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ui.R
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ui.R
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#
# This is the user-interface definition of a Shiny web application. You can
# run the application by clicking 'Run App' above.
#
# Find out more about building applications with Shiny here:
#
# http://shiny.rstudio.com/
#
library(shiny)
library(shinyjs)
library(shinyBS)
library(ggplot2)
library(ggrepel)
library(PhosMap)
library(plotly)
library(colourpicker)
library(ggseqlogo)
library(pheatmap)
library(survminer)
library(survival)
library(zip)
library(stringr)
library(dplyr)
library(DT)
library(png)
library(svglite)
library(ggplotify)
library(shinyWidgets)
library(bslib)
library(PhosMap)
library(qpdf)
library(uwot)
ui <- renderUI(
fluidPage(
shinyjs::useShinyjs(),
useSweetAlert(),
tags$head(
HTML("<title>PhosMap</title>"),
HTML('
<script type="text/javascript" src="popper.js"></script>
<script> window.start.init({Palette:"palette1"})</script>
'),
tags$style(type = "text/css", "
body {padding-top: 70px;}
#loadmessage {
position: fixed;
top: -20px;
left: 0px;
width: 100%;
height:100%;
padding: 300px 0px 5px 0px;
text-align: center;
font-weight: bold;
font-size: 40px;
color: #000000;
background-color: #D6D9E4;
opacity:0.7;
z-index: 1050;
}
",
HTML("
.navbar-nav {float: right; font-size: 16px}
.warning {border-left: 8px solid;}
.tooltitle {text-align:center;}
.toolsubtitle {text-align:center;}
.downloadbutton {}
.viewbutton {}
.runbutton {color: #fff; background-color: #5DA61E; border-color: #ffacae;}
.loadfiledescription {text-align:left;margin-top:15px;margin-left:10px;margin-right:300px;font-size:190%;}
.analysisbutton {color: #fff; background-color: #7650E6; border-color: #ffacae; width:200px; height:40px; }
.plotbutton{color: #fff; background-color:#da5151; border-color: #ffacae; width:150px; height:40px; }
.warningbutton {color: #fff; background-color: #F01616; border-color: #ffacae;}
.runbuttondiv {text-align: right;}
#limmastatic{height:70vh !important;}
#limmainter{height:70vh !important;}
#samstatic{height:70vh !important;}
#saminter{height:70vh !important;}
#anovastatic{height:70vh !important;}
#anovainter{height:70vh !important;}
#timecourse{height:70vh !important;}
#kaptimecourseplot{height:70vh !important;}
#kapstep2plot{height:70vh !important;}
#kseastep2plot{height:70vh !important;}
#kseastep2plotmid{height:110vh !important;}
#kseastep2plotmini{height:140vh !important;}
#kseastep2plotxs{height:200vh !important;}
#resultnav {float: left;}
#resultnavdroppro {float: left;}
#mascotdemodropproresultnav {float: left;}
#usermascotnoproresultnav {float: left;}
#usermascotresultnav {float: left;}
#usermascotdropproresultnav {float: left;}
#demomaxresultnav {float: left;}
#demomaxdropproresultnav {float: left;}
#usermaxnoproresultnav {float: left;}
#usermaxresultnav {float: left;}
#usermaxdropproresultnav {float: left;}
#kapresultnav {float: left;}
")
)
),
conditionalPanel(
condition = "$('html').hasClass('shiny-busy')",
div(h2(strong("PhosMap Calculating..."), img(src = "rmd_loader.gif"), id = "loadmessage"))
),
navbarPage(
div("",img(src = "logo.svg", height = "60px", width = "115px", style = "padding-bottom:10px; padding-right:10px; margin-top: -12px")),
inverse = T,
id = "navbarpage",
position = "fixed-top",
tabPanel(
"Home",
# icon = icon("house"),
column(
3,
panel(
"",
heading = "Update Log",
status = "warning",
h5("PhosMap 1.0.0 was released in April 2023.")
),
panel(
"",
heading = "Notice",
status = "info",
h5("We provide a demo server at https://bio-inf.shinyapps.io/phosmap/. \
This server is single-thread and of low-level hardware, we do recommend\
users to analyze the data using the demo server with small data sets. \
An upgraded hardware is necessary, according to the possible computational\
cost of the data, to reach the potential of PhosMap.")
),
panel(
"",
heading = "Introduction",
status = "success",
h5("PhosMap supports multiple function modules for full landscape of \
phosphoproteomics data analyses including quality control, phosphosite \
mapping, dimension reduction analysis, time course analysis, kinase \
activity analysis and survival analysis. Various of publication ready \
figures and tables could be generated via PhosMap. We provided a downloadable\
R package for local customized analysis of massive data in the R shiny environment\
deploying on the Docker upon the Windows, Linux, and Mac system."),
h5("PhosMap enables researchers and clinicians to process their own \
phosphoproteomics data expediently and efficiently, and whereby \
facilitate better data exploration and interpretation to obtain \
valuable biological insights.")
)
),
column(
9,
# h1(style = "text-align: center;", "PhosMap"),
h3(style = "text-align: center;", "PhosMap: a docker image-based tool to \
accomplish one-stop interactive analysis of quantitative phosphoproteomics"),
div(style = "text-align:center;", img(src = "newmain.jpg", height = "700px", width = "900px", style = "")),
),
column(
12,
br(),
hr(),
h5(style = "text-align: center;","This website is free and open to all users and there is no login requirement.")
)
),
tabPanel(
"Import Data",
# icon = icon("upload", class = "fa-solid fa-upload"),
sidebarLayout(
sidebarPanel(
width = 3,
h3("Import Data"),
div(
id = "pcadatatypediv",
radioGroupButtons(
inputId = "analysisdatatype",
label = NULL,
# choices = list("your data" = 1, "pipeline data" = 2, "example data" = 3),
choices = list("your data" = 1, "example data" = 2),
individual = TRUE,
selected = 1,
checkIcon = list(
yes = tags$i(class = "fa fa-circle",
style = "color: steelblue"),
no = tags$i(class = "fa fa-circle-o",
style = "color: steelblue")),
),
actionButton("pre2analysis", "Go to preprocessing", icon = icon("paper-plane")),
),
# bsTooltip(
# "pcadatatypediv", "1. pipeline data: data obtained through the above preprocessing process; 2. example data: data we provide; 3. your data: formated phosphoomics data",
# placement = "right",
# options = list(container = "body")
# ),
hr(style = "border-color: grey;"),
conditionalPanel(
condition = "input.analysisdatatype == 2",
h4("1. Experimental design file: "),
actionButton("viewanalysisexamdesign", "view", icon("eye")),
hr(style = "border-style: dashed;border-color: grey;"),
h4("2. Phosphorylation data frame: "),
actionButton("viewanalysisexamdf", "view", icon("eye")),
hr(style = "border-style: dashed;border-color: grey;"),
h4("3. Clinical data file[optional]: "),
actionButton("viewanalysisexamclin", "view", icon("eye"))
),
conditionalPanel(
# any
condition = "input.analysisdatatype == 1",
h4("1. Experimental design file: "),
fileInput(
inputId = "analysisupload11",
label = NULL,
accept = ".txt"
),
uiOutput("viewanalysisyourdesign"),
hr(style = "border-style: dashed;border-color: grey;"),
h4("2. Phosphorylation data frame: "),
fileInput(
inputId = "analysisupload12",
label = NULL,
accept = ".csv"
),
uiOutput("viewanalysisyourexpre"),
hr(style = "border-style: dashed;border-color: grey;"),
h4("3. Clinical data file[optional]: "),
fileInput(
inputId = "analysisupload14",
label = NULL,
accept = ".csv"
),
uiOutput("viewanalysisyourclin")
)
),
# 注意： mainpanel 需要根据数据处理情况改变，这里先不变
# 用于已经处理过的mainpanel
mainPanel(
width = 9,
hr(),
htmlOutput("htmlanalysis"),
# wellPanel("Data Overview", class = "warning"),
conditionalPanel(
condition = "input.analysisdatatype == 2",
uiOutput("viewedfileanalysisui"),
dataTableOutput("viewedfileanalysis")
),
conditionalPanel(
condition = "input.analysisdatatype == 1",
uiOutput("viewedfileanalysisuiuser"),
dataTableOutput("viewedfileanalysisuser")
)
)
)
),
tabPanel(
# "Dimension Reduction Analysis",
"DRA",
h2("Dimension Reduction Analysis", class = "tooltitle"),
h4("This module is used to reduce the dimension of phosphosites and visualize samples.", class = "toolsubtitle"),
fluidRow(
column(
4,
panel(
"",
heading = "Parameters Setting",
status = "info",
column(12, h4("PCA:")),
column(6, textInput("pcamain", "main", "PCA")),
column(6, prettyToggle(
inputId = "pcamean",
label_on = "group mean",
icon_on = icon("check"),
status_on = "info",
status_off = "warning",
label_off = "group mean",
icon_off = icon("xmark"),
value = TRUE
)),
column(12, h4("t-SNE:")),
column(6, textInput("tsnemain", "main", "t-SNE")),
column(6,numericInput("tsneseed", "random seed", 42)),
column(6, numericInput('tsneperplexity','perplexity',2,min = 1,step = 1)),
column(12, h4("UMAP:")),
column(6, textInput("umapmain", "main", "UMAP")),
column(6, numericInput('umapneighbors','neighbors',5,min = 1,step = 1)),
column(12, div(actionButton("drbt", "Analysis", icon("magnifying-glass-chart"), class='analysisbutton'), style = "display:flex; justify-content:center; align-item:center;"))
)
),
column(
8,
column(5, plotOutput("pca2")),
column(1, NULL),
column(5, plotOutput("pca1")),
column(1, downloadBttn(
outputId = "pcaplotdl",
label = "",
style = "material-flat",
color = "default",
size = "sm"
)),
column(5, plotOutput("tsne")),
column(1, downloadBttn(
outputId = "tsneplotdl",
label = "",
style = "material-flat",
color = "default",
size = "sm"
)),
column(5, plotOutput("umap")),
column(1, downloadBttn(
outputId = "umapplotdl",
label = "",
style = "material-flat",
color = "default",
size = "sm"
)),
)
)
),
tabPanel(
# "Differential phosphorylation Analysis",
"DPA",
h2("Differential Phosphorylation Analysis", class = "tooltitle"),
h4("This module is used to identify differential phosphorylation sites.", class = "toolsubtitle"),
radioGroupButtons(
inputId = "detools",
label = "",
choices = c("limma",
"SAM", "ANOVA"),
justified = TRUE,
checkIcon = list(
yes = icon("ok",
lib = "glyphicon"))
),
conditionalPanel(
condition = "input.detools == 'limma'",
column(
4,
panel(
"",
heading = "Limma Parameters Setting",
status = "info",
column(6,uiOutput('limmaselect1')),
column(6,uiOutput('limmaselect2')),
column(6, numericInput("limmapvalue", h5("pvalue threshold:"), 0.05, max = 0.05, min = 0.0000001, step = 0.0000001)),
column(
6,
selectInput("limmaadjust", h5("pvalue adjust method:"), choices = c("none", "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr"),selected = 'none')
),
column(6, numericInput("limmafc", h5("FC threshold:"), 2, min = 1, step = 0.5)),
column(6, textAreaInput("limmamain", h5("title:"), "Differential phosphosites with limma")),
column(6, textAreaInput("limmaxaxis", h5("x axis label:"), "log2FC")),
column(6, textAreaInput("limmayaxis", h5("y axis label:"), "-log10(pvalue)")),
column(4, colourInput("limmaupcolor", h5('"UP" colour'), "#FC5C00")),
column(4, colourInput("limmadowncolor", h5('"DOWN" colour'), "#31BDE0")),
column(4, colourInput("limmanotcolor", h5('"NOT" colour'), "#858080")),
conditionalPanel(
condition = "input.deadisplaymode == true",
column(6, numericInput("limmalabelfc", h5("FC threshold for labeling:"), 2, max = 10, min = 1, step = 0.5)),
column(6, numericInput("limmalabelpvalue", h5("pvalue threshold for labeling:"), 0.05, max = 0.05, min = 0.0000001, step = 0.0000001)),
column(12, textAreaInput("limmalabelspec", h5("specified points:"), "NUP35_S279\nPCNP_T139\nSEPTIN9_S30\n"))
),
column(12, div(actionButton("limmabt", "Analysis", icon("magnifying-glass-chart"), class='analysisbutton'), style = "display:flex; justify-content:center; align-item:center;"))
)
),
column(
5,
column(3,actionButton("viewlimmafile", "view result file", icon("eye"))),
column(3, NULL),
column(4, switchInput(
inputId = "deadisplaymode",
label = "mode",
labelWidth = "50px",
size = "mini",
onLabel = "static",
offLabel = "interactive",
value = TRUE,
)),
column(
1,
conditionalPanel(
condition = "input.deadisplaymode == true",
downloadBttn(
outputId = "limmaplotdl",
label = "",
style = "material-flat",
color = "default",
size = "sm"
)
)
),
column(1, NULL),
column(
12,
conditionalPanel(
condition = "input.deadisplaymode == true",
plotOutput("limmastatic", width = "100%"),
),
conditionalPanel(
condition = "input.deadisplaymode == false",
plotlyOutput("limmainter", width = "100%")
)
)
),
column(
3,
panel(
"",
heading = "Heatmap Parameters Setting",
status = "warning",
column(12, selectInput("limmaphscale", h5("scale:"), choices = c("none", "row", "column"), selected = "row")),
column(6, prettyToggle(
inputId = "limmaphcluster",
label_on = "cluster by row",
icon_on = icon("check"),
status_on = "info",
status_off = "warning",
label_off = "no cluster",
icon_off = icon("xmark"),
value = TRUE
)),
column(6, prettyToggle(
inputId = "limmaphrowname",
label_on = "display row name",
icon_on = icon("check"),
status_on = "info",
status_off = "warning",
label_off = "miss row name",
icon_off = icon("xmark"),
value = TRUE
)),
conditionalPanel(
condition = "input.limmaphcluster == 1",
column(6, selectInput("limmaphdistance", h5("clustering distance rows:"), choices = c("euclidean", "correlation"), selected = "euclidean")),
column(6, selectInput("limmaphclusmethod", h5("clustering method:"), choices = c("ward.D2", "ward.D", "single", "complete", "average", "mcquitty", "median", "centroid"), selected = "ward.D2")),
),
column(12, div(actionButton("limmaphbt", "Plot Heatmap", icon("palette"), class="plotbutton")), style = "display:flex; justify-content:center; align-item:center;")
)
)
),
conditionalPanel(
condition = "input.detools == 'SAM'",
column(
4,
panel(
"",
heading = "SAM Parameters Setting",
status = "info",
column(6,uiOutput('samselect1')),
column(6,uiOutput('samselect2')),
column(
6,
numericInput("samnperms",
label = h5("nperms:"),
value = 100,
min = 0,
max = 10000,
step = 10)
),
column(
6,
numericInput("samfdr",
label = h5("minimum FDR:"),
value = 0.05,
min = 0,
max = 0.05,
step = 0.0000001)
),
column(12,div(actionButton('sambt','Analysis', icon("magnifying-glass-chart"), class="analysisbutton")), style = "display:flex; justify-content:center; align-item:center;")
)
),
column(
5,
column(3,actionButton("viewsamfile", "view result file", icon("eye"))),
column(8, NULL),
column(
1,
downloadBttn(
outputId = "samplotdl",
label = "",
style = "material-flat",
color = "default",
size = "sm"
)
),
column(
12,
plotOutput("samstatic", width = "100%")
)
),
column(
3,
panel(
"",
heading = "Heatmap Parameters Setting",
status = "warning",
column(12, selectInput("samphscale", h5("scale:"), choices = c("none", "row", "column"), selected = "row")),
column(6, prettyToggle(
inputId = "samphcluster",
label_on = "cluster by row",
icon_on = icon("check"),
status_on = "info",
status_off = "warning",
label_off = "no cluster",
icon_off = icon("xmark"),
value = TRUE
)),
column(6, prettyToggle(
inputId = "samphrowname",
label_on = "display row name",
icon_on = icon("check"),
status_on = "info",
status_off = "warning",
label_off = "miss row name",
icon_off = icon("xmark"),
value = TRUE
)),
conditionalPanel(
condition = "input.samphcluster == 1",
column(6, selectInput("samphdistance", h5("clustering distance rows:"), choices = c("euclidean", "correlation"), selected = "euclidean")),
column(6, selectInput("samphclusmethod", h5("clustering method:"), choices = c("ward.D2", "ward.D", "single", "complete", "average", "mcquitty", "median", "centroid"), selected = "ward.D2")),
),
column(12,div(actionButton('samphbt','Plot Heatmap', icon("palette"), class="plotbutton")), style = "display:flex; justify-content:center; align-item:center;")
)
)
),
conditionalPanel(
condition = "input.detools == 'ANOVA'",
column(
4,
panel(
"",
heading = "ANOVA Parameters Setting",
status = "info",
column(6, numericInput("anovafc", h5("FC threshold:"), 2, min = 1, step = 0.5)),
column(
6,
selectInput("anovaadjust", h5("p-values adjust method:"), choices = c("none", "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr"), selected = "BH")
),
column(12, numericInput("anovapvalue", h5("pvalue threshold:"), 0.1, max = 0.05, min = 0.0000001, step = 0.0000001)),
column(12, div(actionButton("anovabt", "Analysis", icon("magnifying-glass-chart"), class="analysisbutton")), style = "display:flex; justify-content:center; align-item:center;"),
)
),
column(
5,
dataTableOutput("anovaresult")
),
column(
3,
panel(
"",
heading = "Heatmap Parameters Setting",
status = "warning",
column(12, selectInput("anovaphscale", h5("scale:"), choices = c("none", "row", "column"), selected = "row")),
column(6, prettyToggle(
inputId = "anovaphcluster",
label_on = "cluster by row",
icon_on = icon("check"),
status_on = "info",
status_off = "warning",
label_off = "no cluster",
icon_off = icon("xmark"),
value = TRUE
)),
column(6, prettyToggle(
inputId = "anovaphrowname",
label_on = "display row name",
icon_on = icon("check"),
status_on = "info",
status_off = "warning",
label_off = "miss row name",
icon_off = icon("xmark"),
value = TRUE
)),
conditionalPanel(
condition = "input.anovaphcluster == 1",
column(6, selectInput("anovaphdistance", h5("clustering distance rows:"), choices = c("euclidean", "correlation"), selected = "euclidean")),
column(6, selectInput("anovaphclusmethod", h5("clustering method:"), choices = c("ward.D2", "ward.D", "single", "complete", "average", "mcquitty", "median", "centroid"), selected = "ward.D2")),
),
column(12,div(actionButton('anovaphbt','Plot Heatmap', icon("palette"), class="plotbutton")), style = "display:flex; justify-content:center; align-item:center;")
)
)
)
),
tabPanel(
# "Time Course Analysis(fuzzy clustering)",
"TCA",
h2("Time Course Analysis(fuzzy clustering)", class = "tooltitle"),
h4("Fuzzy clustering is applied to time course analysis for discovering patterns associated with time points in PhosMap.", class = "toolsubtitle"),
fluidRow(
column(
4,
panel(
"",
heading = "Parameters Setting",
status = "info",
column(6, numericInput("tcpvalue", h5("pvalue threshold:"), 0.1, max = 0.05, min = 0.0000001, step = 0.0000001)),
column(6, selectInput("tcadjust", h5("pvalue adjust method:"), choices = c("none", "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr"), selected = "BH")),
column(6, numericInput("tcfc", h5("FC threshold:"), 2, min = 1, step = 0.5)),
column(6, numericInput("tcminmem", h5("minimun membership value:"), 0.1, max = 1, min = 0.0000001, step = 0.0000001)),
column(6, numericInput("tciteration", h5("iteration:"), 100, max = 1000, min = 10, step = 10)),
column(6, numericInput("tckcluster", h5("number of clusters:"), 9, max = 20, min = 2, step = 1)),
column(12, div(actionButton("tcanalysis", "Analysis", icon("magnifying-glass-chart"), class="analysisbutton")), style = "display:flex; justify-content:center; align-item:center;"),
)
),
column(
8,
column(
3,
actionButton("viewtcfile", "view result file", icon("eye"))
),
column(8, NULL),
column(
1,
downloadBttn(
outputId = "tcplotdl",
label = "",
style = "material-flat",
color = "default",
size = "sm"
)
),
column(12, plotOutput("timecourse"))
)
)
),
tabPanel(
# "Kinase-Substrate Enrichment Analysis",
"KSEA",
h2("Kinase-Substrate Enrichment Analysis", class = "tooltitle"),
h4("This module is used to predict kinase activity.", class = "toolsubtitle"),
fluidRow(
column(
4,
radioGroupButtons(
inputId = "kseamode",
label = "",
choices = c("Multiple groups",
"Two groups"),
justified = TRUE,
checkIcon = list(
yes = icon("ok",
lib = "glyphicon"))
),
conditionalPanel(
condition = "input.kseamode == 'Multiple groups'",
panel(
"",
heading = "Parameters Setting [Step 1]",
status = "info",
column(6, numericInput("kappvalue", h5("pvalue threshold:"), 0.1, max = 0.05, min = 0.0000001, step = 0.0000001)),
column(6, selectInput("kapadjust", h5("pvalue adjust method:"), choices = c("none", "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr"), selected = "BH")),
column(6, numericInput("kapfc", h5("FC threshold:"), 2, min = 1, step = 0.5)),
column(6, numericInput("kapminmem", h5("minimun membership value:"), 0.1, max = 1, min = 0.0000001, step = 0.0000001)),
column(6, numericInput("kapiteration", h5("iteration:"), 100, max = 1000, min = 10, step = 10)),
column(6, numericInput("kapkcluster", h5("number of clusters:"), 9, max = 20, min = 2, step = 1)),
column(12, div(actionButton("kapanalysisbt1", "Analysis", icon("magnifying-glass-chart"), class="analysisbutton")), style = "display:flex; justify-content:center; align-item:center;"),
),
panel(
"",
heading = "Parameters Setting [Step 2]",
status = "warning",
column(6, uiOutput("kapstep2cluster")),
column(6, selectInput("kapspecies", h5("species:"), choices = c("human", "mouse", "rattus"))),
column(6, selectInput("kapscale", h5("scale:"), choices = c("none", "row", "column"), selected = "none")),
column(6, selectInput("kapdistance", h5("clustering distance rows:"), choices = c("euclidean", "correlation"), selected = "euclidean")),
column(6, selectInput("kapclusmethod", h5("clustering method:"), choices = c("ward.D2", "ward.D", "single", "complete", "average", "mcquitty", "median", "centroid"), selected = "ward.D2")),
column(6, uiOutput("kapmainui")),
column(12, div(actionButton("kapanalysisbt2", "Analysis", icon("magnifying-glass-chart"), class="analysisbutton")), style = "display:flex; justify-content:center; align-item:center;"),
)
),
conditionalPanel(
condition = "input.kseamode == 'Two groups'",
panel(
"",
heading = "Parameters Setting [Step 1]",
status = "info",
column(6,uiOutput('kseaselect1')),
column(6,uiOutput('kseaselect2')),
column(6, numericInput("kseafc", h5("FC threshold:"), 4, min = 1, step = 0.5)),
column(12, div(actionButton("kseaanalysisbt1", "Analysis", icon("magnifying-glass-chart"), class="analysisbutton")), style = "display:flex; justify-content:center; align-item:center;"),
),
panel(
"",
heading = "Parameters Setting [Step 2]",
status = "warning",
column(12, selectInput("kseaspecies", h5("species:"), choices = c("human", "mouse", "rattus"))),
column(6, selectInput("kseascale", h5("scale:"), choices = c("none", "row", "column"), selected = "none")),
column(6, selectInput("kseadistance", h5("clustering distance rows:"), choices = c("euclidean", "correlation"), selected = "euclidean")),
column(6, selectInput("kseaclusmethod", h5("clustering method:"), choices = c("ward.D2", "ward.D", "single", "complete", "average", "mcquitty", "median", "centroid"), selected = "ward.D2")),
column(6, textAreaInput("kseamain", h5("title:"), value = "Kinase-Substrate Enrichment Analysis")),
column(12, div(actionButton("kseaanalysisbt2", "Analysis", icon("magnifying-glass-chart"), class="analysisbutton")), style = "display:flex; justify-content:center; align-item:center;"),
)
)
),
column(
8,
navbarPage(
title = "Result",
id = "kapresultnav",
tabPanel(
"Step 1",
value = "kapstep1val",
conditionalPanel(
condition = "input.kseamode == 'Multiple groups'",
column(
3,
actionButton("viewkaptimecoursefile", "view result file", icon("eye"))
),
column(8, NULL),
column(
1,
downloadBttn(
outputId = "kaptcplotdl",
label = "",
style = "material-flat",
color = "default",
size = "sm"
)
),
column(12, plotOutput("kaptimecourseplot"))
),
conditionalPanel(
condition = "input.kseamode == 'Two groups'",
dataTableOutput("kseastep1df")
)
),
tabPanel(
"Step 2",
value = "kapstep2val",
conditionalPanel(
condition = "input.kseamode == 'Multiple groups'",
column(11, NULL),
column(
1,
downloadBttn(
outputId = "kapplotdl",
label = "",
style = "material-flat",
color = "default",
size = "sm"
)
),
column(12, plotOutput("kapstep2plot")),
column(12, dataTableOutput("kapstep2df"))
),
conditionalPanel(
condition = "input.kseamode == 'Two groups'",
uiOutput("kseastep2plotui"),
dataTableOutput("kseastep2df")
)
)
)
)
)
),
tabPanel(
# "Motif Enrichment Analysis",
"MEA",
h2("Motif Enrichment Analysis", class = "tooltitle"),
column(3, NULL),
column(6, h4("This module is used to find and visualize enriched motifs.", class = "toolsubtitle")),
column(3, actionLink("infoLink", "Motif-Kinase Relation", class = "btn-info")),
fluidRow(
column(
4,
panel(
"",
heading = "Parameters Setting",
status = "info",
column(6, selectInput("motifspecies", h5("species:"), choices = c("human", "mouse", "rattus"))),
column(6, selectInput("motiffastatype", h5("fasta type:"), choices = c("refseq", "uniprot"))),
column(6, numericInput("motifpvalue", h5("pvalue threshold:"), 0.01, max = 0.05, min = 0.0000001, step = 0.0000001)),
column(12, div(
dropdownButton(
h5("使用motifx算法"),
actionButton("motifanalysisbt", "Analysis", icon("magnifying-glass-chart"), class="analysisbutton"),
h5("有很多motif分析的算法，如streme，根据您的需求和数据特点，\
您可以选择其他算法。为了满足这一需求，我们提供当前的匹配到的以motif为中心的suqence"),
downloadButton("motifseqdownload", label = "download sequence file"),
circle = F, status = "danger",
icon = icon("magnifying-glass-chart"), width = "400px",
label = "Analysis",
# tooltip = tooltipOptions(title = "Click to see inputs !")
tooltip = F
)
), style = "display:flex; justify-content:center; align-item:center;")
),
panel(
"",
heading = "Motif Selection",
status = "warning",
uiOutput("motifenrichrank"),
column(6,div(actionButton('motifplotbt','Plot', icon("palette"), class="plotbutton")), style = "display:flex; justify-content:center; align-item:center;"),
column(6,div(actionButton('motifviewbt','View matched sites')), style = "display:flex; justify-content:center; align-item:center;")
),
panel(
"",
heading = "Heatmap Parameters Setting",
status = "danger",
h5("Assign quantitative values of peptides to their motif", style = "color: grey;"),
column(12, numericInput("minseqs", h5("matched seqs threshold"), 50, min = 1, step = 1)),
column(6, selectInput("motifscale", h5("scale:"), choices = c("none", "row", "column"), selected = "none")),
column(6, selectInput("motifdistance", h5("distance metric:"), choices = c("euclidean", "correlation"), selected = "euclidean")),
column(6, selectInput("motifclusmethod", h5("clustering method:"), choices = c("ward.D2", "ward.D", "single", "complete", "average", "mcquitty", "median", "centroid"), selected = "ward.D2")),
column(12, textAreaInput("motifmain", h5("title:"), "Heatmap of Motif Quantification")),
column(12,div(actionButton('motifplotbt2','Plot', icon("palette"), class="plotbutton")), style = "display:flex; justify-content:center; align-item:center;")
)
),
column(
8,
hr(),
column(
11,
hidden(div(
id = "motifenrichhidden1",
h4("Motif enrichment analysis result:"),
dataTableOutput("motifdfresult")
))
),
column(
1,
downloadBttn(
outputId = "motifenrichdl",
label = "",
style = "material-flat",
color = "default",
size = "sm"
)
)
)
)
),
tabPanel(
# "Survival Analysis",
"SA",
h2("Survival Analysis", class = "tooltitle"),
h4("This module is used to identify phosphorylation sites or kinases associated with clinical outcomes of patients.", class = "toolsubtitle"),
fluidRow(
column(
4,
panel(
"",
heading = "Parameters Setting",
status = "info",
column(6, selectInput("survivalpajust", h5("pvalue adjust method:"), choices = c("none", "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr"), selected = "BH")),
column(6, numericInput("survivalpthreshold", h5("pvalue threshold:"), 0.01, max = 1, min = 0.0000001, step = 0.0000001)),
column(12, div(actionButton("survivalanalysis", "Analysis", icon("magnifying-glass-chart"), class="analysisbutton")), style = "display:flex; justify-content:center; align-item:center;"),
),
panel(
"",
heading = "Feature Selection",
status = "warning",
column(12, uiOutput("survivalui")),
column(6, colourInput("survivalhighcol", h5('"high" colour'), "#3300CC")),
column(6, colourInput("survivallowcol", h5('"low" colour'), "#CC3300")),
column(12,div(actionButton('survivalplotbt1','Plot', icon("palette"), class="plotbutton")), style = "display:flex; justify-content:center; align-item:center;")
)
),
column(
8,
column(
12, dataTableOutput("survivaltable")
),
column(
5, plotOutput("survivalplot1")
),
column(1, downloadBttn(
outputId = "surv1dl",
label = "",
style = "material-flat",
color = "default",
size = "sm"
)),
column(
5, plotOutput("survivalplot2"),
),
column(1, downloadBttn(
outputId = "surv2dl",
label = "",
style = "material-flat",
color = "default",
size = "sm"
))
)
)
),
tabPanel(
"Preprocessing",
sidebarLayout(
sidebarPanel(
width = 3,
h3("Import Data",),
wellPanel(
materialSwitch(
inputId = "loaddatatype",
label = "Load example data",
value = FALSE,
status = "success",
right = TRUE
),
radioGroupButtons(
inputId = "softwaretype",
label = NULL,
choices = list("MaxQuant" = 1, "Firmiana" = 2),
# direction = "vertical",
individual = TRUE,
selected = 1,
checkIcon = list(
yes = tags$i(class = "fa fa-circle",
style = "color: steelblue"),
no = tags$i(class = "fa fa-circle-o",
style = "color: steelblue")),
)
),
conditionalPanel(
condition = "input.loaddatatype == true",
h4("1. Experimental design file: "),
actionButton("viewbt1", "view", icon("eye"), class = "viewbutton"),
hr(style = "border-style: dashed;border-color: grey;")
),
conditionalPanel(
condition = "input.loaddatatype == true & input.softwaretype == 2",
h4("2. Mascot xml file: "),
selectInput("mascotdemoxmlid", NULL, choices = c("Exp027015_F1_R1", "Exp027016_F1_R1", "Exp027017_F1_R1", "Exp027031_F1_R1", "Exp027032_F1_R1", "Exp027033_F1_R1", "Exp027046_F1_R1", "Exp027047_F1_R1", "Exp027048_F1_R1")),
hr(style = "border-style: dashed;border-color: grey;"),
# hr(),
h4("3. Phosphoproteomics peptide file: "),
selectInput("mascotdemopeptidefileid", NULL, choices = c("Exp027015_peptide", "Exp027016_peptide", "Exp027017_peptide", "Exp027031_peptide", "Exp027032_peptide", "Exp027033_peptide", "Exp027046_peptide", "Exp027047_peptide", "Exp027048_peptide")),
hr(style = "border-style: dashed;border-color: grey;"),
h4("4. Proteomics data[optional]"),
br(style = "line-height:1px;"),
h4("4.1 Proteomics experimental design file: "),
actionButton("viewbt5", "view", icon("eye"), class = "viewbutton"),
h4("4.2 Profiling file: "),
selectInput("mascotdemoproid", NULL, choices = c("Exp026982_gene", "Exp026983_gene", "Exp026995_gene", "Exp026996_gene", "Exp027008_gene", "Exp027009_gene"))
),
#Maxquant
conditionalPanel(
condition = "input.loaddatatype == true & input.softwaretype == 1",
h4("2. Phospho (STY)Sites.txt: "),
actionButton("viewdemomaxphosbt", "view", icon("eye"), class = "viewbutton"),
hr(style = "border-style: dashed;border-color: grey;"),
# hr(),
h4("3. Proteomics data[optional]"),
br(),
h4("3.1 Proteomics experimental design file: "),
actionButton("viewdemomaxprodesignbt", "view", icon("eye"), class = "viewbutton"),
h4("3.2 proteinGroups.txt: "),
actionButton("viewdemomaxprobt", "view", icon("eye"), class = "viewbutton"),
),
## user data
# shared phosphoproteomics design file
conditionalPanel(
condition = "input.loaddatatype == false",
h4("1. Experimental design file: "),
fileInput("updesign", NULL, accept = ".txt"),
hidden(
div(
id = "hiddenview1",
actionButton("viewbt4", "view", icon("eye"))
)
),
hr(style = "border-style: dashed;border-color: grey;")
),
# mascot
conditionalPanel(
condition = "input.loaddatatype == false & input.softwaretype == 2",
h4("2. Mascot xml file: "),
fileInput("upmascot", NULL, accept = "application/zip"),
uiOutput("masui"),
hr(style = "border-style: dashed;border-color: grey;"),
h4("3. Phosphoproteomics peptide file: "),
fileInput("uppeptide", NULL, accept = "application/zip"),
uiOutput("pepui"),
hr(style = "border-style: dashed;border-color: grey;"),
h4("4. Proteomics data[optional]"),