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ui.R
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# Copyright (C) 2016 Abhik Seal <abhik1368@gmail.com>
# 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(shinyBS)
library(shinythemes)
library(shiny)
library(shinysky)
library(shinyjs)
library(rCharts)
source("help.R")
library(visNetwork)
library(shinyLP)
library(V8)
library(biomaRt)
library(bsplus)
library(DT)
options(shiny.trace = TRUE)
options(shiny.reactlog = TRUE)
options(shiny.fullstacktrace = TRUE)
library(shinyWidgets)
#library(shinyGridster)
#################################################################################
inputTextarea <- function(inputId, value="", nrows, ncols) {
tagList(
singleton(tags$head(tags$script(src = "textarea.js"))),
tags$textarea(id = inputId,
class = "inputtextarea",
rows = nrows,
cols = ncols,
as.character(value))
)
}
jsCode <- "shinyjs.browseURL = function(URL){window.open(URL,''); ;}"
jscode <- "shinyjs.closeWindow = function() { window.close(); }"
navbarPageWithText <- function(..., text) {
navbar <- navbarPage(...)
textEl <- tags$p(class = "navbar-text pull-right", text)
navbar[[3]][[1]]$children[[1]] <- htmltools::tagAppendChild(
navbar[[3]][[1]]$children[[1]], textEl)
navbar
}
shinyUI(navbarPage(theme = shinytheme("flatly"),img(src = "netpredicter.png", height = 32, width = 35) ,id ="bar",
tabPanel(icon("home",lib = "glyphicon"),
#includeCSS("www/materialize.min.css"),
div(style = " text-align: center;font-family: 'Roboto'",
h2(" NetPredictor brings to you the power of finding missing links in Chem-Biological network .."),
br(),
br(),
img(src = "netpredicter.png", height = 150, width = 150,align = "success")
),
br(),
# br(),
br(),
div(style = "text-align: center;",
bsButton("gofind", label = "Start Prediction", style = "default",
size = "large", disabled = FALSE
)),
br(),br(),br(),br(),br(),br(),
div(style= "text-align: center;",
fluidRow(
column(4,
img(src = "upload.png", height = 90, width = 100,align = "center"),
h3("1. Load your data into NetPredicter ")),
column(4,
img(src = "network.png", height = 90, width = 85,align = "center"),
h3("2. Predict for missing links in a Network")),
column(4,
img(src = "discover.png", height = 90, width = 100,align = "center"),
h3("3. Discover new relations in a network")
)),
br(),
br()
)
),
tabPanel("Start Prediction",icon=icon("dot-circle-o"),
# tags$head(
# tags$style(HTML("
# h1 {
# font-family: 'Verdana', cursive;
# line-height: 1.1;
# font-size : 20px;
# color: #4863A0
# }"))),
headerPanel("Apply NetPredictor to your Data"),
br(),br(),
# fluidRow(
#
# column(4,wellPanel(
sidebarPanel(width=3,
HTML('<style type="text/css">
.well { background-color: #ffffff; }
</style>'),
conditionalPanel(condition="input.conditionedPanels==1",
h4(icon("upload",lib = "glyphicon"),"load your data (or select Example)"),
radioButtons(inputId="data_input_type",
label="",
choices=c("Custom Data"="custom", "Example Data"="example"),
selected="", inline=FALSE),
bsTooltip("data_input_type", "Choose Either example sets or input custom sets of data",
"right", options = list(container = "body")),
## Example data
conditionalPanel(condition = "input.data_input_type == 'example'",
selectInput("datasets", "Example datasets", choices = c("Enzyme","GPCR","Ion Channel","Nuclear Receptor"),selected = "",multiple = FALSE),
h4(icon("magic",lib = "font-awesome"),"Select Network Alogirthms"),
radioButtons(inputId="algorithm_typeI",
label="",
choices=c("HeatS"="heat","Network Based Inference"="nbi","Random Walk with Restart"="rwr","NetCombo"="netcombo"),
selected="", inline=FALSE),
conditionalPanel(condition = "input.algorithm_typeI=='heat'",
sliderInput("heatAlpha", "Alpha", value=0.5, min=0, max=1,width ='200px'),
sliderInput("heatLambda", "Lambda", value=0.5, min=0, max=1,width = '200px')),
conditionalPanel(condition = "input.algorithm_typeI=='nbi'",
sliderInput("nbiAlpha", "Alpha", value=0.5, min=0, max=1,width ='200px'),
sliderInput("nbiLambda", "Lambda", value=0.5, min=0, max=1,width = '200px')),
conditionalPanel(condition = "input.algorithm_typeI=='rwr'",
sliderInput("rwr_restart", "Restart", value=0.8, min=0, max=1,width ='200px')),
conditionalPanel(condition = "input.algorithm_typeI=='netcombo'",
sliderInput("nc_restart", "Restart", value=0.8, min=0, max=1,width ='200px'),
sliderInput("ncAlpha", "Alpha", value=0.5, min=0, max=1,width ='200px'),
sliderInput("ncLambda", "Lambda", value=0.5, min=0, max=1,width ='200px'))),
# Local files
conditionalPanel(condition = "input.data_input_type == 'custom'",
fileInput('dt_file', label="Drug-Target Biparite Network",
multiple=FALSE,
accept=c('text/csv',
'text/comma-separated-values',
'text/tab-separated-values',
'.csv',
'text/plain',
'.tsv')),
bsTooltip("dt_file", "Input CSV file with Binary Drug Target Interactions Matrix",
"right", options = list(container = "body")),
fileInput('drug_file', label="Drug Similarity Matrix",
multiple=FALSE,
accept=c('text/csv',
'text/comma-separated-values',
'text/tab-separated-values',
'.csv',
'text/plain',
'.tsv')),
bsTooltip("drug_file", "Input CSV file with Drug-Drug similarity Matrix",
"right", options = list(container = "body")),
fileInput('target_file', label="Target Similarity Matrix",
multiple=FALSE,
accept=c('text/csv',
'text/comma-separated-values',
'text/tab-separated-values',
'.csv',
'text/plain',
'.tsv')),
bsTooltip("target_file", "Input CSV file with Target-Target similarityMatrix",
"right", options = list(container = "body")),
h4("Select Network Alogirthms"),
radioButtons(inputId="algorithm_typeII",
label="",
choices=c("HeatS"="heat","Network Based Inference"="nbi","Random Walk with Restart"="rwr","NetCombo"="netcombo"),
selected="nbi", inline=FALSE),
conditionalPanel(condition = "input.algorithm_typeII=='heat'",
sliderInput("cheatAlpha", "Alpha", value=0.5, min=0, max=1,width ='200px'),
sliderInput("cheatLambda", "Lambda", value=0.5, min=0, max=1,width = '200px')),
conditionalPanel(condition = "input.algorithm_typeII=='nbi'",
sliderInput("cnbiAlpha", "Alpha", value=0.5, min=0, max=1,width ='200px'),
sliderInput("cnbiLambda", "Lambda", value=0.5, min=0, max=1,width = '200px')),
conditionalPanel(condition = "input.algorithm_typeII=='rwr'",
sliderInput("crwr_restart", "Restart", value=0.8, min=0, max=1,width ='200px')),
conditionalPanel(condition = "input.algorithm_typeII=='netcombo'",
sliderInput("cnc_restart", "Restart", value=0.8, min=0, max=1,width ='200px'),
sliderInput("cncAlpha", "Alpha", value=0.5, min=0, max=1,width ='200px'),
sliderInput("cncLambda", "Lambda", value=0.5, min=0, max=1,width ='200px'))
),
busyIndicator("Calculation In progress",wait = 0),
actionButton('start', label='Run Prediction',
class="btn btn-primary")
),
conditionalPanel(condition="input.conditionedPanels==2",
h4("Get Predictive Metrics"),
numericInput('relinks',width = '200px',
label = 'Choose Random links to be removed',
min = 1, value = 25),
numericInput('freqT',width = '200px',
label = 'Frequency of associations between Biparite Nodes',
min = 1, value = 2),
radioButtons(inputId="predMetrics",
label="",
choices=c("Network Based Inference"="nbi","Random walk with restart"="rwr",
"NetCombo"='nc'),
selected="", inline=FALSE),
conditionalPanel(condition = "input.predMetrics=='nbi'",
sliderInput("pdnbiAlpha", "Alpha", value=0.5, min=0, max=1,width ='200px'),
sliderInput("pdnbiLambda", "Lambda", value=0.5, min=0, max=1,width = '200px')),
conditionalPanel(condition = "input.predMetrics=='rwr'",
sliderInput("pdrwrRestart", "Restart", value=0.8, min=0, max=1,width ='200px')),
conditionalPanel(condition = "input.predMetrics=='nc'",
sliderInput("pdcnc_restart", "Restart", value=0.8, min=0, max=1,width ='200px'),
sliderInput("pdcncAlpha", "Alpha", value=0.5, min=0, max=1,width ='200px'),
sliderInput("pdcncLambda", "Lambda", value=0.5, min=0, max=1,width ='200px')),
busyIndicator("Calculation In progress",wait = 0),
actionButton('submit', label='Submit',
class="btn btn-primary")
# render_helpfile("Advanced Analysis", "mds/analysis.md")
),
conditionalPanel(condition="input.conditionedPanels==3",
h4("Perform Permutations Analysis on your Network"),
numericInput('permute',width = '200px',
label = 'Choose number of random permutations',
min = 5, value = 10),
numericInput('sig',width = '150px',
label = 'Keep Significant links of pvalue',
min = 0.00000000001, value =0.05),
radioButtons(inputId="sigMetrics",
label="",
choices=c("Network Based Inference"="signbi","Random walk with restart"="sigrwr"),
selected="", inline=FALSE),
conditionalPanel(condition = "input.sigMetrics=='signbi'",
sliderInput("sgnbiAlpha", "Alpha", value=0.5, min=0, max=1,width ='200px'),
sliderInput("sgnbiLambda", "Lambda", value=0.5, min=0, max=1,width = '200px')),
conditionalPanel(condition = "input.sigMetrics=='sigrwr'",
sliderInput("sgrwrRestart", "Restart", value=0.8, min=0, max=1,width ='200px')),
busyIndicator("Calculation In progress",wait = 0),
actionButton('sigSubmit', label='Submit',
class="btn btn-primary")
)
),mainPanel(
tabsetPanel(id = "conditionedPanels",
tabPanel("Network Properties",br(),value = 1,
actionButton('netproperty', label='Calculate Properties',class="btn btn-primary"),
h3(textOutput("Data Summary", container = span)),
uiOutput("prop_table"),
tabsetPanel(
id = 'dataset',tabPanel("Proteins Properties",DT::dataTableOutput("btwProteinsdt")),
tabPanel("Drug Properties",DT::dataTableOutput("btwDrugsdt")))
),
#column(width = 5, chartOutput("countProteins","polycharts")),
#column(width = 6, offset = 1, chartOutput("countDrugs","polycharts"))
#),
# fluidRow(
#
# #column(width = 5, chartOutput("btwProteins","polycharts")),
# column(width = 6, offset = 1, chartOutput("btwDrugs","polycharts"))
# )),
# showOutput("btwProteins","polycharts"),
# showOutput("btwDrugs","polycharts")),
tabPanel("Network Modules",value = 1,
br(),
actionButton('mods', label='Calculate Modules',class="btn btn-primary"),
#render_helpfile("Network Modules", "mds/module.md"),
uiOutput('modules'),
dataTableOutput("data_table"),
actionButton('shownet', label='Show Network',class="btn btn-primary"),
div(style="width:1200px; height:225px;",bsCollapse(id = "module1", open = "modules",
bsCollapsePanel("modules",visNetworkOutput("moduleplot",height="550px"))))
),
tabPanel("Prediction Results",value = 1,
h4(textOutput("Prediction Results",container = span)),
dataTableOutput("Result"),
downloadButton("downloadResult", "Download results as csv file")),
tabPanel("networkplot",value = 1,
h4("Network"),
visNetworkOutput("networkplot",height="725px"),
downloadButton("graphResult","Download Graph GML file")),
tabPanel('Statistical Analysis',value =2,
h3(textOutput("Analysis")),
dataTableOutput("advTable"),
downloadButton("downloadadvr", "Download results as csv file")),
tabPanel('Permutation Analysis', value = 3,
h3(textOutput("Permutation Analysis")),
dataTableOutput("sigTable"),
downloadButton("downloadSig", "Download results as csv file"))
)
)),
## Statistical Analysis TAB
tabPanel("PPI Network",
div(id = "demo", class = "collapse in",
sidebarPanel(width = 3,HTML('<style type="text/css">
.well { background-color: #ffffff; }
</style>'),
div(style="display:inline-block",h4(icon("search",lib = "glyphicon"),"Find Interactions")),
radioButtons(inputId="data_search",
label="",
choices=c("Protein-Protein Interactions(PPI)"="ppi", "PPI Shortest Paths"="path","Subgraph Extract"="subgraph" ),
selected="", inline=FALSE),
conditionalPanel(
condition = "input.data_search == 'ppi'",
selectizeInput("ppi", label= "HGNC Symbols", choices = NULL, selected = "", multiple = T,width='85%'),
#uiOutput("location"),
sliderInput("confidence", "Confidence:",
min = 0, max = 1, value = 0.99, step= 0.01)
),
#uiOutput("ppipanel")),
conditionalPanel(
condition = "input.data_search == 'path'",
radioButtons(inputId="weight",
label="",
choices=c("Weighted PPI", "Unweighted PPI"),
selected="Weighted PPI", inline=FALSE),
textInput("from", label = "From :",
value = "",
width = "100px"),
textInput("to", label = "To:",
value = "",
width = "100px"),
numericInput("kpath", label = "Top-Paths :", min = 1,
max = 20, value = 1 , step = 1,
width = "150px"),
checkboxInput('pathway', label = "Enrich Pathways (Reactome)", value = FALSE, width = NULL),
uiOutput("conditionalCheck")),
conditionalPanel(
condition = "input.data_search == 'subgraph'",
inputTextarea('proteinlist', '',8,20),br(),
div(style="display:inline-block", radioGroupButtons(inputId = "ppi_id3",
label = "Interactomes", choices = c("ConsensusPath",
"String"), size="sm",status = "primary", selected = "ConsensusPath",
checkIcon = list(yes = icon("ok",lib = "glyphicon"))))),
div(style="display:inline-block" ,actionButton('ppisearch', label='Search',class="btn btn-primary"))
)
),
mainPanel(conditionalPanel(condition = "input.data_search == 'ppi'",
br(),
bsAlert("alertppi"),
div(style="width:1200px; height:225px;",bsCollapse(id="collapse1",open="PPI Network",
bsCollapsePanel("PPI Network",visNetworkOutput("ppinet",height="675px"))))),
conditionalPanel(condition = "input.data_search == 'path'",
bsAlert("alert"),
div(style="width:1200px; height:225px;",bsCollapse(id="collapse1",open="Shotest paths",bsCollapsePanel("Shotest paths",visNetworkOutput("pathnet",height="675px"))),
bsCollapsePanel("pathways",DT::dataTableOutput("pathTab",width = "40%"), style = "font-size: 40%"))),
conditionalPanel(condition = "input.data_search == 'subgraph'",
div(style="width:1200px; height:225px;",bsCollapse(id = "collapseExample", open = "PPI Subgraph",
bsCollapsePanel("PPI Subgraph",visNetworkOutput("subnet",height = "700px")))))
)
),
tabPanel("Search Drugbank", icon= icon("search"),
tags$head(
tags$style(HTML("
h1 {
font-family: 'Verdana', cursive;
line-height: 1.1;
font-size : 16px;
color: #4863A0
}"))),
headerPanel("Select Drug names/drugbank IDs to search"),br(),
sidebarPanel(width = 3,
# HTML('<style type="text/css">
# .well { background-color: #ffffff; }
# </style>'),
radioButtons(inputId="search_type",
label = "",
choices=c("Search Drugs"="drugs", "Search Proteins"="proteins"),
selected="", inline=FALSE),
conditionalPanel(condition = "input.search_type == 'drugs'",
textInput("did", "Drugbank ID:", width = NULL)),
conditionalPanel(condition = "input.search_type == 'proteins'",
textInput("pid", "Hugo Gene:", width = NULL)),
busyIndicator("Search In progress",wait = 0),
actionButton('dSearch', label='Submit',
class = "btn btn-primary")
),mainPanel(tabPanel('Predicted Links',
dataTableOutput("dtable"),
downloadButton("dBdownload", "Download results as csv file")
))),
tabPanel("Enrichment Analysis" , icon = icon("tasks"),
tags$head(
tags$style(HTML("
h1 {
font-family: 'Verdana', cursive;
line-height: 1.1;
font-size : 16px;
color: #4863A0
}"))),
headerPanel("Paste gene on a new line for enrichment analysis"),br(),
sidebarPanel(width = 3,
# HTML('<style type="text/css">
# .well { background-color: #ffffff; }
# </style>'),
inputTextarea('selectGene', '',8,15 ),
radioButtons(inputId="gopath",
label="Select Ontology or Pathway or Disease",
choices=c("Gene Ontology"="go","Pathway Enrichment"="pathway","Disease Enrichment"="disease"),
selected="", inline=FALSE),
conditionalPanel(condition = "input.gopath=='go'",
textInput("level", label = "Enter GO Level", value = 3)),
busyIndicator("Search In progress",wait = 0),
actionButton('genelist', label='Submit',
class="btn btn-primary")
),mainPanel(tabPanel('Enrichment Results',
dataTableOutput("genePathway"),
downloadButton("Godownload", "Download results as csv file")
))),
tabPanel("About", icon= icon("info-circle"),
HTML(markdown::markdownToHTML("mds/about.md", fragment.only=TRUE, options=c("")))
)
))