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ui.R
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ui.R
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ui <- navbarPage(title = 'ATAC-Seq',
tabPanel('Introduction',
shiny::includeHTML('HTML/ATACBrowserText.html')
),
tabPanel('Explore Data',
useShinyjs(),
sidebarPanel(
titlePanel(h2('ATAC-Seq Explorer', align = 'center')),
width = 2,
chooseSliderSkin("Modern", color = '#428bca'),
shiny::tags$style(type='text/css', '#selected_tf_motifs {background-color: #DCDCDC;}',
".shiny-output-error { visibility: hidden; }",
".shiny-output-error:before { visibility: hidden; }"),
#shiny::tags$head(shiny::tags$style(type='text/css', ".slider-animate-button { font-size: 1pt !important; }")),
# selectInput(
# 'accessionType',
# h4('Accession Type'),
# c('Gene Symbol' = 'gene.symbol', 'Transcript ID' = 'transcript_id')
# ),
h4(''),
hr(style="border-color: grey"),
radioButtons('search_type',
label = 'Search Options',
choices = c('Gene Name','SNP ID'),
selected = 'Gene Name',
inline = TRUE
),
textInput(
"snp_search",
label = 'SNP ID Search',
value = ''
),
verbatimTextOutput('snp_text'),
pickerInput(
inputId = "gene",
label = 'Gene Search',
selected = 'NEUROG2',
choices = gene_names,
options = list(
`live-search` = TRUE,
title = 'Gene Name'
)
),
# selectizeInput(
# 'gene',
# label = h5('Gene Search'),
# choices = '',
# options = list(maxOptions = 5, maxItems = 1),
# multiple = TRUE
# ),
sliderInput(
'xrange',
label = 'Genome Coordinates',
min = 0,
max = 0,
value = c(0,0),
round = T
),
sliderInput(
'ymax',
label = 'Y-axis scale - ATAC-seq signal',
min = 0,
max = 1000,
value = 200
),
conditionalPanel(condition = "!$('html').hasClass('shiny-busy')",
shiny::actionButton("plot_button", h6("Refresh\nPlot"), icon = icon("refresh"),
style="color: #fff; background-color: #64dd17; white-space:normal; width:45%; height:75px"),
downloadButton("report", h6("Download\nSummary"),
style = "white-space:normal; width:45%; height:75px")
),
hr(style="border-color: grey"),
h4('Peak-gene links'),
h6('We defined putative enhancer-gene linkages on the basis of the correlation of accessibility and gene expression across cell lineages. These parameters filter the data displayed by features of those correlations.'),
selectInput(
'transcriptID',
label = h5('Transcript ID'),
choices = '',
multiple = FALSE
),
numericInput(
'cor_cut',
'Correlation Cutoff',
value = NA,
min = -1,
max = 1,
step = 0.05
),
numericInput(
'pval_cut',
'p.value Cutoff',
value = 1,
min = 0,
max = 1,
step = 0.01
),
uiOutput("cluster_select"),
# checkboxGroupInput('clusterID', label = h5('Cluster ID'),
# choiceNames = c('MG1','MG2','GP','PL','EA1','EA2','PN1','PN2','LN1','LN2','CS'),
# choiceValues = c('MG1','MG2','GP','PL','EA1','EA2','PN1','PN2','LN1','LN2','CS'),
# selected = c('MG1','MG2','GP','PL','EA1','EA2','PN1','PN2','LN1','LN2','CS'),
# inline = F),
h4(''),
hr(style="border-color: grey"),
pickerInput(
inputId = "tf_motifs",
label = "Select TF motifs to display",
choices = motifs@colData@rownames,
options = list(
`actions-box` = TRUE,
size = 10,
`selected-text-format` = "count > 3",
`live-search` = TRUE,
`max-options` = 10,
`max-options-text` = "Max of 10 selected"
),
multiple = TRUE
),
textOutput("selected_tf_motifs")
),
# withSpinner(
# uiOutput("rnaExpression.ui"),
# #plotOutput('rnaExpression', height = 400*plotLen),
# type = getOption('spinner.type', default = 4)
# ),
mainPanel(
width = 10,
tabsetPanel(
tabPanel('RNA-seq',
uiOutput("rnaExpression.ui")
#plotOutput('rnaExpression') %>% withSpinner(type = 6)
),
tabPanel('Table',
dataTableOutput('peaks_table') %>% withSpinner(type = 6)
),
tabPanel('ATAC-seq',
plotOutput('gviz', height = 1100) %>% withSpinner(type = 6),
#plotOutput('gvizClust') %>% withSpinner(type = 6),
plotOutput('tf_legend', height = 100)
#busyIndicator(text = '')
)
)
#h5('ATAC-seq Tracks'),
)
)
)