', fixed = TRUE))
fsm.edge.ft <- as.data.frame(fsm.edge.ft, stringsAsFactors = FALSE)
require(Matrix)
require(igraph)
colnames(fsm.edge.ft) <- c('gene.a', 'gene.b')
all.ids <- union(fsm.edge.ft$gene.a,
fsm.edge.ft$gene.b)
fsm.edge.ft <- rbind(fsm.edge.ft,
data.frame('gene.a' = fsm.edge.ft$gene.b,
'gene.b' = fsm.edge.ft$gene.a,
stringsAsFactors = FALSE))
row.idx <- match(fsm.edge.ft$gene.a, table = all.ids)
col.idx <- match(fsm.edge.ft$gene.b, table = all.ids)
adj <- sparseMatrix(i = row.idx,
j = col.idx,
x = 1,
dims = c(length(all.ids), length(all.ids)),
dimnames = list(all.ids, all.ids),
use.last.ij = TRUE)
graph <- graph_from_adjacency_matrix(adj, mode = 'undirected')
graph.cluster <- cluster_louvain(graph)
cluster.data <- data.frame('symbol' = names(membership(graph.cluster)),
'cluster.id' = as.vector(membership(graph.cluster)),
stringsAsFactors = FALSE)
## Cluster Enrichment ##
reactome.data <- read.table('data/Ensembl2Reactome_All_Levels.txt',
header = FALSE,
sep = '\t',
stringsAsFactors = FALSE,
quote = '',
comment.char = '')
reactome.data <- subset(reactome.data, reactome.data$V6 == 'Homo sapiens')
library(biomaRt)
ensembl <- useMart("ENSEMBL_MART_ENSEMBL", dataset = "hsapiens_gene_ensembl")
mapping <- getBM(mart = ensembl,
attributes = c("ensembl_gene_id", "hgnc_symbol"),
filters = "ensembl_gene_id",
values = unique(reactome.data$V1))
reactome.data$V1 <- mapping$hgnc_symbol[match(reactome.data$V1, table = mapping$ensembl_gene_id)]
reactome.list <- split(reactome.data$V1, reactome.data$V2)
reactome.list <- lapply(reactome.list, function(x){unique(na.omit(x))})
reactome.list <- reactome.list[sapply(reactome.list, length) >= 8]
graph.cluster.list <- split(cluster.data$symbol,
cluster.data$cluster.id)
graph.cluster.list <- graph.cluster.list[sapply(graph.cluster.list, length) >= 8]
graph.cluster.list <- graph.cluster.list[sapply(graph.cluster.list, length) < 400]
names(graph.cluster.list) <- paste('Cluster-', 1:length(graph.cluster.list), sep = '')
## Plot Cluster Size ##
library(ggplot2)
cluster.size <- data.frame('size' = sapply(graph.cluster.list,length),
'cluster' = names(graph.cluster.list))
p <- ggplot(cluster.size,
aes(x = reorder(cluster.size$cluster, X = cluster.size$size), y = size)) +
geom_bar(stat = 'identity',
fill = 'grey50') +
theme_minimal() +
xlab("Cluster") +
ylab("Size") +
ggtitle("Cluster Size") +
theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 4),
plot.title = element_text(face = "bold", hjust = 0.5, size = 16),
axis.title.x = element_text(face = "bold", size = 12),
axis.title.y = element_text(face = "bold", size = 12))
ggsave(p,
file = '~/FsmPlots/PANCANCER-GDC-Clusters_InBioMap.pdf',
width = 10,
height = 8)
require(parallel)
cl <- makeCluster(20)
clusterExport(cl, varlist = c('univ', 'reactome.list'))
enrichRes <- parSapply(cl,
graph.cluster.list,
simplify = FALSE,
function(x){
local.enr <- sapply(reactome.list,
simplify = TRUE,
function(t){
test.mat <- matrix(c(sum(x %in% t),
sum(!(x %in% t)),
sum(univ %in% t),
sum(!(univ %in% t))),
byrow = FALSE, ncol = 2)
return(fisher.test(test.mat, alternative = 'greater')$p.value)
})
return(p.adjust(local.enr, method = 'bonferroni'))
})
stopCluster(cl)
enrichRes.ft <- do.call('cbind', lapply(enrichRes, function(x){return(x)}))
p.thr <- 0.01
enrichRes.ft <- enrichRes.ft[rowSums(enrichRes.ft < p.thr) > 0,]
enrichRes.ft <- -1 * log10(enrichRes.ft)
rownames(enrichRes.ft) <- reactome.data$V4[match(rownames(enrichRes.ft), table = reactome.data$V2)]
## Reactome Hierarchy ##
reactome.hier <- read.table('data/ReactomeProcessed.tsv',
header = TRUE,
sep = '\t',
quote = '',
comment.char = '',
stringsAsFactors = FALSE)
require(reshape2)
temp.data <- melt(enrichRes.ft)
colnames(temp.data) <- c('Pathway', 'Cluster', 'Enrichment (Log10 Scale)')
temp.data <- subset(temp.data, temp.data$`Enrichment (Log10 Scale)` > 2)
write.table(temp.data, file = 'results/PANCANCER-GDC_ClusterEnrichment_InBioMap_Support8.tsv',
col.names = TRUE,
row.names = FALSE,
append = FALSE,
quote = FALSE,
sep = '\t')
require(pheatmap)
require(RColorBrewer)
enr.mat <- reshape2::dcast(temp.data, Pathway~Cluster, fun.aggregate = mean, fill = 0, value.var = "Enrichment (Log10 Scale)")
enr.mat.ft <- data.matrix(enr.mat[,-1])
rownames(enr.mat.ft) <- enr.mat$Pathway
row.annotation <- na.omit(data.frame('Root.Pathway' = reactome.hier$root.name[match(rownames(enr.mat.ft),table=reactome.hier$leaf.name)],
row.names = rownames(enr.mat.ft),
stringsAsFactors = FALSE))
enr.mat.ft <- enr.mat.ft[match(rownames(row.annotation),table=rownames(enr.mat.ft)),]
hr.order <- order(row.annotation$Root.Pathway)
hc.order <- na.omit(match(paste('Cluster-',1:length(enrichRes),sep=''),table=colnames(enr.mat.ft)))
breaks <- seq(from = 0, to = max(enr.mat.ft)*1.5, by = 0.1)
colors <- colorRampPalette(colors = c('white', 'red'))(length(breaks)-1)
pheatmap(enr.mat.ft[hr.order,hc.order],
cluster_rows = FALSE,
cluster_cols = FALSE,
breaks = breaks,
color = colors,
annotation_row = row.annotation,
show_rownames = FALSE,
angle_col = 45,
main = 'PANCANCER Cluster Enrichment',
fontsize = 8,
filename = '~/FsmPlots/InBioMap-ClusterEnrichment_Support8.pdf',
width = 10,
height = 8)
graphics.off()
# p <- ggplot(temp.data,aes(x = Cluster,y = Pathway)) +
# geom_point() +
# theme_minimal() +
# ggtitle('Pathway Enrichment') +
# theme(plot.title = element_text(size = 16, face = 'bold'),
# axis.text.x = element_text(angle = 60, hjust = 1, size = 10),
# axis.text.y = element_text(size = 1))
# ggsave(p,filename = '~/FsmPlots/InbioMap-PathwayEnrichment.pdf',width = 12,height = 10)
## Patient Cluster Enrichment ##
library(reshape2)
cl <- makeCluster(20)
clusterExport(cl, varlist = c('graph.cluster.list', 'univ'))
enrichRes.pp <- parSapply(cl,
split(fsm.genes.pp.ft$fsg,fsm.genes.pp.ft$sample.id),
simplify = FALSE,
function(x){
local.enr <- sapply(x, simplify = TRUE, USE.NAMES = FALSE, function(x.local){
x.local <- unlist(strsplit(x.local, split = ',', fixed = TRUE))
local.local.enr <- sapply(graph.cluster.list, simplify = TRUE, function(t){
test.mat <- matrix(c(sum(x.local %in% t),
sum(!(x.local %in% t)),
sum(univ %in% t),
sum(!(univ %in% t))),
byrow = FALSE, ncol = 2)
return(fisher.test(test.mat, alternative = 'greater')$p.value)
})
return(p.adjust(local.local.enr, method = 'bonferroni'))
})
return(apply(local.enr, 1, function(x){min(x, na.rm = TRUE)}))
})
stopCluster(cl)
enrichRes.pp.ft <- do.call('rbind',
lapply(enrichRes.pp, function(x){
return(-1*log10(x))
}))
## t-SNE ##
require(Rtsne)
tsne.res <- Rtsne(enrichRes.pp.ft,
dims = 2,
perplexity = 20,
theta = 0.1,
partial_pca = FALSE,
pca = FALSE,
num_threads = 20,
check_duplicates = FALSE)
plot.data <- data.frame('Dimension.1' = tsne.res$Y[,1],
'Dimension.2' = tsne.res$Y[,2],
'Disease' = pheno.data$disease_type[match(rownames(enrichRes.pp.ft),
table=pheno.data$sample)])
p <- ggplot(plot.data,
aes(x = Dimension.1,
y = Dimension.2,
color = Disease)) +
geom_point(
shape = 20,
size = 1) +
theme_minimal() +
theme(legend.direction = 'vertical',
legend.box = 'vertical') +
ggtitle('PANCANCER t-SNE') +
guides(color = guide_legend(nrow = 27,
override.aes = list(size = 4)))
ggsave(p, filename = '~/FsmPlots/PANCANCER-GDC_ClusterPatientEnrichment_Support8.pdf', width = 12, height = 8)
enrichRes.pp.ft[enrichRes.pp.ft <= 2] <- 0
enrichRes.pp.ft[enrichRes.pp.ft > 2] <- 1
enrichRes.pp.ft <- t(enrichRes.pp.ft)
col.annotation <- data.frame('Disease' = pheno.data$disease_type[match(colnames(enrichRes.pp.ft), table = pheno.data$sample)],
row.names = colnames(enrichRes.pp.ft),
stringsAsFactors = FALSE)
breaksList = seq(0, 1, by = 0.5)
colors <- c('white', 'tomato')
require(fastcluster)
hc <- hclust(dist(t(enrichRes.pp.ft), method = 'manhattan'), method = 'average')
hr <- hclust(dist(enrichRes.pp.ft, method = 'manhattan'), method = 'average')
pheatmap(enrichRes.pp.ft[hr$order, hc$order],
cluster_rows = FALSE,
cluster_cols = FALSE,
annotation_col = col.annotation,
scale = "none",
fontsize = 12,
fontsize_col = 2,
fontsize_row = 8,
legend = FALSE,
border_color = "gray95",
breaks = breaksList,
color = colors,
show_colnames = FALSE,
main = 'Patient Cluster Enrichment',
annotation_colors = list("Disease" = setNames(colors.manual[1:length(unique(col.annotation$Disease))],
unique(col.annotation$Disease))),
filename = '~/FsmPlots/PANCANCER-GDC_InbioMap-Patient-ClusterEnrichmentSupport8.pdf',
width = 12,
height = 10)
graphics.off()
enrichRes.pp.ft <- melt(enrichRes.pp.ft)
enrichRes.pp.ft$phenotype <- pheno.data$X_primary_disease[match(enrichRes.pp.ft$Var2,
table = pheno.data$sample)]
colnames(enrichRes.pp.ft) <- c('cluster.id', 'sample.id', 'enr', 'disease')
enrichRes.pp.ft <- subset(enrichRes.pp.ft, enrichRes.pp.ft$enr == 1)
rownames(enrichRes.pp.ft) <- 1:nrow(enrichRes.pp.ft)
sample <- sapply(unique(enrichRes.pp.ft$cluster.id), simplify = FALSE, function(c.id){
local.data <- subset(enrichRes.pp.ft, enrichRes.pp.ft$cluster.id == c.id)
counts <- table(local.data$disease)
return(data.frame('cluster.id' = rep(c.id, length(counts)),
'disease' = names(counts),
'count' = as.vector(counts),
stringsAsFactors = FALSE))
})
sample.ft <- do.call('rbind', lapply(sample, function(x){return(x)}))
sample.ft$cluster.id <- factor(sample.ft$cluster.id,
levels = paste('Cluster-',
sort(unique(as.numeric(gsub(pattern = '^Cluster-',
replacement = '',
sample.ft$cluster.id)))),
sep = ''))
p <- ggplot(sample.ft,
aes(x = cluster.id,
y = count,
fill = disease)) +
geom_bar(stat = 'identity') +
theme_minimal() +
xlab('Cluster ID') +
ylab('Count') +
ggtitle("Disease Profile") +
theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 8),
legend.text = element_text(size = 8),
legend.key.size = unit(0.5, 'cm'),
plot.title = element_text(hjust = 0.5, face = "bold", size = 16)) +
guides(fill = guide_legend(title = "Disease", ncol = 1)) +
scale_fill_manual(values = setNames(colors.manual[1:length(unique(col.annotation$Disease))], unique(col.annotation$Disease)))
ggsave(p,
filename = "~/FsmPlots/InbioMap-DiseaseClusterEnrichmentSupport8.pdf",
width = 12,
height = 10)
## HotNet2 Enrichment ##
hotnet2 <- read.table('~/pancancer_fsm/data/HotNet2.tsv',
header = TRUE,
sep = '\t',
stringsAsFactors = FALSE,
fill = TRUE)
hotnet2 <- sapply(1:ncol(hotnet2), simplify = FALSE, function(x){
local.res <- trimws(as.character(hotnet2[,x][hotnet2[,x] != '']))
return(local.res[local.res %in% univ])
})
names(hotnet2) <- paste('Subnetwork', 1:length(hotnet2), sep = '')
## Cluster HotNet2 Enrichment ##
cl <- makeCluster(20)
clusterExport(cl, varlist = c('hotnet2', 'univ'))
enrichResClusterHotnet2 <- parSapply(cl, graph.cluster.list, simplify = FALSE, function(x){
local.enr <- sapply(hotnet2, simplify = TRUE, function(t){
test.mat <- matrix(c(sum(x %in% t),
sum(!x %in% t),
sum(univ %in% t),
sum(!univ %in% t)),
byrow = TRUE, ncol = 2)
return(fisher.test(test.mat, alternative = 'greater')$p.value)
})
return(p.adjust(local.enr, method = 'bonferroni'))
})
stopCluster(cl)
enrichResClusterHotnet2.ft <- do.call('rbind', lapply(enrichResClusterHotnet2, function(x){return(x)}))
enrichResClusterHotnet2.ft <- enrichResClusterHotnet2.ft[,colSums(enrichResClusterHotnet2.ft < p.thr) > 0]
enrichResClusterHotnet2.ft <- enrichResClusterHotnet2.ft[rowSums(enrichResClusterHotnet2.ft < p.thr) > 0,]
enrichResClusterHotnet2.ft <- melt(enrichResClusterHotnet2.ft)
colnames(enrichResClusterHotnet2.ft) <- c('Cluster', 'Hotnet2 Subnetwork', 'p-value')
enrichResClusterHotnet2.ft <- subset(enrichResClusterHotnet2.ft, enrichResClusterHotnet2.ft$`p-value` < p.thr)
write.table(enrichResClusterHotnet2.ft,
file = '~/pancancer_fsm/results/ClusterHotnet2Enrichment.tsv',
col.names = TRUE, row.names = FALSE, sep = '\t', append = FALSE, quote = FALSE)
## Patient HotNet2 Enrichment ##
fsm.pp.gene <- sapply(split(fsm.res$fsg, fsm.res$sample.id),
simplify = FALSE,
function(x){
local.genes <- unique(do.call("c",
lapply(stri_match_all(x,
regex = "\\d\\:([^\\(]+?)\\]"),
function(m){
paste(sort(unlist(strsplit(m[,2],split = '.',fixed = TRUE))), collapse = ',')
})))
return(local.genes)
})
cl <- makeCluster(20)
clusterExport(cl, varlist = c('hotnet2', 'univ'))
enrichRes.pp <- parSapply(cl, fsm.pp.gene, simplify = FALSE, function(x){
local.enr <- sapply(x, simplify = TRUE, USE.NAMES = FALSE, function(x.local){
x.local <- unlist(strsplit(x.local, split = ',', fixed = TRUE))
local.local.enr <- sapply(hotnet2, simplify = TRUE, function(t){
test.mat <- matrix(c(sum(x.local %in% t),
sum(!x.local %in% t),
sum(univ %in% t),
sum(!univ %in% t)),
byrow = FALSE, ncol = 2)
return(fisher.test(test.mat, alternative = 'greater')$p.value)
})
return(p.adjust(local.local.enr, method = 'bonferroni'))
})
return(apply(local.enr, 1, function(x){min(x, na.rm = TRUE)}))
})
stopCluster(cl)
enrichRes.pp.ft <- do.call('rbind',
lapply(enrichRes.pp, function(x){
return(x)
}))
enrichRes.pp.ft <- as.matrix(t(enrichRes.pp.ft))
enrichRes.pp.ft <- -1 * log10(enrichRes.pp.ft)
temp <- melt(enrichRes.pp.ft)
colnames(temp) <- c('HotNet2 Subnetwork', 'Sample ID', 'Enrichment (Log Scale)')
temp <- subset(temp, temp$`Enrichment (Log Scale)` > 2)
write.table(temp, file = '~/pancancer_fsm/results/PatientHotNet2Enrichment.tsv', col.names = TRUE, row.names = FALSE, append = FALSE, quote = FALSE, sep = '\t')
## Per Cluster Plot ##
for(clust in unique(sample.ft$cluster.id)){
local.data <- subset(sample.ft, sample.ft$cluster.id == clust)
p <- ggplot(local.data,
aes(x = disease,
y = count,
fill = disease)) +
geom_bar(stat = "identity", position = "dodge_2") +
theme_minimal() +
ggtitle(sprintf('Phenotype Enrichment (%s)',
clust)) +
theme(axis.text.x = element_text(angle = 45, size = 8, hjust = 1),
legend.text = element_blank(),
plot.title = element_text(hjust = 0.5, face = 'bold', size = 16)) +
guides(fill = FALSE) +
xlab('') +
ylab('')
ggsave(p, filename = sprintf('~/pancancer_fsm/plots/PhenotypeEnrichment%s.pdf', clust), width = 12, height = 8)
}
## Plot Network ##
custom.pathways <- read.table('~/pancancer_fsm/data/PathwayGenes.tsv',
header = TRUE,
check.names = FALSE,
sep = '\t',
stringsAsFactors = FALSE,
fill = TRUE)
ppi <- readRDS('~/pancancer_fsm/data/stringdb.hsa.hgnc.medconf.adj.rds')
paths <- sapply(1:ncol(custom.pathways), simplify = FALSE, function(x){
local.genes <- na.omit(as.character(custom.pathways[,x]))
local.genes <- local.genes[local.genes != '']
local.genes <- local.genes[local.genes %in% colnames(ppi)]
return(local.genes)
})
names(paths) <- colnames(custom.pathways)
rm(custom.pathways)
## Filer Matching Genes ##
all.genes <- union(fsm.edge.ft$gene.a,
fsm.edge.ft$gene.b)
all.genes.match <- sapply(all.genes, function(gene.id){
local.match <- names(paths)[sapply(paths, function(x){gene.id %in% x})]
if(length(local.match) > 0)
return(as.character(local.match)[1])
return('Novel')
})
all.genes.match.ft <- names(all.genes.match)[all.genes.match != 'Novel']
fsm.edge.ft$Type <- ifelse(fsm.edge.ft$gene.a %in% all.genes.match.ft |
fsm.edge.ft$gene.b %in% all.genes.match.ft,
1, 0)
fsm.edge.ft.filt <- subset(fsm.edge.ft, fsm.edge.ft$Type == 1)
rownames(fsm.edge.ft.filt) <- 1:nrow(fsm.edge.ft.filt)
## Create Network ##
require(network)
require(sna)
require(ggplot2)
require(ggnet)
graph <- network(fsm.edge.ft.filt,
directed = FALSE,
hyper = FALSE,
loops = FALSE,
bipartite = FALSE,
matrix.type = 'edgelist',
ignore.eval = FALSE)
graph %v% 'Pathway' <- all.genes.match[match(graph %v% 'vertex.names',
table = names(all.genes.match))]
graph %v% 'Size' <- ifelse(graph %v% 'Pathway' == 'Novel', 0.5, 1)
set.edge.attribute(graph, 'Color', ifelse(graph %e% 'Type' == 1, 'orange', 'grey40'))
set.edge.attribute(graph, 'Line', ifelse(graph %e% 'Type' == 1, 1, 2))
set.edge.attribute(graph, 'Size', ifelse(graph %e% 'Type' == 1, 0.05, 0.01))
## Plot ##
p <- ggnet2(graph,
label = TRUE,
label.size = 0.2,
color = 'Pathway',
size = 'Size',
max_size = 1,
alpha = 0.8,
palette = 'Set3',
legend.position = 'bottom',
legend.size = 12,
edge.size = 'Size',
edge.lty = 'Line',
edge.color = 'Color') +
ggtitle('PANCANCER ATLAS FSG Network') +
theme(plot.title = element_text(hjust = 0.5, face = 'bold')) +
guides(size = FALSE)
ggsave(p, filename = '~/FsmPlots/tcga.pancancer.fsg.network.support20.pdf',
width = 12,
height = 10,
dpi = 'retina')
## Check GBM & Thryoid Cancer Patients ##
sig.genes <- graph.cluster.list$`Cluster-63`
local.idx <- colnames(ppi) %in% sig.genes
local.ppi <- ppi[local.idx, local.idx]
local.idx <- colSums(local.ppi) == 0
local.ppi <- local.ppi[!local.idx, !local.idx]
local.graph <- network(as.matrix(local.ppi),
directed = FALSE)
p <- ggnet2(local.graph,
label = TRUE,
label.size = 2,
color = 'orange',
size = 6,
edge.lty = 1,
edge.size = 0.05,
alpha = 0.8) +
guides(size = FALSE) +
ggtitle("FSG Network (Cluster-63)") +
theme_minimal() +
theme(plot.title = element_text(hjust = 0.5, face = 'bold', size = 16),
axis.title = element_blank())
ggsave(p, filename = '~/FsmPlots/Cluster63_Network.pdf', width = 12, height = 10)