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server.R
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server.R
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# LOADING LIBRARIES -------------------------------------------------------
library(igraph)
library(rsbml)
library(shiny)
library(intergraph)
library(ggplot2)
library(visNetwork)
library(xtable)
library(dplyr)
library(sna)
library(shinyBS)
library(rPython)
library(shinythemes)
library(markdown)
library(htmlwidgets)
# FUNCTIONS ---------------------------------------------------------------
fill_blank <- function(x, len) {
#This function is used to make the metabolite name strings the same length. It fills the strings with spaces so the final lengths of each metabolite equals the length of the longest one
if (nchar(x) > len) {
result = x
} else{
len_ori = nchar(x)
len_diff = len - len_ori
len_add = len_diff / 2
len_add_left = ceiling(len_add)
len_add_right = len_diff - len_add_left
add_left = paste(rep(" ", len_add_left), collapse = "")
add_right = paste(rep(" ", len_add_right), collapse = "")
result = paste(add_left, x, add_right, sep = "")
}
return(result)
}
add_dups_new_layout <- function(visdata) {
#This function is used to duplicate the metabolites in order to acheve the textbooky look of the network
visdata$nodes[23, ] = c("M_m01c1", "M_m01c1")#ADP
visdata$nodes[28, ] = c("M_m01c2", "M_m01c2")#ADP
visdata$nodes[22, ] = c("M_m02c1", "M_m02c1")#ATP
visdata$nodes[21, ] = c("M_m06c1", "M_m06c1")#H2O
visdata$nodes[20, ] = c("M_m09c1", "M_m09c1")#Pi
visdata$nodes[24, ] = c("R_E41", "R_E41")#H2O exchange
visdata$nodes[25, ] = c("M_m05c1", "M_m05c1")#H
visdata$nodes[26, ] = c("M_m05m1", "M_m05m1")#H
visdata$nodes[27, ] = c("M_m09c2", "M_m09c2")#Pi
rownames(visdata$nodes) = visdata$nodes[, 1]
visdata$edges[15, 1] = "M_m09c1"
visdata$edges[30, 2] = "M_m09c2"
visdata$edges[8, 1] = "M_m06c1"
visdata$edges[28, 2] = "M_m06c1"
visdata$edges[31, ] = c("M_m06c1", "R_E41", "1")
visdata$edges[16, 1] = "M_m02c1"
visdata$edges[26, 2] = "M_m02c1"
visdata$edges[12, 1] = "M_m01c1"
visdata$edges[29, 2] = "M_m01c2"
visdata$edges[17, 1] = "M_m05c1"
visdata$edges[27, 2] = "M_m05m1"
return(visdata)
}
exclude_trans_exchange <- function(visdata) {
visdata$nodes = visdata$nodes[-grep("port", visdata$nodes[, 2]), ]
visdata$nodes = visdata$nodes[-which(grepl("_e$", rownames(visdata$nodes), perl = T) ==
TRUE), ]
return(visdata)
}
show_basic_network <-
function(model_name = "toycon",
weighting = "none",
exclude = F) {
#This function is used to show a basic network when the tabs are first launched in order to help the user to decide what to do
working_dir = getwd()
path = paste("/data/", model_name, ".xml", sep = "")
if (.Platform$OS.type == "windows") {
path = gsub("\\\\", "/", path)
}
model_file_path = paste(working_dir, path, sep = "")
path = paste("/data/", model_name, "_var.RData", sep = "")
if (.Platform$OS.type == "windows") {
path = gsub("\\\\", "/", path)
}
load(paste(working_dir, path, sep = "")) #formal class SBML object
toycon = readRDS(paste(working_dir, "/data/", model_name, ".rda", sep = "")) #formal class modelorg object
data = rsbml_graph(sbml_model)
toycon_graph = igraph.from.graphNEL(data)
visdata <- toVisNetworkData(toycon_graph)
visdata_ori = visdata
#Adding duplicate metabolites/reactions
if (model_name == "toycon") {
visdata = add_dups_new_layout(visdata)
}
visdata$nodes$group = rep("Metabolite", length(visdata$nodes$id))
visdata$nodes$group[which(grepl("R", visdata$nodes$id))] = "Reaction"
visdata$nodes$group[which(grepl("m\\d*$", visdata$nodes$id))] = "Metabolite mitochondria"
visdata$nodes$group[which(grepl(
perl = T,
pattern = "^M\\S*e$",
x = visdata$nodes$id
))] = "Metabolite external"
visdata$edges$width = 2
visdata$edges$length = 150
net = asNetwork(toycon_graph)
#Setting colors according to node class
color_reaction = "lightblue"
color_metabolite = "lightsalmon"
color_metabolite_mitochondria = "red"
color_metabolite_external = "indianred"
names = rownames(visdata$nodes)
net %v% "type" = ifelse(grepl("R", names), "Reaction", "Metabolite")
edges_names = names
#Setting proper nodes names
for (i in seq(1, length(names))) {
if (model_name == "toycon") {
if (nchar(names[i]) < 6) {
names[i] = substr(names[i], 1, 4)
} else{
names[i] = substr(names[i], 1, 6)
}
}
if (any(names(sbml_model@model@species) == as.character(names[i]))) {
metabolite = sbml_model@model@species[[which(names(sbml_model@model@species) == as.character(names[i]))]]@name
edges_names[i] = metabolite
}
else{
if (any(names(sbml_model@model@reactions) == as.character(names[i]))) {
reaction_name = sbml_model@model@reactions[[which(names(sbml_model@model@reactions) == as.character(names[i]))]]@name
edges_names[i] = reaction_name
}
else{
edges_names[i] = "NoName"
}
}
}
#Make the names equal length (7 is the max length of matabolite name) for the displaying purposes. this way the sizes of the metabolite nodes are all equal
if (model_name == "toycon") {
edges_names = sapply(edges_names, function(x)
fill_blank(x, 7))
}
names_dict = rbind(edges_names, names) #Names and IDs dictionary
visdata$nodes$label = as.vector(edges_names)
edgesize = 0.75
#Read the saved coordinates for the graph dispalying purpose
path = "data/textbooky_coords.csv"
if (.Platform$OS.type == "windows") {
path = gsub("\\\\", "/", path)
}
coords = read.csv(path)
if (model_name == "toycon") {
visdata$nodes = cbind(visdata$nodes, coords)
#Emphasize main reactions
visdata$nodes[which(grepl("^glycolysis$", names_dict[1, ])), "font"] = "20px arial"
visdata$nodes[which(grepl("^respiration$", names_dict[1, ])), "font"] = "20px arial"
visdata$nodes[which(grepl("ATP synthase", names_dict[1, ])), "font"] = "20px arial"
visdata$nodes[which(grepl("ATP demand", names_dict[1, ])), "font"] = "20px arial"
#Emphasize main metabolites
visdata$nodes[which(grepl("^lactate$", names_dict[1, ])), "font"] = "20px arial"
visdata$nodes[which(grepl("^glucose$", names_dict[1, ])), "font"] = "20px arial"
visdata$nodes[which(grepl("ATP", names_dict[1, ])), "font"] = "20px arial"
visdata$nodes[which(grepl("ADP", names_dict[1, ])), "font"] = "20px arial"
}
if (weighting == TRUE) {
#Necessary transformations for the table displaying purposes
ndata = names(data@edgeData)
edges_df = dplyr::mutate(visdata_ori$edges, name = paste(from, to, sep = "|"))
for (i in seq(1, length(data@edgeData@data))) {
hit = which(edges_df$name == ndata[i])
data@edgeData@data[[i]]$coefficient = edges_df[hit, 3]
}
#Rewrites the coefficients to edge's weight slot
for (i in seq(1, length(data@edgeData@data))) {
data@edgeData@data[[i]]$weight = data@edgeData@data[[i]]$coefficient
}
toycon_graph = igraph.from.graphNEL(data)
net = asNetwork(toycon_graph)
net %v% "type" = ifelse(grepl("R", names), "Reaction", "Metabolite")
reactions_names = as.vector(unlist(net$val)[which(names(unlist(net$val)) ==
"vertex.names")][which(grepl("^R_", unlist(net$val)[which(names(unlist(net$val)) ==
"vertex.names")]))])
if (model_name == "toycon") {
visdata1 = visdata
} else{
toycon_graph1 = igraph.from.graphNEL(data)
visdata1 <- toVisNetworkData(toycon_graph1)
}
weights_edges = as.numeric(visdata1$edges$weight)
edgesize = log(abs(weights_edges)) + 1
visdata$edges$width = edgesize
visdata$edges$title = paste("Stoichiometric coefficient: ", round(weights_edges))
}
shape_metabolites = ifelse(model_name == "toycon", "circle", "dot")
shape_reactions = ifelse(model_name == "toycon", "box", "square")
lnodes <-
data.frame(
label = c(
"Cytosolic metabolite",
"Mitochondrial metabolite",
"External metabolite",
"Reaction"
),
shape = c("dot", "dot", "dot", "box"),
color = c("lightsalmon", "red", "indianred", "lightblue"),
title = "Informations"
)
ledges <- data.frame(
color = c("black", "black"),
label = c("flux", "no flux"),
arrows = c("to", "to"),
dashes = c(F, T)
)
if (exclude == T & model_name != "toycon") {
visdata = exclude_trans_exchange(visdata)
}
#Plotting graph
visNetwork(nodes = visdata$nodes, edges = visdata$edges) %>%
visLegend(
position = "right",
stepX = 100,
stepY = 75,
width = 0.2,
useGroups = F,
addNodes = lnodes,
zoom = T,
addEdges = ledges
) %>%
visOptions(highlightNearest = T) %>%
visEdges(
color = "black",
arrows = "to",
smooth = list(
enabled = TRUE,
type = "vertical",
roundness = 0.1,
forceDirection = "vertical"
)
) %>%
visGroups(groupname = "Metabolite",
color = color_metabolite,
shape = shape_metabolites) %>%
visGroups(groupname = "Reaction",
color = color_reaction,
shape = shape_reactions) %>%
visGroups(groupname = "Metabolite mitochondria",
color = color_metabolite_mitochondria,
shape = shape_metabolites) %>%
visGroups(groupname = "Metabolite external",
color = color_metabolite_external,
shape = shape_metabolites) %>%
visLayout(randomSeed = 1) %>%
visPhysics(
solver = "hierarchicalRepulsion",
hierarchicalRepulsion = list(
nodeDistance = 40,
springLength = 10,
springConstant = 0,
damping = 0.1
)
)
}
get_coefficients_DF <- function(model_name = "toycon") {
working_dir = getwd()
path = paste("/data/", model_name, ".xml", sep = "")
if (.Platform$OS.type == "windows") {
path = gsub("\\\\", "/", path)
}
model_file_path = paste(working_dir, path, sep = "")
path = paste("/data/", model_name, "_var.RData", sep = "")
if (.Platform$OS.type == "windows") {
path = gsub("\\\\", "/", path)
}
load(paste(working_dir, path, sep = "")) #formal class SBML object
toycon = readRDS(paste(working_dir, "/data/", model_name, ".rda", sep = "")) #formal class modelorg object
data = rsbml_graph(sbml_model)
toycon_graph = igraph.from.graphNEL(data)
visdata <- toVisNetworkData(toycon_graph)
visdata_ori = visdata
#Adding duplicate metabolites/reactions
if (model_name == "toycon") {
visdata = add_dups_new_layout(visdata)
}
visdata$nodes$group = rep("Metabolite", length(visdata$nodes$id))
visdata$nodes$group[which(grepl("R", visdata$nodes$id))] = "Reaction"
visdata$nodes$group[which(grepl("m\\d*$", visdata$nodes$id))] = "Metabolite mitochondria"
visdata$edges$width = 2
visdata$edges$length = 150
net = asNetwork(toycon_graph)
#Setting colors according to node class
color_reaction = "lightblue"
color_metabolite = "lightsalmon"
color_metabolite_mitochondria = "red"
color_metabolite_external = "indianred"
color_reaction_objective = "lightgreen"
names = rownames(visdata$nodes)
net %v% "type" = ifelse(grepl("R", names), "Reaction", "Metabolite")
edges_names = names
#Setting proper nodes names
for (i in seq(1, length(names))) {
if (model_name == "toycon") {
if (nchar(names[i]) < 6) {
names[i] = substr(names[i], 1, 4)
} else{
names[i] = substr(names[i], 1, 6)
}
}
if (any(names(sbml_model@model@species) == as.character(names[i]))) {
metabolite = sbml_model@model@species[[which(names(sbml_model@model@species) == as.character(names[i]))]]@name
edges_names[i] = metabolite
}
else{
if (any(names(sbml_model@model@reactions) == as.character(names[i]))) {
reaction_name = sbml_model@model@reactions[[which(names(sbml_model@model@reactions) == as.character(names[i]))]]@name
edges_names[i] = reaction_name
}
else{
edges_names[i] = "NoName"
}
}
}
#Make the names equal length (7 is the max length of matabolite name) for the displaying purposes. this way the sizes of the metabolite nodes are all equal
if (model_name == "toycon") {
edges_names = sapply(edges_names, function(x)
fill_blank(x, 7))
}
names_dict = rbind(edges_names, names) #Names and IDs dictionary
visdata$nodes$label = as.vector(edges_names)
edgesize = 0.75
#Read the saved coordinates for the graph dispalying purpose
path = "data/textbooky_coords.csv"
if (.Platform$OS.type == "windows") {
path = gsub("\\\\", "/", path)
}
coords = read.csv(path)
if (model_name == "toycon") {
visdata$nodes = cbind(visdata$nodes, coords)
#Emphasize main reactions
visdata$nodes[which(grepl("^glycolysis$", names_dict[1, ])), "font"] = "20px arial"
visdata$nodes[which(grepl("^respiration$", names_dict[1, ])), "font"] = "20px arial"
visdata$nodes[which(grepl("ATP synthase", names_dict[1, ])), "font"] = "20px arial"
visdata$nodes[which(grepl("ATP demand", names_dict[1, ])), "font"] = "20px arial"
#Emphasize main metabolites
visdata$nodes[which(grepl("^lactate$", names_dict[1, ])), "font"] = "20px arial"
visdata$nodes[which(grepl("^glucose$", names_dict[1, ])), "font"] = "20px arial"
visdata$nodes[which(grepl("ATP", names_dict[1, ])), "font"] = "20px arial"
visdata$nodes[which(grepl("ADP", names_dict[1, ])), "font"] = "20px arial"
}
#Necessary transformations for the table displaying purposes
ndata = names(data@edgeData)
edges_df = dplyr::mutate(visdata_ori$edges, name = paste(from, to, sep = "|"))
for (i in seq(1, length(data@edgeData@data))) {
hit = which(edges_df$name == ndata[i])
data@edgeData@data[[i]]$coefficient = edges_df[hit, 3]
}
#Rewrites the coefficients to edge's weight slot
for (i in seq(1, length(data@edgeData@data))) {
data@edgeData@data[[i]]$weight = data@edgeData@data[[i]]$coefficient
}
new_df = data.frame()
for (i in seq(1, length(data@edgeData@data))) {
df1 = as.data.frame(data@edgeData@data[[i]])
df1$reaction = ndata[i]
new_df = merge(new_df, df1, all = T)
}
selection = union(which(grepl("^R_", new_df$reaction)), which(grepl("\\|R_E", new_df$reaction)))
new_df = new_df[selection,]
new_df$metabolite = sapply(new_df$reaction, function(x)
strsplit(x, split = "\\|")[[1]][2])
new_df$reaction = sapply(new_df$reaction, function(x)
strsplit(x, split = "\\|")[[1]][1])
rotate = which(grepl("M_", new_df$reaction))
cache = new_df[rotate, "reaction"]
new_df[rotate, "reaction"] = new_df[rotate, "metabolite"]
new_df[rotate, "metabolite"] = cache
new_df$reaction = sapply(new_df$reaction, function(x)
names_dict[1, which(names_dict[2,] == x)[1]])
new_df$metabolite = sapply(new_df$metabolite, function(x)
names_dict[1, which(names_dict[2,] == x)[1]])
new_df = new_df[, c(3, 4, 2)]
names(new_df) = c("Reaction", "Metabolite", "Coefficient")
return(new_df)
}
check_flux <- function(model = "toycon") {
# this function gets the objective value when provided with a model name
# rPython package is used for the R Python connectivity, cobrapy is used for FBA calculations
working_dir = getwd()
path = paste("/data/", model, ".xml", sep = "")
if (.Platform$OS.type == "windows") {
path = gsub("\\\\", "/", path)
}
model_file_path = paste(working_dir, path, sep = "")
python.assign("model_file_path", model_file_path)
path = "/scripts/check_flux.py"
if (.Platform$OS.type == "windows") {
path = gsub("\\\\", "/", path)
}
#runs the script specified by the path variable
python.load(paste(working_dir, path, sep = ""))
#gets the python variable after the script execution
flux = python.get(var.name = "flux")
return(flux)
}
create_GPR_df <- function(model) {
model@gpr[which(model@gpr == "")] = "No associated genes"
tab = data.frame(Genes = model@gpr, Reaction = model@react_name)
return(tab)
}
get_model_stats <- function(model_name) {
working_dir = getwd()
toycon = readRDS(paste(working_dir, "/data/", model_name, ".rda", sep = "")) #formal class modelorg object
if (model_name == "toycon") {
compartments = c("c", "m")
} else{
compartments = toycon@mod_compart
}
num_compartments = length(compartments)
num_reactions = toycon@react_num
num_metabolites = toycon@met_num
num_genes = length(toycon@allGenes)
return(t(
data.frame(
# Name = model_name,
Reactions = num_reactions,
Metabolites = num_metabolites,
Genes = num_genes,
Compartments = num_compartments
)
))
}
get_reactionIDs_names <- function(model_name) {
working_dir = getwd()
path = paste("/data/", model_name, "_var.RData", sep = "")
if (.Platform$OS.type == "windows") {
path = gsub("\\\\", "/", path)
}
load(paste(working_dir, path, sep = "")) #formal class SBML object
choices_list = list()
names_choices = c()
for (i in seq_len(length(sbml_model@model@reactions))) {
choices_list[[i]] = sbml_model@model@reactions[[i]]@id
names_choices = append(names_choices, sbml_model@model@reactions[[i]]@name)
}
names(choices_list) = names_choices
return(choices_list)
}
# SERVER ------------------------------------------------------------------
shinyServer(function(input, output, session) {
working_dir = getwd()
path = "/data/toycon.xml"
if (.Platform$OS.type == "windows") {
path = gsub("\\\\", "/", path)
}
model_file_path = paste(working_dir, path, sep = "")
output$model_stats = DT::renderDataTable({
model1 = get_model_stats("toycon")
model2 = get_model_stats("ecoli")
data.frame(cbind(model1, model2))
}, selection = "single", rownames = T, colnames = c("iSIM", "Ecoli core"), options = list(dom = 't'))
# VISUALIZATION UPDATE/LAUNCH APP -----------------------------------------
observeEvent(input$update, {
showNotification(id = "graphInteravtive",ui = "The graph is interactive, play with it!",duration = 10,closeButton = TRUE,type = "default")
model_name = isolate(input$pick_model)
weighting = isolate(input$weighting)
if (model_name != "toycon") {
exclude = isolate(input$exclude)
} else{
exclude = F
}
if (weighting == TRUE) {
output$fluxes = DT::renderDataTable({
get_coefficients_DF(model_name = model_name)
}, selection = "single", options = list(pageLength = 10), rownames = FALSE)
} else{
output$fluxes = DT::renderDataTable({
# intentionally left empty - clears the populated table
})
}
working_dir = getwd()
path = paste("/data/", model_name, ".xml", sep = "")
if (.Platform$OS.type == "windows") {
path = gsub("\\\\", "/", path)
}
model_file_path = paste(working_dir, path, sep = "")
path = paste("/data/", model_name, "_var.RData", sep = "")
if (.Platform$OS.type == "windows") {
path = gsub("\\\\", "/", path)
}
load(paste(working_dir, path, sep = "")) #formal class SBML object
data = rsbml_graph(sbml_model)
toycon_graph = igraph.from.graphNEL(data)
visdata <- toVisNetworkData(toycon_graph)
names = rownames(visdata$nodes)
net = asNetwork(toycon_graph)
net %v% "type" = ifelse(grepl("R", names), "Reaction", "Metabolite")
edges_names = names
#Setting proper nodes names
for (i in seq(1, length(names))) {
if (model_name == "toycon") {
if (nchar(names[i]) < 6) {
names[i] = substr(names[i], 1, 4)
} else{
names[i] = substr(names[i], 1, 6)
}
}
if (any(names(sbml_model@model@species) == as.character(names[i]))) {
metabolite = sbml_model@model@species[[which(names(sbml_model@model@species) == as.character(names[i]))]]@name
edges_names[i] = metabolite
}
else{
if (any(names(sbml_model@model@reactions) == as.character(names[i]))) {
reaction_name = sbml_model@model@reactions[[which(names(sbml_model@model@reactions) == as.character(names[i]))]]@name
edges_names[i] = reaction_name
}
else{
edges_names[i] = "NoName"
}
}
}
#Make the names equal length (7 is the max length of matabolite name) for the displaying purposes. this way the sizes of the metabolite nodes are all equal
if (model_name == "toycon") {
edges_names = sapply(edges_names, function(x)
fill_blank(x, 7))
}
names_dict = rbind(edges_names, names) #Names and IDs dictionary
visdata$nodes$label = as.vector(edges_names)
output$graph = renderVisNetwork({
show_basic_network(model_name = model_name,
weighting = weighting,
exclude = exclude)
})
observe({
s = input$fluxes_rows_selected
df = get_coefficients_DF(model_name = model_name)
rxn_name = df[s, 1]
rxn_id = visdata$nodes$id[which(visdata$nodes$label == rxn_name)]
visNetworkProxy("graph") %>%
visSelectNodes(id = rxn_id)
})
updateNavbarPage(session = session,
inputId = "tabs",
selected = "visualize")
})
observeEvent(input$tabs_popover, ignoreInit = T, {
updateNavbarPage(session = session,
inputId = "tabs",
selected = "help")
})
# SHOW CHANGE MEDIA TAB ---------------------------------------------------
observeEvent(input$change_media,
ignoreInit = F,
{
removeTab(inputId = "tabs",
target = "change_media",
session = getDefaultReactiveDomain())
model_name = (input$pick_model)
if (model_name != "toycon") {
exclude = isolate(input$exclude)
} else{
exclude = F
}
objective = isolate(input$select_objective)
ori_objective = objective
output$fluxes_media = DT::renderDataTable({
# intentionally left empty - clears the populated table
})
output$fluxes_media1 = DT::renderDataTable({
# intentionally left empty - clears the populated table
})
output$text_flux_media = renderText({
# intentionally left empty - clears the field
})
output$text_objective_media = renderText({
# intentionally left empty - clears the field
})
#Prepare the list of reactions to constrain
insertTab(
inputId = "tabs",
target = "help",
tabPanel(
"Change media",
value = "change_media",
sidebarPanel(
conditionalPanel(
condition = "input.pick_model == 'toycon'",
h3("Experiment setup"),
br(),
br(),
fluidRow(class = "myRowText",
column(
8, HTML("<u><b>Use predefined media: </b></u>")
),
column(
1,
offset = 0,
popify(
actionLink("apply_media_popover", "", icon = icon("question-circle")),
title = "Grow the organism in different media conditions",
content = "Media changing can be easily simulated by manipulating the exchange reactions fluxes",
placement = "right",
trigger = "focus",
options = list(container = "body")
)
)),
fluidRow(class = "myRowButton", column(
6,
popify(
bsButton(
inputId = "media1",
block = T,
label = "Glucose free media"
),
title = "Apply glucose free media",
content = "Simulate organism growth in media containing lactate, oxygen, carbon dioxide, water, but no glucose (the import of this metabolite is inhibited by changing <b>the lower and upper bound of glucose exchange to 0</b>). Does the organism produce ATP in this condition?",
trigger = "hover",
placement = "right",
options = list(container = "body")
)
)),
fluidRow(class = "myRowButton", column(
6,
popify(
bsButton(
inputId = "media2",
block = T,
label = "Microaerophilic media"
),
title = "Apply microaerophilic media",
content = "Simulate organism growth in media containing lactate, glucose, carbon dioxide, water, but limited amount of oxygen (the import of this metabolite is inhibited by changing <b>the lower and upper bound of oxygen exchange to -10 and 10, respevtively</b>). Does the organism produce ATP in this condition?",
trigger = "hover",
placement = "right",
options = list(container = "body")
)
)),
fluidRow(class = "myRowButton", column(
6,
popify(
bsButton(
inputId = "media3",
block = T,
label = "Lactate rich media"
),
title = "Apply lactate rich media",
content = "Simulate organism growth in media containing oxygen, carbon dioxide, water and high concentration of lactate (the import of this metabolite is constrained by changing <b>the lower and upper bound of lactate exchange to -700 and 700, respectively</b>). Does the organism produce ATP in this condition?",
trigger = "hover",
placement = "right",
options = list(container = "body")
)
)),
uiOutput("line"),
div(style = "vertical-align:top; width: 80%;height: 30px", htmlOutput("text_own"))
),
conditionalPanel(
condition = "input.pick_model == 'ecoli'",
h3("Experiment setup"),
br(),
br(),
HTML(
"<font color='#808080'>To simulate growth conditions of your choice select any exchange reaction and change its flux limits below</font>"
),
br(),
br()
),
uiOutput("pick_rxn"),
fluidRow(
class = "myRowButton",
column(10, uiOutput("range")),
column(1, offset = 0, uiOutput("range_help"))
),
uiOutput("button_apply_media"),
hr(),
br(),
h3("Results"),
br(),
br(),
fluidRow(class = "myRowButton", column(
5, HTML("<u><b>Biological objective: </b></u>")
), column(
5, offset = 0, htmlOutput("text_objective_media")
)),
fluidRow(
class = "myRowButton",
column(5, HTML("<u><b>Objective value: </b></u>")),
column(1, offset = 0, htmlOutput("text_flux_media")),
column(
1,
offset = 1,
popify(
actionLink("flux_popover_media", "", icon = icon("question-circle")),
title = "Objective value",
content = "It represents flux through the reaction that is a biological objective of the model.",
placement = "right",
trigger = "focus",
options = list(container = "body")
)
)
),
br(),
DT::dataTableOutput('fluxes_media'),
DT::dataTableOutput('fluxes_media1')
),
mainPanel(visNetworkOutput("graph_media", height = "1200"), width = 8)
)
)
updateNavbarPage(session = session,
inputId = "tabs",
selected = "change_media")
output$graph_media = renderVisNetwork({
show_basic_network(model_name, exclude = exclude)
})
#render th text to display in the UI
output$text_media = renderText({
paste("<u><b>Use predefined media: ", "</b></u>")
})
})
# SHOW SIMULATE EXPR TAB --------------------------------------------------
observeEvent(input$simulate_expr,
ignoreInit = T,
{
removeTab(inputId = "tabs",
target = "simulate_expression_changes",
session = getDefaultReactiveDomain())
model_name = isolate(input$pick_model)
if (model_name != "toycon") {
exclude = isolate(input$exclude)
} else{
exclude = F
}
objective = isolate(input$select_objective)
ori_objective = objective
output$fluxes_expr = DT::renderDataTable({
# intentionally left empty - clears the populated table
})
output$fluxes_expr1 = DT::renderDataTable({
# intentionally left empty - clears the populated table
})
output$text_flux_expr = renderText({
# intentionally left empty - clears the field
})
output$text_objective_expr = renderText({
# intentionally left empty - clears the field
})
path = paste("/data/", model_name, ".xml", sep = "")
if (.Platform$OS.type == "windows") {
path = gsub("\\\\", "/", path)
}
model_file_path = paste(working_dir, path, sep = "")
path = paste("/data/", model_name, "_var.RData", sep = "")
if (.Platform$OS.type == "windows") {
path = gsub("\\\\", "/", path)
}
load(paste(working_dir, path, sep = "")) #formal class SBML object
toycon = readRDS(paste(working_dir, "/data/", model_name, ".rda", sep = "")) #formal class modelorg object
#Show the tab in the app
insertTab(
inputId = "tabs",
target = "help",
tabPanel(
"Simulate gene expression changes",
value = "simulate_expression_changes",
sidebarPanel(
h3("Experiment setup"),
br(),
br(),
fluidRow(class = "myRowText",
column(
7, HTML("<b>Change expression of a gene:</b>")
),
column(
1,
offset = 0,
popify(
actionLink("expression_popover", "", icon = icon("question-circle")),
title = "Adjusts the gene expression level",
content = "The expression level scale (0 - 1) corresponds to \"no expression\" and \"maximum overexpression\", respectively.",
placement = "right",
trigger = "focus",
options = list(container = "body")
)
)),
fluidRow(
class = "myRowButton",
column(7, uiOutput("pick_expr_gene")),
column(1, offset = 0, actionLink("show_gpr_expr", "", icon = icon("table")))
),
fluidRow(class = "myRowText", column(
7, HTML("<b>Select the gene expression level:</b>")
)),
uiOutput("expr"),
uiOutput("button_apply_expr"),
hr(),
br(),
h3("Results"),
br(),
br(),
fluidRow(class = "myRowButton", column(
5, HTML("<u><b>Biological objective: </b></u>")
), column(
5, offset = 0, htmlOutput("text_objective_expr")
)),
fluidRow(
class = "myRowButton",
column(5, HTML("<u><b>Objective value: </b></u>")),
column(1, htmlOutput("text_flux_expr")),
column(
1,
offset = 1,
popify(
actionLink("flux_popover_expr", "", icon = icon("question-circle")),
title = "Objective value",
content = "It represents flux through the reaction that is a biological objective of the model.",
placement = "right",
trigger = "focus",
options = list(container = "body")
)
)
),
DT::dataTableOutput('fluxes_expr'),
DT::dataTableOutput('fluxes_expr1'),
bsModal(
id = "modal_expr",
title = "Gene - reaction associations lookup table",
trigger = "show_gpr_expr",
size = "large",
DT::dataTableOutput("gpr_expr")
),
width = 4
),
mainPanel(visNetworkOutput("graph_expr", height = "1200"), width = 8)
)
)
updateNavbarPage(session = session,
inputId = "tabs",
selected = "simulate_expression_changes")
output$graph_expr = renderVisNetwork({
show_basic_network(model_name, exclude = exclude)
})
output$fluxes_expr = DT::renderDataTable({
})
model_name = isolate(input$pick_model)
working_dir = getwd()
path = paste("/data/", model_name, ".xml", sep = "")
if (.Platform$OS.type == "windows") {
path = gsub("\\\\", "/", path)
}
model_file_path = paste(working_dir, path, sep = "")
path = paste("/data/", model_name, "_var.RData", sep = "")
if (.Platform$OS.type == "windows") {
path = gsub("\\\\", "/", path)
}
load(paste(working_dir, path, sep = "")) #formal class SBML object
toycon = readRDS(paste(working_dir, "/data/", model_name, ".rda", sep = "")) #formal class modelorg object
data = rsbml_graph(sbml_model)
toycon_graph = igraph.from.graphNEL(data)
visdata <- toVisNetworkData(toycon_graph)
visdata_ori = visdata
#Adding duplicate metabolites/reactions
if (model_name == "toycon") {
visdata = add_dups_new_layout(visdata)
}
visdata$nodes$group = rep("Metabolite", length(visdata$nodes$id))
visdata$nodes$group[which(grepl("R", visdata$nodes$id))] = "Reaction"
visdata$nodes$group[which(grepl("m\\d*$", visdata$nodes$id))] = "Metabolite mitochondria"
visdata$nodes$group[which(grepl(
perl = T,
pattern = "^M\\S*e$",
x = visdata$nodes$id
))] = "Metabolite external"
visdata$nodes$group[which(visdata$nodes$id == ori_objective)] = "Biological objective"
visdata$edges$width = 2
visdata$edges$length = 150
net = asNetwork(toycon_graph)
#Setting colors according to node class
color_reaction = "lightblue"
color_metabolite = "lightsalmon"
color_metabolite_mitochondria = "red"
color_metabolite_external = "indianred"
color_reaction_objective = "lightgreen"
names = rownames(visdata$nodes)
net %v% "type" = ifelse(grepl("R", names), "Reaction", "Metabolite")
edges_names = names
#Setting proper nodes names
for (i in seq(1, length(names))) {
if (model_name == "toycon") {
if (nchar(names[i]) < 6) {
names[i] = substr(names[i], 1, 4)
} else{
names[i] = substr(names[i], 1, 6)
}
}
if (any(names(sbml_model@model@species) == as.character(names[i]))) {
metabolite = sbml_model@model@species[[which(names(sbml_model@model@species) == as.character(names[i]))]]@name
edges_names[i] = metabolite
}
else{
if (any(names(sbml_model@model@reactions) == as.character(names[i]))) {
reaction_name = sbml_model@model@reactions[[which(names(sbml_model@model@reactions) == as.character(names[i]))]]@name
edges_names[i] = reaction_name
}
else{
edges_names[i] = "NoName"
}
}
}
#Make the names equal length (7 is the max length of matabolite name) for the displaying purposes. this way the sizes of the metabolite nodes are all equal
if (model_name == "toycon") {
edges_names = sapply(edges_names, function(x)
fill_blank(x, 7))
}
names_dict = rbind(edges_names, names) #Names and IDs dictionary
choices_list_expr = as.list(toycon@allGenes)
names(choices_list_expr) = toycon@allGenes
output$pick_expr_gene = renderUI(
selectInput(
inputId = "pick_expr_gene",
label = NULL,
choices = choices_list_expr,
width = "200px"
)
)
#render the apply button for the UI
output$button_apply_expr = renderUI({
bsButton(inputId = "apply_expr",
label = "Run FBA")
})
output$gpr_expr = DT::renderDataTable({
create_GPR_df(toycon)
}, rownames = FALSE)
#Render the selection slider for the expression level adjustment
output$expr = renderUI(
sliderInput(
inputId = "expr",
min = 0,
max = 1,