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ui.R
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ui.R
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#
# This is a Shiny web application. You can run the application by clicking
# the 'Run App' button above.
#
# Find out more about building applications with Shiny here:
#
# http://shiny.rstudio.com/
#
# local_test <- TRUE
## deploy_dir = "/srv/shiny-server/samba_files/"
# for local test
# deploy_dir <- "/home/data/git/samba/files/"
options(max.print=999999)
# biocmanag <- "BiocManager"
# lapply(biocmanag, function(x) if(!require(x,character.only = TRUE)) install.packages(x, dependencies = TRUE))
# list_of_packages = c("shiny","shinydashboard","shinydashboardPlus","shinyFiles","bnlearn","shinyjs","DT","data.table","bnviewer","visNetwork","stringr","shinythemes", "purrr", "seqinr","future","ipc","assertr","promises","dplyr","callr", "parallel","this.path","progressr","compare","future.callr","graph","igraph","zip","tibble", "fresh", "colourpicker", "shinyBS","shinyalert","shinyjqui" )
# lapply(list_of_packages, function(x) if(!require(x,character.only = TRUE)) BiocManager::install(x, dependencies = TRUE))
# if (!require("reticulate")) remotes::install_github("rstudio/reticulate")
# if (!require("reticulate")) install.packages("reticulate")
# if (!require("shinyDirectoryInput")) remotes::install_github("wleepang/shiny-directory-input")
library(shiny)
library(shinydashboard)
library(shinydashboardPlus)
library(shinyFiles)
library(shinyWidgets)
library(bnlearn)
library(shinyjs)
library(dplyr)
library(DT)
library(data.table)
library(bnviewer)
library(visNetwork)
library(stringr)
library(shinythemes)
library(shinyDirectoryInput)
library(reticulate)
library(purrr)
library(seqinr)
library(future)
library(ipc)
library(promises)
library(progressr)
library(future.callr)
library(parallel)
library(this.path)
library(compare)
library(graph)
library(igraph)
library(zip)
library(plyr)
# #
library(fresh)
library(colourpicker)
library(shinyBS)
library(shinyalert)
library(shinyjqui)
#cl <- parallel::makeCluster(2, setup_strategy = "sequential")
#if (Sys.getenv("RSTUDIO") == "1" && !nzchar(Sys.getenv("RSTUDIO_TERM")) &&
# Sys.info()["sysname"] == "Darwin" && getRversion() >= "4.0.0") {
# parallel:::setDefaultClusterOptions(setup_strategy = "sequential")
#}
# Full-screen
js_fs <- "
function openFullscreen(elem) {
if (elem.requestFullscreen) {
elem.requestFullscreen();
} else if (elem.mozRequestFullScreen) { /* Firefox */
elem.mozRequestFullScreen();
} else if (elem.webkitRequestFullscreen) { /* Chrome, Safari and Opera */
elem.webkitRequestFullscreen();
} else if (elem.msRequestFullscreen) { /* IE/Edge */
elem.msRequestFullscreen();
}
}
function closeFullscreen(elem) {
if (document.exitFullscreen) {
document.exitFullscreen();
} else if (elem.mozExitFullscreen) { /* Firefox */
document.mozExitFullScreen();
} else if (elem.webkitExitFullscreen) { /* Chrome, Safari and Opera */
document.webkitExitFullscreen();
} else if (elem.msExitFullscreen) { /* IE/Edge */
document.msExitFullscreen();
}
}"
css_fs <- "
#network_proxy:-webkit-full-screen {
height: 100%;
margin: 0;
}
#network_proxy:-ms-fullscreen {
height: 100%;
}
#network_proxy:fullscreen {
height: 100%;
}
#mynetwork {
width: 600px;
height: 200px;
}
"
# https://stackoverflow.com/questions/69220191/easiest-way-to-set-different-background-images-to-shiny-panels
# https://stackoverflow.com/questions/45178843/how-to-alter-navigation-button-color-in-vis-js/45307412#45307412
dropdownBlock_Rox <- function (..., id, icon = NULL, title = NULL, badgeStatus = "danger")
{
if (!is.null(badgeStatus))
#validateStatus(badgeStatus)
items <- c(list(...))
dropdownClass <- paste0("dropdown")
numItems <- length(items)
if (is.null(badgeStatus)) {
badge <- NULL
}
else {
badge <- dashboardLabel(status = badgeStatus)
}
shiny::tags$li(shiny::singleton(shiny::tags$head(shiny::tags$script(shiny::HTML(paste0("$(document).ready(function(){\n $('#",
id, "').find('ul').click(function(e){\n e.stopPropagation();\n });\n });\n "))))),
class = dropdownClass, id = id, shiny::tags$a(href = "#",
class = "dropdown-toggle", `data-toggle` = "dropdown",
icon, title, badge), shiny::tags$ul(class = "dropdown-menu",
style = "left: 0; right: auto;", shiny::tags$li(shiny::tags$ul(class = "menu",
shiny::tags$div(style = "margin-left: auto; margin-right: auto; width: 80%;",
items)))))
}
# #
DT <- list(
size = 25,
width = 1,
sel_width = 2,
dashes = FALSE,
direction = TRUE,
bounce = FALSE,
E_hidden = FALSE,
opacity = 1,
fix_x = FALSE,
fix_y = FALSE,
# Scaling
scaling_min = 1,
scaling_max = 1,
scaling_label_enabled = FALSE,
scaling_label_min = 14,
scaling_label_max = 14,
# Color
color_background = "#84B8BD",
color_border = "#616161",
color_highlight = "#177782",
shape = "dot",
# Font
show_Nlabel = TRUE,
show_Elabel = FALSE,
font_color = "#343434",
font_size = 14,
font_face = "arial",
font_background = NULL,
font_strokeWidth = 1,
font_strokeColor = "#ffffff",
N_font_align = "center",
E_font_align = "horizontal",
# Shadow
shadow_enabled = FALSE,
shadow_color = "#FFFFFF",
shadow_size = 10,
shadow_x = 5,
shadow_y = 5,
st = " position: absolute;
top: 25px;
text-align: center;
background-color: white;
color: black;
border: 1px solid #e7e7e7;
border-radius: 6px;
padding: 8px 12px;
",
# Otros
# Diseño UI
slider_color = "#95979A",
digits = 3,
# Grado para la seleccion
grade = 0)
Ord_nod_tab <- NULL
Ord_edg_tab <- NULL
nodes <- NULL
edges <- NULL
#Rep <- 0
Sel_nod <- NULL
nodes_info <- NULL
edges_info <- NULL
#Groups <<- list(All = nodes$id, None = NULL)
# #
plan(multisession, workers = 12)
options(shiny.maxRequestSize=100*1024^2)
#setwd(dirname(rstudioapi::getSourceEditorContext()$path))
css <- HTML(".margin-right{float: right !important;}")
js <- HTML("$(function(){
setTimeout(function(){
$('.dataTables_length').addClass('margin-right');
}, 200);
});")
shiny_handler <- make_progression_handler("shiny", reporter = list(
update = function(config, state, progression, ...) {
incProgress(progression$amount / config$max_steps)
}
))
to_log <- function(bn_df, nodes) {
bn_df.log = bn_df
for (node in nodes) {
bn_df.log[,node] = log1p(bn_df.log[,node])
}
bn_df.log
}
inverse_log <- function(bn_df.log, nodes){
bn_df.invlog = bn_df.log
for (node in nodes) {
bn_df.invlog[,node] = exp1m(bn_df.invlog[,node])
}
bn_df.invlog
}
# learning_training_panel <- tabPanel(
# HTML("<b>Learning and training</b>"),
# sidebarPanel(
# width = 12,
# tags$label(h3("Input files, filters and parameters")),
# hr(),
# fileInput("data_variables", "CSV file for experimental variables", accept = ".csv"),
# div(style = "font-size: 10px; padding: 0px 0px; margin-top:-4em", uiOutput("example_data_variables")),
# div(style = "font-size: 10px; padding: 0px 0px; margin-top:2em", fileInput("data_taxas", "CSV file for taxa counts", accept = ".csv")),
# div(style = "font-size: 10px; padding: 0px 0px; margin-top:-4em", uiOutput("example_data_taxas")),
# tags$style("#filter_taxa {height: 35px;}"),
# div(
# style = "border-bottom-style: ridge; border-color:#DAD7D6; border-top-style: ridge; margin-top: 1em; margin-bottom: 1em;", checkboxInput("filter_taxa", label = HTML('<h5 style="position: relative;">Filter taxa data by its presence in samples</h5>'), value = FALSE, width = NULL),
# conditionalPanel(
# condition = "input.filter_taxa == 1",
# pickerInput("before_after_filter", "Select applying abundance filter before or after building the network", choices = c("Before", "After")),
# pickerInput("filter_option", "Select Group to filter by a variables or Total to take into account all variables", choices = c("Total", "Group")),
# conditionalPanel(
# condition = "input.filter_option == 'Total'",
# numericInput("filter_countsT", "Specify a minimum number of counts to apply this filter", value = 10, min = 0, step = 0.5),
# conditionalPanel(
# condition = "input.before_after_filter == 'After'",
# div(style = "padding: 50 px 0 px; width: 100 px", textInput("filter_thrT", "Select global filter threshold", placeholder = "25,50"))
# ),
# conditionalPanel(
# condition = "input.before_after_filter == 'Before'",
# div(style = "padding: 50 px 0 px; width: 100 px", numericInput("filter_thrT", "Select global filter threshold", value = 50, min = 0, max = 100, step = 0.5))
# )
# ),
# conditionalPanel(
# condition = "input.filter_option == 'Group'",
# textInput("filter_variable", "Specify a variable name to apply this filter"),
# numericInput("filter_countsG", "Specify a minimum number of counts to apply this filter", value = 10, min = 0, step = 0.5),
# div(style = "padding: 50 px 0 px; width: 100 px", textInput("filter_thrG", "Select filter threshold for each variable condition", placeholder = "condition1-50,condition2-30..."))
# )
# )
# ),
# div(style = "font-size: 10px; padding: 0px 0px;", textInput("exp_var", "Specify experimental variables to discretize", placeholder = "variableX,variableY")),
# selectInput("dis_method", label = "Select discretization method", choices = c("quantile", "interval", "hartemink"), selected = "quantile"),
# selectInput("net_score", label = "Network score", choices = c("AIC", "BIC", "loglik"), selected = "BIC"),
# numericInput("mi_thr", "Link strength: Mutual Information (MI) threshold", value = 0.05, min = 0, max = 50, step = 0.0005),
# numericInput("bic_thr", "Link strength: Bayesian Information Criterion (BIC) threshold", value = 0, min = -10000000, max = 10000000, step = 0.5),
# tags$style("#blacklist {height: 35px; width: 50 px;}"),
# div(
# style = "border-bottom-style: ridge; border-color:#DAD7D6; border-top-style: ridge; margin-top: 1em; margin-bottom: 1em;", checkboxInput("blacklist", label = HTML('<h5 style="position: relative; top: -50 px">Blacklist: Links not to be included in the network</h5>'), value = FALSE, width = NULL),
# conditionalPanel(
# condition = "input.blacklist == 1",
# fileInput("black_file", "Upload blacklist file"),
# div(style = "font-size: 10px; padding: 0px 0px; margin-top:-4em", uiOutput("example_bl")),
# )
# ),
# tags$style("#whitelist {height: 35px; width: 50 px;}"),
# div(
# style = "border-bottom-style: ridge; border-color:#DAD7D6; border-top-style: ridge; margin-top: 1em; margin-bottom: 1em;", checkboxInput("whitelist", label = HTML('<h5 style="position: relative; top: -50 px">Whitelist: Links to be included in the network</h5>'), value = FALSE, width = NULL),
# conditionalPanel(
# condition = "input.whitelist == 1",
# fileInput("white_file", "Upload whitelist file"),
# div(style = "font-size: 10px; padding: 0px 0px; margin-top:-4em", uiOutput("example_wl"))
# )
# ),
# div(style = "font-size: 10px; padding: 0px 0px;", textInput("directory_net", "Specify an output folder", placeholder = "experiment_1")),
# # directoryInput('directory_net', label = 'Select an output folder'),
# div(class = "buttonagency", style = "display:inline-block; margin-right:10px;", actionBttn(inputId = "start_net", label = "Launch", style = "float", color = "primary", size = "sm", icon = icon("rocket"))),
# div(class = "buttonagency", style = "display:inline-block; margin-right:10px;", actionBttn(inputId = "stop_net", label = "Stop process", style = "float", color = "primary", size = "sm", icon = icon("window-close"))),
# div(class = "buttonagency", style = "display:inline-block", actionBttn(inputId = "check_net", label = "Check status", style = "float", color = "primary", size = "sm", icon = icon("check-square")))
# )
# )
# load_network_panel <- tabPanel(
# HTML("<b>Load Network</b>"),
# sidebarLayout(
# sidebarPanel(
# width = 3,
# tags$label(h3("Input network")),
# p("Load network from a previous build"),
# # hr(),
# fileInput("input_network_file", NULL, accept = c(".RData", ".RDATA", ".rdata"))
# ),
# mainPanel(
# width = 9,
# h3("Network Summary : "),
# # h4("This is a classic simple layout that is often used to keep"),
# uiOutput("current_network_info")
# )
# )
# )
# display_cpts_panel <- tabPanel(
# HTML("<b>CPTs</b>"),
# sidebarLayout(
# sidebarPanel(
# width = 3,
# tags$label(h3("Conditional Probability Tables")),
# p("Select a Node to display CPT"),
# uiOutput("selector_cpt", container = div),
# hr(),
# downloadButton("save_cpt_all", "Download all tables", class = "butt"),
# tags$head(tags$style(".butt{background-color:#F7F7F7;} .butt{color: black;}")),
# downloadButton("save_cpt", "Download current table", class = "butt"),
# ),
# mainPanel(
# width = 9,
# strong("Conditional probability table"),
# tags$hr(style = "margin-left: -1em; max-width: none; max-heigth: 100vh; width: 100vw; heigth: auto; object-fit: contain;"),
# tags$head(tags$style("#conditional_table{overflow: auto; padding: 20px; text-align:justify; overflow-y:scroll; overflow-x:scroll; max-height: 575px; background: #F8F8F8;}")),
# verbatimTextOutput("conditional_table")
# )
# )
# )
# set_network_evidence_panel <- tabPanel(HTML("<b>Evidence/Control</b>"),
# uiOutput("network_evidence_info_ui"))
# prediction_panel <- tabPanel(
# HTML("<b>Prediction</b>"),
# conditionalPanel(
# condition = "output.show_graph",
# sidebarLayout(
# sidebarPanel(
# width = 3,
# tags$label(h3("Predict taxa values")),
# # hr(),
# # ", "Taxas", value = "", placeholder = 'Example: Bacteria_X,Bacteria_Y. Write "all" for all taxas.'),
# uiOutput("selector_predict_taxas", container = div),
# # textInput("evidence", "Evidence", value= "", placeholder = 'Example: season=S,tissue=AI'),
# # verbatimTextOutput("directorypath"),
# # shinyDirButton('directory', 'Select a folder', 'Please select a folder', FALSE),
# # textInput("outputFolder", "Output folder", value= getwd()),
# # div(style = "display:inline-block; float:right;", actionButton("button1", "Submit")))),
# #numericInput("iterations", "Iterations", value = 1, min = 1, max = 50, step = 0.5),
# #numericInput("error_network", "Conditional probability allowed +- error", value = 0.3, min = 0, max = 1, step = 0.05),
# tags$style(HTML(pickerInput_select)),
# tags$style(HTML(pickerInput_deselect)),
# uiOutput("evidence_selector", container = div),
# checkboxInput("use_data_as_strong_proir", label = "Use Input Data as strong proir for network prediction", value = TRUE, width = NULL),
# checkboxInput("show_org_data_dist", label = "Show Original Data summary", value = TRUE, width = NULL),
# div(class = "buttonagency", actionBttn(inputId = "button1", label = "Submit", style = "float", color = "primary", size = "sm"))
# ),
# mainPanel(
# width = 9,
# #tags$hr(style = "margin-left: -1em; max-width: none; max-heigth: 100vh; heigth: auto; object-fit: contain;"),
# div(style = "align: center",
# tabsetPanel(
# type = "pills",
# tabPanel(
# strong("Predict abundances"),
# strong("Predict abundances"),
# tags$hr(style = "margin-left: -1em; max-width: none; max-heigth: 100vh; width: 100vw; heigth: auto; object-fit: contain;"),
# dataTableOutput("predicted_value")
# ),
# # tabPanel(
# # strong("Conditional probability table"),
# # tags$hr(style = "margin-left: -1em; max-width: none; max-heigth: 100vh; width: 100vw; heigth: auto; object-fit: contain;"),
# # uiOutput("selector_cpt", container = div),
# # downloadButton("save_cpt_all", "Download all tables", class = "butt"),
# # tags$head(tags$style(".butt{background-color:#F7F7F7;} .butt{color: black;}")),
# # downloadButton("save_cpt", "Download current table", class = "butt"),
# # tags$head(tags$style("#conditional_table{overflow: auto; padding: 20px; text-align:justify; overflow-y:scroll; overflow-x:scroll; max-height: 575px; background: #F8F8F8;}")),
# # verbatimTextOutput("conditional_table")
# # ),
# tabPanel(
# strong("Predict Metagenomes"),
# strong("Predict Metagenomes"),
# tags$hr(style = "margin-left: -1em; max-width: none; max-heigth: 100vh; width: 100vw; heigth: auto; object-fit: contain;"),
# fileInput("counts", "Raw counts text file", accept = ".txt"),
# div(style = "font-size: 10px; padding: 0px 0px; margin-top:-4em", uiOutput("example_counts")),
# div(style = "font-size: 10px; padding: 0px 0px; margin-top:2em", fileInput("seqs", "Sequences fasta file", accept = c(".fa", ".fasta"))),
# div(style = "font-size: 10px; padding: 0px 0px; margin-top:-4em", uiOutput("example_seqs")),
# # shinyDirButton('directory', 'Select an output folder', 'Please select a folder'),
# div(style = "font-size: 10px; padding: 0px 0px; margin-top:2em", directoryInput("directory", label = "Select an output folder")),
# span(tags$i(h6(HTML("<b>Remember to remove white spaces in fasta headers.</b>"))), style = "color:#52C4DD"),
# tags$style(".buttonpicrust .bttn-primary{color: #3B3B3B; border-color: #7D7D7D; background-color: #E7E7E7;}"),
# div(class = "buttonpicrust", style = "font-size: 10px; padding: 0px 0px; margin-top:2em;", actionBttn(inputId = "button_picrust", label = "Launch", style = "float", color = "primary", size = "sm", icon = icon("rocket"))),
# mainPanel(fluidRow(
# align = "center", shinyjs::useShinyjs(), style = "background-color:#F8F8F8;",
# textOutput("predicted_metagenome")
# ), width = 12)
# )
# ))
# # ,
# # style = "width: 100%; height: 1000px"
# )
# )
# )
# )
# Define UI for application that draws a histogram
shinyUI(
fluidPage(
tags$style(".buttonagency .bttn-primary{color: #3B3B3B; border-color: #7D7D7D; background-color: #E7E7E7; margin-top:1em;}"),
navbarPage(
theme = shinytheme("yeti"),
collapsible = TRUE,
HTML( paste('<a style="text-decoration:none;cursor:default;color:#FFFFFF;" class = "active" href = "#"><b>','SAMBA </b></a>')),
id = "nav",
windowTitle = HTML("Metagenomic network"),
navbarMenu(HTML("<b>Build</b>"),
# learning_training_panel,
build_network_ui(),
load_network_ui(),
evidence_info_ui("evidence_ui")),
navbarMenu(HTML("<b>Inference</b>"),
nodes_cpts_ui("nodes_cpts_ui"),
nodes_dags_ui("nodes_dags_ui")),
navbarMenu(HTML("<b>Prediction</b>"),
abundances_prediction_ui(),
metagenomes_prediction_ui()
),
network_viewer_ui(),
download_result_ui()
# tabPanel(
# HTML("<b>Downloads</b>"),
# tags$label(h3("Experiment results")),
# # radioButtons("down_files","Download Files", choiceNames = as.list(list.files('/srv/shiny-server/samba/files/', full.names = FALSE)), choiceValues = as.list(list.files('/srv/shiny-server/samba/files/', full.names = FALSE)), selected = "")
# selectInput("down_files", "Download Files", as.list(list.files(deploy_dir, full.names = FALSE)), selected = ""),
# downloadButton("downloadResults", "Download")
# )
)
)
)