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Patterns computations.R
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Patterns computations.R
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library(GGally)
library(sda)
library(ggplot2)
library(network)
library(graph)
library(igraph)
library(KEGGREST)
library(progress)
kegg.pathws <- c("00010", "00020", "00030", "00040", "00051", "00052", "00053", "00061",
"00071", "00130", "00190", "00220", "00230", "00240", "00250", "00260",
"00261", "00270", "00280", "00290", "00300", "00310", "00330", "00340",
"00350", "00360", "00362", "00380", "00400", "00410", "00440", "00450",
"00480", "00500", "00520", "00521", "00540", "00550", "00561", "00564",
"00620", "00627", "00630", "00640", "00650", "00660", "00670", "00680",
"00730", "00740", "00750", "00760", "00770", "00780", "00790", "00860",
"00900", "00910", "00970", "02010", "02020", "02030", "02040", "02060",
"03010", "03018", "03030", "03060", "03070", "03410", "03420", "03430",
"03440", "04122")
# eco.df.paths <- data.frame()
# for(i in 1:length(kegg.pathws))
# {
# df<-as.data.frame(keggList(paste("path:eco",kegg.pathws[[i]], sep="")))
# colnames(df) <- "name.path"
# eco.df.paths<-rbind(eco.df.paths , df)
# }
#
#
# fname <- "./eco.df.paths.Rdata"
# save(eco.df.paths, file=fname)
load("./gene_assoc.Rdata")
load("./list.kegg.path.igraph.Rdata")
load("./ecocyc.kegg.igraph.Rdata")
load("./norfloxacin.treats.model1.Rdata")
load("./suzukietall.treats.model1.Rdata")
#get(load("./norfloxacin.treats.model1.approx.Rdata"))
#get(load("./suzukietall.treats.model1.approx.Rdata"))
load("./operons.matrix.Rdata")
load("./complex.matrix.Rdata")
load("./computations.steady.state.Rdata")
load("./eco.df.paths.Rdata")
global.pattern.names <- sub("eco:", "", V(ecocyc.kegg.igraph)$name)
global.pattern <- gene_assoc[global.pattern.names,]
global.pattern <- global.pattern[!is.na(global.pattern$gene_loc),]
names.global.net <- paste("eco:", global.pattern$gene_loc, sep="")
global.network <- induced.subgraph(graph=ecocyc.kegg.igraph, vids=names.global.net )
global.pattern_names <- as.vector(global.pattern$gene_loc)
global.pattern.net <- as.vector(global.pattern$list.caipa)
names(global.pattern.net) <- global.pattern_names
global.network <- set.vertex.attribute(global.network , "name", value=global.pattern_names)
APL <- ceiling(calc.mean.shortest.path(global.network ))
get.mov.js <- function(list.cai, list.pa)
{
list.cai_hat <- scale(list.cai, center = TRUE, scale=TRUE)
list.pa_hat <- scale(list.pa, center = TRUE, scale=TRUE)
list.summed <- scale( ((list.cai_hat + list.pa_hat) / 2), center = TRUE, scale=TRUE )
list.summed[list.summed>=0] <- 1
list.summed[list.summed<0] <- 0
return(as.vector(list.summed))
}
list.cai <- gene_assoc$CAI
list.pa <- gene_assoc$pa_mc
list.cai_hat <- scale(list.cai, center = TRUE, scale=TRUE)
list.pa_hat <- scale(list.pa, center = TRUE, scale=TRUE)
list.summed <- scale( ((list.cai_hat + list.pa_hat) / 2), center = TRUE, scale=TRUE )
list.summed[list.summed>=0] <- 1
list.summed[list.summed<0] <- 0
gene_assoc$list.caipa <- list.summed
sym.to.b.orderd <- function(gene_assoc, gene_sym)
{
path.ext <- gene_assoc[gene_assoc$gene_sym %in% gene_sym,]
path.ext <- path.ext[!is.na(path.ext$gene_loc),]
path.ext <- path.ext[with(path.ext, order(orf_from)), ]
return(as.vector(path.ext$gene_loc))
}
b.to.sym.orderd <- function(gene_assoc, gene_list)
{
path.ext <- gene_assoc[gene_list,]
path.ext <- path.ext[!is.na(path.ext$gene_loc),]
path.ext <- path.ext[with(path.ext, order(orf_from)), ]
return(as.vector(path.ext$gene_sym))
}
get.the.patterns <- function(i, gene_assoc, list.kegg.path.igraph, global.network)
{
#extract the graph
pathway_clean_name <- gsub("eco:","", V(list.kegg.path.igraph[[i]])$name)
pathway_clean <- set.vertex.attribute(list.kegg.path.igraph[[i]] , "name", value=pathway_clean_name)
not.found <- setdiff(V(pathway_clean)$name, V(global.network)$name)
graph_ext_name <- delete_vertices(pathway_clean, not.found)
graph_ext <- induced.subgraph(graph=global.network, vids=V(graph_ext_name)$name)
gene_list <- gsub("eco:","", V(graph_ext )$name)
path.ext <- gene_assoc[gene_list,]
path.ext <- path.ext[!is.na(path.ext$gene_loc),]
path.ext <- path.ext[with(path.ext, order(orf_from)), ]
list.cai <- path.ext$CAI
list.pa <- path.ext$pa_mc
local_pattern <- get.mov.js(list.cai, list.pa)
names(local_pattern) <- path.ext$gene_loc
global_pattern <- as.vector(path.ext$list.caipa)
names(global_pattern) <- path.ext$gene_loc
return(list(local_pattern_path=local_pattern,
global_pattern_path=global_pattern,
lost.nodes= not.found, size.pattern = length(local_pattern),
list.cai = list.cai, list.pa = list.pa))
}
#treat <- treats2[,1]
get.global.pattern.treat <- function(treat, gene_assoc)
{
gene_assoc_mod <- gene_assoc[gene_assoc$gene_sym %in% names(treat),]
list.cai.glb <- as.vector(gene_assoc_mod$CAI)
names(list.cai.glb) <- rownames(gene_assoc_mod)
list.pa.glb <- as.vector(treat[gene_assoc_mod$gene_sym])
names(list.pa.glb) <- rownames(gene_assoc_mod)
list.pa.glb <- list.pa.glb[!is.na(list.pa.glb)]
list.cai.glb <- list.cai.glb[names(list.pa.glb)]
global_pattern <- get.mov.js(list.cai.glb , list.pa.glb )
names(global_pattern) <- names(list.cai.glb)
return(global_pattern)
}
get.pattern.treatment <- function(treat, gene_assoc, pattern_path, global_pattern)
{ pattern_path1 <- pattern_path[unique(names(pattern_path))]
genes_df <- gene_assoc[names(pattern_path1),]
gene_sym <- b.to.sym.orderd(gene_assoc, as.vector(genes_df$gene_loc))
tts <- treat[gene_sym]
gene_lost <- sym.to.b.orderd(gene_assoc, gene_sym[which(is.na(tts))])
gene_sym_lost <- gene_sym[which(is.na(tts))]
tts <- tts[!is.na(tts)]
n.n <- sym.to.b.orderd(gene_assoc, names(tts))
list.cai <- gene_assoc[n.n,]$CAI
list.pa <- as.vector(tts)
local_pattern <- get.mov.js(list.cai, list.pa)
names(local_pattern) <- n.n
names(list.cai) <- n.n
names(list.pa) <- n.n
l.p <- local_pattern[names(pattern_path)]
l.p <- l.p[!is.na(l.p)]
list.cai.ext <- c()
list.pa.ext <- c()
n.cai.ext <- c()
n.pa.ext <- c()
n.l.p <- names(l.p)
for(i in 1:length(l.p))
{
index1 <- grep(n.l.p[i], names(list.cai))
cai.g <- list.cai[[index1[[1]]]]
n.cai.ext <- c(n.cai.ext , n.l.p[i])
list.cai.ext <- c(list.cai.ext, cai.g )
index2 <- grep(n.l.p[i], names(list.pa))
cai.g <- list.pa[[index1[[1]]]]
n.pa.ext <- c(n.pa.ext , n.l.p[i])
list.pa.ext <- c(list.pa.ext, cai.g )
}
list.cai <- list.cai.ext
names(list.cai) <- n.cai.ext
list.pa <- list.pa.ext
names(list.pa) <- n.pa.ext
global_pattern <- global_pattern[names(l.p)]
return(list(local_pattern_path=l.p,
global_pattern_path=global_pattern,
lost.nodes= gene_lost, size.pattern = length(l.p),
list.cai = list.cai, list.pa = list.pa))
}
##omics in OR
compute.effective.pattern <- function(patterns)
{
alternation_local <- compute.alternations(patterns$local_pattern, N=2)
alternation_global <- compute.alternations(patterns$global_pattern, N=2)
## Compute path extensions with the best possible score.
effective_alternation <- alternation_global | alternation_local
effective_alternation[effective_alternation == TRUE] <- 1
effective_alternation[effective_alternation == FALSE] <- 0
effective_pattern <- c(rep(0,length(patterns$local_pattern)))
names(effective_pattern) <- names(patterns$local_pattern)
for(i in 1:length(effective_alternation))
{
genes <- unlist(strsplit(names(effective_alternation[i]), "|", fixed = TRUE))
loc.alt <- abs(patterns$local_pattern[genes][[1]] - patterns$local_pattern[genes][[2]])
glb.alt <- abs(patterns$global_pattern[genes][[1]] - patterns$global_pattern[genes][[2]])
if(loc.alt == 1)
{
effective_pattern[names(patterns$local_pattern[genes][1])] <- patterns$local_pattern[genes][[1]]
effective_pattern[names(patterns$local_pattern[genes][2])] <- patterns$local_pattern[genes][[2]]
} else
{
effective_pattern[names(patterns$global_pattern[genes][1])] <- patterns$global_pattern[genes][[1]]
effective_pattern[names(patterns$global_pattern[genes][2])] <- patterns$global_pattern[genes][[2]]
}}
return(effective_pattern)
}
get.the.operon<-function(gene_assoc, operons.matrix, gene){
operon <- operons.matrix[!is.na(operons.matrix[, grep(gene, colnames(operons.matrix))]),]
operon <- names(operon[!is.na(operon)])
return(list(b.operon = operon, sym.operon = b.to.sym.orderd(gene_assoc, operon)))
}
get.the.complex<-function(gene_assoc, complex.matrix, gene){
complex <- complex.matrix[!is.na(complex.matrix[, grep(gene, colnames(complex.matrix))]),]
complex <- names(complex[!is.na(complex)])
return(list(b.complex = complex, sym.complex = b.to.sym.orderd(gene_assoc, complex)))
}
enrich.with.operons <- function(gene_assoc, operons.matrix, pattern_path)
{ op <- 1
op.c <- -1
searched<- c()
genes <- names(pattern_path)
operon.label <- rep(0, length(genes))
names(operon.label) <- genes
operon.path.list <- list()
for(i in 1:length(genes))
{ gene <- genes[i]
gto <- get.the.operon(gene_assoc, operons.matrix, gene)
gg <- grep(gene, searched)
if(!is.null(gto$b.operon) && length(gg)==0)
{ #print(gto)
if(length(operon.label[names(operon.label) %in% gto$b.operon ]) > 1)
{ operon.label[names(operon.label) %in% gto$b.operon ] <- op
op <- op +1 }
else
{
operon.label[names(operon.label) %in% gto$b.operon ] <- op.c
op.c <- op.c -1
}
operon.path.list[[length(operon.path.list)+1]] <- gto
searched <- c(searched, gto$b.operon)
}
}
return(list(operon.adjacent = operon.label, all.operons.involved = operon.path.list))
}
enrich.with.complex <- function(gene_assoc, complex.matrix, pattern_path)
{ cp <- 1
cp.c <- -1
searched<- c()
genes <- names(pattern_path)
complex.label <- rep(0, length(genes))
names(complex.label) <- genes
complex.path.list <- list()
for(i in 1:length(genes))
{ gene <- genes[i]
gto <- get.the.complex(gene_assoc, complex.matrix, gene)
gg <- grep(gene, searched)
if(!is.null(gto$b.complex) && length(gg)==0)
{ #print(gto)
if(length(complex.label[names(complex.label) %in% gto$b.complex ]) > 1)
{ complex.label[names(complex.label) %in% gto$b.complex ] <- cp
cp <- cp +1 }
else
{
complex.label[names(complex.label) %in% gto$b.complex ] <- cp.c
cp.c <- cp.c -1
}
complex.path.list[[length(complex.path.list)+1]] <- gto
searched <- c(searched, gto$b.complex)
}
}
return(list(complex.adjacent = complex.label, all.complex.involved = complex.path.list))
}
clear.deviations <- function(clear.dev.pattern)
{
nn <- names(clear.dev.pattern)
nn1 <- gsub("[^dev]", "", nn)
nn1 <- gsub("dev", 1, nn1)
nn1 <- as.numeric(nn1)
nn1[is.na(nn1)] <- 0
nn <- gsub("dev\\[", "", nn)
nn <- gsub("\\]", "", nn)
names(clear.dev.pattern) <- nn
return(list(clear.dev= clear.dev.pattern, dev = nn1))
}
compute.operon.compression <- function(rx)
{ #rx <- r2
gg <- grep("dev", rownames(rx))
devN = FALSE
if(length(gg)!=0)
{ devN = TRUE
dev <- rx["dev",]
operon_t <- rx["operon", ]
col_n <- colnames(rx)
col_comp <- c()
col_hidden <- c()
oper_comp <- c()
for(i in 1:length(dev))
{
if(dev[[i]]==1)
{
oper_comp <- c(oper_comp, operon_t[[i]])
operon_t[[i]] = 0
col_hidden <- c(col_hidden, paste("hidden",i, sep=""))
col_comp <- c(col_comp, col_n[[i]])
col_n[[i]] = paste("hidden",i, sep="")
}
}
rx["operon", ] <- operon_t
colnames(rx) <- col_n
}
temp <- data.frame()
operons.to.compress <- rx[grep("operon", rownames(rx)),]
distinct.op <- unique(operons.to.compress[operons.to.compress > 0 ])
dim.flag=TRUE
if(length(distinct.op)!=0)
{
distinct.op1 <- distinct.op
for(j in 1:length(distinct.op))
{ o.t.c <- names(operons.to.compress[operons.to.compress == distinct.op[[j]]])
if(length(o.t.c)!=1){
compress.block <- rx[, o.t.c]
n.c <- ncol(compress.block)
if(is.null(n.c))
{ g1 <- grep(distinct.op[[j]], distinct.op1)
if(length(g1)!=0)
{ distinct.op1 = distinct.op1[-g1[[1]]]
} }
else{
compress.block <- as.data.frame(compress.block[, !duplicated(colnames(compress.block))])
n.r <- nrow(compress.block)
if(is.null(n.r))
{
g1 <- grep(distinct.op[[j]], distinct.op1)
if(length(g1)!=0)
{ distinct.op1 = distinct.op1[-g1[[1]]]
}
}}
} else{
g1 <- grep(distinct.op[[j]], distinct.op1)
if(length(g1)!=0)
{ distinct.op1 = distinct.op1[-g1[[1]]]
}
}}
if(length(distinct.op1)!=0){
distinct.op <- distinct.op1
for(j in 1:length(distinct.op))
{ o.t.c <- names(operons.to.compress[operons.to.compress == distinct.op[[j]]])
compress.block <- as.data.frame(rx[, o.t.c])
if(ncol(compress.block)>1 && ncol(compress.block) != ncol(rx)){
compress.block <- as.data.frame(compress.block[, !duplicated(colnames(compress.block))])
#compress.block
compress.col <- c()
for(i in 1:nrow(compress.block))
{ tt.comp <- as.data.frame(table(compress.block[i,]))
tt.comp <- tt.comp[with( tt.comp, order(-Freq)), ]
if(nrow(tt.comp) == 1)
{ compress.col <- as.numeric(c(compress.col, paste(tt.comp[1,1])))
} else
{
compress.col <- as.numeric(c(compress.col, paste(tt.comp[1,1])))
}
}
compress.col <- as.data.frame(compress.col)
colnames(compress.col) <- paste("(", paste(colnames(compress.block), collapse ="-"), ")", sep="")
rownames(compress.col) <- rownames(rx)
gg <- grep(colnames(compress.block)[[1]], colnames(rx))
gg.x <- grep(colnames(compress.block)[[length(colnames(compress.block))]],colnames(rx))
#print(paste("-->",gg))
if(gg[[1]]==1)
{ rx = cbind(compress.col, rx[, !(colnames(rx) %in% colnames(compress.block))])}
if(gg.x[[1]] == length(colnames(rx)))
{
rx = cbind(rx[, !(colnames(rx) %in% colnames(compress.block))], compress.col)
}
else
{
left.rx <- rx[, colnames(rx)[1:gg[[1]]-1]]
gg2 <- grep(colnames(compress.block)[[length(colnames(compress.block))]], colnames(rx))
if(length(gg2)!=0)
{ limit <- gg2[[1]]
l.c.n <- length(colnames(rx))
#l.c.n <- abs(l.c.n - limit)
plus.one <- gg2[[1]]+1
#print(rx)
nn.name <- colnames(rx)[c(plus.one:l.c.n)]
#print(nn.name)
right.rx <- rx[,paste(nn.name)]
rx <- cbind(left.rx, compress.col)
rx <- cbind(rx, right.rx)
} else { print("Something goes wrong.") }
}
}
}
}
}
if(devN)
{ if(!is.null(col_hidden)){
for(i in 1:length(col_hidden))
{
gg <- grep(col_hidden[[i]], colnames(rx))
if(length(gg)!=0){
rx["operon",gg[[1]]] <- oper_comp[[i]]
colnames(rx)[gg[[1]]] <- col_comp[[i]]
}
}}
}
return(rx)
}
#########################################################################################################
#### Steady state conditions for all the pathways.
#### Multi-omic patterns with operons compressions
#### Multi omic patterns with path extensions
#########################################################################################################
#pathway.under.examinations <- 1
steady.state.an <- function(pathway.under.examinations, eco.df.paths,
gene_assoc, list.kegg.path.igraph, global.network, global.pattern.net,
operons.matrix, complex.matrix, APL=3)
{
name.path <- paste(eco.df.paths[pathway.under.examinations,])
path.code <- paste(rownames(eco.df.paths)[[pathway.under.examinations]])
patterns <- get.the.patterns(pathway.under.examinations, gene_assoc, list.kegg.path.igraph, global.network )
#global and local effect influences
effective_pattern_path <- compute.effective.pattern(patterns)
#pathway multi-omic alternations on arrays
effective_alternation_path <- alternation.scores(0,0, effective_pattern_path, 2)
effective_altern_path <- effective_alternation_path$alter
#compute path extensions need global pattern
deviations.frame <- compute.deviations(effective_altern_path ,
global.network, global.pattern.net, N=2)
# #compute alternative path with path extensions
effective_pattern_path_dev <- compute.extension.by.deviations(deviations.frame,
global.pattern.net, effective_pattern_path )
#compute alternations on arrays for the extended pathway
effective_alternation_path_dev <- alternation.scores(0,0,effective_pattern_path_dev,2)
#effective_alternation_path
#effective_alternation_path_dev
clear.dds <- clear.deviations(effective_pattern_path_dev)
effective_pattern_path_dev.c <- clear.dds$clear.dev
dds <- clear.dds$dev
#compute network alternations
net.alt <- network.alternations.m(effective_pattern_path , global.network)
net.dev.alt <- network.alternations.m(effective_pattern_path_dev.c , global.network)
count.path <- get.pattern.adjacent.influences(names(effective_pattern_path), delta=2, psi=2:APL, global.network)
count.path.dev <- get.pattern.adjacent.influences(names(effective_pattern_path_dev.c), delta=2, psi=2:APL, global.network)
count.path.dev.order <- c()
count.path.dev.name <- c()
for(i in 1:length(names(effective_pattern_path_dev.c)))
{
count.path.dev.order <-c(count.path.dev.order, count.path.dev[names(effective_pattern_path_dev.c)[[i]]][[1]])
count.path.dev.name <-c(count.path.dev.name , names(effective_pattern_path_dev.c)[[i]])
}
names(count.path.dev.order) <- count.path.dev.name
count.path.dev <- count.path.dev.order
#compute operon compressions
operons.list.path <- enrich.with.operons(gene_assoc, operons.matrix, effective_pattern_path )
complex.list.path <- enrich.with.complex(gene_assoc, complex.matrix, effective_pattern_path )
r1<-rbind(effective_pattern_path,operons.list.path$operon.adjacent)
r1<-rbind(r1,complex.list.path$complex.adjacent)
r1<-rbind(r1, count.path)
r1<-rbind(r1, patterns$list.cai)
r1<-rbind(r1, patterns$list.pa)
rownames(r1) <- c("altern", "operon", "complex", "weight", "CAI", "pv")
operons.list.path.d <- enrich.with.operons(gene_assoc, operons.matrix, effective_pattern_path_dev.c )
complex.list.path.d <- enrich.with.complex(gene_assoc, complex.matrix, effective_pattern_path_dev.c )
list.cai <- gene_assoc[names(effective_pattern_path_dev.c),]$CAI
list.pa <- gene_assoc[names(effective_pattern_path_dev.c),]$pa_mc
r2 <- rbind(effective_pattern_path_dev.c , operons.list.path.d$operon.adjacent)
r2 <- rbind(r2, complex.list.path.d$complex.adjacent)
r2 <- rbind(r2, count.path.dev)
r2<-rbind(r2, list.cai)
r2<-rbind(r2, list.pa)
r2 <- rbind(r2, dds)
rownames(r2) <- c("altern", "operon", "complex","weight", "CAI", "pv","dev" )
op.compr <- compute.operon.compression(r1)
effective_pattern_path_op <- round(op.compr [1,],0)
op.compr.dev <- compute.operon.compression(r2)
effective_pattern_path_op_dev <- round(op.compr.dev[1,],0)
if(is.data.frame(effective_pattern_path_op)){
e_p_p_o <- c()
val.val.o <- c()
for(u in 1:length(effective_pattern_path_op)){
e_p_p_o <- c(e_p_p_o, gsub("\\)", "",gsub("\\(", "",gsub("-", "", names(effective_pattern_path_op)[[u]]))))
val.val.o <- c(val.val.o, effective_pattern_path_op[1,u])
}
val.val.o <- as.numeric(val.val.o)
names(val.val.o)<- e_p_p_o
op.alt <- alternation.scores(0,0,val.val.o,2)
} else
{
op.alt <- alternation.scores(0,0,effective_pattern_path_op,2)
val.val.o <- effective_pattern_path_op
}
if(is.data.frame(effective_pattern_path_op_dev)){
e_p_p_o_d <- c()
val.val.o.d <- c()
for(u in 1:length(effective_pattern_path_op_dev )){
e_p_p_o_d <- c(e_p_p_o_d, gsub("\\)", "",gsub("\\(", "",gsub("-", "", names(effective_pattern_path_op_dev)[[u]]))))
val.val.o.d <- c(val.val.o.d, effective_pattern_path_op_dev[1,u])
}
val.val.o.d <- as.numeric(val.val.o.d)
names(val.val.o.d) <- e_p_p_o_d
op.compr.alt <- alternation.scores(0,0,val.val.o.d,2)
} else { op.compr.alt <- alternation.scores(0,0,effective_pattern_path_op_dev,2)
val.val.o.d <- effective_pattern_path_op_dev}
return(list(name.path = name.path ,
path.code = path.code ,
multi.omic.pattern = effective_pattern_path ,
multi.omic.pattern.d = effective_pattern_path_dev ,
multi.omic.pattern.c = effective_pattern_path_dev.c,
multi.omic.pattern.o = val.val.o,
multi.omic.pattern.o.d = val.val.o.d,
array.alternations = effective_alternation_path,
array.alternations.d = effective_alternation_path_dev,
array.alternations.o = op.alt,
array.alternations.o.d = op.compr.alt,
network.alternations = net.alt,
network.alternations.d = net.dev.alt,
operon.enric = operons.list.path,
operon.enric.d = operons.list.path.d,
complex.enrich = complex.list.path,
complex.enrich.d = complex.list.path.d,
multi.omic.df = round(r1[-c(5,6),],2),
multi.omic.df.dev = round(r2[-c(5,6),],2),
omic.amount.df = r1[c(5,6),],
omic.amount.df.dev = r2[c(5,6),],
multi.omic.df.compr = round(op.compr[-c(5,6),],2),
multi.omic.df.compr.d = round(op.compr.dev[-c(5,6),],2),
omic.amount.df.compr = op.compr[c(5,6),] ,
omic.amount.df.compr.d = op.compr.dev[c(5,6),]
))
}
########################################################################################################
### Suzuki et all 10 treatments.
###
########################################################################################################
#pattern_under_considerations <- i <- 1
#treatment.i <- 2 #primo esperimento
#treats1 = suzukietall.treats.model1
#treats2 = norfloxacin.treats.model1
#treats <- treats1
# res <- treatment.an(i, treats2, steady.state, treatment.i,
# eco.df.paths, gene_assoc, list.kegg.path.igraph, global.network,
# global.pattern.net, operons.matrix, complex.matrix, APL)
treatment.an <- function(pattern_under_considerations, treats, steady.state, treatment.i,
eco.df.paths, gene_assoc, list.kegg.path.igraph, global.network,
global.pattern.net, operons.matrix, complex.matrix, APL=3){
name.path <- paste(eco.df.paths[pattern_under_considerations,])
path.code <- paste(rownames(eco.df.paths)[[pattern_under_considerations]])
ith.treatment <- treats[,treatment.i]
global_pattern_treat <- get.global.pattern.treat(ith.treatment, gene_assoc)
global.pattern.treat <- c(global_pattern_treat, global.pattern.net[setdiff(names(global.pattern.net), names(global_pattern_treat))])
put.it <- steady.state[[pattern_under_considerations]]$multi.omic.pattern
patterns.treat <- get.pattern.treatment(ith.treatment, gene_assoc, put.it , global.pattern.treat)
effective_pattern_treat <- compute.effective.pattern(patterns.treat)
effective_pattern_treat <- effective_pattern_treat[names(effective_pattern_treat) %in% names(global_pattern_treat)]
effective_alternation_path_treat <- alternation.scores(0,0, effective_pattern_treat , 2)
effective_altern_path_treat <- effective_alternation_path_treat$alter
put.it.dev <- steady.state[[pattern_under_considerations]]$multi.omic.pattern.c
patterns.treat.dev <- get.pattern.treatment(ith.treatment, gene_assoc, put.it.dev , global.pattern.treat)
effective_pattern_treat.dev <- compute.effective.pattern(patterns.treat.dev)
effective_pattern_path_dev_treat <- effective_pattern_treat.dev[names(effective_pattern_treat.dev) %in% names(global_pattern_treat)]
effective_alternation_path_dev_treat <- alternation.scores(0,0,effective_pattern_path_dev_treat ,2)
effective_pattern_path_dev.c <- effective_pattern_path_dev_treat
data.group <- effective_pattern_path_dev.c[names(patterns.treat.dev$local_pattern_path)]
effective_pattern_path_dev.c <- data.group[!is.na(data.group)]
dds <- steady.state[[pattern_under_considerations]]$multi.omic.df.dev
dds <- dds[,names(effective_pattern_path_dev.c) ]
net.alt <- network.alternations.m(effective_pattern_treat, global.network)
net.dev.alt <- network.alternations.m(effective_pattern_path_dev.c, global.network)
count.path <- steady.state[[pattern_under_considerations]]$multi.omic.df[4,names(effective_pattern_treat)]
count.path.dev <- steady.state[[pattern_under_considerations]]$multi.omic.df.dev[4,names(effective_pattern_path_dev.c)]
operons.list.path <- enrich.with.operons(gene_assoc, operons.matrix, effective_pattern_treat )
complex.list.path <- enrich.with.complex(gene_assoc, complex.matrix, effective_pattern_treat )
r1 <- rbind(effective_pattern_treat ,operons.list.path$operon.adjacent)
r1 <- rbind(r1,complex.list.path$complex.adjacent)
r1 <- rbind(r1, count.path)
a <- patterns.treat$list.pa
names(a) <- names(patterns.treat$local_pattern_path)
list.cai <- a[names(a) %in% names(effective_pattern_treat) ]
b <- patterns.treat$list.cai
names(b) <- names(patterns.treat$local_pattern_path)
list.pa <- b[names(b) %in% names(effective_pattern_treat) ]
r1 <- rbind(r1, list.cai)
r1 <- rbind(r1, list.pa )
rownames(r1) <- c("altern", "operon", "complex", "weight", "CAI", "pv")
operons.list.path.d <- enrich.with.operons(gene_assoc, operons.matrix, effective_pattern_path_dev.c )
complex.list.path.d <- enrich.with.complex(gene_assoc, complex.matrix, effective_pattern_path_dev.c )
r2 <- rbind(effective_pattern_path_dev.c ,operons.list.path.d$operon.adjacent)
r2 <- rbind(r2, complex.list.path.d$complex.adjacent)
r2 <- rbind(r2, count.path.dev)
a <- patterns.treat.dev$list.pa
names(a) <- names(patterns.treat.dev$local_pattern_path)
list.cai <- a[names(a) %in% names(effective_pattern_path_dev.c ) ]
b <- patterns.treat.dev$list.cai
names(b) <- names(patterns.treat.dev$local_pattern_path)
list.pa <- b[names(b) %in% names(effective_pattern_path_dev.c ) ]
r2 <- rbind(r2, list.cai)
r2 <- rbind(r2, list.pa)
r2 <- rbind(r2, dds[5,])
rownames(r2) <- c("altern", "operon", "complex","weight", "CAI", "pv","dev" )
op.compr <- compute.operon.compression(r1)
effective_pattern_path_op <- round(op.compr [1,],0)
r2.not.dup <- r2[,!duplicated(colnames(r2))]
op.compr.dev <- compute.operon.compression(r2.not.dup )
effective_pattern_path_op_dev <- round(op.compr.dev[1,],0)
if(is.data.frame(effective_pattern_path_op)){
e_p_p_o <- c()
val.val.o <- c()
for(u in 1:length(effective_pattern_path_op)){
e_p_p_o <- c(e_p_p_o, gsub("\\)", "",gsub("\\(", "",gsub("-", "", names(effective_pattern_path_op)[[u]]))))
val.val.o <- c(val.val.o, effective_pattern_path_op[1,u])
}
val.val.o <- as.numeric(val.val.o)
names(val.val.o)<- e_p_p_o
op.alt <- alternation.scores(0,0,val.val.o,2)
} else
{
op.alt <- alternation.scores(0,0,effective_pattern_path_op,2)
val.val.o <- effective_pattern_path_op
}
if(is.data.frame(effective_pattern_path_op_dev)){
e_p_p_o_d <- c()
val.val.o.d <- c()
for(u in 1:length(effective_pattern_path_op_dev )){
e_p_p_o_d <- c(e_p_p_o_d, gsub("\\)", "",gsub("\\(", "",gsub("-", "", names(effective_pattern_path_op_dev)[[u]]))))
val.val.o.d <- c(val.val.o.d, effective_pattern_path_op_dev[1,u])
}
val.val.o.d <- as.numeric(val.val.o.d)
names(val.val.o.d) <- e_p_p_o_d
op.compr.alt <- alternation.scores(0,0,val.val.o.d,2)
} else { op.compr.alt <- alternation.scores(0,0,effective_pattern_path_op_dev,2)
val.val.o.d <- effective_pattern_path_op_dev}
return(list(name.path = name.path ,
path.code = path.code ,
multi.omic.pattern = effective_pattern_treat ,
multi.omic.pattern.d = effective_pattern_path_dev_treat,
multi.omic.pattern.c = effective_pattern_path_dev.c,
multi.omic.pattern.o = val.val.o,
multi.omic.pattern.o.d = val.val.o.d,
array.alternations = effective_alternation_path_treat ,
array.alternations.d = effective_alternation_path_dev_treat,
array.alternations.o = op.alt,
array.alternations.o.d = op.compr.alt,
network.alternations = net.alt,
network.alternations.d = net.dev.alt,
operon.enric = operons.list.path,
operon.enric.d = operons.list.path.d,
complex.enrich = complex.list.path,
complex.enrich.d = complex.list.path.d,
multi.omic.df = round(r1[-c(5,6),],2),
multi.omic.df.dev = round(r2[-c(5,6),],2),
omic.amount.df = r1[c(5,6),],
omic.amount.df.dev = r2[c(5,6),],
multi.omic.df.compr = round(op.compr[-c(5,6),],2),
multi.omic.df.compr.d = round(op.compr.dev[-c(5,6),],2),
omic.amount.df.compr = op.compr[c(5,6),] ,
omic.amount.df.compr.d = op.compr.dev[c(5,6),]
))
}
compute.ith.treatment<- function(treatment, treatment.i, steady.state,
eco.df.paths, gene_assoc, list.kegg.path.igraph, global.network,
global.pattern.net, operons.matrix, complex.matrix, APL)
{ ll <- list()
pb <- progress_bar$new(
format = " computing [:bar] :percent in :elapsed - :current on :total",
total = length(eco.df.paths[,1]), clear = FALSE, width= 60)
#pb <- progress_bar$new(total = length(eco.df.paths[,1]))
for(i in 1:length(eco.df.paths[,1]))
{ #print(i)
pb$tick()
diffs.v <- setdiff(V(list.kegg.path.igraph[[i]])$name, V(ecocyc.kegg.igraph)$name)
v.list <- V(list.kegg.path.igraph[[i]])$name
v.list <- v.list[!(v.list %in% diffs.v)]
if(length(v.list)!=0){
if(length(E(induced.subgraph(graph=ecocyc.kegg.igraph, vids=v.list)))==0)
{ll[[length(ll)+1]] <- paste(eco.df.paths[i,], "does not presents edges...", sep="")}
else{
ll[[length(ll)+1]] <- treatment.an(i, treatment, steady.state, treatment.i,
eco.df.paths, gene_assoc, list.kegg.path.igraph, global.network,
global.pattern.net, operons.matrix, complex.matrix, APL)
}
} else
{
ll[[length(ll)+1]] <- paste(eco.df.paths[i,], "does not presents edges...", sep="")
}
}
treatment.list <- ll
return(treatment.list)
}
#######################################################################################
## Steady state extractions 3 hours
#######################################################################################
#ll1 <- steady.state.an(1,eco.df.paths, gene_assoc, list.kegg.path.igraph, global.network,
# global.pattern.net, operons.matrix, complex.matrix, APL)
#
compute.steady.state <- function(eco.df.paths, gene_assoc,
list.kegg.path.igraph,
global.network,
global.pattern.net,
operons.matrix,
complex.matrix, APL) {
ll.i <- list()
pb <- progress_bar$new(
format = " computing [:bar] :percent in :elapsed - :current on :total",
total = length(eco.df.paths[,1]), clear = FALSE, width= 60)
for(i in 1:length(eco.df.paths[,1]))
{ #print(i)
pb$tick()
diffs.v <- setdiff(V(list.kegg.path.igraph[[i]])$name, V(ecocyc.kegg.igraph)$name)
v.list <- V(list.kegg.path.igraph[[i]])$name
v.list <- v.list[!(v.list %in% diffs.v)]
if(length(v.list)!=0){
if(length(E(induced.subgraph(graph=ecocyc.kegg.igraph, vids=v.list)))==0)
{ll.i[[length(ll.i)+1]] <- paste(eco.df.paths[i,], "does not presents edges...", sep="")}
else{
ll.i[[length(ll.i)+1]] <- steady.state.an(i,eco.df.paths, gene_assoc, list.kegg.path.igraph, global.network,
global.pattern.net, operons.matrix, complex.matrix, APL)
}
} else
{
ll.i[[length(ll.i)+1]] <- paste(eco.df.paths[i,], "does not presents edges...", sep="")
}
}
steady.state <- ll.i
return(steady.state)
}
steady.state <- compute.steady.state(eco.df.paths, gene_assoc,
list.kegg.path.igraph,
global.network,
global.pattern.net,
operons.matrix,
complex.matrix, APL)
fname <- "./computations.steady.state.Rdata"
save(steady.state, file=fname)
####################################################################################
##
## Suzuki et All. 10 experiments with path extentions and oepron compressions. 7 min for exp
##
####################################################################################
#pattern_under_considerations <- i <- 1
treatment = suzukietall.treats.model1
#treats2 = norfloxacin.treats.model1
treatment <- suzukietall.treats.model1
treatment.i <- 1 #primo esperimento
suzuki1 <- compute.ith.treatment(treatment, treatment.i, steady.state,
eco.df.paths, gene_assoc, list.kegg.path.igraph, global.network,
global.pattern.net, operons.matrix, complex.matrix, APL)
fname <- "./suzuki1.Rdata"
save(suzuki1, file=fname)
treatment.i <- 2 #primo esperimento
suzuki2 <- compute.ith.treatment(treatment, treatment.i, steady.state,
eco.df.paths, gene_assoc, list.kegg.path.igraph, global.network,
global.pattern.net, operons.matrix, complex.matrix, APL)
fname <- "./suzuki2.Rdata"
save(suzuki2, file=fname)
treatment.i <- 3 #primo esperimento
suzuki3 <- compute.ith.treatment(treatment, treatment.i, steady.state,
eco.df.paths, gene_assoc, list.kegg.path.igraph, global.network,
global.pattern.net, operons.matrix, complex.matrix, APL)
fname <- "./suzuki3.Rdata"
save(suzuki3, file=fname)
treatment.i <- 4 #primo esperimento
suzuki4 <- compute.ith.treatment(treatment, treatment.i, steady.state,
eco.df.paths, gene_assoc, list.kegg.path.igraph, global.network,
global.pattern.net, operons.matrix, complex.matrix, APL)
fname <- "./suzuki4.Rdata"
save(suzuki4, file=fname)
treatment.i <- 5 #primo esperimento
suzuki5 <- compute.ith.treatment(treatment, treatment.i, steady.state,
eco.df.paths, gene_assoc, list.kegg.path.igraph, global.network,
global.pattern.net, operons.matrix, complex.matrix, APL)
fname <- "./suzuki5.Rdata"
save(suzuki5, file=fname)
treatment.i <- 6 #primo esperimento
suzuki6 <- compute.ith.treatment(treatment, treatment.i, steady.state,
eco.df.paths, gene_assoc, list.kegg.path.igraph, global.network,
global.pattern.net, operons.matrix, complex.matrix, APL)
fname <- "./suzuki6.Rdata"
save(suzuki6, file=fname)
treatment.i <- 7 #primo esperimento
suzuki7 <- compute.ith.treatment(treatment, treatment.i, steady.state,
eco.df.paths, gene_assoc, list.kegg.path.igraph, global.network,
global.pattern.net, operons.matrix, complex.matrix, APL)
fname <- "./suzuki7.Rdata"
save(suzuki7, file=fname)
treatment.i <- 8 #primo esperimento
suzuki8 <- compute.ith.treatment(treatment, treatment.i, steady.state,
eco.df.paths, gene_assoc, list.kegg.path.igraph, global.network,
global.pattern.net, operons.matrix, complex.matrix, APL)
fname <- "./suzuki8.Rdata"
save(suzuki8, file=fname)
treatment.i <- 9 #primo esperimento
suzuki9 <- compute.ith.treatment(treatment, treatment.i, steady.state,
eco.df.paths, gene_assoc, list.kegg.path.igraph, global.network,
global.pattern.net, operons.matrix, complex.matrix, APL)
fname <- "./suzuki9.Rdata"
save(suzuki9, file=fname)
treatment.i <- 10 #primo esperimento
suzuki10 <- compute.ith.treatment(treatment, treatment.i, steady.state,
eco.df.paths, gene_assoc, list.kegg.path.igraph, global.network,
global.pattern.net, operons.matrix, complex.matrix, APL)
fname <- "./suzuki10.Rdata"
save(suzuki10, file=fname)
####################################################################################
##
## Suzuki et All. 10 experiments with path extensions and operon compressions. 7 min for exp
##
####################################################################################
#pattern_under_considerations <- i <- 1
treatment = norfloxacin.treats.model1
treatment.i <- 1 #primo esperimento
faith1 <- compute.ith.treatment(treatment, treatment.i, steady.state,
eco.df.paths, gene_assoc, list.kegg.path.igraph, global.network,
global.pattern.net, operons.matrix, complex.matrix, APL)
fname <- "./faith1.Rdata"
save(faith1, file=fname)
treatment.i <- 2 #primo esperimento
faith2 <- compute.ith.treatment(treatment, treatment.i, steady.state,
eco.df.paths, gene_assoc, list.kegg.path.igraph, global.network,
global.pattern.net, operons.matrix, complex.matrix, APL)
fname <- "./faith2.Rdata"
save(faith2, file=fname)
treatment.i <- 3 #primo esperimento
faith3 <- compute.ith.treatment(treatment, treatment.i, steady.state,
eco.df.paths, gene_assoc, list.kegg.path.igraph, global.network,
global.pattern.net, operons.matrix, complex.matrix, APL)
fname <- "./faith3.Rdata"
save(faith3, file=fname)
treatment.i <- 4 #primo esperimento
faith4 <- compute.ith.treatment(treatment, treatment.i, steady.state,
eco.df.paths, gene_assoc, list.kegg.path.igraph, global.network,
global.pattern.net, operons.matrix, complex.matrix, APL)
fname <- "./faith4.Rdata"
save(faith4, file=fname)
treatment.i <- 5 #primo esperimento
faith5 <- compute.ith.treatment(treatment, treatment.i, steady.state,
eco.df.paths, gene_assoc, list.kegg.path.igraph, global.network,
global.pattern.net, operons.matrix, complex.matrix, APL)
fname <- "./faith5.Rdata"
save(faith5, file=fname)
treatment.i <- 6 #primo esperimento
faith6 <- compute.ith.treatment(treatment, treatment.i, steady.state,
eco.df.paths, gene_assoc, list.kegg.path.igraph, global.network,
global.pattern.net, operons.matrix, complex.matrix, APL)
fname <- "./faith6.Rdata"
save(faith6, file=fname)
treatment.i <- 7 #primo esperimento
faith7 <- compute.ith.treatment(treatment, treatment.i, steady.state,
eco.df.paths, gene_assoc, list.kegg.path.igraph, global.network,
global.pattern.net, operons.matrix, complex.matrix, APL)
fname <- "./faith7.Rdata"
save(faith7, file=fname)
treatment.i <- 8 #primo esperimento
faith8 <- compute.ith.treatment(treatment, treatment.i, steady.state,
eco.df.paths, gene_assoc, list.kegg.path.igraph, global.network,
global.pattern.net, operons.matrix, complex.matrix, APL)
fname <- "./faith8.Rdata"
save(faith8, file=fname)
treatment.i <- 9 #primo esperimento
faith9 <- compute.ith.treatment(treatment, treatment.i, steady.state,
eco.df.paths, gene_assoc, list.kegg.path.igraph, global.network,
global.pattern.net, operons.matrix, complex.matrix, APL)
fname <- "./faith9.Rdata"
save(faith9, file=fname)
treatment.i <- 10 #primo esperimento
faith10 <- compute.ith.treatment(treatment, treatment.i, steady.state,
eco.df.paths, gene_assoc, list.kegg.path.igraph, global.network,
global.pattern.net, operons.matrix, complex.matrix, APL)
fname <- "./faith10.Rdata"
save(faith10, file=fname)
treatment.i <- 11 #primo esperimento
faith11 <- compute.ith.treatment(treatment, treatment.i, steady.state,
eco.df.paths, gene_assoc, list.kegg.path.igraph, global.network,
global.pattern.net, operons.matrix, complex.matrix, APL)
fname <- "./faith11.Rdata"
save(faith11, file=fname)
treatment.i <- 12 #primo esperimento
faith12 <- compute.ith.treatment(treatment, treatment.i, steady.state,
eco.df.paths, gene_assoc, list.kegg.path.igraph, global.network,
global.pattern.net, operons.matrix, complex.matrix, APL)
fname <- "./faith12.Rdata"
save(faith12, file=fname)