}) %>% t %>% data.frame %>% set_names(metrics) %>%
mutate(Network = topoFiles %>% str_remove(".topo"))
df <- sapply(topoFiles, function(topoFile) {
freqDf <- read_csv(str_replace(topoFile, ".topo", "_finFlagFreq.csv"),
col_types = cols(), lazy = F) %>% filter(flag == 1)
if (nrow(freqDf) < 3)
return(rep(NA, 17))
frust <- freqDf$frust0
freq <- freqDf$Avg0
coh <- freqDf$coherence0
frustration <- c(frust %>% min(na.rm = T), frust %>% max(na.rm = T),
frust %>% mean(na.rm = T), frust*freq %>% sum(na.rm = T))
coherence <- c(coh %>% min(na.rm = T), coh %>% max(na.rm = T),
coh %>% mean(na.rm = T), coh*freq %>% sum(na.rm = T))
cohFreq <- cor.test(coh, freq, method = "spearman")
cohFrust <- cor.test(coh, frust, method = "spearman")
freqFrust <- cor.test(freq, frust, method = "spearman")
cors <- c(freqFrust$estimate, freqFrust$p.value,
cohFreq$estimate, cohFreq$p.value,
cohFrust$estimate, cohFrust$p.value)
bimodalities <- c(bimodality_coefficient(freq),
bimodality_coefficient(frust),
bimodality_coefficient(coh))
c(frustration, coherence, cors, bimodalities)
}) %>% t %>% data.frame
View(df)
topoFile <- topoFiles[1]
freqDf <- read_csv(str_replace(topoFile, ".topo", "_finFlagFreq.csv"),
col_types = cols(), lazy = F) %>% filter(flag == 1)
if (nrow(freqDf) < 3)
return(rep(NA, 17))
frust <- freqDf$frust0
freq <- freqDf$Avg0
coh <- freqDf$coherence0
frustration <- c(frust %>% min(na.rm = T), frust %>% max(na.rm = T),
frust %>% mean(na.rm = T), frust*freq %>% sum(na.rm = T))
coherence <- c(coh %>% min(na.rm = T), coh %>% max(na.rm = T),
coh %>% mean(na.rm = T), coh*freq %>% sum(na.rm = T))
cohFreq <- cor.test(coh, freq, method = "spearman")
cohFrust <- cor.test(coh, frust, method = "spearman")
freqFrust <- cor.test(freq, frust, method = "spearman")
cors <- c(freqFrust$estimate, freqFrust$p.value,
cohFreq$estimate, cohFreq$p.value,
cohFrust$estimate, cohFrust$p.value)
bimodalities <- c(bimodality_coefficient(freq),
bimodality_coefficient(frust),
bimodality_coefficient(coh))
c(frustration, coherence, cors, bimodalities)
df <- sapply(topoFiles, function(topoFile) {
freqDf <- read_csv(str_replace(topoFile, ".topo", "_finFlagFreq.csv"),
col_types = cols(), lazy = F) %>% filter(flag == 1)
if (nrow(freqDf) < 3)
return(rep(NA, 17))
frust <- freqDf$frust0
freq <- freqDf$Avg0
coh <- freqDf$coherence0
frustration <- c(frust %>% min(na.rm = T), frust %>% max(na.rm = T),
frust %>% mean(na.rm = T), frust*freq %>% sum(na.rm = T))
coherence <- c(coh %>% min(na.rm = T), coh %>% max(na.rm = T),
coh %>% mean(na.rm = T), coh*freq %>% sum(na.rm = T))
cohFreq <- cor.test(coh, freq, method = "spearman")
cohFrust <- cor.test(coh, frust, method = "spearman")
freqFrust <- cor.test(freq, frust, method = "spearman")
cors <- c(freqFrust$estimate, freqFrust$p.value,
cohFreq$estimate, cohFreq$p.value,
cohFrust$estimate, cohFrust$p.value)
bimodalities <- c(bimodality_coefficient(freq),
bimodality_coefficient(frust),
bimodality_coefficient(coh))
c(frustration, coherence, cors, bimodalities)
})
df[[1]]
sapply(df, length)
coh %>% min(na.rm = T)
coh %>% max(na.rm = T)
coh %>% mean(na.rm = T)
coh*freq %>% sum(na.rm = T)
coh*freq
coh
freq
x <- coh*freq
sum(x)
coh*freq %>% sum(na.rm = T)
coherence <- c(coh %>% min(na.rm = T), coh %>% max(na.rm = T),
coh %>% mean(na.rm = T), sum(coh*freq,na.rm = T))
source("D:/Github/Rpackages/Teams/R/compileData.R")
AllDataFile(net)
source("D:/Github/Rpackages/Teams/R/utils.R")
AllDataFile(net)
AllDataFile(net)
metrics <- c("minFrust", "maxFrust", "meanFrust", "meanNetFrust",
"minCoh", "maxCoh", "meanCoh", "meanNetCoh",
"corFreqFrust", "pFreqFrust", "corFreqCoh", "pFreqCoh",
"corFrustCoh", "pFrustCoh", "bmSSF", "bmCoh", "bmFrust")
topoFiles <- list.files(".", ".topo$")
df <- sapply(topoFiles, function(topoFile) {
freqDf <- read_csv(str_replace(topoFile, ".topo", "_finFlagFreq.csv"),
col_types = cols(), lazy = F) %>% filter(flag == 1)
if (nrow(freqDf) < 3)
return(rep(NA, 17))
frust <- freqDf$frust0
freq <- freqDf$Avg0
coh <- freqDf$coherence0
frustration <- c(frust %>% min(na.rm = T), frust %>% max(na.rm = T),
frust %>% mean(na.rm = T), sum(frust*freq,na.rm = T))
coherence <- c(coh %>% min(na.rm = T), coh %>% max(na.rm = T),
coh %>% mean(na.rm = T), sum(coh*freq,na.rm = T))
cohFreq <- cor.test(coh, freq, method = "spearman")
cohFrust <- cor.test(coh, frust, method = "spearman")
freqFrust <- cor.test(freq, frust, method = "spearman")
cors <- c(freqFrust$estimate, freqFrust$p.value,
cohFreq$estimate, cohFreq$p.value,
cohFrust$estimate, cohFrust$p.value)
bimodalities <- c(bimodality_coefficient(freq),
bimodality_coefficient(frust),
bimodality_coefficient(coh))
c(frustration, coherence, cors, bimodalities)
})
getwd()
setwd("..")
source("D:/Github/Rpackages/Teams/R/compileData.R")
AllDataFile(net)
AllDataFileNoFlag(net)
# Multinode Perturbation
setwd(gsCausation)
setwd(net)
topoFiles <- list.files(".", ".topo$")
logDf <- read.csv("LogFile.csv")
sapply(topoFiles, CoherenceAllNode, logDf = logDf)
source("D:/Github/Rpackages/Teams/R/coherence.R")
sapply(topoFiles, CoherenceAllNode, logDf = logDf)
source("D:/Github/Rpackages/Teams/R/coherence.R")
sapply(topoFiles, CoherenceAllNode, logDf = logDf)
source("D:/Github/Rpackages/Teams/R/coherence.R")
sapply(topoFiles, CoherenceAllNode, logDf = logDf)
devtools::load_all()
?require
devtools::install_github("askhari139/Teams")
devtools::install_github("askhari139/Teams")
devtools::install_github("askhari139/Teams")
library(Teams)
### Setup data folder structure
SetupFunc(mainFolder = "D:/TeamsTest", topoFolder = "D:/TopoFiles", numThreads = 3)
Teams::
paths <- readRDS("C:/Users/askha/OneDrive/Documents/R/win-library/4.1/Teams/help/paths.rds")
setwd("D:/Github/Projects/Ongoing/Canalilzation/topos/topoFiles")
setwd("D:/Github/Projects/Ongoing/Canalilzation/RandomNetworkAnalysis/EffectivenessGs")
Analysis <- function() {
topoFiles <- list.files(".", ".topo$")
sapply(topoFiles, function(x) {
topoDf <- read.delim(x, sep = "", row.names = NULL) %>%
mutate_if(is.character, str_replace_all, pattern = regex("\\W+"), replace = "") %>%
mutate(Type = ifelse(Type == 1, 1, 2))
write_delim(topoDf, x, delim = " ", quote = "none")
})
}
library(tidyverse)
library(miscFuncs)
setwd("D:/Github/Projects/Ongoing/Canalilzation/RandomNetworkAnalysis/EffectivenessGs")
dirs <- list.dirs(".", recursive = F)
sapply(dirs, function(x) {
setwd(x)
Analysis()
LogFileGen()
GroupStrengthAll(x %>% str_remove("./"), plotOut = T)
setwd("..")
})
source("D:/Github/Rpackages/Teams/R/influenceAndGs.R")
library(compiler)
source("D:/Github/Rpackages/Teams/R/influenceAndGs.R")
source("D:/Github/Rpackages/Teams/R/topoGenAndSim.R")
setwd("D:/Github/Projects/Ongoing/Canalilzation/RandomNetworkAnalysis/EffectivenessGs")
dirs <- list.dirs(".", recursive = F)
sapply(dirs, function(x) {
setwd(x)
Analysis()
LogFileGen()
GroupStrengthAll(x %>% str_remove("./"), plotOut = T)
setwd("..")
})
source("D:/Github/Rpackages/Teams/R/utils.R")
setwd("D:/Github/Projects/Ongoing/Canalilzation/RandomNetworkAnalysis/EffectivenessGs")
dirs <- list.dirs(".", recursive = F)
sapply(dirs, function(x) {
setwd(x)
Analysis()
LogFileGen()
GroupStrengthAll(x %>% str_remove("./"), plotOut = T)
setwd("..")
})
getwd()
topoFiles <- list.files(".", ".topo")
sapply(dirs[-1], function(x) {
setwd(x)
Analysis()
LogFileGen()
GroupStrengthAll(x %>% str_remove("./"), plotOut = T)
setwd("..")
})
setwd("D:/Github/Projects/Ongoing/Canalilzation/RandomNetworkAnalysis/EffectivenessGs")
sapply(dirs[-1], function(x) {
setwd(x)
Analysis()
LogFileGen()
GroupStrengthAll(x %>% str_remove("./"), plotOut = T)
setwd("..")
})
setwd("D:/Github/Projects/Ongoing/Canalilzation/RandomNetworkAnalysis/EffectivenessGs")
dirs <- list.dirs(".", recursive = F)
sapply(dirs[-1], function(x) {
setwd(x)
Analysis()
LogFileGen()
GroupStrengthAll(x %>% str_remove("./"), plotOut = T)
setwd("..")
})
sapply(dirs, function(x) {
x <- x %>% str_remove("./")
df <- read_csv(paste0(x, "/CompiledData/", x, "_TeamStrengths.csv"), lazy = F)
df$Type <- df$Network %>% str_extract("_del_.*") %>% str_remove("_del_")
df$Type[is.na(df$Type)] <- "original"
df$Network <- df$Network %>% str_remove("_del_.*")
# df$Class <- ifelse(str_detect(df$Network, "_\\d"), "Rand", "WT")
df1 <- df %>% gather(key = "Group", value = "Strength", -Network, -Type) %>%
spread(key = Type, value = Strength) %>% drop_na %>%
gather(key = "Type", value = "Strength",-Network, -Group)
ggplot(df1, aes(x = Type, y = Strength)) + geom_boxplot() +
facet_wrap(~Group, ncol = 2, scales = "free_y") + theme_Publication() +
theme(axis.text.x = element_text(angle = 60, vjust = 1, hjust = 1))
ggsave(paste0(x, "_strengthDist.png"), width = 7, height = 9)
})
sapply(dirs, function(x) {
x <- x %>% str_remove("./")
df <- read_csv(paste0(x, "/CompiledData/", x, "_TeamStrengths.csv"), lazy = F)
df$Type <- df$Network %>% str_extract("_del_.*") %>% str_remove("_del_")
df$Type[is.na(df$Type)] <- "original"
df$Network <- df$Network %>% str_remove("_del_.*")
# df$Class <- ifelse(str_detect(df$Network, "_\\d"), "Rand", "WT")
df1 <- df %>% gather(key = "Group", value = "Strength", -Network, -Type) %>%
spread(key = Type, value = Strength) %>% drop_na %>%
gather(key = "Type", value = "Strength",-Network, -Group)
ggplot(df1, aes(x = Type, y = Strength)) + geom_boxplot() +
facet_wrap(~Group, ncol = 2, scales = "free_y") + theme_Publication() +
labs(title = x) +
theme(axis.text.x = element_text(angle = 60, vjust = 1, hjust = 1))
ggsave(paste0(x, "_strengthDist.png"), width = 7, height = 9)
})
setwd("D:/Github/Rpackages/Teams")
library(philentropy)
library(tidyverse)
discretFunc <- function(x)
{
x <- (x- mean(x))/sd(x)
ifelse(x > 0, 1, 0)
}
naZero <- function(df) {
df[is.na(df)] <- 0
df
}
jsdCalc <- function(WT, n2) {
WTNodes <- read.delim(paste0(WT, ".prs")) %>% filter(str_detect(Parameter, "Prod")) %>%
select(Parameter) %>% unlist %>% str_remove("Prod_of_")
write.csv(WTNodes, "WTNodes.csv", row.names = F)
WTSols <- read_delim(paste0(WT, "_solution.dat"), delim = "\t", col_names = F) %>%
set_names(c("ParIndex", "nStates", "Basin", WTNodes))
write.csv(WTSols, "WTsols1.csv", row.names = F)
WTSols[, -(1:3)] <- WTSols %>% select(all_of(WTNodes)) %>% sapply(discretFunc)
WTSols <- WTSols %>% group_by(across(all_of(WTNodes))) %>%
summarise(Count = n(), .groups = "drop") %>%
mutate(Frequency = Count/sum(Count))
write.csv(WTSols, "WTsols2.csv", row.names = F)
n2Nodes <- read.delim(paste0(n2, ".prs")) %>% filter(str_detect(Parameter, "Prod")) %>%
select(Parameter) %>% unlist %>% str_remove("Prod_of_")
write.csv(n2Nodes, "n2Nodes.csv", row.names = F)
n2Sols <- read_delim(paste0(n2, "_solution.dat"), delim = "\t", col_names = F) %>%
set_names(c("ParIndex", "nStates", "Basin", n2Nodes))
write.csv(n2Sols, "n2Sols.csv", row.names = F)
n2Sols[, -(1:3)] <- n2Sols %>% select(all_of(WTNodes)) %>% sapply(discretFunc)
n2Sols <- n2Sols %>% group_by(across(all_of(n2Nodes))) %>%
summarise(Count = n(), .groups = "drop") %>%
mutate(Frequency = Count/sum(Count))
write.csv(n2Sols, "n2Sols1.csv", row.names = F)
if (all(WTNodes %in% n2Nodes))
{
n2Sols <- n2Sols %>% select(all_of(WTNodes), Frequency) %>%
unite("State", all_of(WTNodes), sep = "")
WTSols <- WTSols %>% select(all_of(WTNodes), Frequency) %>%
unite("State", all_of(WTNodes), sep = "") %>%
merge(n2Sols, by = "State", all = T) %>% naZero %>%
select(contains("Frequency")) %>% t%>% JSD
}
else
{
commons <- WTNodes[which(WTNodes %in% n2Nodes)]
n2Sols <- n2Sols %>% select(all_of(commons), Frequency) %>% unite("State", all_of(commons), sep = "")
WTSols <- WTSols %>%
group_by(across(all_of(commons))) %>% summarise(Frequency = sum(Frequency), .groups = "drop") %>%
select(all_of(commons), Frequency) %>% unite("State", all_of(commons), sep = "") %>%
merge(n2Sols, by = "State", all = T) %>% naZero %>%
select(contains("Frequency")) %>% t%>% JSD
}
WTSols
}
nets <- c("EMT_RACIPE", "EMT_RACIPE2", "SIL", "silviera2", "EMT_MET_reduced")
df <- sapply(nets, function(net) {
WT <- net
n1 <- paste0(net, "_del_bottom5")
n2 <- paste0(net, "_del_top5")
jsd <- c(jsdCalc(WT, n1), jsdCalc(WT, n2), jsdCalc(n1, n2))
print(jsd)
}) %>% t %>% data.frame %>% set_names(c("WTbottom", "WTtop", "bottomTop"))
df <- sapply(nets, function(net) {
print(net)
WT <- net
n1 <- paste0(net, "_del_bottom5")
n2 <- paste0(net, "_del_top5")
jsd <- c(jsdCalc(WT, n1), jsdCalc(WT, n2), jsdCalc(n1, n2))
}) %>% t %>% data.frame %>% set_names(c("WTbottom", "WTtop", "bottomTop"))
debug(jsdCalc)
df <- sapply(nets, function(net) {
print(net)
WT <- net
n1 <- paste0(net, "_del_bottom5")
n2 <- paste0(net, "_del_top5")
jsd <- c(jsdCalc(WT, n1), jsdCalc(WT, n2), jsdCalc(n1, n2))
}) %>% t %>% data.frame %>% set_names(c("WTbottom", "WTtop", "bottomTop"))
getwd()
setwd("D:/Github/Projects/Ongoing/Canalilzation/RACIPE")
df <- sapply(nets, function(net) {
print(net)
WT <- net
n1 <- paste0(net, "_del_bottom5")
n2 <- paste0(net, "_del_top5")
jsd <- c(jsdCalc(WT, n1), jsdCalc(WT, n2), jsdCalc(n1, n2))
}) %>% t %>% data.frame %>% set_names(c("WTbottom", "WTtop", "bottomTop"))
jsdCalc <- function(WT, n2) {
WTNodes <- read.delim(paste0(WT, ".prs")) %>% filter(str_detect(Parameter, "Prod")) %>%
select(Parameter) %>% unlist %>% str_remove("Prod_of_")
write.csv(WTNodes, "WTNodes.csv", row.names = F)
WTSols <- read_delim(paste0(WT, "_solution.dat"), delim = "\t", col_names = F) %>%
set_names(c("ParIndex", "nStates", "Basin", WTNodes))
write.csv(WTSols, "WTsols1.csv", row.names = F)
WTSols[, -(1:3)] <- WTSols %>% select(all_of(WTNodes)) %>% sapply(discretFunc)
WTSols <- WTSols %>% group_by(across(all_of(WTNodes))) %>%
summarise(Count = n(), .groups = "drop") %>%
mutate(Frequency = Count/sum(Count))
write.csv(WTSols, "WTsols2.csv", row.names = F)
n2Nodes <- read.delim(paste0(n2, ".prs")) %>% filter(str_detect(Parameter, "Prod")) %>%
select(Parameter) %>% unlist %>% str_remove("Prod_of_")
write.csv(n2Nodes, "n2Nodes.csv", row.names = F)
n2Sols <- read_delim(paste0(n2, "_solution.dat"), delim = "\t", col_names = F) %>%
set_names(c("ParIndex", "nStates", "Basin", n2Nodes))
write.csv(n2Sols, "n2Sols.csv", row.names = F)
n2Sols[, -(1:3)] <- n2Sols %>% select(all_of(WTNodes)) %>% sapply(discretFunc)
n2Sols <- n2Sols %>% group_by(across(all_of(n2Nodes))) %>%
summarise(Count = n(), .groups = "drop") %>%
mutate(Frequency = Count/sum(Count))
write.csv(n2Sols, "n2Sols1.csv", row.names = F)
if (all(WTNodes %in% n2Nodes))
{
n2Sols <- n2Sols %>% select(all_of(WTNodes), Frequency) %>%
unite("State", all_of(WTNodes), sep = "")
WTSols <- WTSols %>% select(all_of(WTNodes), Frequency) %>%
unite("State", all_of(WTNodes), sep = "") %>%
merge(n2Sols, by = "State", all = T) %>% naZero %>%
select(contains("Frequency")) %>% t%>% JSD
}
else
{
commons <- WTNodes[which(WTNodes %in% n2Nodes)]
n2Sols <- n2Sols %>% select(all_of(commons), Frequency) %>% unite("State", all_of(commons), sep = "")
WTSols <- WTSols %>%
group_by(across(all_of(commons))) %>% summarise(Frequency = sum(Frequency), .groups = "drop") %>%
select(all_of(commons), Frequency) %>% unite("State", all_of(commons), sep = "") %>%
merge(n2Sols, by = "State", all = T) %>% naZero %>%
select(contains("Frequency")) %>% t%>% JSD
}
WTSols
}
nets <- c("EMT_RACIPE", "EMT_RACIPE2", "SIL", "silviera2", "EMT_MET_reduced")
df <- sapply(nets, function(net) {
print(net)
WT <- net
n1 <- paste0(net, "_del_bottom5")
n2 <- paste0(net, "_del_top5")
jsd <- c(jsdCalc(WT, n1), jsdCalc(WT, n2), jsdCalc(n1, n2))
}) %>% t %>% data.frame %>% set_names(c("WTbottom", "WTtop", "bottomTop"))
df <- sapply(nets, function(net) {
print(net)
WT <- net
n1 <- paste0(net, "_del_bottom5")
n2 <- paste0(net, "_del_top5")
jsd <- c(jsdCalc(WT, n1), jsdCalc(WT, n2), jsdCalc(n1, n2))
})
jsdCalc <- function(WT, n2) {
WTNodes <- read.delim(paste0(WT, ".prs")) %>% filter(str_detect(Parameter, "Prod")) %>%
select(Parameter) %>% unlist %>% str_remove("Prod_of_")
write.csv(WTNodes, "WTNodes.csv", row.names = F)
WTSols <- read_delim(paste0(WT, "_solution.dat"), delim = "\t", col_names = F) %>%
set_names(c("ParIndex", "nStates", WTNodes))
write.csv(WTSols, "WTsols1.csv", row.names = F)
WTSols[, -(1:3)] <- WTSols %>% select(all_of(WTNodes)) %>% sapply(discretFunc)
WTSols <- WTSols %>% group_by(across(all_of(WTNodes))) %>%
summarise(Count = n(), .groups = "drop") %>%
mutate(Frequency = Count/sum(Count))
write.csv(WTSols, "WTsols2.csv", row.names = F)
n2Nodes <- read.delim(paste0(n2, ".prs")) %>% filter(str_detect(Parameter, "Prod")) %>%
select(Parameter) %>% unlist %>% str_remove("Prod_of_")
write.csv(n2Nodes, "n2Nodes.csv", row.names = F)
n2Sols <- read_delim(paste0(n2, "_solution.dat"), delim = "\t", col_names = F) %>%
set_names(c("ParIndex", "nStates", n2Nodes))
write.csv(n2Sols, "n2Sols.csv", row.names = F)
n2Sols[, -(1:3)] <- n2Sols %>% select(all_of(WTNodes)) %>% sapply(discretFunc)
n2Sols <- n2Sols %>% group_by(across(all_of(n2Nodes))) %>%
summarise(Count = n(), .groups = "drop") %>%
mutate(Frequency = Count/sum(Count))
write.csv(n2Sols, "n2Sols1.csv", row.names = F)
if (all(WTNodes %in% n2Nodes))
{
n2Sols <- n2Sols %>% select(all_of(WTNodes), Frequency) %>%
unite("State", all_of(WTNodes), sep = "")
WTSols <- WTSols %>% select(all_of(WTNodes), Frequency) %>%
unite("State", all_of(WTNodes), sep = "") %>%
merge(n2Sols, by = "State", all = T) %>% naZero %>%
select(contains("Frequency")) %>% t%>% JSD
}
else
{
commons <- WTNodes[which(WTNodes %in% n2Nodes)]
n2Sols <- n2Sols %>% select(all_of(commons), Frequency) %>% unite("State", all_of(commons), sep = "")
WTSols <- WTSols %>%
group_by(across(all_of(commons))) %>% summarise(Frequency = sum(Frequency), .groups = "drop") %>%
select(all_of(commons), Frequency) %>% unite("State", all_of(commons), sep = "") %>%
merge(n2Sols, by = "State", all = T) %>% naZero %>%
select(contains("Frequency")) %>% t%>% JSD
}
WTSols
}
nets <- c("EMT_RACIPE", "EMT_RACIPE2", "SIL", "silviera2", "EMT_MET_reduced")
df <- sapply(nets, function(net) {
print(net)
WT <- net
n1 <- paste0(net, "_del_bottom5")
n2 <- paste0(net, "_del_top5")
jsd <- c(jsdCalc(WT, n1), jsdCalc(WT, n2), jsdCalc(n1, n2))
}) %>% t %>% data.frame %>% set_names(c("WTbottom", "WTtop", "bottomTop"))
jsdCalc <- function(WT, n2) {
WTNodes <- read.delim(paste0(WT, ".prs")) %>% filter(str_detect(Parameter, "Prod")) %>%
select(Parameter) %>% unlist %>% str_remove("Prod_of_")
write.csv(WTNodes, "WTNodes.csv", row.names = F)
WTSols <- read_delim(paste0(WT, "_solution.dat"), delim = "\t", col_names = F) %>%
set_names(c("ParIndex", "nStates", WTNodes))
write.csv(WTSols, "WTsols1.csv", row.names = F)
WTSols[, -(1:2)] <- WTSols %>% select(all_of(WTNodes)) %>% sapply(discretFunc)
WTSols <- WTSols %>% group_by(across(all_of(WTNodes))) %>%
summarise(Count = n(), .groups = "drop") %>%
mutate(Frequency = Count/sum(Count))
write.csv(WTSols, "WTsols2.csv", row.names = F)
n2Nodes <- read.delim(paste0(n2, ".prs")) %>% filter(str_detect(Parameter, "Prod")) %>%
select(Parameter) %>% unlist %>% str_remove("Prod_of_")
write.csv(n2Nodes, "n2Nodes.csv", row.names = F)
n2Sols <- read_delim(paste0(n2, "_solution.dat"), delim = "\t", col_names = F) %>%
set_names(c("ParIndex", "nStates", n2Nodes))
write.csv(n2Sols, "n2Sols.csv", row.names = F)
n2Sols[, -(1:2)] <- n2Sols %>% select(all_of(WTNodes)) %>% sapply(discretFunc)
n2Sols <- n2Sols %>% group_by(across(all_of(n2Nodes))) %>%
summarise(Count = n(), .groups = "drop") %>%
mutate(Frequency = Count/sum(Count))
write.csv(n2Sols, "n2Sols1.csv", row.names = F)
if (all(WTNodes %in% n2Nodes))
{
n2Sols <- n2Sols %>% select(all_of(WTNodes), Frequency) %>%
unite("State", all_of(WTNodes), sep = "")
WTSols <- WTSols %>% select(all_of(WTNodes), Frequency) %>%
unite("State", all_of(WTNodes), sep = "") %>%
merge(n2Sols, by = "State", all = T) %>% naZero %>%
select(contains("Frequency")) %>% t%>% JSD
}
else
{
commons <- WTNodes[which(WTNodes %in% n2Nodes)]
n2Sols <- n2Sols %>% select(all_of(commons), Frequency) %>% unite("State", all_of(commons), sep = "")
WTSols <- WTSols %>%
group_by(across(all_of(commons))) %>% summarise(Frequency = sum(Frequency), .groups = "drop") %>%
select(all_of(commons), Frequency) %>% unite("State", all_of(commons), sep = "") %>%
merge(n2Sols, by = "State", all = T) %>% naZero %>%
select(contains("Frequency")) %>% t%>% JSD
}
WTSols
}
nets <- c("EMT_RACIPE", "EMT_RACIPE2", "SIL", "silviera2", "EMT_MET_reduced")
df <- sapply(nets, function(net) {
print(net)
WT <- net
n1 <- paste0(net, "_del_bottom5")
n2 <- paste0(net, "_del_top5")
jsd <- c(jsdCalc(WT, n1), jsdCalc(WT, n2), jsdCalc(n1, n2))
}) %>% t %>% data.frame %>% set_names(c("WTbottom", "WTtop", "bottomTop"))
df$Network <- nets
df1 <- df %>% gather(key = "Comparision", value = "JSD", -Network)
ggplot(df1, aes(x = Network, y = JSD, color = Comparision)) + geom_point(size = 3) + theme(legend.position = "top", axis.text.x = element_text(angle = 60, vjust = 1, hjust = 1)) + ylim(c(0, 1.1))
ggplot(df1, aes(x = Network, y = JSD, color = Comparision)) + geom_point(size = 3) +
theme_Publication() +
theme(legend.position = "top", axis.text.x = element_text(angle = 60, vjust = 1, hjust = 1)) +
ylim(c(0, 1.1))
ggsave("JSDPlot.png", width = 5.5, height = 6)
write.csv(df, "JSDDat.csv", row.names = F)
setwd("D:/Github/Rpackages/Teams")
setwd("D:/Github/Projects/Ongoing/Canalilzation/RandomNetworkAnalysis/EffectivenessGs")
devtools::install_github("askhari139/Teams")
devtools::install_github("askhari139/Teams")
devtools::install_github("askhari139/Teams")