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required_function.R
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required_function.R
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library(tidyverse)
library(dplyr)
library(igraph)
library(visNetwork)
# library(xlsx)
library(data.table)
# library(MKmisc)
library(gplots)
library(gtools)
build_char_table <- function(raw_data, network_node){
class <- raw_data$feature %>% str_extract('[A-Za-z]+( O-)*')
class_included <- class %in% network_node$Abbreviation
totallength <- raw_data$feature %>% str_extract_all('\\d+:') %>%
map_int(~str_sub(.x, end = -2) %>% as.integer() %>% sum)
totaldb <- raw_data$feature %>% str_extract_all('\\d+;') %>%
map_int(~str_sub(.x, end = -2) %>% as.integer() %>% sum)
totaloh <- raw_data$feature %>% str_extract_all(';\\d+') %>%
map_int(~str_sub(.x, start = 2) %>% as.integer() %>% sum)
FA_sum <- str_c(totallength, ':', totaldb, ';', totaloh)
FA_split <- raw_data$feature %>% str_extract_all('\\d+:\\d+;\\d+') %>%
map_chr(~str_c(.x, collapse = '_'))
char_table <- data.frame(feature=raw_data$feature, class=class, totallength=totallength,
totaldb=totaldb, totaloh=totaloh, FA_sum=FA_sum, FA_split=FA_split) %>%
.[class_included,] %>% left_join(network_node[c('Abbreviation','FA')], by=c('class'='Abbreviation'))
colnames(char_table)[8] <- 'FA_num'
FA_exact <- map_int(str_split(char_table$FA_split, '_'), ~length(.x))==char_table$FA_num
char_table$FA_split[!FA_exact] <- ''
each_FA <- str_extract_all(char_table$FA_split, '\\d+:\\d+;\\d+') %>% map(.f = function(x){x[x!='0:0;0']})
char_table <- char_table %>% mutate(each_FA=each_FA)
raw_data <- raw_data[class_included,]
return(list(raw_data, char_table))
}
unprocessed_data_test <- function(exp_data, char_table, method='t.test',
significant=p_value, ctrl_group,
exp_group){
ctrl_group <- ctrl_group+1
exp_group <- exp_group+1
#FA expression
char_sel <- char_table %>% filter(feature %in% exp_data$feature)
each_FA <- str_extract_all(char_sel$FA_split, '\\d+:\\d+;\\d+')
FA_mapping <- unique(unlist(each_FA)) %>% sort()
Lipid_FA_freq_list <- each_FA %>%
map(.f = function(x){factor(sort(x[x!='0:0;0']), levels = FA_mapping) %>%
table() %>% as.integer()})
Lipid_FA_freq_matrix <- matrix(unlist(Lipid_FA_freq_list), ncol = length(Lipid_FA_freq_list))
exp_matrix <- exp_data[-1] %>% as.matrix()
FA_exp <- Lipid_FA_freq_matrix %*% exp_matrix %>% as.data.frame()
FA_exp <- FA_exp %>% mutate(feature=FA_mapping, type='FA') %>%
dplyr::select(feature, type, everything())
#lipid class expression
exp_data[-1][is.na(exp_data[-1])] <- 0
lipid_class_exp <- exp_data %>% left_join(char_table[c('feature', 'class')], by='feature') %>%
dplyr::select(-1)
lipid_class_exp <- lipid_class_exp[!is.na(lipid_class_exp[['class']]),]
if(nrow(lipid_class_exp)==0){
lipid_class_exp <- data.frame()
}else{
lipid_class_exp <- lipid_class_exp %>%
aggregate(as.formula(str_c('. ~ class')), ., sum)
}
colnames(lipid_class_exp)[1] <- 'feature'
lipid_class_exp <- lipid_class_exp %>% mutate(type='class') %>%
dplyr::select(feature, type, everything())
lipid_species_exp <- exp_data %>% mutate(type='species') %>%
dplyr::select(feature, type, everything())
all_exp_data <- rbind(lipid_species_exp, lipid_class_exp, FA_exp)
#statistics test
lipid <- character()
type <- character()
mean_ctrl <- numeric()
mean_exp <- numeric()
mean_all <- numeric()
FC <- numeric()
sd_ctrl <- numeric()
sd_exp <- numeric()
p_value <- numeric()
statistics <- numeric()
ctrl_group <- ctrl_group+1
exp_group <- exp_group+1
if(method=='mod.t.test'){
g2 <- rep("group 2", length(c(ctrl_group, exp_group)))
g2[(exp_group-2)] <- "group 1"
g2 <- factor(g2)
print(g2)
mod_t_test <- tryCatch({mod.t.test(as.matrix(all_exp_data[-c(1:2)]), group = g2)},
error=function(e){return(NULL)})
if(!is.null(mod_t_test)){
p_value <- mod_t_test$p.value
statistics <- mod_t_test$t
}else{
p_value <- NA
statistics <- NA
}
}
for (a in 1:nrow(all_exp_data)) {
lipid[a] <- all_exp_data$feature[a]
type[a] <- all_exp_data$type[a]
mean_ctrl[a] <- all_exp_data[a, ctrl_group] %>% unlist() %>% mean(na.rm=T)
mean_exp[a] <- all_exp_data[a, exp_group] %>% unlist() %>% mean(na.rm=T)
mean_all[a] <- all_exp_data[a, c(ctrl_group, exp_group)] %>% unlist() %>% mean(na.rm=T)
FC[a] <- mean_exp[a]/mean_ctrl[a]
sd_ctrl[a] <- all_exp_data[a, ctrl_group] %>% unlist() %>% sd(na.rm=T)
sd_exp[a] <- all_exp_data[a, exp_group] %>% unlist() %>% sd(na.rm=T)
if(method=='t.test'){
p_value[a] <- tryCatch({t.test(unlist(all_exp_data[a, exp_group]), unlist(all_exp_data[a, ctrl_group]), var.equal=T)$p.value},
error=function(e){return(NA)})
statistics[a] <- tryCatch({t.test(unlist(all_exp_data[a, exp_group]), unlist(all_exp_data[a, ctrl_group]), var.equal=T)$statistic},
error=function(e){return(NA)})
}else if(method=='wilcox.test'){
p_value[a] <- tryCatch({wilcox.test(unlist(all_exp_data[a, exp_group]), unlist(all_exp_data[a, ctrl_group]), var.equal=T)$p.value},
error=function(e){return(NA)})
statistics[a] <- tryCatch({wilcox.test(unlist(all_exp_data[a, exp_group]), unlist(all_exp_data[a, ctrl_group]), var.equal=T)$statistic},
error=function(e){return(NA)})
}
}
result <- data.frame(lipid=lipid, type=type, mean_all=mean_all, mean_ctrl=mean_ctrl, mean_exp=mean_exp, sd_ctrl=sd_ctrl,
sd_exp=sd_exp, FC=FC, log2FC=log2(FC), statistics=statistics, p_value=p_value, mlog10p=-log10(p_value))
result <- result %>% group_by(type) %>%
mutate(adj_p_value = stats::p.adjust(p_value, method = 'fdr')) %>%
mutate(mlog10padj=-log10(adj_p_value))
if(significant=='p_value'){
result <- result %>% mutate(sig=ifelse(p_value<0.05, 'yes', 'no'))
}else{
result <- result %>% mutate(sig=ifelse(adj_p_value<0.05, 'yes', 'no'))
}
return(list(all_exp_data, result))
}
build_FA_net <- function(FA_network, unprocessed_data_result){
non_processed_data_result <- unprocessed_data_result
all_FA <- non_processed_data_result[[2]] %>%
filter(type=='FA') %>% .$lipid %>% unique()
FA_below_16 <- min(which(FA_network$S1[1:7] %in%all_FA))
if(is.infinite(FA_below_16)){
FA_network <- FA_network[-c(1:7),]
endo_start <- data.frame(S1=str_c('endo: 2:0;0--14:0;0'),
P1='16:0;0',
S1_detail='endo: 2:0;0--14:0;0',
P1_detail='16:0;0',pathway='Non_essential_FA_synthesis')
FA_network <- FA_network %>%
rbind(endo_start)
exo <- ''
}
else{
exo <- FA_network[FA_below_16:7,]
exo$S1 <- str_c('exo: ',exo$S1)
exo$P1 <- str_c('exo: ',exo$P1)
exo$S1_detail <- str_c('exo: ',exo$S1_detail)
exo$P1_detail <- str_c('exo: ',exo$P1_detail)
endo <- data.frame(lapply(exo, function(x){gsub('exo','endo',x)}))
endo_start <- data.frame(S1=str_c('endo: 2:0;0--',2*(FA_below_16-1),':0;0'),
P1=endo[1,1],
S1_detail=str_c('endo: 2:0;0--',2*(FA_below_16-1),':0;0'),
P1_detail=endo[1,1],pathway='Non_essential_FA_synthesis')
FA_network <- FA_network[-c(1:7),] %>%
rbind(exo) %>% rbind(endo) %>% rbind(endo_start)
FA_network <- data.frame(lapply(FA_network, function(x){gsub('exo: 16:0;0|endo: 16:0;0','16:0;0',x)}))
}
return(FA_network)
}
FA_sub_transform <- function(FA_network, unprocessed_data_result,
unmapped_FA=NULL){
non_processed_data_result <- unprocessed_data_result
all_FA <- non_processed_data_result[[2]] %>%
filter(type=='FA') %>% .$lipid %>% unique()
endo_start <- FA_network %>% filter(str_detect(S1, 'endo: 2:0;0'))
exo <- FA_network %>% filter(str_detect(S1, 'exo: ')) %>% .[1,]
graph <- graph_from_data_frame(FA_network[c('S1','P1')], directed = T,
vertices = unique(c(FA_network$S1,FA_network$P1)))
De_novo <- FA_network %>% filter(pathway=='Non_essential_FA_synthesis') %>% unlist()
Omega6 <- FA_network %>% filter(pathway=='Omega_6_FA_synthesis') %>% unlist()
Omega3 <- FA_network %>% filter(pathway=='Omega_3_FA_synthesis') %>% unlist()
lipid <- character()
G3P_start_path <- list()
lipid_list <- unique(c(FA_network$S1,FA_network$P1))
num_class <- 1
num_path <- 1
while(!is.na(lipid_list[num_class])){
if(str_detect(lipid_list[num_class], 'exo|w6-18:2;0|w3-18:3;0')){
lipid[num_path] <- lipid_list[num_class]
G3P_start_path[[num_path]] <- lipid_list[num_class]
num_path <- num_path+1
num_class <- num_class+1
next
}
if(lipid_list[num_class] %in% De_novo){
start=c(exo[1,1],endo_start[1,1])
}
else if(lipid_list[num_class] %in% Omega6){
start='w6-18:2;0'
}
else if(lipid_list[num_class] %in% Omega3){
start='w3-18:3;0'
}
for(starts in start){
shortest_path <- tryCatch({all_simple_paths(graph,starts,
lipid_list[num_class],
mode='out')},
error=function(e){NULL})
if(!is.null(shortest_path)){
if(length(shortest_path)!=0){
for (path_num in 1:length(shortest_path)) {
lipid[num_path] <- lipid_list[num_class]
G3P_start_path[[num_path]] <- shortest_path[[path_num]] %>%
attributes() %>% .$names
num_path <- num_path+1
}
}
else{
lipid[num_path] <- lipid_list[num_class]
G3P_start_path[[num_path]] <- ''
num_path <- num_path+1
}
}
else{
lipid[num_path] <- lipid_list[num_class]
G3P_start_path[[num_path]] <- ''
num_path <- num_path+1
}
}
num_class <- num_class+1
}
names(G3P_start_path) <- lipid
FA_substructure <- plyr::ldply(G3P_start_path, rbind) %>% unique()
FA_substructure[is.na(FA_substructure)] <- ''
colnames(FA_substructure) <- c('FA', str_c('Unit', 1:(ncol(FA_substructure)-1)))
FA_substructure <- FA_substructure %>% filter(Unit1!='')
FA_substructure$FA <- str_extract(FA_substructure$FA, '\\d+:.+')
special_FA <- all_FA[all_FA!='']
special_FA <- special_FA[!special_FA %in% FA_substructure$FA]
FA_substructure <- FA_substructure %>% bind_rows(data.frame(FA=special_FA, Unit1=special_FA))
FA_substructure[is.na(FA_substructure)]=''
if(!is.null(unmapped_FA)){
rm_FA <- which(apply(FA_substructure, MARGIN = 1, FUN = function(x){last(x[x!=''])}) %in% unmapped_FA)
FA_substructure <- FA_substructure[-rm_FA,]
}
return(FA_substructure)
}
FA_sub_extract <- function(char_table, FA_substructure,
unprocessed_data_result,
exact_FA='no', exo_lipid=NULL){
if(class(unprocessed_data_result)=='list'){
unprocessed_data_result <- unprocessed_data_result[[2]] %>% filter(type=='FA')
}
non_processed_data_result <- unprocessed_data_result %>% filter(lipid %in% FA_substructure$FA)
if(exact_FA=='no'){
FA_t_test <- FA_substructure[-1] %>% t() %>% as.data.frame() %>%
apply(MARGIN = 2, FUN = function(x){as.data.frame(x) %>% filter(x!='') %>%
mutate(Lipid=str_extract(x, '\\d+:\\d+;\\d+')) %>% #modify
left_join(non_processed_data_result,by=c('Lipid'='lipid'))})
}
else{
FA_t_test <- FA_substructure[-1] %>% t() %>% as.data.frame() %>%
apply(MARGIN = 2, FUN = function(x){as.data.frame(x) %>% filter(x!='') %>%
mutate(Lipid=x) %>% #modify
mutate(Lipid=str_replace_all(Lipid, 'endo: ','')) %>%
mutate(Lipid=str_replace_all(Lipid, 'exo: ','')) %>%
left_join(non_processed_data_result,by=c('Lipid'='lipid'))})
}
if(!is.null(exo_lipid)){
exo_lipid_with_neighbor <- unlist(filter(FA_network, S1==exo_lipid | P1==exo_lipid)[c('S1','P1')]) %>% unique()
}
else{
exo_lipid_with_neighbor <- ''
}
FA_sub_stop <- list()
FA_name <- character()
for(num in 1:length(FA_t_test)){
FA_sub <- FA_t_test[[num]]
FA_name[num] <- last(FA_sub$x)
if(is.na(last(FA_sub$log2FC))){
FA_sub_stop[[num]] <- ''
}
else if(last(FA_sub$x)=='16:0;0'){
FA_sub_stop[[num]] <- FA_sub$x
}
else if(exo_lipid_with_neighbor!='' && last(FA_sub$x) %in% exo_lipid_with_neighbor){
FA_sub_stop[[num]] <- last(FA_sub$x)
}
else{
if(last(FA_sub$log2FC>0)){
stop_point <- which(FA_sub$log2FC<0)
if(length(stop_point)==0){
FA_sub_stop[[num]] <- FA_sub$x
}
else if(max(stop_point)==(nrow(FA_sub)-1)){
FA_sub_stop[[num]] <- ''
}
else{
stop_loc <- max(stop_point)+1
FA_sub_stop[[num]] <- FA_sub$x[stop_loc:length(FA_sub$x)]
}
}
else{
stop_point <- which(FA_sub$log2FC>0)
if(length(stop_point)==0){
FA_sub_stop[[num]] <- FA_sub$x
}
else if(max(stop_point)==(nrow(FA_sub)-1)){
FA_sub_stop[[num]] <- ''
}
else{
stop_loc <- max(stop_point)+1
FA_sub_stop[[num]] <- FA_sub$x[stop_loc:length(FA_sub$x)]
}
}
}
names(FA_sub_stop) <- FA_name #modify
}
FA_sub_stop <- plyr::ldply(FA_sub_stop, rbind)
FA_sub_stop[is.na(FA_sub_stop)] <- ''
colnames(FA_sub_stop) <- c('FA', str_c('Unit', 1:(ncol(FA_sub_stop)-1)))
FA_sub_stop <- unique(FA_sub_stop) %>% filter(Unit1!='')
exist_FA <- FA_substructure %>% filter(FA %in% non_processed_data_result$lipid) %>%
apply(MARGIN = 1, FUN = function(x){last(x[x!=''])}) %>% unique()
FA_sub_exist_FA <- FA_sub_stop %>% apply(MARGIN = 1, FUN = function(x){last(x[x!=''])}) %>%
unique()
lost_FA <- exist_FA[!exist_FA%in%FA_sub_exist_FA]
if(length(lost_FA)!=0){
FA_sub_stop <- FA_sub_stop %>% add_row(FA = lost_FA, Unit1 =lost_FA)
FA_sub_stop[is.na(FA_sub_stop)] <- ''
}
if(exact_FA=='no'){
FA_sub_stop <- FA_sub_stop %>%
mutate(FA=str_extract(FA, '\\d+:\\d+;\\d+'))
}
else{
FA_sub_stop <- FA_sub_stop %>%
mutate(FA=ifelse(str_detect(FA, '(exo)|(endo)'), str_extract(FA, '\\d+:\\d+;\\d+'), FA))
}
FA_substructure_transform <- function(char_table, FA_substructure){
#Filter lipid and FA with substructure
char <- char_table %>% filter(FA_split!='')
lipid_name <- list()
lipid_sub_list <- list()
add <- 1
for (var1 in 1:nrow(char)) {
char[var1, 'feature']
FA <- str_split(char[var1, 'FA_split'], '_') %>% unlist()
FA_sub <- FA_sub_trans(char[var1, 'feature'], FA, FA_substructure)
lipid_name[[add]] <- FA_sub[[1]]
lipid_sub_list[[add]] <- FA_sub[[2]]
add <- add+1
}
lipid_name <- unlist(lipid_name, recursive = F)
lipid_sub_list <- unlist(lipid_sub_list, recursive = F)
return(list(lipid_name, lipid_sub_list))
}
FA_sub_trans <- function(lipid, FA_list, FA_substructure){
add <- 1
#FA substructure: all possible path method
all_FA_sub <- list()
for(FA_num in 1:length(FA_list)){
FA_sub_all <- FA_substructure %>% filter(FA==FA_list[FA_num])
if(nrow(FA_sub_all)==0){
FA_sub_all <- data.frame(FA=FA_list[FA_num],Unit1=FA_list[FA_num])
}
all_FA_sub[[FA_num]] <- FA_sub_all
}
lipid_name <- character()
FA_sub_list <- list()
for(var1 in 1:nrow(all_FA_sub[[1]])){
if(length(all_FA_sub)>1){
for(var2 in 1:nrow(all_FA_sub[[2]])){
if(length(all_FA_sub)>2){
for(var3 in 1:nrow(all_FA_sub[[3]])){
if(length(all_FA_sub)>3){
for(var4 in 1:nrow(all_FA_sub[[4]])){
FA1_sub <- all_FA_sub[[1]][var1,] %>% dplyr::select(-FA) %>% unlist()
FA1_sub <- FA1_sub[FA1_sub!='']
FA2_sub <- all_FA_sub[[2]][var2,] %>% dplyr::select(-FA) %>% unlist()
FA2_sub <- FA2_sub[FA2_sub!='']
FA3_sub <- all_FA_sub[[3]][var3,] %>% dplyr::select(-FA) %>% unlist()
FA3_sub <- FA3_sub[FA3_sub!='']
FA4_sub <- all_FA_sub[[4]][var4,] %>% dplyr::select(-FA) %>% unlist()
FA4_sub <- FA4_sub[FA4_sub!='']
FA_sub <- c(FA1_sub, FA2_sub, FA3_sub, FA4_sub)
names(FA_sub) <- c(rep('FA1', length(FA1_sub)),
rep('FA2', length(FA2_sub)),
rep('FA3', length(FA3_sub)),
rep('FA4', length(FA4_sub)))
lipid_name[add] <- lipid
FA_sub_list[[add]] <- FA_sub
add <- add+1
}
}
else{
FA1_sub <- all_FA_sub[[1]][var1,] %>% dplyr::select(-FA) %>% unlist()
FA1_sub <- FA1_sub[FA1_sub!='']
FA2_sub <- all_FA_sub[[2]][var2,] %>% dplyr::select(-FA) %>% unlist()
FA2_sub <- FA2_sub[FA2_sub!='']
FA3_sub <- all_FA_sub[[3]][var3,] %>% dplyr::select(-FA) %>% unlist()
FA3_sub <- FA3_sub[FA3_sub!='']
FA_sub <- c(FA1_sub, FA2_sub, FA3_sub)
names(FA_sub) <- c(rep('FA1', length(FA1_sub)),
rep('FA2', length(FA2_sub)),
rep('FA3', length(FA3_sub)))
lipid_name[add] <- lipid
FA_sub_list[[add]] <- FA_sub
add <- add+1
}
}
}
else{
FA1_sub <- all_FA_sub[[1]][var1,] %>% dplyr::select(-FA) %>% unlist()
FA1_sub <- FA1_sub[FA1_sub!='']
FA2_sub <- all_FA_sub[[2]][var2,] %>% dplyr::select(-FA) %>% unlist()
FA2_sub <- FA2_sub[FA2_sub!='']
FA_sub <- c(FA1_sub, FA2_sub)
names(FA_sub) <- c(rep('FA1', length(FA1_sub)),
rep('FA2', length(FA2_sub)))
lipid_name[add] <- lipid
FA_sub_list[[add]] <- FA_sub
add <- add+1
}
}
}
else{
FA_sub <- all_FA_sub[[1]][var1,] %>% dplyr::select(-FA) %>% unlist()
#20:2;0: 18:1;0,9, 18:2;0,6,9, 20:2;0,8,11
FA_sub <- FA_sub[FA_sub!='']
names(FA_sub) <- c(rep('FA1', length(FA_sub)))
lipid_name[add] <- lipid
FA_sub_list[[add]] <- FA_sub
add <- add+1
}
}
return(list(lipid_name, FA_sub_list))
}
FA_sub_stop <- FA_substructure_transform(char_table, FA_sub_stop)
return(FA_sub_stop)
}
lipid_sub_matrix <- function(exp_data, sub_data,
sub_type='species'){
sub_mat <- list()
if(sub_type=='FA'){
sub_data[[2]] <- sub_data[[2]] %>% map(.f = function(x){x[str_detect(names(x), 'FA')]})
}
else if(sub_type=='Class'){
sub_data[[2]] <- sub_data[[2]] %>% map(.f = function(x){x <- x[!str_detect(names(x), 'FA')]
x <- unique(str_replace(x, '_.+', ''))})
}
else{
sub_data[[2]] <- sub_data[[2]] %>% map(.f = function(x){x <- x[!str_detect(names(x), 'FA')]})
}
all_sub <- sub_data[[2]] %>% unlist() %>% unique() %>% sort()
for (num in 1:nrow(exp_data)) {
loc <- which(sub_data[[1]]==exp_data[num,1])
if(length(loc)!=0){
#Substructure weight: all possible path divided by path number
sub_mat[[num]] <- as.integer(table(factor(unlist(sub_data[[2]][loc]), levels = all_sub)))/length(loc)
}else{
sub_mat[[num]] <- integer(length(all_sub))
}
}
sub_mat <- sub_mat %>% as.data.frame()
colnames(sub_mat) <- exp_data$feature
rownames(sub_mat) <- sub_data[[2]] %>% unlist() %>% unique() %>% sort()
sub_mat <- sub_mat[rowSums(sub_mat)!=0,]
sub_mat <- as.matrix(sub_mat)
exp_mat <- exp_data[-1] %>% as.matrix()
sub_exp <- sub_mat %*% exp_mat
return(list(sub_mat,exp_mat, sub_exp))
}
t_test <- function(data, ctrl, exp, method='t.test', significant='p_value'){
lipid <- character()
mean_ctrl <- numeric()
mean_exp <- numeric()
mean_all <- numeric()
FC <- numeric()
sd_ctrl <- numeric()
sd_exp <- numeric()
p_value <- numeric()
statistics <- numeric()
if(method=='mod.t.test'){
g2 <- rep("group 2", length(c(ctrl, exp)))
g2[exp] <- "group 1"
g2 <- factor(g2)
data_m <- as.matrix(data)
mod_t_test <- tryCatch({mod.t.test(data_m, group = g2)},
error=function(e){return(NULL)})
if(!is.null(mod_t_test)){
p_value <- mod_t_test$p.value
statistics <- mod_t_test$t
}else{
p_value <- NA
statistics <- NA
}
}
for (a in 1:nrow(data)) {
lipid[a] <- rownames(data)[a]
mean_ctrl[a] <- data[a, ctrl] %>% unlist() %>% mean(na.rm=T)
mean_exp[a] <- data[a, exp] %>% unlist() %>% mean(na.rm=T)
mean_all[a] <- data[a, c(ctrl, exp)] %>% unlist() %>% mean(na.rm=T)
FC[a] <- mean_exp[a]/mean_ctrl[a]
sd_ctrl[a] <- data[a, ctrl] %>% unlist() %>% sd(na.rm=T)
sd_exp[a] <- data[a, exp] %>% unlist() %>% sd(na.rm=T)
if(method=='t.test'){
p_value[a] <- tryCatch({t.test(unlist(data[a, exp]), unlist(data[a, ctrl]), var.equal=T)$p.value},
error=function(e){return(NA)})
statistics[a] <- tryCatch({t.test(unlist(data[a, exp]), unlist(data[a, ctrl]), var.equal=T)$statistic},
error=function(e){return(NA)})
}
else if(method=='wilcox.test'){
p_value[a] <- tryCatch({wilcox.test(unlist(data[a, exp]), unlist(data[a, ctrl]), var.equal=T)$p.value},
error=function(e){return(NA)})
statistics[a] <- tryCatch({wilcox.test(unlist(data[a, exp]), unlist(data[a, ctrl]), var.equal=T)$statistic},
error=function(e){return(NA)})
}
}
adj_p_value <- p.adjust(p_value, method = 'fdr', n = length(p_value))
result <- data.frame(lipid=lipid, mean_all=mean_all, mean_ctrl=mean_ctrl, mean_exp=mean_exp, sd_ctrl=sd_ctrl,
sd_exp=sd_exp, FC=FC, log2FC=log2(FC), statistics=statistics, p_value=p_value, mlog10p=-log10(p_value),
adj_p_value=adj_p_value, mlog10padj=-log10(adj_p_value))
if(significant=='p_value'){
result <- result %>% mutate(sig=ifelse(p_value<0.05, 'yes', 'no'))
}
else{
result <- result %>% mutate(sig=ifelse(adj_p_value<0.05, 'yes', 'no'))
}
result$lipid <- str_replace(result$lipid, '_FA\\d+','')
return(result)
}
draw_network <- function(network_data, DE_data, if_species=F, significant='p_value',
path_scoring_result, reaction_scoring_result,
top_n=3, path_type='both'){
if(path_type=='both'){
active=T
suppressed=T
}
else if(path_type=='active'){
active=T
suppressed=F
}
else{
active=F
suppressed=T
}
FA_node <- data.frame(lipid=unique(c(network_data$S1,network_data$P1)), stringsAsFactors = F) %>%
left_join(DE_data, by=c('lipid'))
replace_inf <- max(abs(FA_node$log2FC[!is.infinite(FA_node$log2FC)]), na.rm = T)+0.5
if(significant=='p_value'){
FA_node <- FA_node %>% mutate(sig=ifelse(p_value<0.05, 'yes','no'))
}
else{
FA_node <- FA_node %>% mutate(sig=ifelse(adj_p_value<0.05, 'yes','no')) %>%
mutate(mlog10p=-log10(adj_p_value))
}
FA_node <- FA_node %>%
mutate(in_ref=ifelse(!is.na(FC), 'yes','no')) %>%
mutate(sig=ifelse(is.na(sig),'na',sig)) %>%
mutate(log2FC=ifelse(is.infinite(log2FC) & log2FC>0, replace_inf, log2FC)) %>%
mutate(log2FC=ifelse(is.infinite(log2FC) & log2FC<0, -replace_inf, log2FC)) %>%
mutate(mlog10p=ifelse(is.na(mlog10p), 0, mlog10p)) %>%
mutate(mean_all=ifelse(is.na(mean_all), 0, mean_all))
FC_color <- cut(FA_node$log2FC, breaks = seq(-replace_inf-0.1, replace_inf+0.1, length.out=100), labels = bluered(99)) %>%
as.character()
FC_color[is.na(FC_color)] <- 'black'
node <- data.frame(id=FA_node$lipid,
label=FA_node$lipid,
color.background=FC_color,
color.border=recode(FA_node$sig,
"yes"='purple',
"no"='black',
'na'='transparent'),
borderWidth=recode(FA_node$sig,
"yes"=2.5,
"no"=1,
'na'=0),
#value=FA_node$mlog10p,
value=(FA_node$mlog10p),
font.size = 35)
edge <- data.frame(from=network_data$S1,to=network_data$P1,
#label = paste("Edge", 1:8),
color='gray',
arrows=c('to'),
length=100)
path_color <- function(network_edge, path_scoring_result,
reaction_scoring_result, top_n=3,
active=T, suppressed=T){
if(active==T){
#top paths
sig_active_path <- path_scoring_result %>% filter(Type=='Active') %>%
.[!duplicated(.$rep_sub_path),] %>% filter(Significant=='yes')
if(nrow(sig_active_path)==0){
top_n_new <- nrow(sig_active_path)
topN_active_path <- data.frame()
}
else if(nrow(sig_active_path)<top_n){
top_n_new <- nrow(sig_active_path)
topN_active_path <- sig_active_path[1:top_n_new,] %>%
mutate(rank=1:top_n_new)
}
else{
top_n_new <- top_n
topN_active_path <- sig_active_path[1:top_n_new,] %>%
mutate(rank=1:top_n_new)
}
#top edges
sig_active_edge <- reaction_scoring_result %>% filter(Mode=='Increase') %>%
filter(p_value<0.05)
if(nrow(sig_active_edge)==0){
topN_active_edge <- data.frame()
}
else if(nrow(sig_active_edge)<top_n){
top_n_new <- nrow(sig_active_edge)
topN_active_edge <- sig_active_edge[1:top_n_new,] %>%
mutate(rank=1:top_n_new)
}
else{
top_n_new <- top_n
topN_active_edge <- sig_active_edge[1:top_n_new,] %>%
mutate(rank=1:top_n_new)
}
}
else{
topN_active_path <- data.frame()
topN_active_edge <- data.frame()
}
if(suppressed==T){
#top paths
sig_suppressed_path <- path_scoring_result %>% filter(Type=='Suppressed') %>%
arrange(cal_score) %>%.[!duplicated(.$rep_sub_path),] %>%
filter(Significant=='yes')
if(nrow(sig_suppressed_path)==0){
topN_suppressed_path <- data.frame()
}
else if(nrow(sig_suppressed_path)<top_n){
top_n_new <- nrow(sig_suppressed_path)
topN_suppressed_path <- sig_suppressed_path[1:top_n_new,] %>%
mutate(rank=c(rep(5, top_n_new)+1:top_n_new))
}
else{
top_n_new <- top_n
topN_suppressed_path <- sig_suppressed_path[1:top_n_new,] %>%
mutate(rank=c(rep(5, top_n_new)+1:top_n_new))
}
#top edges
sig_suppressed_edge <- reaction_scoring_result %>% filter(Mode=='Decrease') %>%
arrange(perturbation_score) %>% filter(p_value<0.05)
if(nrow(sig_suppressed_edge)==0){
topN_suppressed_edge <- data.frame()
}
else if(nrow(sig_suppressed_edge)<top_n){
top_n_new <- nrow(sig_suppressed_edge)
topN_suppressed_edge <- sig_suppressed_edge[1:top_n_new,] %>%
mutate(rank=c(rep(5, top_n_new)+1:top_n_new))
}
else{
top_n_new <- top_n
topN_suppressed_edge <- sig_suppressed_edge[1:top_n_new,] %>%
mutate(rank=c(rep(5, top_n_new)+1:top_n_new))
}
}
else{
topN_suppressed_path <- data.frame()
topN_suppressed_edge <- data.frame()
}
topN_path <- rbind(topN_active_path, topN_suppressed_path)
topN_edge <- rbind(topN_active_edge, topN_suppressed_edge)
path <- data.frame(rank=1:10, edge_color=bluered(100)[c(seq(100,56, length.out=5),seq(1,45, length.out=5))],
width=rep(seq(12,4, length.out=5),2))
#
edge <- data.frame(rank=1:10, perturb_type=c(rep('Increase',5),rep('Decrease',5)),
fontsize=rep(rep(40,5),2),
fontcolor=c(rep('red',5),rep('blue',5)))
if(nrow(topN_path)!=0){
path_score <- topN_path %>% left_join(path, by='rank')
}
else{
path_score <- data.frame()
}
if(nrow(topN_edge)!=0){
perturbation_score <- topN_edge %>% left_join(edge, by='rank')
}
else{
perturbation_score <- data.frame()
}
edge_color <- character()
width <- numeric()
perturb_type <- character()
fontsize <- numeric()
fontcolor <- character()
for (edge_num in 1:nrow(network_edge)) {
from <- network_edge$from[edge_num]
to <- network_edge$to[edge_num]
edge_in_path <- str_detect(path_score$path, str_c(from,' --> ',to))
if(sum(edge_in_path)==0){
edge_color[edge_num] <- 'black'
width[edge_num] <- 1
}
else{
names(edge_in_path) <- path_score$rank
path_rank <- names(edge_in_path)[which.max(edge_in_path)]
edge_color[edge_num] <- filter(path_score, rank==path_rank)$edge_color
width[edge_num] <- filter(path_score, rank==path_rank)$width
}
edge_in_perturb <- perturbation_score$edge_name==str_c(from,' --> ',to)
if(sum(edge_in_perturb)==0){
perturb_type[edge_num] <- ''
fontsize[edge_num] <- 0
fontcolor[edge_num] <- 'black'
}
else{
names(edge_in_perturb) <- perturbation_score$rank
perturb_rank <- names(edge_in_perturb)[which.max(edge_in_perturb)]
perturb_type[edge_num] <- filter(perturbation_score, rank==perturb_rank)$perturb_type
fontsize[edge_num] <- filter(perturbation_score, rank==perturb_rank)$fontsize
fontcolor[edge_num] <- filter(perturbation_score, rank==perturb_rank)$fontcolor
}
}
network_edge <- network_edge %>% mutate(color=edge_color,
width=width,
label=perturb_type,
font.size=fontsize,
font.color=fontcolor)
return(network_edge)
}
if(if_species==T){
if(active==T && suppressed==T){
topN_rep_path <- c(path_scoring_result %>% filter(Type=='Active') %>%
.[!duplicated(.$rep_sub_path),] %>% .[1:top_n,] %>% .$rep_sub_path,
path_scoring_result %>% filter(Type=='Suppressed') %>%
arrange(cal_score) %>% .[!duplicated(.$rep_sub_path),] %>%
.[1:top_n,] %>% .$rep_sub_path)
}
else if(active==T && suppressed==F){
topN_rep_path <- path_scoring_result %>% filter(Type=='Active') %>%
.[!duplicated(.$rep_sub_path),] %>% .[1:top_n,] %>% .$rep_sub_path
}
else if(active==F && suppressed==T){
topN_rep_path <-path_scoring_result %>% filter(Type=='Suppressed') %>%
arrange(cal_score) %>% .[!duplicated(.$rep_sub_path),] %>%
.[1:top_n,] %>% .$rep_sub_path
}
topN_rep_path <- path_scoring_result %>% filter(rep_sub_path%in%topN_rep_path, Significant=='yes') %>%
.$path %>% str_split(' --> ') %>% unlist() %>% unique()
edge_in_topN_path <- reaction_scoring_result$edge_name %>% str_split(' --> ') %>%
map_lgl(.f = function(x){x[1] %in% topN_rep_path && x[2] %in% topN_rep_path})
edge_in_topN_path <- reaction_scoring_result[edge_in_topN_path,]$edge_name
topN_net_edge <- network_data %>% filter(S1 %in% topN_rep_path, P1 %in% topN_rep_path)
topN_net_edge <- topN_net_edge %>% mutate(color='gray', arrows='to',length=100)
colnames(topN_net_edge)[1:2] <- c('from','to')
edge <- topN_net_edge
topN_net_node <- node %>%
filter(id %in% unlist(topN_net_edge))
reaction_scoring_result <- reaction_scoring_result%>%
filter(edge_name %in% edge_in_topN_path)
}
edge <- path_color(edge, path_scoring_result,
reaction_scoring_result, top_n,
active, suppressed)
if(if_species==T){
node <- topN_net_node %>% filter(id %in%c(edge$from, edge$to))
}
return(list(node, edge))
}
path_scoring <- function(network, sub_t, calibrate=T, data_type='FA'){
options(scipen = 999)
sub_t <- sub_t %>% dplyr::select(-sig)
sub_t <- sub_t %>%
mutate(zscore=qnorm(1-sub_t$p_value/2)) %>%
mutate(zscore=ifelse(is.infinite(zscore), 8, zscore)) %>%
mutate(zscore=ifelse(statistics>0, zscore, -zscore))
Network_w <- data.frame(node=unique(unlist(network[c('S1','P1')]))) %>%
left_join(sub_t, by=c('node'='lipid')) %>%
mutate(weight=zscore) %>%
dplyr::select(node, weight)
graph <- graph_from_data_frame(network[c('S1','P1')], directed = T, vertices = unique(c(network$S1,network$P1)))
all_node <- network[c('S1','P1')] %>% unlist() %>% unique()
combo <- permutations(length(all_node), 2, all_node)
path <- character()
path_full <- character()
score <- numeric()
type <- character()
from <- character()
to <- character()
num_path <- 1
for (num in 1:nrow(combo)) {