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server.R
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server.R
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# Copyright (C) 2016 Abhik Seal <[email protected]>
# This program CC BY-NC-SA 3.0 license.
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
#################################################################################
## Load the libraries
library(netpredictor)
library(igraph)
library(gdata)
library(shiny)
require(shinysky)
library(data.table)
library(networkD3)
library(visNetwork)
library(DBI)
library(rCharts)
library(RSQLite)
library(clusterProfiler)
library(ReactomePA)
library(biomaRt)
library(DT)
source('global.R')
set.seed(12345)
options(shiny.trace = TRUE)
options(shiny.reactlog=TRUE)
options(shiny.fullstacktrace = TRUE)
# con1 <- dbConnect(SQLite(), "ppi.sqlite")
# Q1 <- sprintf("SELECT distinct Approved_Symbol FROM hgnc_data")
# hgnc <- dbGetQuery(con1,Q1)
# rownames(hgnc) <- hgnc[,1]
# dbDisconnect(con1)
#
mod2 <- readRDS("data/genes.rds")
## trim forward and trainling spaces
trim <- function (x) gsub("^\\s+|\\s+$", "", x)
#source('netpredictUI.R')
#################################################################################
mart = useMart("ensembl")
human = useMart(biomart="ensembl", dataset="hsapiens_gene_ensembl",host="www.ensembl.org")
shinyServer( function(input, output,session) {
#
# login <- reactiveValues(login = FALSE, user = NULL, role = NULL, email = NULL)
#
# # initially display the login modal
# observe({
# composeLoginModal()
# })
#
# observeEvent(input$logout_ok, {
# shiny::removeModal()
#
# # clear the values when logout is confirmed
# login$login <- FALSE
# login$user <- NULL
# login$role <- NULL
# login$email <- NULL
#
# composeLoginModal(
# div(
# id = "modal-logout-message"
# , style = "margin-bottom: 10px"
# , span(class = "text-muted", "Successfully Logged Out")
# ) #/ modal-logout-message
# ) #/ composeLoginModal
# })
#
# # once a login is attempted, do some checks
# observeEvent(input$login_button, {
#
# # remove the modal while we check
# shiny::removeModal()
#
# # query the database for that user will return NAs if not populated
# stored <- sendUserGetQuery(input$login_user)
#
# # if any are NA then the record doesn't exist or the record is corrupted
# user_invalid <- stored %>% sapply(is.na) %>% any
#
# # try to login, will automatically handle NULL-y objects
# login$login <- validateLogin(stored$password, input$login_passwd)
#
# # if the login is not successful, toss up another login modal,
# # this time with a message
# if (isTRUE(user_invalid) | login$login == FALSE) {
# composeLoginModal(
# div(
# id = "modal-login-message"
# , style = "margin-bottom: 10px"
# , span(style = "color: red; font-weight:bold", "Incorrect Login/Password")
# ) #/ modal-login-message
# ) #/ composeLoginModal
# } else {
# # if the login is successful, populate the known values
# login$user <- stored$user
# login$role <- stored$role
# login$email <- stored$email
#
# rm(stored)
# } #/ fi
# }) #/ login_button Observer
#
# # close database conncention on exit
# session$onSessionEnded(function() {
# dbDisconnect(db)
# })
#
# observeEvent(input$logout, {
# helpText("Are you sure you want to Logout? Any unsaved work will be lost!") %>%
# div(style = "margin-bottom: 15px", .) %>%
# showConfirmModal("logout", .)
# })
#
# observeEvent(input$logout_cancel, {
# shiny::removeModal()
# })
#
# output$welcome <- renderUI({
# # wait to render until the login is truthy
# req(login$login)
#
# # a crude login card
# div( netpredict(login$user)
# # div(style = "width: 50px; margin-top: 15px"
# # # , wellPanel(
# # # span(class = "h5", login$user)
# # # , p("(", login$role, ")")
# # # , tags$small(class = "text-muted", tolower(login$email))
# # # , actionLink("logout", "Logout")
# # # )
# # )
# )
# })
#
observe({
if(input$gofind){
updateTabsetPanel(session, "bar", selected = "Start Prediction")
}
})
observe({
if(input$start){
updateTabsetPanel(session, "datatabs", selected = "Prediction Results")
}
})
updateSelectizeInput(session = session,inputId = "ppi", label= "HGNC Symbols", choices = mod2,server = TRUE)
## Get the properties
prop <- reactive({
if (input$netproperty <= 0){
return(NULL)
}
result <- isolate({
input$netproperty
tryCatch ({
if(input$data_input_type=="example"){
if(input$datasets == "Enzyme"){
load("data/Enzyme.rda")
data <- t(adjm)
props <- getProp(data)
props
} else if (input$datasets == "GPCR"){
load("data/GPCR.rda")
data <- t(adjm)
props <- getProp(data)
props
} else if (input$datasets == "Nuclear Receptor"){
load("data/Nuclear Receptor.rda")
data <- t(adjm)
props <- getProp(data)
props
} else if (input$datasets == "Ion Channel") {
load("data/Ion Channel.rda")
data <- t(adjm)
props <- getProp(data)
props
}
} else if (input$data_input_type=="custom"){
if (is.null(input$dt_file))
return(NULL)
inFile <- input$dt_file
dataDT <- as.matrix(read.csv(inFile$datapath, sep=",", header=TRUE, fill=TRUE,row.names = 1,quote = '"'))
props <- getProp(dataDT)
print (props)
props
}},
error = function(err) {
print(paste("Select Example or custom data set"))
})
})
result
})
## Get the names of PPI proteins with HGNC symbols.
# output$PPI <- renderUI({
#
# selectInput("ppi", label= "HGNC Symbols", choices = mod2, selected = "", multiple = T,width='85%')
# })
## Get the modules of the bipartite network
totModules <- reactive({
if (input$mods <= 0){
return(NULL)
}
input$mods
results <- isolate({
input$mods
if(input$data_input_type=="example"){
dset <- input$datasets
exdata <- paste(dset,".rda",sep="")
load(exdata)
dm <-as.matrix(adjm)
mod <- getMod(dm)
mod
} else if(input$data_input_type=="custom") {
if (is.null(input$dt_file))
return(NULL)
DTFile <- input$dt_file
dataDT <- as.matrix(read.csv(DTFile$datapath, sep=",", header=TRUE, fill=TRUE,row.names = 1,quote = '"'))
mod <- getMod(dataDT)
mod
}
})
results
})
#####################################################################################
## Calculation of counts of Proteins each Drug is having
#####################################################################################
topDrugs <- reactive({
if (input$netproperty <= 0){
return(NULL)
}
# Use isolate() to avoid dependency on input$obs
results <- isolate({
input$netproperty
if(input$data_input_type=="example"){
dset <- input$datasets
exdata <- paste(dset,".rda",sep="")
load(exdata)
dt <- t(adjm)
topRows <- data.frame(rowSums(dt))
colnames(topRows)[1] <- "count"
topRows$Drugs <- row.names(topRows)
res <- topRows[order(-topRows$count),][1:20,]
rownames(res)<- NULL
plt <- rPlot(x = list(var= "Drugs",sort = "count"),color= list(const='red'), y="count",data=res,type="bar")
plt$addParams(width = 600, height = 300,title = "Top 20 Drugs interaction counts")
plt
} else if(input$data_input_type=="custom") {
if (is.null(input$dt_file))
return(NULL)
DTFile <- input$dt_file
dataDT <- as.matrix(read.csv(DTFile$datapath, sep=",", header=TRUE, fill=TRUE,row.names = 1,quote = '"'))
topRows <- data.frame(colSums(dataDT))
colnames(topRows)[1] <- "count"
topRows$Drugs <- row.names(topRows)
res <- topRows[order(-topRows$count),][1:15,]
rownames(res)<- NULL
plt1 <- rPlot(x = list(var= "Drugs",sort = "count"),color= list(const='red'), y="count",data=res,type="bar")
plt1$addParams(width = 600, height = 300,title = "Top 20 Drugs interaction counts")
plt1
}
})
results
})
#####################################################################################
## Calculation of counts of Drugs each Protein is having
#####################################################################################
topProteins <- reactive({
if (input$netproperty <= 0){
return(NULL)
}
# Use isolate() to avoid dependency on input$obs
results <- isolate({
input$netproperty
if(input$data_input_type=="example"){
dset <- input$datasets
exdata <- paste(dset,".rda",sep="")
load(exdata)
dt <- t(adjm)
topRows <- data.frame(colSums(dt))
colnames(topRows)[1] <- "count"
topRows$Proteins <- row.names(topRows)
res <- topRows[order(-topRows$count),][1:20,]
rownames(res)<- NULL
plt2 <- rPlot(x = list(var= "Proteins",sort = "count"), y="count",data=res,type="bar")
plt2$addParams(width = 600, height = 300,title = "Top 20 proteins interaction counts")
plt2
} else if(input$data_input_type=="custom") {
if (is.null(input$dt_file))
return(NULL)
DTFile <- input$dt_file
dataDT <- as.matrix(read.csv(DTFile$datapath, sep=",", header=TRUE, fill=TRUE,row.names = 1,quote = '"'))
topRows <- data.frame(rowSums(dataDT))
colnames(topRows)[1] <- "count"
topRows$Proteins <- row.names(topRows)
res <- topRows[order(-topRows$count),][1:20,]
rownames(res)<- NULL
plt2 <- rPlot(x = list(var= "Proteins",sort = "count"),y="count",data=res,type="bar")
plt2$addParams(width = 600, height = 300,title = "Top 20 proteins interaction counts")
plt2
}
})
results
})
#####################################################################################
## Calculation of Betweenness of Drugs
#####################################################################################
betweennessDrugs <- reactive({
if (input$netproperty <= 0){
return(NULL)
}
# Use isolate() to avoid dependency on input$obs
results <- isolate({
input$netproperty
if(input$data_input_type=="example"){
dset <- input$datasets
exdata <- paste(dset,".rda",sep="")
load(exdata)
data <- t(adjm)
topRows <- data.frame(rowSums(data))
colnames(topRows)[1] <- "count"
net2 <- graph_from_incidence_matrix(data)
net2.bp <- bipartite.projection(net2)
btw1 <- betweenness(net2.bp[[1]], directed=F, weights=NA)
btwDF <- data.frame(btw1)
btwDrugs <- cbind(btwDF,topRows,rownames(btwDF))
colnames(btwDrugs)[1] <- "Betweenness"
colnames(btwDrugs)[2] <- "Degree"
colnames(btwDrugs)[3] <- "Drugs"
rownames(btwDrugs) <- NULL
#res <- btwDrugs[order(-btwDrugs$Betweenness),][1:20,]
btwDrugs
} else if(input$data_input_type=="custom") {
if (is.null(input$dt_file))
return(NULL)
DTFile <- input$dt_file
dataDT <- as.matrix(read.csv(DTFile$datapath, sep=",", header=TRUE, fill=TRUE,row.names = 1,quote = '"'))
topRows <- data.frame(rowSums(dataDT))
colnames(topRows)[1] <- "count"
net2 <- graph_from_incidence_matrix(dataDT)
net2.bp <- bipartite.projection(net2)
btw1 <- betweenness(net2.bp[[1]], directed=F, weights=NA)
btwDF <- data.frame(btw1)
btwDrugs <- cbind(btwDF,topRows,rownames(btwDF))
colnames(btwDrugs)[1] <- "Betweenness"
colnames(btwDrugs)[2] <- "Degree"
colnames(btwDrugs)[3] <- "Drugs"
rownames(btwDrugs) <- NULL
btwDrugs
}
})
results
})
#####################################################################################
## Calculation of Betweenness of Proteins
#####################################################################################
betweennessProteins <- reactive({
if (input$netproperty <= 0){
return(NULL)
}
# Use isolate() to avoid dependency on input$obs
results <- isolate({
input$netproperty
if(input$data_input_type=="example"){
#dset <- "Enzyme"
dset <- input$datasets
exdata <- paste(dset,".rda",sep="")
load(exdata)
data <- t(adjm)
topRows <- data.frame(colSums(data))
colnames(topRows)[1] <- "count"
net2 <- graph_from_incidence_matrix(data)
net2.bp <- bipartite.projection(net2)
btw1 <- betweenness(net2.bp[[2]], directed=F, weights=NA)
btwDF <- data.frame(btw1)
btwProteins <- cbind(btwDF,topRows,rownames(btwDF))
colnames(btwProteins)[1] <- "Betweenness"
colnames(btwProteins)[2] <- "degree"
colnames(btwProteins)[3] <- "Proteins"
rownames(btwProteins) <- NULL
print(head(btwProteins))
#res <- btwProteins[order(-btwProteins$Betweenness),][1:20,]
btwProteins
#plt4 <- rPlot(x = list(var= "Proteins",sort = "Betweenness"), y="Betweenness",data=res,type="bar")
#plt4$addParams(width = 600, height = 300,title = "Top 20 High Betwee nness Proteins")
#plt4
} else if(input$data_input_type=="custom") {
if (is.null(input$dt_file))
return(NULL)
DTFile <- input$dt_file
dataDT <- as.matrix(read.csv(DTFile$datapath, sep=",", header=TRUE, fill=TRUE,row.names = 1,quote = '"'))
topRows <- data.frame(colSums(dataDT))
colnames(topRows)[1] <- "count"
net2 <- graph_from_incidence_matrix(dataDT)
net2.bp <- bipartite.projection(net2)
btwDF <- data.frame(betweenness(net2.bp[[1]], directed=F, weights=NA))
btwProteins <- cbind(btwDF,topRows,rownames(btwDF))
colnames(btwProteins)[1] <- "Betweenness"
colnames(btwProteins)[2] <- "degree"
colnames(btwProteins)[3] <- "Proteins"
rownames(btwProteins) <- NULL
#res <- btwProteins[order(-btwProteins$Betweenness),][1:20,]
#plt4 <- rPlot(x = list(var= "Proteins",sort = "Betweenness"), y="Betweenness",data=res,type="bar")
#plt4$addParams(width = 600, height = 300,title = "Top 20 High Betweenness Proteins")
#plt4
DT::datatable(btwProteins)
}
})
results
})
#####################################################################################
## Output of 4 types of histograms in Network properties tab
#####################################################################################
## Output properties table
output$prop_table <- renderTable({
prop()
})
## Output count distribution of drugs
# output$countDrugs <- renderChart2({
# topDrugs()
# })
#
# ## Output count distribution of proteins
# output$countProteins <- renderChart2({
# topProteins()
# })
## Output count distribution of proteins
# output$btwProteins <- renderChart2({
# betweennessProteins()
# })
output$btwProteinsdt <- renderDataTable({
betweennessProteins()
},
extensions = 'Buttons',
filter = 'top',
class = 'cell-border stripe',
options = list(
autoWidth = FALSE,
autoWidth = TRUE,
"dom" = 'T<"clear">lBfrtip',
buttons = list('csv', 'excel')
))
# output$btwDrugs <- renderChart2({
# betweennessDrugs()
# })
output$btwDrugsdt <- renderDataTable({
betweennessDrugs()
},
extensions = 'Buttons',
filter = 'top',
class = 'cell-border stripe',
options = list(
autoWidth = FALSE,
autoWidth = TRUE,
"dom" = 'T<"clear">lBfrtip',
buttons = list('csv', 'excel')
))
#####################################################################################
## Calculation of modules using lpbrim
#####################################################################################
## Get the count of modules
tot_modules <- reactive({
mod <- totModules()
len <- length(mod)
modules <- c()
for (i in 1:len){
modules[i] <- paste("module",toString(i),sep="")
}
return(modules)
})
## Get the list of modules
full_modules <- reactive({
mod <- totModules()
return(mod)
})
#####################################################################################
## Update dynamically the list of modules
#####################################################################################
output$modules = renderUI({
selectInput('module', 'Modules', tot_modules())
})
#####################################################################################
## Output the data table of modules
#####################################################################################
data_table <- reactive({
# If missing input, return to avoid error later in function
if(is.null(input$module))
return()
# Get the module
dat <- input$module
if (dat %in% tot_modules())
modIndx <- match(dat, tot_modules())
modMat <- full_modules()
mod <- modMat[[modIndx]]
gMod <- graph.incidence(mod)
el <- get.edgelist(gMod)
colnames(el) <- c("column1","column2")
return (el)
})
## Out the module list on the data table
output$data_table <- renderDataTable( {
data_table()
},options = list(pageLength = 10,
autoWidth = TRUE,
columnDefs = list(list(width = '50px', targets = "_all")))
)
#####################################################################################
## Generate the module network using visNetwork
#####################################################################################
modnetwork <- reactive({
if (input$shownet <= 0){
return(NULL)
}
netResult <- isolate({
input$shownet
if(is.null(input$module))
return()
# Get the module
dat <- input$module
if (dat %in% tot_modules())
modIndx <- match(dat, tot_modules())
modMat <- full_modules()
mod <- modMat[[modIndx]]
gMod <- graph.incidence(mod)
el <- data.frame(get.edgelist(gMod))
colnames(el) <- c("column1","column2")
nodes <- unique(c(as.character(el$column1), as.character(el$column2)))
group1 <- length(unique(el$column1))
group2 <- length(unique(el$column2))
id=seq(0,length(nodes)-1,1)
label=nodes
print (dim(el))
nodeData <- data.frame(id ,label,group=c(rep("Proteins",group1),rep("Drugs",group2)),stringsAsFactors=FALSE)
colnames(el)[1] <- "from"
colnames(el)[2] <- "to"
edgeList <- el[, c("from","to")]
edgeList$from <- with(nodeData, id[match(edgeList$from, nodes)])
edgeList$to <- with(nodeData, id[match(edgeList$to,nodes)])
#edgeList$dashes <- ifelse(mynet$type == "True Interactions",FALSE,TRUE)
netresult <- visNetwork(nodeData, edgeList,width = "70%") %>% visNodes(size = 25) %>% visInteraction(navigationButtons = TRUE,tooltipDelay = 0) %>% visLegend() %>% visNodes(scaling=list(min=20),font=list(size=24)) %>%
visOptions(selectedBy = "group", nodesIdSelection = TRUE,highlightNearest = TRUE)%>% visPhysics(solver = "barnesHut",barnesHut = list(gravitationalConstant = -1000,avoidOverlap=0.5,springLength=250)) %>% visExport()
})
netResult
})
#####################################################################################
## Send it to output
#####################################################################################
output$moduleplot <- renderVisNetwork({
modnetwork()
})
# observe({
# if (input$start == 0)
# return()
# showshinyalert(session, "shinyalert1", paste("Computation in progress", "success"),
# styleclass = "success")
# })
#####################################################################################
## Calculate prediction models using different algorithms
#####################################################################################
Result <- reactive({
if (input$start <= 0){
return(NULL)
}
# Take a dependency on input$start
input$start
if(input$data_input_type=="example"){
# Use isolate() to avoid dependency on input$obs
results <- isolate({
dset <- input$datasets
exdata <- paste(dset,".rda",sep="")
if (input$algorithm_typeI == "heat"){
heat_alpha = input$heatAlpha
heat_lamda = input$heatLambda
results <- getheat(exdata,heat_alpha,heat_lamda)
results
} else if (input$algorithm_typeI == "nbi"){
nbi_alpha = input$nbiAlpha
nbi_lamda = input$nbiLambda
results <- getnbi(exdata,nbi_alpha,nbi_lamda)
results
} else if (input$algorithm_typeI == "rwr"){
restart = input$rwr_restart
results <- getrwr(exdata,restart)
results
} else if (input$algorithm_typeI == "netcombo"){
restart = input$nc_restart
nc_alpha = input$ncAlpha
nc_lamda = input$ncLambda
results <- getcombo(exdata,nc_alpha,nc_lamda,restart)
results
}
})
results
} else if (input$data_input_type=="custom"){
if (is.null(input$dt_file))
return(NULL)
if (is.null(input$drug_file))
return(NULL)
if (is.null(input$target_file))
return(NULL)
results <- isolate({
DTFile <- input$dt_file
dataDT <- as.matrix(read.csv(DTFile$datapath, sep=",", header=TRUE, fill=TRUE,row.names = 1,quote = '"'))
DFile <- input$drug_file
dataD <- as.matrix(read.csv(DFile$datapath, sep=",", header=TRUE, fill=TRUE,row.names = 1,quote = '"'))
TFile <- input$target_file
dataT <- as.matrix(read.csv(TFile$datapath, sep=",", header=TRUE, fill=TRUE,row.names = 1,quote = '"'))
if (input$algorithm_typeII == "heat"){
heat_alpha = input$cheatAlpha
heat_lamda = input$cheatLambda
results <- getCustomHeat(dataDT,heat_alpha,heat_lamda)
results
} else if (input$algorithm_typeII == "nbi"){
nbi_alpha = input$cnbiAlpha
nbi_lamda = input$cnbiLambda
results <- getCustomNBI(dataDT,dataD,dataT,nbi_alpha,nbi_lamda)
results
} else if (input$algorithm_typeII == "rwr"){
restart = input$crwr_restart
results <- getCustomRWR(dataDT,dataD,dataT,restart)
results
} else if (input$algorithm_typeII == "netcombo"){
restart = input$cnc_restart
nc_alpha = input$cncAlpha
nc_lamda = input$cncLambda
results <- getCustomCombo(dataDT,dataD,dataT,nc_alpha,nc_lamda,restart)
results
}
})
results
}
})
## Get the output Result to data table output
output$Result <- renderDataTable(Result(),options = list(pageLength = 10))
output$downloadResult <- downloadHandler(
filename = function() { paste("Results.txt") },
content = function(file) {
write.table(Result(), file, row.names=FALSE, quote=FALSE, sep="\t")
})
# output$networkplot <- renderForceNetwork({
#
# if (input$start <= 0){
# return(NULL)
# }
# input$start
# netResult <- isolate({
# mynet <- Result()
# nodes <- unique(c(mynet$Proteins, mynet$Drugs))
# group1 <- length(unique(mynet$Proteins))
# group2 <- length(unique(mynet$Drugs))
# id = nodes
# name = nodes
# #nodeData <- data.frame(name,stringsAsFactors=FALSE)
# nodeData <- data.frame(name,group=c(rep(10,group1),rep(25,group2)),stringsAsFactors=FALSE)
# colnames(mynet)[1] <- "source"
# colnames(mynet)[2] <- "target"
# colnames(mynet)[3] <- "value"
# edgeList <- mynet[, c("source","target","value")]
# #edgeData <- edgeList
#
# nodesID <- data.frame(nodes,id=seq(0,length(nodes)-1,1))
# edgeList$source <- with(nodesID, id[match(edgeList$source, nodes)])
# edgeList$target <- with(nodesID, id[match(edgeList$target,nodes)])
# nodeData$name <- factor(nodeData$name)
#
#
# forceNetwork(Links = edgeList, Nodes = nodeData, Source = "source",
# Target = "target", NodeID = "name",zoom = TRUE,Value="value",
# Group = "group",fontFamily="verdana",linkColour = "#afafaf",opacity = 0.8,legend=T)
# #cyNetwork <- createCytoscapeJsNetwork(nodeData, edgeData)
# #rcytoscapejs(nodeEntries=cyNetwork$nodes, edgeEntries=cyNetwork$edges)
# })
# netResult
# })
#
#####################################################################################
## Generate network at networkplot tab
#####################################################################################
output$networkplot <- renderVisNetwork({
if (input$start <= 0){
return(NULL)
}
input$start
netResult <- isolate({
mynet <- Result()
nodes <- unique(c(as.character(mynet$Proteins), as.character(mynet$Drugs)))
group1 <- length(unique(mynet$Proteins))
group2 <- length(unique(mynet$Drugs))
id=seq(0,length(nodes)-1,1)
label=nodes
nodeData <- data.frame(id ,label,group=c(rep("Proteins",group1),rep("Drugs",group2)),stringsAsFactors=FALSE)
colnames(mynet)[1] <- "from"
colnames(mynet)[2] <- "to"
edgeList <- mynet[, c("from","to")]
edgeList$from <- with(nodeData, id[match(edgeList$from, nodes)])
edgeList$to <- with(nodeData, id[match(edgeList$to,nodes)])
edgeList$dashes <- ifelse(mynet$type == "True Interactions",FALSE,TRUE)
netresult <- visNetwork(nodeData, edgeList,height="725px",width = "100%",maxVelocity = 10,minVelocity = 1.0) %>% visInteraction(navigationButtons = TRUE,tooltipDelay = 0) %>% visNodes(size = 25,scaling=list(min=20),borderWidth = 2,physics=TRUE ,font=list(size=18),color = list(border = "black",highlight = "yellow")) %>% visEdges(smooth=FALSE) %>% visLegend(width=0.05) %>%
visOptions(selectedBy = "group",highlightNearest = list(enabled = TRUE ,algorithm="hierarchical",hover=TRUE) ,nodesIdSelection = TRUE) %>% visPhysics(solver = "forceAtlas2Based",forceAtlas2Based = list(gravitationalConstant = -100,avoidOverlap=0.8,springLength=50,springConstant = 0.002),stabilization = list(iterations = 200, enabled = TRUE)) %>%
visLayout(improvedLayout = TRUE) %>% visExport()
})
netResult
})
#####################################################################################
## Download options for Network
#####################################################################################
output$graphResult <- downloadHandler(
filename = function() { paste("graph.gml") },
content = function(file) {
netResult <- Result()
g<-graph.data.frame(netResult[,1:2],directed=FALSE)
## Set the edge values
g <- set.edge.attribute(g, "weight", value=netResult[,3])
saveGML(g,file ,"netresult")
})
#####################################################################################
## Function Advanced Analysis for statistical metrics
#####################################################################################
advancedResult <- reactive({
input$submit
if(input$data_input_type=="example"){
# Use isolate() to avoid dependency on input$obs
adresults <- isolate({
dset <- input$datasets
exdata <- paste(dset,".rda",sep="")
#print(exdata)
load(exdata)
rl = input$relinks
nT = input$freqT
if (input$predMetrics == "nbi"){
nbi_alpha = input$pdnbiAlpha
nbi_lamda = input$pdnbiLambda
results <- data.frame(net.perf(adjm,protSim,compSim,alpha=nbi_alpha,lamda=nbi_lamda,relinks =rl,numT=nT,Calgo="nbi"))
results$num_links_remove = rl
results$Freq_Association = nT
results$method = "NBI"
results
} else if (input$predMetrics == "rwr"){
rt = input$pdrwrRestart
results <- data.frame(net.perf(adjm,protSim,compSim,restart=rt, relinks =rl,numT=nT,Calgo="rwr"))
results$num_links_remove = rl
results$Freq_Association = nT
results$method = "RWR"
print (head(results))
results
} else if (input$predMetrics == "nc"){
nc_rt = input$pdcnc_restart
nc_alpha = input$pdcncAlpha
nc_lamda = input$pdcncLambda
results <- data.frame(net.perf(adjm,protSim,compSim,restart=nc_rt,alpha=nc_alpha,lamda=nc_lamda,relinks =rl,numT=nT,Calgo="netcombo"))
results$num_links_remove = rl
results$Freq_Association = nT
results$method = "NetCombo"
results
}
})
} else if (input$data_input_type=="custom"){
if (is.null(input$dt_file))
return(NULL)
if (is.null(input$drug_file))
return(NULL)
if (is.null(input$target_file))
return(NULL)
adresults <- isolate({
DTFile <- input$dt_file
dataDT <- as.matrix(read.csv(DTFile$datapath, sep=",", header=TRUE, fill=TRUE,row.names = 1,quote = '"'))
DFile <- input$drug_file
dataD <- as.matrix(read.csv(DFile$datapath, sep=",", header=TRUE, fill=TRUE,row.names = 1,quote = '"'))
TFile <- input$target_file
dataT <- as.matrix(read.csv(TFile$datapath, sep=",", header=TRUE, fill=TRUE,row.names = 1,quote = '"'))
rl = input$relinks
nT = input$freqT
if (input$predMetrics == "nbi"){
nbi_alpha = input$pdnbiAlpha
nbi_lamda = input$pdnbiLambda
results <- data.frame(net.perf(dataDT,dataT,dataD,alpha=nbi_alpha,lamda=nbi_lamda,relinks =rl,numT=nT,Calgo="nbi"))
results$num_links_remove = rl
results$Freq_Association = nT
results$method = "NBI"
results
} else if (input$predMetrics == "rwr"){
rt = input$pdrwrRestart
results <- data.frame(net.perf(dataDT,dataT,dataD,restart=rt, relinks =rl,numT=nT,Calgo="rwr"))
results$num_links_remove = rl
results$Freq_Association = nT
results$method = "RWR"
print(head(results))
results
} else if (input$predMetrics == "nc"){
nc_rt = input$pdcnc_restart
nc_alpha = input$pdcncAlpha
nc_lamda = input$pdcncLambda
results <- data.frame(net.perf(dataDT,dataT,dataD,restart=nc_rt,alpha=nc_alpha,lamda=nc_lamda,relinks =rl,numT=nT,Calgo="netcombo"))
results$num_links_remove = rl
results$Freq_Association = nT
results$method = "NetCombo"
results
}
})
}
})
myresult <- reactiveValues()
myresult$df <- data.frame()
observe({
if(input$submit>0){
newdata <- isolate(advancedResult())
isolate(myresult$df <- rbind(myresult$df,newdata))
}
})
#####################################################################################
## Function Significance analysis tab
#####################################################################################
## This function for the significance results tab
sigResult <- reactive({
if (input$sigSubmit <= 0){
return(NULL)
}
input$sigSubmit
if(input$data_input_type=="example"){
adresults <- isolate({
permutation <- input$permute
significance <- input$sig
dset <- input$datasets
exdata <- paste(dset,".rda",sep="")
#print(exdata)
load(exdata)
if (input$sigMetrics == "signbi"){
nbi_alpha = input$sgnbiAlpha
nbi_lamda = input$sgnbiLambda
sigResults <- permTest(adjm,compSim,protSim,alpha=nbi_alpha, lamda=nbi_lamda, permute=permutation, sig=significance,calgo="nbi")
sigResults
} else if (input$sigMetrics == "sigrwr"){
rt = input$sgrwrRestart
sigResults <- permTest(adjm,compSim,protSim,restart=rt, perm=permutation, sig=significance, calgo="rwr")
sigresults
}
})
} else if(input$data_input_type=="custom"){
if (is.null(input$dt_file))
return(NULL)
if (is.null(input$drug_file))
return(NULL)
if (is.null(input$target_file))
return(NULL)
adresults <- isolate({
DTFile <- input$dt_file
dataDT <- as.matrix(read.csv(DTFile$datapath, sep=",", header=TRUE, fill=TRUE,row.names = 1,quote = '"'))
DFile <- input$drug_file
dataD <- as.matrix(read.csv(DFile$datapath, sep=",", header=TRUE, fill=TRUE,row.names = 1,quote = '"'))
TFile <- input$target_file
dataT <- as.matrix(read.csv(TFile$datapath, sep=",", header=TRUE, fill=TRUE,row.names = 1,quote = '"'))