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scaleFreeGlasso.R
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library(openxlsx)
library(ggplot2)
library(ggfortify)
library(jcolors)
require(gridExtra)
library(grid)
library(matrixStats)
library(tidyverse)
library(RColorBrewer)
library(sctransform)
library(glassoFast)
library(igraph)
library(ggraph)
library(graphlayouts)
library(fdrtool)
library(parallel)
num.cores <- detectCores()
getNetwork <- function(gl, genes, prod.desc){
par.cor <- -cov2cor(gl$wi)
tmp <- par.cor
diag(tmp) <- 0
tmp <- c(tmp)
ind <- which(tmp != 0)
if(length(ind) > 0){
cutoff.cor <- quantile(abs(tmp[ind]), probs = 0.9)
}else{
cutoff.cor <- 0
}
#cutoff.cor <- par.cor %>% as.data.frame() %>% summarise(across(everything(), ~ quantile(abs(.x), prob = 0.25))) %>%
# quantile(prob = 0.75)
recovered.network <- par.cor
recovered.network[which(abs(par.cor) <= as.numeric(cutoff.cor))] <- 0
#recovered.network[which(abs(par.cor) > as.numeric(cutoff.cor))] <- 1
diag(recovered.network) <- 0
recovered.network[upper.tri(recovered.network)] <- 0
colnames(recovered.network) <- genes
recovered.network.adj <- recovered.network
recovered.network.adj[which(abs(par.cor) > as.numeric(cutoff.cor))] <- 1
node.con <- colSums(recovered.network.adj)
node.con <- data.frame(id = names(node.con), size = as.numeric(node.con))
edge.list <- recovered.network %>% as.data.frame() %>% mutate(from = genes) %>%
pivot_longer(-from, names_to = 'to', values_to = 'weight') %>% dplyr::filter(weight != 0) %>%
transmute(from = from, to = to, weight = abs(weight), sgn = sign(weight))
nodes <- data.frame(GeneID = gsub("-", "_", genes), id = genes) %>% left_join(prod.desc, by = 'GeneID')
nodes.list <- nodes %>% dplyr::filter(id %in% unique(c(edge.list$from, edge.list$to))) %>%
transmute(id = id, type = ProductDescription) %>% left_join(node.con, by = 'id')
nodes.list$class <- 'Protein'
nodes.list$class[grepl('hypothetical', nodes.list$type)] <- 'hypo'
nodes.list$class[grepl('conserved', nodes.list$type)] <- 'cons'
nodes.list$class[grepl('histone', nodes.list$type)] <- 'hist'
nodes.list$class[grepl('domain', nodes.list$type)] <- 'domain'
nodes.list$class[grepl('AP2', nodes.list$type)] <- 'AP2'
network <- graph_from_data_frame(d = edge.list, vertices = nodes.list, directed = F)
return(network)
}
networksScaleFreeStats <- function(genes, input.dir, prod.desc){
in.files <- list.files(input.dir)
networks <- mclapply(1:length(in.files), function(i){
tryCatch(
expr = {
gl <- readRDS(paste(input.dir, in.files[i], sep = ''))
network <- getNetwork(gl, genes, prod.desc)
d <- degree(network, mode="all")
fit <- fit_power_law(d+1)
tmp <- rle(sort(d))
r2 <- summary(lm(log(tmp$lengths)~log(tmp$values)))$adj.r.squared
net.stats <- c(as.numeric(gsub('\\.RData', '', strsplit(in.files[i], split = '_')[[1]][2])),
gsize(network),
fit$xmin, fit$KS.p, r2)
return(list(network = network, net.stats = net.stats))
},
error = function(v){
message(paste('error:', v))
},
warning = function(w){
message(paste('error:', w))
}
) }, mc.cores = num.cores)
err.ind <- which(unlist(mclapply(networks, function(x) is.null(x$net.stats), mc.cores = num.cores)))
if(length(err.ind) > 0){
file.info <- as.data.frame(do.call('rbind', mclapply(networks[-err.ind], `[[`, 2, mc.cores = num.cores)))
}else{
file.info <- as.data.frame(do.call('rbind', mclapply(networks, `[[`, 2, mc.cores = num.cores)))
}
colnames(file.info) <- c('rho', 'size', 'xmin', 'pval', 'r2')
return(list(file.info = file.info, networks = networks))
}
genOptNetwork <- function(genes, file.info, networks, prod.desc){
file.info.filt <- file.info %>% dplyr::filter(!is.na(r2))
ind.high.r2 <- which(file.info.filt$r2 > 0.7)
edge.high.r2 <- mclapply(ind.high.r2, function(i){
tmp <- as_data_frame(simplify(networks[[i]]$network))[,1:2]
colnames(tmp) = c('src', 'trg')
tmp.reverse <- data.frame(src = tmp[,2], trg = tmp[,1])
tmp <- rbind(tmp, tmp.reverse)
edge.hash <- paste(tmp$src, tmp$trg, sep = '_')
return(edge.hash)
}, mc.cores = num.cores)
edge.rle <- rle(sort(unlist(edge.high.r2)))
const.edge <- edge.rle$values[edge.rle$lengths >= quantile(edge.rle$lengths, probs = 0.10)]
split.edges <- strsplit(const.edge, split = '_')
cons.edge.list <- data.frame(from = unlist(lapply(split.edges, `[[`, 1)), to = unlist(lapply(split.edges, `[[`, 2)))
nodes <- data.frame(GeneID = gsub("-", "_", genes), id = genes) %>% left_join(prod.desc, by = 'GeneID')
nodes.list <- nodes %>% dplyr::filter(id %in% unique(c(cons.edge.list$from, cons.edge.list$to))) %>%
transmute(id = id, type = ProductDescription)
nodes.list$class <- 'Protein'
nodes.list$class[grepl('hypothetical', nodes.list$type)] <- 'hypo'
nodes.list$class[grepl('conserved', nodes.list$type)] <- 'cons'
nodes.list$class[grepl('histone', nodes.list$type)] <- 'hist'
nodes.list$class[grepl('domain', nodes.list$type)] <- 'domain'
nodes.list$class[grepl('AP2', nodes.list$type)] <- 'AP2'
network.opt <- graph_from_data_frame(d = cons.edge.list, vertices = nodes.list, directed = F)
network.opt <- set_vertex_attr(network.opt, 'size', index = V(network.opt), degree(network.opt))
network.opt <- simplify(network.opt)
return(network.opt)
}
getClustSubNet <- function(network.opt, phase, genes.clust){
all.edges <- as_data_frame(simplify(network.opt))[,1:2]
clust.ind <- which(all.edges[,1] %in% genes.clust$GeneID[genes.clust$phase %in% phase] &
all.edges[,2] %in% genes.clust$GeneID[genes.clust$phase %in% phase])
nodes <- data.frame(GeneID = unique(c(all.edges[clust.ind, 1], all.edges[clust.ind, 2])))
nodes <- left_join(nodes, genes.clust, by = 'GeneID')
clust.edges <- data.frame(all.edges[clust.ind, ])
g.nds <- graph_from_data_frame(d = clust.edges, vertices = nodes, directed = F)
node.size <- degree(g.nds, v = V(g.nds))
g.nds <- set_vertex_attr(g.nds, 'size', index = V(g.nds), node.size)
g.nds <- set_vertex_attr(g.nds, 'clust', index = V(g.nds), nodes$phase)
return(g.nds)
}
rankedNetDeg <- function(net, prod.desc, genes.clust){
opt.d <- degree(net)
opt.d <- data.frame(GeneID = names(opt.d), degree = opt.d)
opt.d$GeneID <- gsub('-', '_', opt.d$GeneID)
opt.d <- left_join(opt.d, prod.desc, by = 'GeneID') %>% arrange(desc(degree))
tmp <- genes.clust
tmp$GeneID <- gsub('-', '_', tmp$GeneID)
opt.d <- left_join(opt.d, tmp, by = 'GeneID')
return(opt.d)
}
getEdgeList <- function(clust.net, prod.desc){
e.l <- as_data_frame(simplify(clust.net))[,1:2]
colnames(e.l) <- c('src', 'trg')
e.l.reverse <- data.frame(src = e.l[,2], trg = e.l[,1])
e.l <- rbind(e.l, e.l.reverse)
e.l$src <- gsub('-', '_', e.l$src)
e.l$trg <- gsub('-', '_', e.l$trg)
e.l <- left_join(e.l, prod.desc, by = c('src' = 'GeneID'))
e.l <- left_join(e.l, prod.desc, by = c('trg' = 'GeneID'))
colnames(e.l) <- c('src', 'trg', 'srcDesc', 'trgDesc')
return(e.l)
}
getNetFromVert <- function(g.obj, nds){
nds <- gsub('_', '-', nds)
all.edges <- as_data_frame(simplify(g.obj))[,1:2]
ind <- which(all.edges[,1] %in% nds |
all.edges[,2] %in% nds)
nodes <- data.frame(GeneID = unique(c(all.edges[ind, 1], all.edges[ind, 2])))
node.phase <- data.frame(GeneID = V(g.obj)$name, phase = vertex_attr(g.obj, 'phase', index = V(g.obj)))
nodes <- left_join(nodes, node.phase, by = 'GeneID')
edges <- data.frame(all.edges[ind, ])
g.nds <- graph_from_data_frame(d = edges, vertices = nodes, directed = F)
node.size <- degree(g.nds, v = V(g.nds))
g.nds <- set_vertex_attr(g.nds, 'size', index = V(g.nds), node.size)
#node.phase <- vertex_attr(g.obj, 'phase', index = V(g.obj))
#g.nds <- set_vertex_attr(g.nds, 'phase', index = V(g.nds), node.phase[match(V(g.nds), V(g.obj))])
return(g.nds)
}
highlightNetwork <- function(g.obj, nds){
nds <- gsub('_', '-', nds)
g.obj <- set_edge_attr(g.obj, 'subnet', E(g.obj)[from(nds)], nds)
nodes <- data.frame(GeneID = unique(c(all.edges[ind, 1], all.edges[ind, 2])))
edges <- data.frame(all.edges[ind, ])
g.nds <- graph_from_data_frame(d = edges, vertices = nodes, directed = F)
node.size <- degree(g.nds, v = V(g.nds))
g.nds <- set_vertex_attr(g.nds, 'size', index = V(g.nds), node.size)
node.phase <- vertex_attr(g.obj, 'phase', index = V(g.obj))
g.nds <- set_vertex_attr(g.nds, 'phase', index = V(g.nds), node.phase[match(V(g.nds), V(g.obj))])
return(g.nds)
}
tg.prod.desc <- read.xlsx('../Input/compScBdTgPb/genes/ProductDescription_GT1.xlsx')
#tg.in.Cov <- readRDS('../Input/compScBdTgPb/glasso/S_tg_smooth_gam.RData')
tg.in.Cov <- readRDS('../Input/compScBdTgPb/glasso/S_tg_smooth_gam_de_trended.RData')
#tg.input.dir <- "../Input/compScBdTgPb/glasso/Tg_out/"
tg.input.dir <- "../Input/compScBdTgPb/glasso/Tg_de_trended_out/"
tg.sc.tc.mus.scale <- readRDS('../Input/compScBdTgPb/RData/tg_sc.tc.mus.scale.RData')
tg.genes <- rownames(tg.in.Cov)
tg.L <- networksScaleFreeStats(tg.genes, tg.input.dir, tg.prod.desc)
tg.opt.network <- genOptNetwork(tg.genes, tg.L$file.info, tg.L$networks, tg.prod.desc)
saveRDS(tg.opt.network, '../Input/compScBdTgPb/RData/tg_opt_net.RData')
tg.genes.clust <- tg.sc.tc.mus.scale %>% dplyr::select(GeneID, cluster, phase) %>% distinct()
tg.genes.clust$GeneID <- gsub('_', '-', tg.genes.clust$GeneID)
tg.clust.net <- getClustSubNet(tg.opt.network, c('G1', 'S', 'M', 'C'), tg.genes.clust)
saveRDS(tg.clust.net, '../Input/compScBdTgPb/RData/tg_clust_net.RData')
# define a custom color palette
got_palette <- c("#1A5878", "#C44237", "#AD8941", "#E99093", "#50594B")
node.size <- degree(tg.clust.net, v = V(tg.clust.net))
g.nds <- set_vertex_attr(tg.clust.net, 'size', index = V(tg.clust.net), node.size)
g.nds.filt <- induced_subgraph(g.nds, V(g.nds)[node.size > 1])
p <- ggraph(g.nds.filt,layout = "stress")+
#geom_edge_link0(aes(edge_width = weight),edge_colour = "grey66")+
geom_edge_link0(edge_colour = "grey66")+
geom_node_point(aes(fill = phase, size = size),shape=21)+
geom_node_text(aes(filter = size > 20, label = name),family="serif")+
scale_fill_manual(values = got_palette)+
scale_edge_width(range = c(0.2,3))+
scale_size(range = c(1,6))+
theme_graph(base_family = 'Helvetica') +
theme(legend.position = "right")
plot(p)
ggsave(filename="../Output/compScBdTgPb/figs/tg_cyclic_genes_network.pdf",
plot=p,
width = 10, height = 8,
units = "in", # other options are "in", "cm", "mm"
dpi = 300
)
tg.clust.net.d <- rankedNetDeg(tg.clust.net, tg.prod.desc, tg.genes.clust)
saveRDS(tg.clust.net.d, '../Input/compScBdTgPb/RData/tg_clust_net_d.RData')
write.xlsx(tg.clust.net.d, '../Output/compScBdTgPb/tables/tg_genes_cyclic_network_nodes.xlsx')
tg.opt.net.d <- rankedNetDeg(tg.opt.network, tg.prod.desc, tg.genes.clust)
saveRDS(tg.opt.net.d, '../Input/compScBdTgPb/RData/tg_opt_net_d.RData')
write.xlsx(tg.opt.net.d, '../Output/compScBdTgPb/tables/tg_genes_opt_network_nodes.xlsx')
tg.e.l <- getEdgeList(tg.clust.net, tg.prod.desc)
saveRDS(tg.e.l, '../Input/compScBdTgPb/RData/tg_e_l.RData')
write.xlsx(tg.e.l, '../Output/compScBdTgPb/tables/tg_cyclic_network_edge_list.xlsx')
tg.opt.e.l <- getEdgeList(tg.opt.network, tg.prod.desc)
saveRDS(tg.opt.e.l, '../Input/compScBdTgPb/RData/tg_opt_e_l.RData')
write.xlsx(tg.opt.e.l, '../Output/compScBdTgPb/tables/tg_opt_network_edge_list.xlsx')
### Interactome of selected gens
## B.d egress Markers
bd.sc.egress.markers.tc.mus.long <- readRDS('../Input/compScBdTgPb/RData/bd_sc_egress._markers_tc_mus_long.RData')
egress.clust1 <- bd.sc.egress.markers.tc.mus.long %>% dplyr::select(GeneID, cluster) %>% dplyr::filter(cluster == 1) %>%
distinct()
egress.clust2 <- bd.sc.egress.markers.tc.mus.long %>% dplyr::select(GeneID, cluster) %>% dplyr::filter(cluster == 2) %>%
distinct()
egress.clust3 <- bd.sc.egress.markers.tc.mus.long %>% dplyr::select(GeneID, cluster) %>% dplyr::filter(cluster == 3) %>%
distinct()
egress.clust4 <- bd.sc.egress.markers.tc.mus.long %>% dplyr::select(GeneID, cluster) %>% dplyr::filter(cluster == 4) %>%
distinct()
egress.clust1.net <- getNetFromVert(bd.clust.net, egress.clust1$GeneID)
egress.clust2.net <- getNetFromVert(bd.clust.net, egress.clust2$GeneID)
egress.clust3.net <- getNetFromVert(bd.clust.net, egress.clust3$GeneID)
egress.clust4.net <- getNetFromVert(bd.clust.net, egress.clust4$GeneID)
egress.clust1.edge.list <- getEdgeList(egress.clust1.net, bd.prod.desc)
egress.clust2.edge.list <- getEdgeList(egress.clust2.net, bd.prod.desc)
egress.clust3.edge.list <- getEdgeList(egress.clust3.net, bd.prod.desc)
egress.clust4.edge.list <- getEdgeList(egress.clust4.net, bd.prod.desc)
write.xlsx(egress.clust1.edge.list, '../Output/compScBdTgPb/tables/bd_egress_clust1_edges.xlsx')
write.xlsx(egress.clust2.edge.list, '../Output/compScBdTgPb/tables/bd_egress_clust2_edges.xlsx')
write.xlsx(egress.clust3.edge.list, '../Output/compScBdTgPb/tables/bd_egress_clust3_edges.xlsx')
write.xlsx(egress.clust4.edge.list, '../Output/compScBdTgPb/tables/bd_egress_clust4_edges.xlsx')
p4 <- ggraph(egress.clust4.net,layout = "stress") +
#geom_edge_link0(aes(edge_width = weight),edge_colour = "grey66")+
geom_edge_link0(edge_colour = "grey66")+
geom_node_point(aes(fill = phase, size = 3),shape=21)+
geom_node_text(aes(filter = size > 1, label = name),family="serif")+
scale_fill_manual(values = got_palette)+
scale_edge_width(range = c(0.2,3))+
scale_size(range = c(1,6))+
theme_graph()+
theme_graph(base_family = 'Helvetica') +
theme(legend.position = "right")
plot(p4)
ggsave(filename="../Output/compScBdTgPb/figs/bd_egress_clust4_network.pdf",
plot=p4,
width = 6, height = 6,
units = "in", # other options are "in", "cm", "mm"
dpi = 300
)
### T. gondii AP2 network
AP2s <- tg.prod.desc$GeneID[grep('AP2', tg.prod.desc$ProductDescription)]
AP2.net <- getNetFromVert(tg.clust.net, AP2s)
AP2.edge.list <- getEdgeList(AP2.net, tg.prod.desc)
write.xlsx(AP2.edge.list, '../Output/compScBdTgPb/tables/tg_AP2_edges.xlsx')
p5 <- ggraph(AP2.net,layout = "stress") +
#geom_edge_link0(aes(edge_width = weight),edge_colour = "grey66")+
geom_edge_link0(edge_colour = "grey66")+
geom_node_point(aes(fill = phase, size = 3),shape=21)+
geom_node_text(aes(filter = size > 1, label = name),family="serif")+
scale_fill_manual(values = got_palette)+
scale_edge_width(range = c(0.2,3))+
scale_size(range = c(1,6))+
theme_graph()+
theme_graph(base_family = 'Helvetica') +
theme(legend.position = "right")
plot(p5)
ggsave(filename="../Output/compScBdTgPb/figs/bd_egress_clust4_network.pdf",
plot=p4,
width = 6, height = 6,
units = "in", # other options are "in", "cm", "mm"
dpi = 300
)
## Node networks
nds <- 'TGGT1-214840'
tg.clust.net.nds <- set_edge_attr(tg.clust.net, 'subnet', E(tg.clust.net)[from(nds)], nds)
tg.clust.net.nds <- set_edge_attr(tg.clust.net.nds, 'subnet.weight', E(tg.clust.net.nds)[from(nds)], 2)
tg.clust.net.nds <- set_edge_attr(tg.clust.net.nds, 'subnet.weight', E(tg.clust.net.nds)[!from(nds)], 1)
ggraph(tg.clust.net.nds,layout = "stress") +
#geom_edge_link(aes(colour = subnet, width = subnet.weight, alpha = subnet.weight)) +
geom_edge_link(aes(colour = subnet)) +
#geom_edge_link0(aes(colour = subnet))+
#geom_edge_link0(edge_colour = "grey66")+
geom_node_point(aes(fill = phase, size = size), shape=21)+
geom_node_text(aes(filter = name == nds, label = name),family="serif")+
scale_fill_manual(values = got_palette)+
scale_edge_width(range = c(0.8,2))+
scale_size(range = c(1,6))+
theme_graph()+
theme(legend.position = "top")
node.net <- getNetFromVert(tg.clust.net, 'TGGT1_271930')
ggraph(node.net,layout = "stress") +
#geom_edge_link0(aes(edge_width = weight),edge_colour = "grey66")+
geom_edge_link0(edge_colour = "grey66")+
geom_node_point(aes(fill = phase, size = 3),shape=21)+
geom_node_text(aes(filter = size > 1, label = name),family="serif")+
scale_fill_manual(values = got_palette)+
scale_edge_width(range = c(0.2,3))+
scale_size(range = c(1,6))+
theme_graph()+
theme(legend.position = "right")
#saveRDS(file.info, '../Input/BdivCellCycle/RDS/file_info.RData')
## Fitting the rho/r2 curve with smooth splines
file.info.filt <- file.info %>% dplyr::filter(!is.na(r2))
spline.fit <- smooth.spline(x = file.info.filt$rho,
y = file.info.filt$r2,lambda = 0.0005)
## Compute the derivatives of the fitted splines
s.0 <- predict(spline.fit, spline.fit$x, deriv=0)
s.1 <- predict(spline.fit, spline.fit$x, deriv=1)
s.derv <- data.frame(s0=s.0$y, s1=s.1$y)
## Get the location of the extrema
locs <- rle(den.sign <- sign(s.derv$s1))
plot(file.info.filt$rho, file.info.filt$r2, pch = 20, xlab = 'rho', ylab = 'r^2')
points(s.0$x, s.0$y, type = 'l', lwd=2, col = 'blue')
points(file.info.filt$rho[ind.high.r2], file.info.filt$r2[ind.high.r2], pch = 21, col = 'red', )
# inc.ind <- which(locs$values == 1)
# maxima.ind = {}
# for(i in inc.ind){
# maxima.ind = c(maxima.ind, sum(locs$lengths[1:i]))
# }
#
# ## Interpolate a point between the location where derivative changes sign
# maxima = (spline.fit$x[maxima.ind] + spline.fit$x[(maxima.ind + 1)]) / 2
# maxima = maxima[!is.na(maxima)]
# ## Get the maximum values
# maxval = predict(spline.fit, maxima)
#
# ## Get the outliers
# maxima.outliers = which(maxval$y >= quantile(maxval$y, prob = 0.8))
#
# ## Peaks for entities of interest
# entity.x = maxval$x[maxima.outliers]
# entity.y = maxval$y[maxima.outliers]
#
# plot(file.info.filt$rho, file.info.filt$r2, pch = 20, col = 'blue')
# points(s.0$x, s.0$y, type = 'l')
# points(entity.x, entity.y, col = 'red', pch = 8)
#
# network.opt <- networks[[which(file.info$rho >= entity.x[length(entity.x)])[1]]]$network
#### Network similarity/consistency
saveRDS(network.opt, '../Input/BdivCellCycle/RDS/bd_network_opt.RData')
clust.net <- getClustSubNet(network.opt, c('G1b', 'G1c'), tc_clust)
# define a custom color palette
got_palette <- c("#1A5878", "#C44237", "#AD8941", "#E99093", "#50594B")
ggraph(clust.net,layout = "stress")+
#geom_edge_link0(aes(edge_width = weight),edge_colour = "grey66")+
geom_edge_link0(edge_colour = "grey66")+
geom_node_point(aes(fill = clust, size = size),shape=21)+
geom_node_text(aes(filter = size>40, label = name),family="serif")+
scale_fill_manual(values = got_palette)+
scale_edge_width(range = c(0.2,3))+
scale_size(range = c(1,6))+
theme_graph()+
theme(legend.position = "right")
clust.net.d <- rankedNetDeg(clust.net, prod.desc, tc_clusts)
########### OLDE STUFF
## calculate the jaccard distance
# getJD <- function(edge.high.r2, i, j){
# return(length(intersect(edge.high.r2[[i]], edge.high.r2[[j]]))/length(union(edge.high.r2[[i]], edge.high.r2[[j]])))
# }
#
# distMat.list <- mclapply(1:(length(edge.high.r2) - 1), function(i){
# mclapply((i+1):length(edge.high.r2), function(j){
# return(getJD(edge.high.r2, i, j))
# }, mc.cores = num.cores - 1)
# }, mc.cores = 1)
#
# dist.mat <- do.call('rbind', distMat.list)
# dist.mat <- as.dist(dist.mat[upper.tri(dist.mat)] <- dist.mat[lower.tri(dist.mat)])
# hc_eucledian <- hclust(dist.mat, method = "ward.D" )
####
d <- degree(bd_network.opt, mode="all")
fit <- fit_power_law(d+1)
fit
hist(d, nclass = 200)
tmp <- rle(sort(d))
ff <- lm(log(tmp$lengths)~log(tmp$values))
ff.sum <- summary(ff)
plot(log(tmp$lengths)~log(tmp$values))
abline(b = ff.sum$coefficients[2], a = ff.sum$coefficients[1], col='red')
nodes.degree <- dplyr::bind_rows(vertex_attr(bd_network.opt, index = V(bd_network.opt)))
#putative.regs <- nodes.degree$name[which(nodes.degree$size > quantile(nodes.degree$size, probs = 0.3))]
putative.regs <- nodes.degree$name
e.l <- as.data.frame(get.edgelist(bd_network.opt))
colnames(e.l) <- c('src', 'trg')
e.l.reverse <- data.frame(src = e.l[,2], trg = e.l[,1])
e.l <- rbind(e.l, e.l.reverse)
e.l.regs <- e.l %>% dplyr::filter(src %in% putative.regs) %>% group_by(src) %>%
summarise(targs = list(trg), num.trg = length(trg))
tc_clusts <- read.xlsx('../Output/scClockOut/sc_time_course_gene_clusters.xlsx')
tc_clusts <- tc_clusts %>% dplyr::select(GeneID, cluster, phase) %>% distinct()
tc_clusts$GeneID <- gsub('_', '-', gsub('___.*', '', tc_clusts$GeneID))
clust.nums <- tc_clusts %>% group_by(phase) %>% summarise(clust.genes = list(GeneID), clust.gene.num = n())
clust.nums$dummy = 1
e.l.regs$dummy <- 1
e.l.regs.clust <- left_join(e.l.regs, clust.nums, by = 'dummy')
e.l.regs.clust$all <- length(genes)
e.l.regs.clust <- e.l.regs.clust %>% rowwise() %>%
mutate(overlap = length(intersect(c(targs), c(clust.genes))),
overlap.genes = list(intersect(c(targs), c(clust.genes)))) %>%
mutate(pvalue = fisher.test(matrix(c(overlap, num.trg- overlap, clust.gene.num - overlap,
all - (num.trg + clust.gene.num - overlap) ), byrow = T, ncol = 2, nrow = 2),
alternative = "greater")$p.value)
e.l.regs.clust$qval <- fdrtool(e.l.regs.clust$pvalue, statistic = 'pvalue', plot = F)$qval
e.l.regs.clust$proportion <- e.l.regs.clust$overlap / e.l.regs.clust$clust.gene.num
con.stat <- e.l.regs.clust %>% dplyr::filter(qval < 0.01) %>% group_by(phase) %>% summarise(quant = quantile(proportion, prob = 0.5))
e.l.regs.clust <- left_join(e.l.regs.clust, con.stat, by = 'phase')
## matching with Caroline's list
TFs <- read.xlsx('../Input/BdivCellCycle/regulators/gene_regulators_kz.xlsx', sheet = 1)
colnames(TFs) <- c('Pf', 'Bdiv','annotation','notes')
TFs <- TFs %>% mutate(Bdiv = strsplit(Bdiv, ',')) %>% unnest(Bdiv)
TFs$Bdiv <- as.character(gsub('\\t', '', gsub('.*best', '', gsub(' ', '', TFs$Bdiv ))))
TFs <- TFs %>% dplyr::filter(!grepl('noo', Bdiv))
TFs$classification <- 'TF'
TFs$domain <- 'NA'
TFs$GeneName <- 'NA'
epigenetic.regs <- read.xlsx('../Input/BdivCellCycle/regulators/gene_regulators_kz.xlsx', sheet = 2)
colnames(epigenetic.regs) <- c('classification', 'Pf', 'GeneName', 'annotation', 'domain', 'Bdiv', 'notes')
epigenetic.regs <- epigenetic.regs %>% mutate(Bdiv = strsplit(Bdiv, ',')) %>% unnest(Bdiv)
epigenetic.regs$Bdiv <- as.character(gsub('\\t', '', gsub('.*best', '', gsub(' ', '', epigenetic.regs$Bdiv ))))
tmp1 <- TFs %>% transmute(Bdiv = Bdiv, Pf = Pf, GeneName = GeneName, domain = domain,
classification = classification, annotation = annotation, notes = notes)
tmp2 <- epigenetic.regs %>% transmute(Bdiv = Bdiv, Pf = Pf, GeneName = GeneName, domain = domain,
classification = classification, annotation = annotation, notes = notes)
known.regs <- rbind(tmp1, tmp2)
known.regs$Bdiv <- gsub('_', '-', known.regs$Bdiv)
e.l.regs.clust <- left_join(e.l.regs.clust, known.regs, by = c('src' = 'Bdiv'))
saveRDS(e.l.regs.clust, '../Input/BdivCellCycle/RDS/e_l_regs_clust.RData')
#putative.clust.regs <- e.l.regs.clust %>%
# dplyr::filter(qval < 0.01, proportion >= quant) %>% arrange(desc(overlap))
putative.clust.regs <- e.l.regs.clust %>%
dplyr::filter(qval < 0.01) %>% arrange(desc(overlap))
putative.clust.regs$src <- gsub('-', '_', putative.clust.regs$src)
putative.clust.regs <- left_join(putative.clust.regs, prod.desc, by = c('src' = 'GeneID'))
saveRDS(putative.clust.regs, '../Input/BdivCellCycle/RDS/putative_clust_regs.RData')
write.xlsx(putative.clust.regs, '../Output/BdivCellCycle/cluster_putative_regulatrs.xlsx')
cat('top 2 overall regulators\n')
putative.clust.regs %>% ungroup() %>%
dplyr::select(src, num.trg, phase, clust.gene.num, overlap, qval, proportion, ProductDescription) %>%
group_by(phase) %>% slice_max(order_by = proportion, n = 2)
cat('\ntop 2 know regulators\n')
putative.clust.regs %>% ungroup() %>% dplyr::filter(!is.na(Pf)) %>%
dplyr::select(src, num.trg, phase, clust.gene.num, overlap, qval,
proportion, Pf, GeneName, ProductDescription) %>%
group_by(phase) %>% slice_max(order_by = proportion, n = 2)
### Graph regulators
# define a custom color palette
got_palette <- c("#1A5878", "#C44237", "#AD8941", "#E99093", "#50594B")
getNetFromVert <- function(g.obj,nds, reg.info){
all.edges <- get.edgelist(g.obj)
nds.edges <- unique(c(which(all.edges[,1] %in% nds), which(all.edges[,2] %in% nds)))
nodes <- data.frame(GeneID = unique(c(all.edges[nds.edges, ])))
reg.info$src <- gsub('_', '-', reg.info$src)
nodes <- cbind(nodes, reg.info[match(nodes$GeneID, reg.info$src),])
nodes$reg <- ifelse(nodes$GeneID %in% nds, 'Y', 'N')
g.nds <- graph_from_data_frame(d = data.frame(all.edges[nds.edges, ]), vertices = nodes, directed = F)
node.size <- degree(g.nds, v = V(g.nds))
g.nds <- set_vertex_attr(g.nds, 'size', index = V(g.nds), node.size)
return(g.nds)
}
putative.clust.regs.known <- putative.clust.regs %>% dplyr::filter(!is.na(Pf))
nds <- gsub('_', '-',unique(putative.clust.regs.known$src))
g.nds <- getNetFromVert(bd_network.opt,nds,putative.clust.regs.known)
ggraph(g.nds,layout = "stress")+
#geom_edge_link0(aes(edge_width = weight),edge_colour = "grey66")+
geom_edge_link0(edge_colour = "grey66")+
geom_node_point(aes(fill = phase, size = size),shape=21)+
geom_node_text(aes(filter = reg == 'Y', label = name),family="serif")+
scale_fill_manual(values = got_palette)+
scale_edge_width(range = c(0.2,3))+
scale_size(range = c(1,6))+
theme_graph()+
theme(legend.position = "right")
## Plot expression curves of regulators
## Plot a few curves to check the alignments
library(sme)
source('util_funcs.R')
sc.tc.df.adj <- readRDS('../Input/scClock/sc.tc.df.adj.RData')
sc.tc.fits <- readRDS("../Input/scClock/sme_fits_sc_tc_20min.RData")
vs = gsub('-', '_', nds)
pdf(file = "../Output/scClockFigs/putative_regulators_sme_fits_sc.pdf", # The directory you want to save the file in
width = 10, # The width of the plot in inches
height = 10) # The height of the plot in inches
par(mfrow = c(4,4))
for(v in vs){
ind <- which(unique(sc.tc.df.adj$variable) == v)
plot.sme(sc.tc.fits[[ind]], v)
}
dev.off()
## Regulators
tb <- putative.clust.regs %>% ungroup() %>% dplyr::filter(!is.na(Pf)) %>%
dplyr::select(src, phase,Pf, GeneName, annotation, ProductDescription) %>%
group_by(src) %>% summarise(phase = phase[1], Pf = Pf[1],
GeneName = gsub('NA', '', paste(unique(GeneName), collapse = ',')),
annotation = gsub('NA', '', paste(unique(annotation), collapse = ',')),
Product.Description = Product.Description[1])
print(tb, width = Inf)
######################################## OLDER stuff
#######################################
## Examine sub network connectivity
sub.net <- list()
ph <- unique(tc_clusts$phase)
sub.net.info <- data.frame(phase = rep(0, length(ph)),
num.nodes = rep(0, length(ph)),
num.edges = rep(0, length(ph)))
for(i in 1:length(ph)){
ind <- tc_clusts$GeneID[tc_clusts$phase == ph[i]] %in% names(V(bd_network.opt))
putative.clust.regs.ph <- putative.clust.regs %>% dplyr::filter(phase == ph[i])
regs <- putative.clust.regs.ph %>% dplyr::filter(!is.na(Pf))
g.sub <- induced_subgraph(bd_network.opt, unique(c(tc_clusts$GeneID[tc_clusts$phase == ph[i]][ind],
gsub('_', '-',unique(regs$src)))))
reg <- ifelse(names(V(g.sub)) %in% gsub('_', '-',unique(regs$src)), 'Y', 'N')
g.sub <- set_vertex_attr(g.sub, 'reg', index = V(g.sub), reg)
sub.net <- c(sub.net, list(g.sub))
d <- degree(g.sub, mode="all")
sub.net.info$phase[i] <- ph[i]
sub.net.info$num.nodes[i] <- length(V(g.sub))
sub.net.info$num.edges[i] <- gsize(g.sub)
}
# define a custom color palette
got_palette <- c("#1A5878", "#C44237", "#AD8941", "#E99093", "#50594B")
my.graph <- sub.net[[3]]
Isolated = which(degree(my.graph) < 3)
my.graph = delete.vertices(my.graph, Isolated)
ggraph(my.graph,layout = "stress")+
#geom_edge_link0(aes(edge_width = weight),edge_colour = "grey66")+
geom_edge_link0(edge_colour = "grey66")+
geom_node_point(aes(fill = class, size = size),shape=21)+
geom_node_text(aes(filter = reg == 'Y', label = name),family="serif")+
scale_fill_manual(values = got_palette)+
scale_edge_width(range = c(0.2,3))+
scale_size(range = c(1,6))+
theme_graph()+
theme(legend.position = "right")
###########
file.info <- data.frame(rho = rep(0, length(in.files)), size = rep(0, length(in.files)),
xmin = rep(0, length(in.files)), pval = rep(0, length(in.files)))
plot(file.info$rho, file.info$r2)
which(file.info$rho==0.1376)
bd_network <- bd_networks[[which(file.info$rho==0.1376)]]$bd_network
d <- degree(bd_network, mode="all")
fit <- fit_power_law(d+1)
fit
hist(d, nclass = 200)
tmp <- rle(sort(d))
ff <- lm(log(tmp$lengths)~log(tmp$values))
plot(log(tmp$lengths)~log(tmp$values))
abline(b = -1.122650, a = 6.335302)
plot(file.info$rho,file.info$r2)
for(i in 1:length(in.files)){
cat(paste('processing file', i))
cat('\n')
gl <- readRDS(paste(input.dir, in.files[i], sep = ''))
bd_network <- getNetwork(gl, genes, prod.desc)
bd_networks <- c(bd_networks, list(bd_network))
d <- degree(bd_network, mode="all")
## Find best fitting xmin
#qs <- quantile(d+1, probs = c(0.75, 0.99))
#fits <- lapply(unique(sort(d[d+1 >qs[1] & d+1 <qs[2]] + 1)), function(xmin) fit_power_law(d+1, xmin))
#xmins <- unlist(lapply(fits, `[[`, 3))
#pvals <- unlist(lapply(fits, `[[`, 6))
#best.ind <- which.min(pvals)
fit <- fit_power_law(d+1)
file.info$rho[i] <- as.numeric(gsub('\\.RData', '', strsplit(in.files[i], split = '_')[[1]][2]))
file.info$size[i] <- gsize(bd_network)
#file.info$xmin[i] <- xmins[best.ind]
#file.info$pval[i] <- pvals[best.ind]
file.info$xmin[i] <- fit$xmin
file.info$pval[i] <- fit$KS.p
}
#saveRDS(bd_networks, '../Input/scClock/glasso/bd_networks.RData')
##ff <- fdrtool(file.info$pval,statistic = 'pvalue', color.figure = F)
##file.info$qval <- ff$qval
file.info.filt <- file.info %>% dplyr::filter(size > 0 & size < quantile(file.info$size, probs = 0.95) & pval < 0.05)
plot(file.info.filt$size, file.info.filt$pval)
rho.opt <- file.info.filt$rho[which.min(file.info.filt$pval)]
opt.network <- bd_networks[[which(file.info$rho == rho.opt)]]
## identify hubs
d <- degree(opt.network, mode="all")
pl <- power.law.fit(d+1)
hist(d)
xx <- as.data.frame(do.call('cbind',vertex_attr(opt.network, index = V(opt.network))))
xx[xx$name %in% names(d[order(d,decreasing=TRUE)][1:20]),] %>% arrange(desc(size))
## Get the gene clusters
tc_clusts <- read.xlsx('../Output/scClockOut/sc_time_course_gene_clusters.xlsx')
tc_clusts <- tc_clusts %>% dplyr::select(GeneID, cluster, phase) %>% distinct()
tc_clusts$GeneID <- gsub('_', '-', gsub('___.*', '', tc_clusts$GeneID))
clust.nums <- tc_clusts %>% group_by(phase) %>% summarise(total.genes = n())
e.l <- as.data.frame(get.edgelist(opt.network))
colnames(e.l) <- c('src', 'trg')
## sub network containing gene clusters
cluster.src.all <- left_join(tc_clusts, e.l, by = c('GeneID' = 'trg')) %>% na.omit() %>%
group_by(src) %>% summarise(targs = list(GeneID), num.targs = length(GeneID)) %>% ungroup()
cluster.src.all <- cluster.src.all%>% mutate(proportion = num.targs/nrow(tc_clusts))
cluster.src.all <- cluster.src.all %>% arrange(desc(proportion))
hubs <- cluster.src.all$src[cluster.src.all$num.targs > quantile(cluster.src.all$num.targs, probs = 0.9)]
orthologs <- read.xlsx('../Input/orthologs/all_reciprocal_bet_hits.xlsx')
orthologs.Bd.Tg <- read.xlsx('../Input/orthologs/GT1_BDiv.xlsx')
tmp <- cluster.src.all %>% dplyr::filter(num.targs > quantile(cluster.src.all$num.targs, probs = 0.9)) %>%
dplyr::select(src, num.targs)
tmp$src <- gsub('-', '_', tmp$src)
putative.regs <- inner_join(orthologs.Bd.Tg, tmp, by = c('BDiv' = 'src')) %>% arrange(desc(num.targs))
ind <- tc_clusts$GeneID %in% names(V(opt.network))
g.sub <- induced_subgraph(opt.network, c(hubs, tc_clusts$GeneID[ind]))
# define a custom color palette
got_palette <- c("#1A5878", "#C44237", "#AD8941", "#E99093", "#50594B")
my.graph <- g.sub
Isolated = which(degree(g.sub) <= 3)
my.graph = delete.vertices(my.graph, Isolated)
ggraph(my.graph,layout = "stress")+
#geom_edge_link0(aes(edge_width = weight),edge_colour = "grey66")+
geom_edge_link0(edge_colour = "grey66")+
geom_node_point(aes(fill = class, size = size),shape=21)+
geom_node_text(aes(filter = size >= 150, label = name),family="serif")+
scale_fill_manual(values = got_palette)+
scale_edge_width(range = c(0.2,3))+
scale_size(range = c(1,6))+
theme_graph()+
theme(legend.position = "right")
## per phase sub network containing gene clusters
cluster.src <- left_join(tc_clusts, e.l, by = c('GeneID' = 'trg')) %>% na.omit() %>%
group_by(src, phase) %>% summarise(targs = list(GeneID), num.targs = length(GeneID)) %>% ungroup()
cluster.src <- left_join(cluster.src , clust.nums, by = 'phase') %>% mutate(proportion = num.targs/total.genes)
cluster.src <- cluster.src %>% arrange(desc(proportion))
## Examine sub network connectivity
sub.net <- list()
ph <- unique(tc_clusts$phase)
sub.net.info <- data.frame(phase = rep(0, length(ph)),
num.nodes = rep(0, length(ph)),
num.edges = rep(0, length(ph)))
for(i in 1:length(ph)){
ind <- tc_clusts$GeneID[tc_clusts$phase == ph[i]] %in% names(V(opt.network))
g.sub <- induced_subgraph(opt.network, tc_clusts$GeneID[tc_clusts$phase == ph[i]][ind])
sub.net <- c(sub.net, list(g.sub))
d <- degree(g.sub, mode="all")
sub.net.info$phase[i] <- ph[i]
sub.net.info$num.nodes[i] <- length(V(g.sub))
sub.net.info$num.edges[i] <- gsize(g.sub)
}
## Get hubs of each modules
regs <- list()
for(i in 1:length(ph)){
ph.reg <- cluster.src %>% dplyr::filter(phase == ph[i] & proportion > 0.2) %>% dplyr::select(src, num.targs)
regs <- c(regs, ph.reg)
}
S.M.reg <- cluster.src %>% dplyr::filter(phase == 'S/M' & proportion > 0.3) %>% dplyr::select(src, num.targs)
G1a.reg <- cluster.src %>% dplyr::filter(phase == 'G1c' & proportion > 0.2) %>% dplyr::select(src, num.targs)
# define a custom color palette
got_palette <- c("#1A5878", "#C44237", "#AD8941", "#E99093", "#50594B")
my.graph <- sub.net[[1]]
Isolated = which(degree(my.graph) < 3)
my.graph = delete.vertices(my.graph, Isolated)
ggraph(my.graph,layout = "stress")+
#geom_edge_link0(aes(edge_width = weight),edge_colour = "grey66")+
geom_edge_link0(edge_colour = "grey66")+
geom_node_point(aes(fill = class, size = size),shape=21)+
geom_node_text(aes(filter = size >= 150, label = name),family="serif")+
scale_fill_manual(values = got_palette)+
scale_edge_width(range = c(0.2,3))+
scale_size(range = c(1,6))+
theme_graph()+
theme(legend.position = "right")
xx <- as.data.frame(do.call('cbind',vertex_attr(bd_networks[[1440]], index = V(bd_networks[[1440]]))))
d <- degree(bd_networks[[1440]], mode="all")
xx[xx$name %in% names(d[order(d,decreasing=TRUE)][1:20]),] %>% arrange(desc(size))
pl <- power.law.fit(d+1)
d = degree(bd_network, mode = "all")
hist(d)
## Get the gene clusters
tc_clusts <- read.xlsx('../Output/scClockOut/sc_time_course_gene_clusters.xlsx')
tc_clusts <- tc_clusts %>% dplyr::select(GeneID, cluster, phase) %>% distinct()
tc_clusts$GeneID <- gsub('_', '-', gsub('___.*', '', tc_clusts$GeneID))
clust.nums <- tc_clusts %>% group_by(phase) %>% summarise(total.genes = n())
e.l <- as.data.frame(get.edgelist(bd_networks[[1440]]))
colnames(e.l) <- c('src', 'trg')
cluster.src <- left_join(tc_clusts, e.l, by = c('GeneID' = 'trg')) %>% na.omit() %>%
group_by(src, phase) %>% summarise(targs = list(GeneID), num.targs = length(GeneID)) %>% ungroup()
cluster.src <- left_join(cluster.src , clust.nums, by = 'phase') %>% mutate(proportion = num.targs/total.genes)
cluster.src <- cluster.src %>% arrange(desc(proportion))
S.M.reg <- cluster.src %>% dplyr::filter(phase == 'S/M' & proportion > 0.3) %>% dplyr::select(src, num.targs)
G1a.reg <- cluster.src %>% dplyr::filter(phase == 'G1c' & proportion > 0.2) %>% dplyr::select(src, num.targs)
g.sub <- induced_subgraph(bd_networks[[1440]], c(S.M.reg$src, tc_clusts$GeneID[tc_clusts$phase == 'S/M']))
ind <- c(G1a.reg$src, tc_clusts$GeneID[tc_clusts$phase == 'G1c']) %in% names(V(bd_networks[[1440]]))
g.sub <- induced_subgraph(bd_networks[[1440]], c(G1a.reg$src, tc_clusts$GeneID[tc_clusts$phase == 'G1c'])[ind])
got_palette <- c("#1A5878", "#C44237", "#AD8941", "#E99093", "#50594B")
vertex_attr(g.sub, "reg", S.M.reg$src) <- 'reg'
vertex_attr(g.sub, "reg", G1a.reg$src) <- 'reg'
ggraph(g.sub,layout = "stress")+
#geom_edge_link0(aes(edge_width = weight),edge_colour = "grey66")+
geom_edge_link0(edge_colour = "grey66")+
geom_node_point(aes(fill = class,size = size),shape=21)+
geom_node_text(aes(filter = !is.na(reg), label = name),family="serif")+
scale_fill_manual(values = got_palette)+
scale_edge_width(range = c(0.2,3))+
scale_size(range = c(1,6))+
theme_graph()+
theme(legend.position = "right")
orthologs <- read.xlsx('../Input/orthologs/all_reciprocal_bet_hits.xlsx')
orthologs.Bd.Tg <- read.xlsx('../Input/orthologs/GT1_BDiv.xlsx')
orthologs[which(orthologs$Bdiv %in% gsub('-', '_', S.M.reg$src)),]
orthologs[which(orthologs$Bdiv %in% gsub('-', '_', G1a.reg$src)),]
xx <- as.data.frame(do.call('cbind',vertex_attr(g.sub, index = V(g.sub))))
d <- degree(g.sub, mode="all")
xx[xx$name %in% names(d[order(d,decreasing=TRUE)][1:20]),]