R/ui.R
In satijalab/azimuth: A Shiny App Demonstrating a Query-Reference Mapping Algorithm for Single-Cell Data
#' @include zzz.R
#'
#' @importFrom DT DTOutput
#' @importFrom htmltools div h3 h4 HTML includeCSS p tagList tags
#' @importFrom shinyjs disabled useShinyjs
#' @importFrom shiny actionButton checkboxInput column downloadButton fileInput
#' fluidRow htmlOutput icon numericInput plotOutput radioButtons selectizeInput
#' tableOutput textOutput textAreaInput uiOutput verbatimTextOutput hoverOpts
#' checkboxGroupInput
#' @importFrom shinyBS bsButton bsPopover bsTooltip
#' @importFrom plotly plotlyOutput renderPlotly toWebGL
#' @importFrom shinydashboard box dashboardBody dashboardHeader dashboardSidebar
#' dashboardPage menuItem sidebarMenu sidebarMenuOutput tabItem tabItems
#' valueBoxOutput
#'
NULL
AzimuthUI <- tagList(
useShinyjs(),
includeCSS(path = GetCSS()),
dashboardPage(
dashboardHeader(title = app.title),
dashboardSidebar(
fileInput(
inputId = 'file',
label = p(
'File Upload',
bsButton(
'q1',
label = '',
icon = icon(name = 'question'),
style = 'info',
size = 'extra-small'
)
),
accept = c('.h5', '.h5ad', '.h5seurat', '.rds')
),
bsPopover(
id = 'q1',
title = 'Supported file types',
content = paste(
'10x Genomics H5',
'Seurat object (RDS)',
'H5AD',
'H5Seurat',
'Matrix/matrix/data.frame (RDS)',
sep = '; '
),
placement = 'right',
trigger = 'focus',
options = list(container = 'body')
),
div(
id = "demobuttons"
),
htmlOutput(outputId = 'message', inline = FALSE),
sidebarMenu(
menuItem(
text = 'Welcome',
tabName = 'tab_welcome',
icon = icon(name = 'map'),
selected = TRUE
),
sidebarMenuOutput(outputId = 'menu1'),
sidebarMenuOutput(outputId = 'menu2'),
sidebarMenuOutput(outputId = 'menu3')
),
htmlOutput(outputId = 'containerid', inline = FALSE)
),
dashboardBody(
tags$head(
tags$style(
HTML(".content-wrapper { overflow: auto }
.wrapper {height: auto !important; position:relative; overflow-x:hidden; overflow-y:hidden}
.shiny-notification {
position: fixed;
font-size: 15px;
left: calc(50% - 100px);
top: calc(90%);
width: 350px;
}
.shiny-notification-close {
display: none;
}
.small-box {height: 110px}
"
)
)
),
tabItems(
# Welcome tab
tabItem(
tabName = 'tab_welcome',
div(
fluidRow(
box(
htmlOutput(outputId = 'welcomebox'),
htmlOutput(outputId = 'refdescriptor'),
width=12
),
width=12
),
fluidRow(
div(
style = "position:relative",
plotOutput(
outputId = 'refdim_intro',
hover = hoverOpts(
id = "refdim_intro_hover_location",
delay = 5,
delayType = "debounce",
nullOutside = TRUE
),
height='1000px'
),
uiOutput("refdim_intro_hover_box")
),
width = 12,
height='1000px'
),
)
),
# Preprocessing + QC Tab
tabItem(
tabName = 'tab_preproc',
fluidRow(
box(
title = p(
'QC Filters',
bsButton(
inputId = 'q2',
label = '',
icon = icon(name = 'question'),
style = 'info',
size = 'extra-small'
)
),
div(
id = 'ncount',
numericInput(
inputId = 'num.ncountmin',
label = NULL,
value = 0,
width = '90%'
),
numericInput(
inputId = 'num.ncountmax',
label = NULL,
value = 0,
width = '90%'
)
),
div(
id = 'nfeature',
numericInput(
inputId = 'num.nfeaturemin',
label = NULL,
value = 0,
width = '90%'
),
numericInput(
inputId = 'num.nfeaturemax',
label = NULL,
value = 0,
width = '90%'
)
),
div(
id = 'pctmt',
numericInput(
inputId = 'minmt',
label = NULL,
value = 0,
width = '90%'
),
numericInput(
'maxmt',
label = NULL,
value = 0,
width = '90%'
)
),
textOutput(outputId = 'text.cellsremain'),
div(
id = 'xferopts',
h4(
'Transfer Options',
bsButton(
inputId = 'xferinput',
label = '',
icon = icon(name = 'question'),
style = 'info',
size = 'extra-small'
)
),
bsPopover(
id = 'xferinput',
title = 'Transfer Options',
content = 'Select the meta.data fields to transfer from the reference',
placement = 'right',
trigger = 'focus',
options = list(container = 'body')
),
selectizeInput(
inputId = 'metadataxfer',
label = 'Reference Metadata to Transfer',
choices = '',
multiple = TRUE
)
),
disabled(actionButton(
inputId = 'map',
label = 'Map cells to reference'
)),
width = 4
),
bsPopover(
id = 'q2',
title = 'QC Filters',
content = paste(
'Select a minimum and maximum value for nCount (number of molecules)',
'nFeature (number of genes expressed)',
'and mitochondrial percentage (if applicable)',
sep = ', '
),
placement = 'right',
trigger = 'focus',
options = list(container = 'body')
),
box(
checkboxGroupInput(inputId = "check.qc", label = NULL, choiceNames = c("Log-scale Y-axis", "Hide points"), choiceValues = c("qcscale", "qcpoints"), inline = TRUE),
plotOutput(outputId = 'plot.qc'),
tableOutput(outputId = 'table.qc'),
width = 8
),
),
uiOutput(outputId = "all_qc"),
),
tabItem(
tabName = 'tab_cell',
div(
id = "topdim",
width = 12
),
div(
id = "bottomdim",
width = 12
),
box(
title = p(
'Metadata table',
bsButton(
inputId = 'q5',
label = '',
icon = icon(name = 'question'),
style = 'info',
size = 'extra-small'
)
),
bsPopover(
id = 'q5',
title = 'Metadata table',
content = paste(
'A (usually) 2D table where each dimension represents a query metadata field, ',
'thus revealing the breakdown of any one query attribute when grouping by another. ',
'By default, a 1D table is produced where one dimension has constant value (\\"query\\") and ',
'the other is a predicted class (\\"predicted.XXX\\"), thus showing the overall breakdown of the ',
'predicted class.'),
placement = 'right',
trigger = 'focus',
options = list(container = 'body')
),
div(
style = 'display: inline-block; vertical-align: top; width: 25%',
selectizeInput(
inputId = 'metarow',
label = 'Table rows',
choices = ''
)
),
div(
style = 'display: inline-block; vertical-align: top; width: 25%',
selectizeInput(
inputId = 'metacol',
label = 'Table columns',
choices = ''
)
),
div(
style = 'display: inline-block; vertical-align: top; width: 50%',
radioButtons(
inputId = 'radio.pct',
label = NULL,
choices = c('Percentage','Frequency'),
inline = TRUE
)
),
div(
id = 'tablemetadata'
),
width = 12,
height = 'auto'
)
),
# Motif tab
tabItem(
tags$head(tags$style(HTML(".selectize-dropdown .optgroup-header { font-weight: bold; font-size: 13px; color: black; background: #f6f6f6}"))),
tabName = 'tab_motif',
# box(
# title = 'Motif Plots',
# div(
# id = 'motifinput',
# class = 'thirds',
# selectizeInput(
# inputId = 'motif.feature',
# label = 'Motif',
# choices = ''
# )
# ),
# div(
# id = 'continput.motif',
# class = 'thirds',
# selectizeInput(
# inputId = 'metadata.cont.motif',
# label = 'Prediction Scores and Metadata',
# choices = ''
# )
# ),
# plotOutput(outputId = 'motifdim'),
# selectizeInput(
# inputId = 'metagroup.motif',
# label = 'Metadata to group by',
# choices = '',
# width = '25%'
# ),
# checkboxInput(inputId = 'check.featpoints', label = 'Hide points'),
# plotOutput(outputId = 'motifvln'),
# width = 12
# ),
box(
title = p(
'Predicted cell type cluster motifs',
bsButton(
inputId = 'q6',
label = '',
icon = icon(name = 'question'),
style = 'info',
size = 'extra-small'
)
),
bsPopover(
id = 'q6',
title = 'Motif Analysis Table',
content = paste(
'Only available for clusters with at least 15 cells.',
paste(
# 'avg_diff: log fold-change between cells in the cluster specified and other cells',
'padj: Benjamini-Hochberg adjusted p value',
'pct_in: percent of cells in the cluster with nonzero feature value',
'pct_out: percent of cells out of the cluster with nonzero feature value',
sep = '; '
)
),
placement = 'right',
trigger = 'focus',
options = list(container = 'body')
),
div(
id = 'markerclustersgroupinput.motif',
class = 'halves',
selectizeInput(
inputId = 'markerclustersgroup.motif',
label = 'Metadata group',
choices = ''
)
),
div(
id = 'markerclustersgroupinput.motif',
class = 'halves',
selectizeInput(
inputId = 'markerclusters.motif',
label = 'Predicted cell type',
choices = ''
)
),
div(
id = 'motiftable',
class = 'full',
h3('Motifs'),
DTOutput(outputId = 'motifs')
),
width = 12
)
),
# Feature tab
tabItem(
tags$head(tags$style(HTML(".selectize-dropdown .optgroup-header { font-weight: bold; font-size: 13px; color: black; background: #f6f6f6}"))),
tabName = 'tab_feature',
box(
title = 'Gene Activity Scores',
div(
id = 'featureinput',
class = 'thirds',
selectizeInput(
inputId = 'feature',
label = 'Feature',
choices = ''
)
),
div(
id = 'imputedinput',
class = 'thirds',
selectizeInput(
inputId = 'adtfeature',
label = 'Imputed protein',
choices = ''
)
),
div(
id = 'continput',
class = 'thirds',
selectizeInput(
inputId = 'metadata.cont',
label = 'Prediction Scores and Metadata',
choices = ''
)
),
plotOutput(outputId = 'edim'),
selectizeInput(
inputId = 'metagroup',
label = 'Metadata to group by',
choices = '',
width = '25%'
),
checkboxInput(inputId = 'check.featpoints', label = 'Hide points'),
plotOutput(outputId = 'evln'),
width = 12
),
box(
title = p(
'Predicted cell type cluster biomarkers',
bsButton(
inputId = 'q3',
label = '',
icon = icon(name = 'question'),
style = 'info',
size = 'extra-small'
)
),
bsPopover(
id = 'q3',
title = 'Biomarkers Table',
content = paste(
'Only available for clusters with at least 15 cells.',
paste(
# 'logFC: log fold-change between cells in the cluster specified and other cells',
'auc: area under ROC',
'padj: Benjamini-Hochberg adjusted p value',
'pct_in: percent of cells in the cluster with nonzero feature value',
'pct_out: percent of cells out of the cluster with nonzero feature value',
sep = '; '
)
),
placement = 'right',
trigger = 'focus',
options = list(container = 'body')
),
div(
id = 'markerclustersgroupinput',
class = 'halves',
selectizeInput(
inputId = 'markerclustersgroup',
label = 'Metadata group',
choices = ''
)
),
div(
id = 'markerclustersgroupinput',
class = 'halves',
selectizeInput(
inputId = 'markerclusters',
label = 'Predicted cell type',
choices = ''
)
),
div(
id = 'biotable',
class = 'halves',
h3('RNA biomarkers'),
DTOutput(outputId = 'biomarkers')
),
div(
id = 'imputedtable',
class = 'halves',
uiOutput(outputId = 'imputedlabel'),
DTOutput(outputId = 'adtbio')
),
width = 12
)
),
# Downloads tab
tabItem(
tabName = 'tab_download',
div(
id = 'scriptdl',
box(
title = 'Analysis script template ',
downloadButton(
outputId = 'dlscript',
label = 'Download'
),
width = 6
)
),
div(
id = 'umapdl',
box(
title = 'UMAP (Seurat Reduction RDS)',
verbatimTextOutput(outputId = 'text.dlumap'),
downloadButton(
outputId = 'dlumap',
label = 'Download'
),
width = 6
)
),
div(
id = 'imputeddl',
box(
title = 'Imputed protein (Seurat Assay RDS)',
verbatimTextOutput(outputId = 'text.dladt'),
downloadButton(
outputId = 'dladt',
label = 'Download'
),
width = 6
)
),
div(
id = 'predictionsdl',
box(
title = 'Predicted cell types and scores (TSV)',
verbatimTextOutput(outputId = 'text.dlpred'),
downloadButton(
outputId = 'dlpred',
label = 'Download'
),
width = 6
)
),
div(
id = 'alldl',
box(
title = 'All results',
verbatimTextOutput(outputId = 'text.dlall'),
downloadButton(
outputId = 'dlall',
label = 'Download'
),
width = 6
)
)
),
# Feedback tab
tabItem(
tabName = 'tab_feedback',
box(
div(
h3('Tell us anything!'),
textAreaInput(
inputId = 'feedback',
label = NULL,
value = '',
width = '100%',
height = '300px',
resize = 'none',
placeholder = 'Were the results helpful? Did you encounter any bugs? Any new feature requests?'
),
actionButton(inputId = "submit_feedback", label = "Submit"),
),
width = 8
)
)
)
)
)
)
#'
#' ################### Bridge Integration #####################
#'
#' AzimuthBridgeUI <- tagList(
#' useShinyjs(),
#' includeCSS(path = GetCSS()),
#' dashboardPage(
#' dashboardHeader(title = app.title),
#' dashboardSidebar(
#' fileInput(
#' inputId = 'file',
#' label = p(
#' 'File Upload',
#' bsButton(
#' 'q1',
#' label = '',
#' icon = icon(name = 'question'),
#' style = 'info',
#' size = 'extra-small'
#' )
#' ),
#' accept = c('.h5', '.h5ad', '.h5seurat', '.rds')
#' ),
#' bsPopover(
#' id = 'q1',
#' title = 'Supported file types',
#' content = paste(
#' '10x Genomics H5',
#' 'Seurat object (RDS)',
#' 'H5AD',
#' 'H5Seurat',
#' 'Matrix/matrix/data.frame (RDS)',
#' sep = '; '
#' ),
#' placement = 'right',
#' trigger = 'focus',
#' options = list(container = 'body')
#' ),
#' div(
#' id = "demobuttons"
#' ),
#' htmlOutput(outputId = 'message', inline = FALSE),
#' sidebarMenu(
#' menuItem(
#' text = 'Welcome',
#' tabName = 'tab_welcome',
#' icon = icon(name = 'map'),
#' selected = TRUE
#' ),
#' sidebarMenuOutput(outputId = 'menu1'),
#' sidebarMenuOutput(outputId = 'menu2'),
#' sidebarMenuOutput(outputId = 'menu3')
#' ),
#' htmlOutput(outputId = 'containerid', inline = FALSE)
#' ),
#' dashboardBody(
#' tags$head(
#' tags$style(
#' HTML(".content-wrapper { overflow: auto }
#' .wrapper {height: auto !important; position:relative; overflow-x:hidden; overflow-y:hidden}
#' .shiny-notification {
#' position: fixed;
#' font-size: 15px;
#' left: calc(50% - 100px);
#' top: calc(90%);
#' width: 350px;
#' }
#' .shiny-notification-close {
#' display: none;
#' }
#' .small-box {height: 110px}
#' "
#' )
#' )
#' ),
#' tabItems(
#' # Welcome tab
#' tabItem(
#' tabName = 'tab_welcome',
#' div(
#' fluidRow(
#' box(
#' htmlOutput(outputId = 'welcomebox'),
#' htmlOutput(outputId = 'refdescriptor'),
#' width=12
#' ),
#' width=12
#' ),
#' fluidRow(
#' div(
#' style = "position:relative",
#' plotOutput(
#' outputId = 'refdim_intro',
#' hover = hoverOpts(
#' id = "refdim_intro_hover_location",
#' delay = 5,
#' delayType = "debounce",
#' nullOutside = TRUE
#' ),
#' height='1000px'
#' ),
#' uiOutput("refdim_intro_hover_box")
#' ),
#' width = 12,
#' height='1000px'
#' ),
#' )
#' ),
#' # Preprocessing + QC Tab
#' tabItem(
#' tabName = 'tab_preproc',
#' fluidRow(
#' column(4,
#' wellPanel(
#' box(
#' title = p(
#' 'QC Filters',
#' bsButton(
#' inputId = 'q2',
#' label = '',
#' icon = icon(name = 'question'),
#' style = 'info',
#' size = 'extra-small'
#' )
#' ),
#' div(
#' id = 'ncount',
#' numericInput(
#' inputId = 'num.ncountmin',
#' label = NULL,
#' value = 0,
#' width = '90%'
#' ),
#' numericInput(
#' inputId = 'num.ncountmax',
#' label = NULL,
#' value = 0,
#' width = '90%'
#' )
#' ),
#' div(
#' id = 'nfeature',
#' numericInput(
#' inputId = 'num.nfeaturemin',
#' label = NULL,
#' value = 0,
#' width = '90%'
#' ),
#' numericInput(
#' inputId = 'num.nfeaturemax',
#' label = NULL,
#' value = 0,
#' width = '90%'
#' )
#' ),
#' textOutput(outputId = 'text.cellsremain'),
#' div(
#' id = 'xferopts',
#' h4(
#' 'Transfer Options',
#' bsButton(
#' inputId = 'xferinput',
#' label = '',
#' icon = icon(name = 'question'),
#' style = 'info',
#' size = 'extra-small'
#' )
#' ),
#' bsPopover(
#' id = 'xferinput',
#' title = 'Transfer Options',
#' content = 'Select the meta.data fields to transfer from the reference',
#' placement = 'right',
#' trigger = 'focus',
#' options = list(container = 'body')
#' ),
#' selectizeInput(
#' inputId = 'metadataxfer',
#' label = 'Reference Metadata to Transfer',
#' choices = '',
#' multiple = TRUE
#' )
#' ),
#' disabled(actionButton(
#' inputId = 'map',
#' label = 'Map cells to reference'
#' )),
#' width = 3
#' ),
#' bsPopover(
#' id = 'q2',
#' title = 'QC Filters',
#' content = paste(
#' 'Select a minimum and maximum value for nCount (number of molecules)',
#' 'nFeature (number of genes expressed)',
#' #'and mitochondrial percentage (if applicable)',
#' sep = ', '
#' ),
#' placement = 'right',
#' trigger = 'focus',
#' options = list(container = 'body')
#' ),
#' box(
#' checkboxGroupInput(inputId = "check.qc", label = NULL, choiceNames = c("Log-scale Y-axis", "Hide points"), choiceValues = c("qcscale", "qcpoints"), inline = TRUE),
#' plotOutput(outputId = 'plot.qc'),
#' tableOutput(outputId = 'table.qc'),
#' width = 8
#' ),
#' ),
#' fluidRow(
#' valueBoxOutput(outputId = 'valuebox.upload', width = 3),
#' valueBoxOutput(outputId = 'valuebox_overlap', width = 3),
#' valueBoxOutput(outputId = 'valuebox_jaccard', width = 3),
#' valueBoxOutput(outputId = 'valuebox.preproc', width = 3),
#' div(
#' id = 'panchors_popup',
#' valueBoxOutput(outputId = "valuebox_panchors", width = 3),
#' bsTooltip(id = "valuebox_panchors", title = "Click for more info", placement = "top", trigger = 'hover'),
#' ),
#' div(
#' id = 'overlap_popup',
#' valueBoxOutput(outputId = "valuebox_overlap", width = 3),
#' bsTooltip(id = "valuebox_overlap", title = "Click for more info", placement = "top", trigger = 'hover')
#' ),
#' div(
#' id = 'mappingqcstat_popup',
#' valueBoxOutput(outputId = "valuebox_mappingqcstat", width = 3),
#' bsTooltip(id = "valuebox_mappingqcstat", title = "Click for more info", placement = "top", trigger = 'hover'),
#' ),
#' valueBoxOutput(outputId = 'valuebox.mapped', width = 3),
#' ),
#' ),
#' tabItem(
#' tabName = 'tab_cell',
#' div(
#' id = "topdim",
#' width = 12
#' ),
#' div(
#' id = "bottomdim",
#' width = 12
#' ),
#' box(
#' title = p(
#' 'Metadata table',
#' bsButton(
#' inputId = 'q5',
#' label = '',
#' icon = icon(name = 'question'),
#' style = 'info',
#' size = 'extra-small'
#' )
#' ),
#' bsPopover(
#' id = 'q5',
#' title = 'Metadata table',
#' content = paste(
#' 'A (usually) 2D table where each dimension represents a query metadata field, ',
#' 'thus revealing the breakdown of any one query attribute when grouping by another. ',
#' 'By default, a 1D table is produced where one dimension has constant value (\\"query\\") and ',
#' 'the other is a predicted class (\\"predicted.XXX\\"), thus showing the overall breakdown of the ',
#' 'predicted class.'),
#' placement = 'right',
#' trigger = 'focus',
#' options = list(container = 'body')
#' ),
#' div(
#' style = 'display: inline-block; vertical-align: top; width: 25%',
#' selectizeInput(
#' inputId = 'metarow',
#' label = 'Table rows',
#' choices = ''
#' )
#' ),
#' div(
#' style = 'display: inline-block; vertical-align: top; width: 25%',
#' selectizeInput(
#' inputId = 'metacol',
#' label = 'Table columns',
#' choices = ''
#' )
#' ),
#' div(
#' style = 'display: inline-block; vertical-align: top; width: 50%',
#' radioButtons(
#' inputId = 'radio.pct',
#' label = NULL,
#' choices = c('Percentage','Frequency'),
#' inline = TRUE
#' )
#' ),
#' div(
#' id = 'tablemetadata'
#' ),
#' width = 12,
#' height = 'auto'
#' )
#' ),
#' # Motif tab
#' tabItem(
#' tags$head(tags$style(HTML(".selectize-dropdown .optgroup-header { font-weight: bold; font-size: 13px; color: black; background: #f6f6f6}"))),
#' tabName = 'tab_motif',
#' box(
#' title = 'Motif Plots',
#' div(
#' id = 'motifinput',
#' class = 'thirds',
#' selectizeInput(
#' inputId = 'chromvar.feature',
#' label = 'Motif',
#' choices = ''
#' )
#' ),
#' div(
#' id = 'continput.motif',
#' class = 'thirds',
#' selectizeInput(
#' inputId = 'metadata.cont.motif',
#' label = 'Prediction Scores and Metadata',
#' choices = ''
#' )
#' ),
#' plotOutput(outputId = 'motifdim'),
#' selectizeInput(
#' inputId = 'metagroup.motif',
#' label = 'Metadata to group by',
#' choices = '',
#' width = '25%'
#' ),
#' checkboxInput(inputId = 'check.featpoints', label = 'Hide points'),
#' plotOutput(outputId = 'motifvln'),
#' width = 12
#' ),
#' box(
#' title = p(
#' 'Predicted cell type cluster motifs',
#' bsButton(
#' inputId = 'q6',
#' label = '',
#' icon = icon(name = 'question'),
#' style = 'info',
#' size = 'extra-small'
#' )
#' ),
#' bsPopover(
#' id = 'q6',
#' title = 'Motif Analysis Table',
#' content = paste(
#' 'Only available for clusters with at least 15 cells.',
#' paste(
#' # 'avg_diff: log fold-change between cells in the cluster specified and other cells',
#' 'padj: Benjamini-Hochberg adjusted p value',
#' 'pct_in: percent of cells in the cluster with nonzero feature value',
#' 'pct_out: percent of cells out of the cluster with nonzero feature value',
#' sep = '; '
#' )
#' ),
#' placement = 'right',
#' trigger = 'focus',
#' options = list(container = 'body')
#' ),
#' div(
#' id = 'markerclustersgroupinput.motif',
#' class = 'full',
#' selectizeInput(
#' inputId = 'markerclustersgroup.motif',
#' label = 'Metadata group',
#' choices = ''
#' )
#' ),
#' div(
#' id = 'markerclustersgroupinput.motif',
#' class = 'full',
#' selectizeInput(
#' inputId = 'markerclusters.motif',
#' label = 'Predicted cell type',
#' choices = ''
#' )
#' ),
#' div(
#' id = 'motiftable',
#' class = 'full',
#' h3('Motifs'),
#' DTOutput(outputId = 'motifs')
#' ),
#' width = 12
#' )
#' ),
#' # Feature tab
#' tabItem(
#' tags$head(tags$style(HTML(".selectize-dropdown .optgroup-header { font-weight: bold; font-size: 13px; color: black; background: #f6f6f6}"))),
#' tabName = 'tab_feature',
#' box(
#' title = 'Feature Plots',
#' div(
#' id = 'featureinput',
#' class = 'thirds',
#' selectizeInput(
#' inputId = 'feature',
#' label = 'Feature',
#' choices = ''
#' )
#' ),
#' div(
#' id = 'imputedinput',
#' class = 'thirds',
#' selectizeInput(
#' inputId = 'adtfeature',
#' label = 'Imputed protein',
#' choices = ''
#' )
#' ),
#' div(
#' id = 'continput',
#' class = 'thirds',
#' selectizeInput(
#' inputId = 'metadata.cont',
#' label = 'Prediction Scores and Metadata',
#' choices = ''
#' )
#' ),
#' plotOutput(outputId = 'edim'),
#' selectizeInput(
#' inputId = 'metagroup',
#' label = 'Metadata to group by',
#' choices = '',
#' width = '25%'
#' ),
#' checkboxInput(inputId = 'check.featpoints', label = 'Hide points'),
#' plotOutput(outputId = 'evln'),
#' width = 12
#' ),
#' box(
#' title = p(
#' 'Predicted cell type cluster biomarkers',
#' bsButton(
#' inputId = 'q3',
#' label = '',
#' icon = icon(name = 'question'),
#' style = 'info',
#' size = 'extra-small'
#' )
#' ),
#' bsPopover(
#' id = 'q3',
#' title = 'Biomarkers Table',
#' content = paste(
#' 'Only available for clusters with at least 15 cells.',
#' paste(
#' # 'logFC: log fold-change between cells in the cluster specified and other cells',
#' 'auc: area under ROC',
#' 'padj: Benjamini-Hochberg adjusted p value',
#' 'pct_in: percent of cells in the cluster with nonzero feature value',
#' 'pct_out: percent of cells out of the cluster with nonzero feature value',
#' sep = '; '
#' )
#' ),
#' placement = 'right',
#' trigger = 'focus',
#' options = list(container = 'body')
#' ),
#' div(
#' id = 'markerclustersgroupinput',
#' class = 'halves',
#' selectizeInput(
#' inputId = 'markerclustersgroup',
#' label = 'Metadata group',
#' choices = ''
#' )
#' ),
#' div(
#' id = 'markerclustersgroupinput',
#' class = 'halves',
#' selectizeInput(
#' inputId = 'markerclusters',
#' label = 'Predicted cell type',
#' choices = ''
#' )
#' ),
#' div(
#' id = 'biotable',
#' class = 'halves',
#' h3('RNA biomarkers'),
#' DTOutput(outputId = 'biomarkers')
#' ),
#' div(
#' id = 'imputedtable',
#' class = 'halves',
#' uiOutput(outputId = 'imputedlabel'),
#' DTOutput(outputId = 'adtbio')
#' ),
#' width = 12
#' )
#' ),
#' # Downloads tab
#' tabItem(
#' tabName = 'tab_download',
#' div(
#' id = 'scriptdl',
#' box(
#' title = 'Analysis script template ',
#' downloadButton(
#' outputId = 'dlscript',
#' label = 'Download'
#' ),
#' width = 6
#' )
#' ),
#' div(
#' id = 'umapdl',
#' box(
#' title = 'UMAP (Seurat Reduction RDS)',
#' verbatimTextOutput(outputId = 'text.dlumap'),
#' downloadButton(
#' outputId = 'dlumap',
#' label = 'Download'
#' ),
#' width = 6
#' )
#' ),
#' div(
#' id = 'imputeddl',
#' box(
#' title = 'Imputed protein (Seurat Assay RDS)',
#' verbatimTextOutput(outputId = 'text.dladt'),
#' downloadButton(
#' outputId = 'dladt',
#' label = 'Download'
#' ),
#' width = 6
#' )
#' ),
#' div(
#' id = 'predictionsdl',
#' box(
#' title = 'Predicted cell types and scores (TSV)',
#' verbatimTextOutput(outputId = 'text.dlpred'),
#' downloadButton(
#' outputId = 'dlpred',
#' label = 'Download'
#' ),
#' width = 6
#' )
#' ),
#' div(
#' id = 'alldl',
#' box(
#' title = 'All results',
#' verbatimTextOutput(outputId = 'text.dlall'),
#' downloadButton(
#' outputId = 'dlall',
#' label = 'Download'
#' ),
#' width = 6
#' )
#' )
#' ),
#' # Feedback tab
#' tabItem(
#' tabName = 'tab_feedback',
#' box(
#' div(
#' h3('Tell us anything!'),
#' textAreaInput(
#' inputId = 'feedback',
#' label = NULL,
#' value = '',
#' width = '100%',
#' height = '300px',
#' resize = 'none',
#' placeholder = 'Were the results helpful? Did you encounter any bugs? Any new feature requests?'
#' ),
#' actionButton(inputId = "submit_feedback", label = "Submit"),
#' ),
#' width = 8
#' )
#' )
#' )
#' )
#' )
#' )
#'
satijalab/azimuth documentation built on Nov. 19, 2023, 8:34 a.m.
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#' @include zzz.R
#'
#' @importFrom DT DTOutput
#' @importFrom htmltools div h3 h4 HTML includeCSS p tagList tags
#' @importFrom shinyjs disabled useShinyjs
#' @importFrom shiny actionButton checkboxInput column downloadButton fileInput
#' fluidRow htmlOutput icon numericInput plotOutput radioButtons selectizeInput
#' tableOutput textOutput textAreaInput uiOutput verbatimTextOutput hoverOpts
#' checkboxGroupInput
#' @importFrom shinyBS bsButton bsPopover bsTooltip
#' @importFrom plotly plotlyOutput renderPlotly toWebGL
#' @importFrom shinydashboard box dashboardBody dashboardHeader dashboardSidebar
#' dashboardPage menuItem sidebarMenu sidebarMenuOutput tabItem tabItems
#' valueBoxOutput
#'
NULL
AzimuthUI <- tagList(
useShinyjs(),
includeCSS(path = GetCSS()),
dashboardPage(
dashboardHeader(title = app.title),
dashboardSidebar(
fileInput(
inputId = 'file',
label = p(
'File Upload',
bsButton(
'q1',
label = '',
icon = icon(name = 'question'),
style = 'info',
size = 'extra-small'
)
),
accept = c('.h5', '.h5ad', '.h5seurat', '.rds')
),
bsPopover(
id = 'q1',
title = 'Supported file types',
content = paste(
'10x Genomics H5',
'Seurat object (RDS)',
'H5AD',
'H5Seurat',
'Matrix/matrix/data.frame (RDS)',
sep = '; '
),
placement = 'right',
trigger = 'focus',
options = list(container = 'body')
),
div(
id = "demobuttons"
),
htmlOutput(outputId = 'message', inline = FALSE),
sidebarMenu(
menuItem(
text = 'Welcome',
tabName = 'tab_welcome',
icon = icon(name = 'map'),
selected = TRUE
),
sidebarMenuOutput(outputId = 'menu1'),
sidebarMenuOutput(outputId = 'menu2'),
sidebarMenuOutput(outputId = 'menu3')
),
htmlOutput(outputId = 'containerid', inline = FALSE)
),
dashboardBody(
tags$head(
tags$style(
HTML(".content-wrapper { overflow: auto }
.wrapper {height: auto !important; position:relative; overflow-x:hidden; overflow-y:hidden}
.shiny-notification {
position: fixed;
font-size: 15px;
left: calc(50% - 100px);
top: calc(90%);
width: 350px;
}
.shiny-notification-close {
display: none;
}
.small-box {height: 110px}
"
)
)
),
tabItems(
# Welcome tab
tabItem(
tabName = 'tab_welcome',
div(
fluidRow(
box(
htmlOutput(outputId = 'welcomebox'),
htmlOutput(outputId = 'refdescriptor'),
width=12
),
width=12
),
fluidRow(
div(
style = "position:relative",
plotOutput(
outputId = 'refdim_intro',
hover = hoverOpts(
id = "refdim_intro_hover_location",
delay = 5,
delayType = "debounce",
nullOutside = TRUE
),
height='1000px'
),
uiOutput("refdim_intro_hover_box")
),
width = 12,
height='1000px'
),
)
),
# Preprocessing + QC Tab
tabItem(
tabName = 'tab_preproc',
fluidRow(
box(
title = p(
'QC Filters',
bsButton(
inputId = 'q2',
label = '',
icon = icon(name = 'question'),
style = 'info',
size = 'extra-small'
)
),
div(
id = 'ncount',
numericInput(
inputId = 'num.ncountmin',
label = NULL,
value = 0,
width = '90%'
),
numericInput(
inputId = 'num.ncountmax',
label = NULL,
value = 0,
width = '90%'
)
),
div(
id = 'nfeature',
numericInput(
inputId = 'num.nfeaturemin',
label = NULL,
value = 0,
width = '90%'
),
numericInput(
inputId = 'num.nfeaturemax',
label = NULL,
value = 0,
width = '90%'
)
),
div(
id = 'pctmt',
numericInput(
inputId = 'minmt',
label = NULL,
value = 0,
width = '90%'
),
numericInput(
'maxmt',
label = NULL,
value = 0,
width = '90%'
)
),
textOutput(outputId = 'text.cellsremain'),
div(
id = 'xferopts',
h4(
'Transfer Options',
bsButton(
inputId = 'xferinput',
label = '',
icon = icon(name = 'question'),
style = 'info',
size = 'extra-small'
)
),
bsPopover(
id = 'xferinput',
title = 'Transfer Options',
content = 'Select the meta.data fields to transfer from the reference',
placement = 'right',
trigger = 'focus',
options = list(container = 'body')
),
selectizeInput(
inputId = 'metadataxfer',
label = 'Reference Metadata to Transfer',
choices = '',
multiple = TRUE
)
),
disabled(actionButton(
inputId = 'map',
label = 'Map cells to reference'
)),
width = 4
),
bsPopover(
id = 'q2',
title = 'QC Filters',
content = paste(
'Select a minimum and maximum value for nCount (number of molecules)',
'nFeature (number of genes expressed)',
'and mitochondrial percentage (if applicable)',
sep = ', '
),
placement = 'right',
trigger = 'focus',
options = list(container = 'body')
),
box(
checkboxGroupInput(inputId = "check.qc", label = NULL, choiceNames = c("Log-scale Y-axis", "Hide points"), choiceValues = c("qcscale", "qcpoints"), inline = TRUE),
plotOutput(outputId = 'plot.qc'),
tableOutput(outputId = 'table.qc'),
width = 8
),
),
uiOutput(outputId = "all_qc"),
),
tabItem(
tabName = 'tab_cell',
div(
id = "topdim",
width = 12
),
div(
id = "bottomdim",
width = 12
),
box(
title = p(
'Metadata table',
bsButton(
inputId = 'q5',
label = '',
icon = icon(name = 'question'),
style = 'info',
size = 'extra-small'
)
),
bsPopover(
id = 'q5',
title = 'Metadata table',
content = paste(
'A (usually) 2D table where each dimension represents a query metadata field, ',
'thus revealing the breakdown of any one query attribute when grouping by another. ',
'By default, a 1D table is produced where one dimension has constant value (\\"query\\") and ',
'the other is a predicted class (\\"predicted.XXX\\"), thus showing the overall breakdown of the ',
'predicted class.'),
placement = 'right',
trigger = 'focus',
options = list(container = 'body')
),
div(
style = 'display: inline-block; vertical-align: top; width: 25%',
selectizeInput(
inputId = 'metarow',
label = 'Table rows',
choices = ''
)
),
div(
style = 'display: inline-block; vertical-align: top; width: 25%',
selectizeInput(
inputId = 'metacol',
label = 'Table columns',
choices = ''
)
),
div(
style = 'display: inline-block; vertical-align: top; width: 50%',
radioButtons(
inputId = 'radio.pct',
label = NULL,
choices = c('Percentage','Frequency'),
inline = TRUE
)
),
div(
id = 'tablemetadata'
),
width = 12,
height = 'auto'
)
),
# Motif tab
tabItem(
tags$head(tags$style(HTML(".selectize-dropdown .optgroup-header { font-weight: bold; font-size: 13px; color: black; background: #f6f6f6}"))),
tabName = 'tab_motif',
# box(
# title = 'Motif Plots',
# div(
# id = 'motifinput',
# class = 'thirds',
# selectizeInput(
# inputId = 'motif.feature',
# label = 'Motif',
# choices = ''
# )
# ),
# div(
# id = 'continput.motif',
# class = 'thirds',
# selectizeInput(
# inputId = 'metadata.cont.motif',
# label = 'Prediction Scores and Metadata',
# choices = ''
# )
# ),
# plotOutput(outputId = 'motifdim'),
# selectizeInput(
# inputId = 'metagroup.motif',
# label = 'Metadata to group by',
# choices = '',
# width = '25%'
# ),
# checkboxInput(inputId = 'check.featpoints', label = 'Hide points'),
# plotOutput(outputId = 'motifvln'),
# width = 12
# ),
box(
title = p(
'Predicted cell type cluster motifs',
bsButton(
inputId = 'q6',
label = '',
icon = icon(name = 'question'),
style = 'info',
size = 'extra-small'
)
),
bsPopover(
id = 'q6',
title = 'Motif Analysis Table',
content = paste(
'Only available for clusters with at least 15 cells.',
paste(
# 'avg_diff: log fold-change between cells in the cluster specified and other cells',
'padj: Benjamini-Hochberg adjusted p value',
'pct_in: percent of cells in the cluster with nonzero feature value',
'pct_out: percent of cells out of the cluster with nonzero feature value',
sep = '; '
)
),
placement = 'right',
trigger = 'focus',
options = list(container = 'body')
),
div(
id = 'markerclustersgroupinput.motif',
class = 'halves',
selectizeInput(
inputId = 'markerclustersgroup.motif',
label = 'Metadata group',
choices = ''
)
),
div(
id = 'markerclustersgroupinput.motif',
class = 'halves',
selectizeInput(
inputId = 'markerclusters.motif',
label = 'Predicted cell type',
choices = ''
)
),
div(
id = 'motiftable',
class = 'full',
h3('Motifs'),
DTOutput(outputId = 'motifs')
),
width = 12
)
),
# Feature tab
tabItem(
tags$head(tags$style(HTML(".selectize-dropdown .optgroup-header { font-weight: bold; font-size: 13px; color: black; background: #f6f6f6}"))),
tabName = 'tab_feature',
box(
title = 'Gene Activity Scores',
div(
id = 'featureinput',
class = 'thirds',
selectizeInput(
inputId = 'feature',
label = 'Feature',
choices = ''
)
),
div(
id = 'imputedinput',
class = 'thirds',
selectizeInput(
inputId = 'adtfeature',
label = 'Imputed protein',
choices = ''
)
),
div(
id = 'continput',
class = 'thirds',
selectizeInput(
inputId = 'metadata.cont',
label = 'Prediction Scores and Metadata',
choices = ''
)
),
plotOutput(outputId = 'edim'),
selectizeInput(
inputId = 'metagroup',
label = 'Metadata to group by',
choices = '',
width = '25%'
),
checkboxInput(inputId = 'check.featpoints', label = 'Hide points'),
plotOutput(outputId = 'evln'),
width = 12
),
box(
title = p(
'Predicted cell type cluster biomarkers',
bsButton(
inputId = 'q3',
label = '',
icon = icon(name = 'question'),
style = 'info',
size = 'extra-small'
)
),
bsPopover(
id = 'q3',
title = 'Biomarkers Table',
content = paste(
'Only available for clusters with at least 15 cells.',
paste(
# 'logFC: log fold-change between cells in the cluster specified and other cells',
'auc: area under ROC',
'padj: Benjamini-Hochberg adjusted p value',
'pct_in: percent of cells in the cluster with nonzero feature value',
'pct_out: percent of cells out of the cluster with nonzero feature value',
sep = '; '
)
),
placement = 'right',
trigger = 'focus',
options = list(container = 'body')
),
div(
id = 'markerclustersgroupinput',
class = 'halves',
selectizeInput(
inputId = 'markerclustersgroup',
label = 'Metadata group',
choices = ''
)
),
div(
id = 'markerclustersgroupinput',
class = 'halves',
selectizeInput(
inputId = 'markerclusters',
label = 'Predicted cell type',
choices = ''
)
),
div(
id = 'biotable',
class = 'halves',
h3('RNA biomarkers'),
DTOutput(outputId = 'biomarkers')
),
div(
id = 'imputedtable',
class = 'halves',
uiOutput(outputId = 'imputedlabel'),
DTOutput(outputId = 'adtbio')
),
width = 12
)
),
# Downloads tab
tabItem(
tabName = 'tab_download',
div(
id = 'scriptdl',
box(
title = 'Analysis script template ',
downloadButton(
outputId = 'dlscript',
label = 'Download'
),
width = 6
)
),
div(
id = 'umapdl',
box(
title = 'UMAP (Seurat Reduction RDS)',
verbatimTextOutput(outputId = 'text.dlumap'),
downloadButton(
outputId = 'dlumap',
label = 'Download'
),
width = 6
)
),
div(
id = 'imputeddl',
box(
title = 'Imputed protein (Seurat Assay RDS)',
verbatimTextOutput(outputId = 'text.dladt'),
downloadButton(
outputId = 'dladt',
label = 'Download'
),
width = 6
)
),
div(
id = 'predictionsdl',
box(
title = 'Predicted cell types and scores (TSV)',
verbatimTextOutput(outputId = 'text.dlpred'),
downloadButton(
outputId = 'dlpred',
label = 'Download'
),
width = 6
)
),
div(
id = 'alldl',
box(
title = 'All results',
verbatimTextOutput(outputId = 'text.dlall'),
downloadButton(
outputId = 'dlall',
label = 'Download'
),
width = 6
)
)
),
# Feedback tab
tabItem(
tabName = 'tab_feedback',
box(
div(
h3('Tell us anything!'),
textAreaInput(
inputId = 'feedback',
label = NULL,
value = '',
width = '100%',
height = '300px',
resize = 'none',
placeholder = 'Were the results helpful? Did you encounter any bugs? Any new feature requests?'
),
actionButton(inputId = "submit_feedback", label = "Submit"),
),
width = 8
)
)
)
)
)
)
#'
#' ################### Bridge Integration #####################
#'
#' AzimuthBridgeUI <- tagList(
#' useShinyjs(),
#' includeCSS(path = GetCSS()),
#' dashboardPage(
#' dashboardHeader(title = app.title),
#' dashboardSidebar(
#' fileInput(
#' inputId = 'file',
#' label = p(
#' 'File Upload',
#' bsButton(
#' 'q1',
#' label = '',
#' icon = icon(name = 'question'),
#' style = 'info',
#' size = 'extra-small'
#' )
#' ),
#' accept = c('.h5', '.h5ad', '.h5seurat', '.rds')
#' ),
#' bsPopover(
#' id = 'q1',
#' title = 'Supported file types',
#' content = paste(
#' '10x Genomics H5',
#' 'Seurat object (RDS)',
#' 'H5AD',
#' 'H5Seurat',
#' 'Matrix/matrix/data.frame (RDS)',
#' sep = '; '
#' ),
#' placement = 'right',
#' trigger = 'focus',
#' options = list(container = 'body')
#' ),
#' div(
#' id = "demobuttons"
#' ),
#' htmlOutput(outputId = 'message', inline = FALSE),
#' sidebarMenu(
#' menuItem(
#' text = 'Welcome',
#' tabName = 'tab_welcome',
#' icon = icon(name = 'map'),
#' selected = TRUE
#' ),
#' sidebarMenuOutput(outputId = 'menu1'),
#' sidebarMenuOutput(outputId = 'menu2'),
#' sidebarMenuOutput(outputId = 'menu3')
#' ),
#' htmlOutput(outputId = 'containerid', inline = FALSE)
#' ),
#' dashboardBody(
#' tags$head(
#' tags$style(
#' HTML(".content-wrapper { overflow: auto }
#' .wrapper {height: auto !important; position:relative; overflow-x:hidden; overflow-y:hidden}
#' .shiny-notification {
#' position: fixed;
#' font-size: 15px;
#' left: calc(50% - 100px);
#' top: calc(90%);
#' width: 350px;
#' }
#' .shiny-notification-close {
#' display: none;
#' }
#' .small-box {height: 110px}
#' "
#' )
#' )
#' ),
#' tabItems(
#' # Welcome tab
#' tabItem(
#' tabName = 'tab_welcome',
#' div(
#' fluidRow(
#' box(
#' htmlOutput(outputId = 'welcomebox'),
#' htmlOutput(outputId = 'refdescriptor'),
#' width=12
#' ),
#' width=12
#' ),
#' fluidRow(
#' div(
#' style = "position:relative",
#' plotOutput(
#' outputId = 'refdim_intro',
#' hover = hoverOpts(
#' id = "refdim_intro_hover_location",
#' delay = 5,
#' delayType = "debounce",
#' nullOutside = TRUE
#' ),
#' height='1000px'
#' ),
#' uiOutput("refdim_intro_hover_box")
#' ),
#' width = 12,
#' height='1000px'
#' ),
#' )
#' ),
#' # Preprocessing + QC Tab
#' tabItem(
#' tabName = 'tab_preproc',
#' fluidRow(
#' column(4,
#' wellPanel(
#' box(
#' title = p(
#' 'QC Filters',
#' bsButton(
#' inputId = 'q2',
#' label = '',
#' icon = icon(name = 'question'),
#' style = 'info',
#' size = 'extra-small'
#' )
#' ),
#' div(
#' id = 'ncount',
#' numericInput(
#' inputId = 'num.ncountmin',
#' label = NULL,
#' value = 0,
#' width = '90%'
#' ),
#' numericInput(
#' inputId = 'num.ncountmax',
#' label = NULL,
#' value = 0,
#' width = '90%'
#' )
#' ),
#' div(
#' id = 'nfeature',
#' numericInput(
#' inputId = 'num.nfeaturemin',
#' label = NULL,
#' value = 0,
#' width = '90%'
#' ),
#' numericInput(
#' inputId = 'num.nfeaturemax',
#' label = NULL,
#' value = 0,
#' width = '90%'
#' )
#' ),
#' textOutput(outputId = 'text.cellsremain'),
#' div(
#' id = 'xferopts',
#' h4(
#' 'Transfer Options',
#' bsButton(
#' inputId = 'xferinput',
#' label = '',
#' icon = icon(name = 'question'),
#' style = 'info',
#' size = 'extra-small'
#' )
#' ),
#' bsPopover(
#' id = 'xferinput',
#' title = 'Transfer Options',
#' content = 'Select the meta.data fields to transfer from the reference',
#' placement = 'right',
#' trigger = 'focus',
#' options = list(container = 'body')
#' ),
#' selectizeInput(
#' inputId = 'metadataxfer',
#' label = 'Reference Metadata to Transfer',
#' choices = '',
#' multiple = TRUE
#' )
#' ),
#' disabled(actionButton(
#' inputId = 'map',
#' label = 'Map cells to reference'
#' )),
#' width = 3
#' ),
#' bsPopover(
#' id = 'q2',
#' title = 'QC Filters',
#' content = paste(
#' 'Select a minimum and maximum value for nCount (number of molecules)',
#' 'nFeature (number of genes expressed)',
#' #'and mitochondrial percentage (if applicable)',
#' sep = ', '
#' ),
#' placement = 'right',
#' trigger = 'focus',
#' options = list(container = 'body')
#' ),
#' box(
#' checkboxGroupInput(inputId = "check.qc", label = NULL, choiceNames = c("Log-scale Y-axis", "Hide points"), choiceValues = c("qcscale", "qcpoints"), inline = TRUE),
#' plotOutput(outputId = 'plot.qc'),
#' tableOutput(outputId = 'table.qc'),
#' width = 8
#' ),
#' ),
#' fluidRow(
#' valueBoxOutput(outputId = 'valuebox.upload', width = 3),
#' valueBoxOutput(outputId = 'valuebox_overlap', width = 3),
#' valueBoxOutput(outputId = 'valuebox_jaccard', width = 3),
#' valueBoxOutput(outputId = 'valuebox.preproc', width = 3),
#' div(
#' id = 'panchors_popup',
#' valueBoxOutput(outputId = "valuebox_panchors", width = 3),
#' bsTooltip(id = "valuebox_panchors", title = "Click for more info", placement = "top", trigger = 'hover'),
#' ),
#' div(
#' id = 'overlap_popup',
#' valueBoxOutput(outputId = "valuebox_overlap", width = 3),
#' bsTooltip(id = "valuebox_overlap", title = "Click for more info", placement = "top", trigger = 'hover')
#' ),
#' div(
#' id = 'mappingqcstat_popup',
#' valueBoxOutput(outputId = "valuebox_mappingqcstat", width = 3),
#' bsTooltip(id = "valuebox_mappingqcstat", title = "Click for more info", placement = "top", trigger = 'hover'),
#' ),
#' valueBoxOutput(outputId = 'valuebox.mapped', width = 3),
#' ),
#' ),
#' tabItem(
#' tabName = 'tab_cell',
#' div(
#' id = "topdim",
#' width = 12
#' ),
#' div(
#' id = "bottomdim",
#' width = 12
#' ),
#' box(
#' title = p(
#' 'Metadata table',
#' bsButton(
#' inputId = 'q5',
#' label = '',
#' icon = icon(name = 'question'),
#' style = 'info',
#' size = 'extra-small'
#' )
#' ),
#' bsPopover(
#' id = 'q5',
#' title = 'Metadata table',
#' content = paste(
#' 'A (usually) 2D table where each dimension represents a query metadata field, ',
#' 'thus revealing the breakdown of any one query attribute when grouping by another. ',
#' 'By default, a 1D table is produced where one dimension has constant value (\\"query\\") and ',
#' 'the other is a predicted class (\\"predicted.XXX\\"), thus showing the overall breakdown of the ',
#' 'predicted class.'),
#' placement = 'right',
#' trigger = 'focus',
#' options = list(container = 'body')
#' ),
#' div(
#' style = 'display: inline-block; vertical-align: top; width: 25%',
#' selectizeInput(
#' inputId = 'metarow',
#' label = 'Table rows',
#' choices = ''
#' )
#' ),
#' div(
#' style = 'display: inline-block; vertical-align: top; width: 25%',
#' selectizeInput(
#' inputId = 'metacol',
#' label = 'Table columns',
#' choices = ''
#' )
#' ),
#' div(
#' style = 'display: inline-block; vertical-align: top; width: 50%',
#' radioButtons(
#' inputId = 'radio.pct',
#' label = NULL,
#' choices = c('Percentage','Frequency'),
#' inline = TRUE
#' )
#' ),
#' div(
#' id = 'tablemetadata'
#' ),
#' width = 12,
#' height = 'auto'
#' )
#' ),
#' # Motif tab
#' tabItem(
#' tags$head(tags$style(HTML(".selectize-dropdown .optgroup-header { font-weight: bold; font-size: 13px; color: black; background: #f6f6f6}"))),
#' tabName = 'tab_motif',
#' box(
#' title = 'Motif Plots',
#' div(
#' id = 'motifinput',
#' class = 'thirds',
#' selectizeInput(
#' inputId = 'chromvar.feature',
#' label = 'Motif',
#' choices = ''
#' )
#' ),
#' div(
#' id = 'continput.motif',
#' class = 'thirds',
#' selectizeInput(
#' inputId = 'metadata.cont.motif',
#' label = 'Prediction Scores and Metadata',
#' choices = ''
#' )
#' ),
#' plotOutput(outputId = 'motifdim'),
#' selectizeInput(
#' inputId = 'metagroup.motif',
#' label = 'Metadata to group by',
#' choices = '',
#' width = '25%'
#' ),
#' checkboxInput(inputId = 'check.featpoints', label = 'Hide points'),
#' plotOutput(outputId = 'motifvln'),
#' width = 12
#' ),
#' box(
#' title = p(
#' 'Predicted cell type cluster motifs',
#' bsButton(
#' inputId = 'q6',
#' label = '',
#' icon = icon(name = 'question'),
#' style = 'info',
#' size = 'extra-small'
#' )
#' ),
#' bsPopover(
#' id = 'q6',
#' title = 'Motif Analysis Table',
#' content = paste(
#' 'Only available for clusters with at least 15 cells.',
#' paste(
#' # 'avg_diff: log fold-change between cells in the cluster specified and other cells',
#' 'padj: Benjamini-Hochberg adjusted p value',
#' 'pct_in: percent of cells in the cluster with nonzero feature value',
#' 'pct_out: percent of cells out of the cluster with nonzero feature value',
#' sep = '; '
#' )
#' ),
#' placement = 'right',
#' trigger = 'focus',
#' options = list(container = 'body')
#' ),
#' div(
#' id = 'markerclustersgroupinput.motif',
#' class = 'full',
#' selectizeInput(
#' inputId = 'markerclustersgroup.motif',
#' label = 'Metadata group',
#' choices = ''
#' )
#' ),
#' div(
#' id = 'markerclustersgroupinput.motif',
#' class = 'full',
#' selectizeInput(
#' inputId = 'markerclusters.motif',
#' label = 'Predicted cell type',
#' choices = ''
#' )
#' ),
#' div(
#' id = 'motiftable',
#' class = 'full',
#' h3('Motifs'),
#' DTOutput(outputId = 'motifs')
#' ),
#' width = 12
#' )
#' ),
#' # Feature tab
#' tabItem(
#' tags$head(tags$style(HTML(".selectize-dropdown .optgroup-header { font-weight: bold; font-size: 13px; color: black; background: #f6f6f6}"))),
#' tabName = 'tab_feature',
#' box(
#' title = 'Feature Plots',
#' div(
#' id = 'featureinput',
#' class = 'thirds',
#' selectizeInput(
#' inputId = 'feature',
#' label = 'Feature',
#' choices = ''
#' )
#' ),
#' div(
#' id = 'imputedinput',
#' class = 'thirds',
#' selectizeInput(
#' inputId = 'adtfeature',
#' label = 'Imputed protein',
#' choices = ''
#' )
#' ),
#' div(
#' id = 'continput',
#' class = 'thirds',
#' selectizeInput(
#' inputId = 'metadata.cont',
#' label = 'Prediction Scores and Metadata',
#' choices = ''
#' )
#' ),
#' plotOutput(outputId = 'edim'),
#' selectizeInput(
#' inputId = 'metagroup',
#' label = 'Metadata to group by',
#' choices = '',
#' width = '25%'
#' ),
#' checkboxInput(inputId = 'check.featpoints', label = 'Hide points'),
#' plotOutput(outputId = 'evln'),
#' width = 12
#' ),
#' box(
#' title = p(
#' 'Predicted cell type cluster biomarkers',
#' bsButton(
#' inputId = 'q3',
#' label = '',
#' icon = icon(name = 'question'),
#' style = 'info',
#' size = 'extra-small'
#' )
#' ),
#' bsPopover(
#' id = 'q3',
#' title = 'Biomarkers Table',
#' content = paste(
#' 'Only available for clusters with at least 15 cells.',
#' paste(
#' # 'logFC: log fold-change between cells in the cluster specified and other cells',
#' 'auc: area under ROC',
#' 'padj: Benjamini-Hochberg adjusted p value',
#' 'pct_in: percent of cells in the cluster with nonzero feature value',
#' 'pct_out: percent of cells out of the cluster with nonzero feature value',
#' sep = '; '
#' )
#' ),
#' placement = 'right',
#' trigger = 'focus',
#' options = list(container = 'body')
#' ),
#' div(
#' id = 'markerclustersgroupinput',
#' class = 'halves',
#' selectizeInput(
#' inputId = 'markerclustersgroup',
#' label = 'Metadata group',
#' choices = ''
#' )
#' ),
#' div(
#' id = 'markerclustersgroupinput',
#' class = 'halves',
#' selectizeInput(
#' inputId = 'markerclusters',
#' label = 'Predicted cell type',
#' choices = ''
#' )
#' ),
#' div(
#' id = 'biotable',
#' class = 'halves',
#' h3('RNA biomarkers'),
#' DTOutput(outputId = 'biomarkers')
#' ),
#' div(
#' id = 'imputedtable',
#' class = 'halves',
#' uiOutput(outputId = 'imputedlabel'),
#' DTOutput(outputId = 'adtbio')
#' ),
#' width = 12
#' )
#' ),
#' # Downloads tab
#' tabItem(
#' tabName = 'tab_download',
#' div(
#' id = 'scriptdl',
#' box(
#' title = 'Analysis script template ',
#' downloadButton(
#' outputId = 'dlscript',
#' label = 'Download'
#' ),
#' width = 6
#' )
#' ),
#' div(
#' id = 'umapdl',
#' box(
#' title = 'UMAP (Seurat Reduction RDS)',
#' verbatimTextOutput(outputId = 'text.dlumap'),
#' downloadButton(
#' outputId = 'dlumap',
#' label = 'Download'
#' ),
#' width = 6
#' )
#' ),
#' div(
#' id = 'imputeddl',
#' box(
#' title = 'Imputed protein (Seurat Assay RDS)',
#' verbatimTextOutput(outputId = 'text.dladt'),
#' downloadButton(
#' outputId = 'dladt',
#' label = 'Download'
#' ),
#' width = 6
#' )
#' ),
#' div(
#' id = 'predictionsdl',
#' box(
#' title = 'Predicted cell types and scores (TSV)',
#' verbatimTextOutput(outputId = 'text.dlpred'),
#' downloadButton(
#' outputId = 'dlpred',
#' label = 'Download'
#' ),
#' width = 6
#' )
#' ),
#' div(
#' id = 'alldl',
#' box(
#' title = 'All results',
#' verbatimTextOutput(outputId = 'text.dlall'),
#' downloadButton(
#' outputId = 'dlall',
#' label = 'Download'
#' ),
#' width = 6
#' )
#' )
#' ),
#' # Feedback tab
#' tabItem(
#' tabName = 'tab_feedback',
#' box(
#' div(
#' h3('Tell us anything!'),
#' textAreaInput(
#' inputId = 'feedback',
#' label = NULL,
#' value = '',
#' width = '100%',
#' height = '300px',
#' resize = 'none',
#' placeholder = 'Were the results helpful? Did you encounter any bugs? Any new feature requests?'
#' ),
#' actionButton(inputId = "submit_feedback", label = "Submit"),
#' ),
#' width = 8
#' )
#' )
#' )
#' )
#' )
#' )
#'
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