Nothing
inst/shinyApp/ui.R
In IntLIM: Integration of Omics Data Using Linear Modeling
headerbar <- shinydashboard::dashboardHeader(
title = "IntLIM",
titleWidth = 270,
shinydashboard::dropdownMenu(
type = "notifications",
shinydashboard::notificationItem(
text = "Plots might take some time to display",
icon("truck"),
status = "warning"
)
)
)
sidebar <- shinydashboard::dashboardSidebar(
width = 270,
shinydashboard::sidebarMenu(
id="sidebar",
shinydashboard::menuItem("About",
tabName = "about",
icon = icon("info")),
shinydashboard::menuItem(
"Load Data",
tabName = "loaddata",
icon = icon("folder-open"),
badgeLabel = "step 1"
),
shinydashboard::menuItem(
"Filter Data (optional)",
tabName = "Filterdata",
icon = icon("bullseye"),
badgeLabel = "step 2"
),
shinydashboard::menuItem(
"Run Linear Models",
tabName = "RunLM",
icon = icon("bullseye"),
badgeLabel = "step 3"
),
shinydashboard::menuItem(
"Process result",
tabName = "processresult",
icon = icon("bullseye"),
badgeLabel = "step 4"
),
shinydashboard::menuItem(
"Scatter plot",
tabName = "scatterplot",
icon = icon("bullseye"),
badgeLabel = "step 5"
),
shinydashboard::menuItem(
"Exit app",
tabName = "stop",
icon = icon("sign-out"),
badgeLabel = "exit"
)
)
)
body <- shinydashboard::dashboardBody(
shinydashboard::tabItems(
shinydashboard::tabItem(tabName = "about",
fluidRow(
shinydashboard::box(
width = 12,
solidHeader = TRUE,
includeMarkdown("README.md")
)
)
),
shinydashboard::tabItem(tabName = "loaddata",
fluidRow(
shinydashboard::box(
title = strong("Load Data"),
width = 8,
solidHeader = TRUE,
h5("This step takes all the relevant CSV files as input, including the following (See About for more details):"),
tags$ul(
tags$li("input.csv (required): contains the names of all files input (See About)"),
tags$li("analyteType1Data (required): rows are analytes of the first type, columns are samples; the first row is assumed to have sample ids and these ids should be unique; the first column is assumed to have feature ids and those should be unique."),
tags$li("sampleMetaData (required): rows are samples, features are columns"),
tags$li("analyteType2Data (required): rows are analytes of the second type, columns are samples; the first row is assumed to have sample ids and these ids should be unique; the first column is assumed to have feature ids and those should be unique."),
tags$li("analyteType1MetaData (optional): rows are analytes of the first type, features are columns"),
tags$li("analyteType2MetaData (optional): rows are analytes of the second type, features are columns")
)
),
shinydashboard::box(
width = 4,
infoBoxOutput("statusbox1", width = NULL)
)
),#end of info flow
fluidRow(
shinydashboard::box(
fileInput("file1", "Select all CSV Files (input, analyteType1Data, analyteType2Data, and sampleMetaData are required)", multiple = TRUE, accept = ".csv"),
verbatimTextOutput('filename'),
uiOutput('idChooseType1'),
uiOutput('idChooseType2'),
hr(),
actionButton("run", "Run"),
conditionalPanel(condition="$('html').hasClass('shiny-busy')",
tags$div("Loading...",id="loadmessage"))
),
shinydashboard::box(
tags$b("Summary Statistics"),
pre(dataTableOutput('stats')),
tags$style(type="text/css", '#stats tfoot {display:none;}')
)
),#end of select file and stats flow
fluidRow(
shinydashboard::box(
width = 12,
tags$b("Distribution of Input Data"),
pre(htmlOutput("plot"))
)
)#end of plot flow
), # end tab loaddata
shinydashboard::tabItem(tabName = "Filterdata",
fluidRow(
shinydashboard::box(
title = strong("Filter Data (optional)") ,
width = 8,
solidHeader = TRUE,
h5("This step allows you to filter the data by a user-defined percentile cutoff."),
hr(),
numericInput("analyteType1perc", "percentile cutoff (0-1) for filtering analyte type 1 (e.g. remove analytes with mean values < cutoff):", 0, min = 0, max = 1),
numericInput("analyteType2perc", "percentile cutoff (0-1) for filtering analyte type 2 (e.g. remove analytes with mean values < cutoff):", 0, min = 0, max = 1),
numericInput("analyteMiss", "missing value percent cutoff (0-1) for filtering analytes (e.g. analytes with > % cutoff missing values will be removed)", 0,min=0,max=1),
numericInput("cov.cutoff", "coefficient of variation percentile cutoff for filtering analytes (e.g. analytes with cov > cutoff)", 0,min=0,max=1),
actionButton("run2", "Run")
),
shinydashboard::box(
width=4,
infoBoxOutput("statusbox2", width = NULL),
downloadButton('downloadFdata', 'Download')
)
), # end filter option flow
fluidRow(
shinydashboard::box(
tags$b("The statistic summary of origin data"),
dataTableOutput('Ostats'),
tags$style(type="text/css", '#Ostats tfoot {display:none;}')
),
shinydashboard::box(
tags$b("The statistic summary of filtered data"),
dataTableOutput('Fstats'),
tags$style(type="text/css", '#Fstats tfoot {display:none;}')
)
),#end stats comparison flow
fluidRow(
shinydashboard::box(
tags$b("The distribution of the origin data."),
uiOutput('Oplot')
),
shinydashboard::box(
tags$b("Verify the distribution of the filtered data."),
uiOutput('Fplot')
)
)#end plot comparison flow
),
shinydashboard::tabItem(tabName = "RunLM",
fluidRow(
shinydashboard::box(
title = strong("Run IntLIM") ,
width = 6,
height =800,
solidHeader = TRUE,
h5("This step performs the linear models for all combinations of analyte pairs and then plots distribution of p-values."),
h5("The linear model performed is 'a_i ~ a_j + p + a_j:p' where "),
tags$ul(
tags$li("'a_i' is the outcome analyte level (may be of types 1 or 2)"),
tags$li("a_j is the independent analyte level (may be of types 1 or 2"),
tags$li("p is the phenotype (e.g. tumor vs non-tumor)"),
tags$li("a_j:p is the interaction between phenotype and independent analyte level")
),
h5("A statistically significant p-value of the the interaction term a_j:p indicates that the analyte pair relationship is phenotype-specific. Please see manuscript for more details."),
hr(),
uiOutput('choosestype'),
numericInput("nrpoints", "number of points to be plotted in lowest density areas:", 10000, min = 0, max = 30000),
numericInput("pvalcutoff1","cutoff of FDR-adjusted p-value for filtering(0 - 1) :", 0.05, min = 0, max = 1),
numericInput("rsquared1", "cutoff of R^2 value for filtering (0-1):", 0.5, min = 0, max = 1),
numericInput("interactionCoeff", "cutoff of interaction coefficient percentile (0-1):", 0.9, min = 0, max = 1),
checkboxInput("continuous", "stype is continuous", value = FALSE, width = NULL),
actionButton("run3", "Run")
),
shinydashboard::box(
width = 6,
height =750,
numericInput("breaks","Breaks of histogram",100,min=10,max = 500),
plotOutput("Pdist"),
hr(),
textOutput("Ptext")
)
),
fluidRow(
shinydashboard::box(
width = 15,
height = 550,
tags$head(tags$style(type="text/css", "
loadmessage {
position: fixed;
top: 0px;
left: 0px;
width: 100%;
padding: 5px 0px 5px 0px;
text-align: center;
font-weight: bold;
font-size: 100%;
color: #000000;
background-color: #CCFF66;
z-index: 105;
}
")),
conditionalPanel(condition="$('html').hasClass('shiny-busy')",
tags$div("Loading...(It might take several minutes depending on the size of the dataset,please be patient!)",id="loadmessage")),
plotOutput("volcanoPlot")
),
shinydashboard::box(
width = 4,
infoBoxOutput("statusbox3", width = NULL)
)
)
),
shinydashboard::tabItem(tabName = "processresult",
fluidRow(
shinydashboard::box(
title = strong("Process the result") ,
width = 8,
height = 200,
solidHeader = TRUE,
h5("Process the results and filter pairs of analytes based on
adjusted p-values, R^2 values, and interaction coefficient cutoffs between the two groups being compared."),
h5("Then plot beta graph of significant gene-metabolite pairs by filling out parameters below and clicking 'Run'.")
),
shinydashboard::box(
width = 4,
height = 200,
infoBoxOutput("statusbox4", width = NULL),
downloadButton('downloadData', 'Download')
)
),#end of info floww
fluidRow(
shinydashboard::box(
width = 4,
uiOutput("numericChoice1"),
uiOutput("numericChoice2"),
numericInput("pvalcutoff2","cutoff of FDR-adjusted p-value for filtering(0 - 1) :", 0.05, min = 0, max = 1),
numericInput("rsquared2", "cutoff of R^2 value for filtering (0-1):", 0.5, min = 0, max = 1),
numericInput("interactionCoeff2", "cutoff of interaction coefficient percentile (0-1):", 0.9, min = 0, max = 1),
actionButton("run4", "Run")
),
shinydashboard::box(
width = 8,
tags$head(tags$style(type="text/css", "
loadmessage {
position: fixed;
top: 0px;
left: 0px;
width: 100%;
padding: 5px 0px 5px 0px;
text-align: center;
font-weight: bold;
font-size: 100%;
color: #000000;
background-color: #CCFF66;
z-index: 105;
}
")),
conditionalPanel(condition="$('html').hasClass('shiny-busy')",
tags$div("Loading...",id="loadmessage")),
downloadButton('downloadplot1',label='Download graph as pdf'),
plotOutput("betagraph")
)
)
),
shinydashboard::tabItem(tabName = "scatterplot",
fluidRow(
shinydashboard::box(
title = strong("Scatter plot") ,
width = 8,
solidHeader = TRUE,
h5("This step presents the table of analyte pairs that are significant."),
h5("You can plot the scatter plot of preferred analyte pairs by clicking table")
),
shinydashboard::box(
width = 4,
infoBoxOutput("statusbox5", width = NULL)
)
),# end of info flow
fluidRow(
shinydashboard::box(
width = NULL,
tags$b("Significant pairs"),
DT::DTOutput('table'),
hr(),
actionButton("run5", "Run"),
tags$head(tags$style(type="text/css", "
loadmessage {
position: fixed;
top: 0px;
left: 0px;
width: 100%;
padding: 5px 0px 5px 0px;
text-align: center;
font-weight: bold;
font-size: 100%;
color: #37649b;
background-color: #CCFF66;
z-index: 105;
}
")),
conditionalPanel(condition="$('html').hasClass('shiny-busy')",
tags$div("Loading...",id="loadmessage"))
)
),#end of table flow
fluidRow(
shinydashboard::box(
width = 12,
pre(plotOutput("scatterplot")),
)
)#end of scatterplot flow
)
)
)
shinyUI(fluidPage(
tags$head(includeHTML(("www/google_analytics.html"))),
shinydashboard::dashboardPage(
headerbar,
sidebar,
body
)
)) # end shinUI
Try the IntLIM package in your browser
Any scripts or data that you put into this service are public.
IntLIM documentation built on Aug. 22, 2022, 5:05 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
headerbar <- shinydashboard::dashboardHeader(
title = "IntLIM",
titleWidth = 270,
shinydashboard::dropdownMenu(
type = "notifications",
shinydashboard::notificationItem(
text = "Plots might take some time to display",
icon("truck"),
status = "warning"
)
)
)
sidebar <- shinydashboard::dashboardSidebar(
width = 270,
shinydashboard::sidebarMenu(
id="sidebar",
shinydashboard::menuItem("About",
tabName = "about",
icon = icon("info")),
shinydashboard::menuItem(
"Load Data",
tabName = "loaddata",
icon = icon("folder-open"),
badgeLabel = "step 1"
),
shinydashboard::menuItem(
"Filter Data (optional)",
tabName = "Filterdata",
icon = icon("bullseye"),
badgeLabel = "step 2"
),
shinydashboard::menuItem(
"Run Linear Models",
tabName = "RunLM",
icon = icon("bullseye"),
badgeLabel = "step 3"
),
shinydashboard::menuItem(
"Process result",
tabName = "processresult",
icon = icon("bullseye"),
badgeLabel = "step 4"
),
shinydashboard::menuItem(
"Scatter plot",
tabName = "scatterplot",
icon = icon("bullseye"),
badgeLabel = "step 5"
),
shinydashboard::menuItem(
"Exit app",
tabName = "stop",
icon = icon("sign-out"),
badgeLabel = "exit"
)
)
)
body <- shinydashboard::dashboardBody(
shinydashboard::tabItems(
shinydashboard::tabItem(tabName = "about",
fluidRow(
shinydashboard::box(
width = 12,
solidHeader = TRUE,
includeMarkdown("README.md")
)
)
),
shinydashboard::tabItem(tabName = "loaddata",
fluidRow(
shinydashboard::box(
title = strong("Load Data"),
width = 8,
solidHeader = TRUE,
h5("This step takes all the relevant CSV files as input, including the following (See About for more details):"),
tags$ul(
tags$li("input.csv (required): contains the names of all files input (See About)"),
tags$li("analyteType1Data (required): rows are analytes of the first type, columns are samples; the first row is assumed to have sample ids and these ids should be unique; the first column is assumed to have feature ids and those should be unique."),
tags$li("sampleMetaData (required): rows are samples, features are columns"),
tags$li("analyteType2Data (required): rows are analytes of the second type, columns are samples; the first row is assumed to have sample ids and these ids should be unique; the first column is assumed to have feature ids and those should be unique."),
tags$li("analyteType1MetaData (optional): rows are analytes of the first type, features are columns"),
tags$li("analyteType2MetaData (optional): rows are analytes of the second type, features are columns")
)
),
shinydashboard::box(
width = 4,
infoBoxOutput("statusbox1", width = NULL)
)
),#end of info flow
fluidRow(
shinydashboard::box(
fileInput("file1", "Select all CSV Files (input, analyteType1Data, analyteType2Data, and sampleMetaData are required)", multiple = TRUE, accept = ".csv"),
verbatimTextOutput('filename'),
uiOutput('idChooseType1'),
uiOutput('idChooseType2'),
hr(),
actionButton("run", "Run"),
conditionalPanel(condition="$('html').hasClass('shiny-busy')",
tags$div("Loading...",id="loadmessage"))
),
shinydashboard::box(
tags$b("Summary Statistics"),
pre(dataTableOutput('stats')),
tags$style(type="text/css", '#stats tfoot {display:none;}')
)
),#end of select file and stats flow
fluidRow(
shinydashboard::box(
width = 12,
tags$b("Distribution of Input Data"),
pre(htmlOutput("plot"))
)
)#end of plot flow
), # end tab loaddata
shinydashboard::tabItem(tabName = "Filterdata",
fluidRow(
shinydashboard::box(
title = strong("Filter Data (optional)") ,
width = 8,
solidHeader = TRUE,
h5("This step allows you to filter the data by a user-defined percentile cutoff."),
hr(),
numericInput("analyteType1perc", "percentile cutoff (0-1) for filtering analyte type 1 (e.g. remove analytes with mean values < cutoff):", 0, min = 0, max = 1),
numericInput("analyteType2perc", "percentile cutoff (0-1) for filtering analyte type 2 (e.g. remove analytes with mean values < cutoff):", 0, min = 0, max = 1),
numericInput("analyteMiss", "missing value percent cutoff (0-1) for filtering analytes (e.g. analytes with > % cutoff missing values will be removed)", 0,min=0,max=1),
numericInput("cov.cutoff", "coefficient of variation percentile cutoff for filtering analytes (e.g. analytes with cov > cutoff)", 0,min=0,max=1),
actionButton("run2", "Run")
),
shinydashboard::box(
width=4,
infoBoxOutput("statusbox2", width = NULL),
downloadButton('downloadFdata', 'Download')
)
), # end filter option flow
fluidRow(
shinydashboard::box(
tags$b("The statistic summary of origin data"),
dataTableOutput('Ostats'),
tags$style(type="text/css", '#Ostats tfoot {display:none;}')
),
shinydashboard::box(
tags$b("The statistic summary of filtered data"),
dataTableOutput('Fstats'),
tags$style(type="text/css", '#Fstats tfoot {display:none;}')
)
),#end stats comparison flow
fluidRow(
shinydashboard::box(
tags$b("The distribution of the origin data."),
uiOutput('Oplot')
),
shinydashboard::box(
tags$b("Verify the distribution of the filtered data."),
uiOutput('Fplot')
)
)#end plot comparison flow
),
shinydashboard::tabItem(tabName = "RunLM",
fluidRow(
shinydashboard::box(
title = strong("Run IntLIM") ,
width = 6,
height =800,
solidHeader = TRUE,
h5("This step performs the linear models for all combinations of analyte pairs and then plots distribution of p-values."),
h5("The linear model performed is 'a_i ~ a_j + p + a_j:p' where "),
tags$ul(
tags$li("'a_i' is the outcome analyte level (may be of types 1 or 2)"),
tags$li("a_j is the independent analyte level (may be of types 1 or 2"),
tags$li("p is the phenotype (e.g. tumor vs non-tumor)"),
tags$li("a_j:p is the interaction between phenotype and independent analyte level")
),
h5("A statistically significant p-value of the the interaction term a_j:p indicates that the analyte pair relationship is phenotype-specific. Please see manuscript for more details."),
hr(),
uiOutput('choosestype'),
numericInput("nrpoints", "number of points to be plotted in lowest density areas:", 10000, min = 0, max = 30000),
numericInput("pvalcutoff1","cutoff of FDR-adjusted p-value for filtering(0 - 1) :", 0.05, min = 0, max = 1),
numericInput("rsquared1", "cutoff of R^2 value for filtering (0-1):", 0.5, min = 0, max = 1),
numericInput("interactionCoeff", "cutoff of interaction coefficient percentile (0-1):", 0.9, min = 0, max = 1),
checkboxInput("continuous", "stype is continuous", value = FALSE, width = NULL),
actionButton("run3", "Run")
),
shinydashboard::box(
width = 6,
height =750,
numericInput("breaks","Breaks of histogram",100,min=10,max = 500),
plotOutput("Pdist"),
hr(),
textOutput("Ptext")
)
),
fluidRow(
shinydashboard::box(
width = 15,
height = 550,
tags$head(tags$style(type="text/css", "
loadmessage {
position: fixed;
top: 0px;
left: 0px;
width: 100%;
padding: 5px 0px 5px 0px;
text-align: center;
font-weight: bold;
font-size: 100%;
color: #000000;
background-color: #CCFF66;
z-index: 105;
}
")),
conditionalPanel(condition="$('html').hasClass('shiny-busy')",
tags$div("Loading...(It might take several minutes depending on the size of the dataset,please be patient!)",id="loadmessage")),
plotOutput("volcanoPlot")
),
shinydashboard::box(
width = 4,
infoBoxOutput("statusbox3", width = NULL)
)
)
),
shinydashboard::tabItem(tabName = "processresult",
fluidRow(
shinydashboard::box(
title = strong("Process the result") ,
width = 8,
height = 200,
solidHeader = TRUE,
h5("Process the results and filter pairs of analytes based on
adjusted p-values, R^2 values, and interaction coefficient cutoffs between the two groups being compared."),
h5("Then plot beta graph of significant gene-metabolite pairs by filling out parameters below and clicking 'Run'.")
),
shinydashboard::box(
width = 4,
height = 200,
infoBoxOutput("statusbox4", width = NULL),
downloadButton('downloadData', 'Download')
)
),#end of info floww
fluidRow(
shinydashboard::box(
width = 4,
uiOutput("numericChoice1"),
uiOutput("numericChoice2"),
numericInput("pvalcutoff2","cutoff of FDR-adjusted p-value for filtering(0 - 1) :", 0.05, min = 0, max = 1),
numericInput("rsquared2", "cutoff of R^2 value for filtering (0-1):", 0.5, min = 0, max = 1),
numericInput("interactionCoeff2", "cutoff of interaction coefficient percentile (0-1):", 0.9, min = 0, max = 1),
actionButton("run4", "Run")
),
shinydashboard::box(
width = 8,
tags$head(tags$style(type="text/css", "
loadmessage {
position: fixed;
top: 0px;
left: 0px;
width: 100%;
padding: 5px 0px 5px 0px;
text-align: center;
font-weight: bold;
font-size: 100%;
color: #000000;
background-color: #CCFF66;
z-index: 105;
}
")),
conditionalPanel(condition="$('html').hasClass('shiny-busy')",
tags$div("Loading...",id="loadmessage")),
downloadButton('downloadplot1',label='Download graph as pdf'),
plotOutput("betagraph")
)
)
),
shinydashboard::tabItem(tabName = "scatterplot",
fluidRow(
shinydashboard::box(
title = strong("Scatter plot") ,
width = 8,
solidHeader = TRUE,
h5("This step presents the table of analyte pairs that are significant."),
h5("You can plot the scatter plot of preferred analyte pairs by clicking table")
),
shinydashboard::box(
width = 4,
infoBoxOutput("statusbox5", width = NULL)
)
),# end of info flow
fluidRow(
shinydashboard::box(
width = NULL,
tags$b("Significant pairs"),
DT::DTOutput('table'),
hr(),
actionButton("run5", "Run"),
tags$head(tags$style(type="text/css", "
loadmessage {
position: fixed;
top: 0px;
left: 0px;
width: 100%;
padding: 5px 0px 5px 0px;
text-align: center;
font-weight: bold;
font-size: 100%;
color: #37649b;
background-color: #CCFF66;
z-index: 105;
}
")),
conditionalPanel(condition="$('html').hasClass('shiny-busy')",
tags$div("Loading...",id="loadmessage"))
)
),#end of table flow
fluidRow(
shinydashboard::box(
width = 12,
pre(plotOutput("scatterplot")),
)
)#end of scatterplot flow
)
)
)
shinyUI(fluidPage(
tags$head(includeHTML(("www/google_analytics.html"))),
shinydashboard::dashboardPage(
headerbar,
sidebar,
body
)
)) # end shinUI
Try the IntLIM package in your browser
Any scripts or data that you put into this service are public.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
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