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ui.R
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ui.R
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################################## UI #####################################
.libPaths(c(.libPaths(), '/home/ubuntu/R/x86_64-pc-linux-gnu-library/3.6/'))
library(shiny)
library(shinydashboard)
library(shinydashboardPlus)
library(shinyjs)
#library(shinyWidgets)
library(shinycssloaders)
library(plotly)
library(shinythemes)
### TCGA Datasets
# tcga.datasets <- readRDS('data/TCGA_Projects.RDS')
# projects.tcga <- c("TCGA-ACC","TCGA-BLCA","TCGA-BRCA","TCGA-CESC",
# "TCGA-CHOL","TCGA-COAD","TCGA-DLBC","TCGA-ESCA",#"TCGA-GBM",
# "TCGA-HNSC","TCGA-KICH","TCGA-KIRC","TCGA-KIRP",
# "TCGA-LAML","TCGA-LGG","TCGA-LIHC","TCGA-LUAD",
# "TCGA-LUSC","TCGA-MESO","TCGA-OV","TCGA-PAAD",
# "TCGA-PCPG","TCGA-PRAD","TCGA-READ","TCGA-SARC",
# "TCGA-SKCM","TCGA-STAD","TCGA-TGCT","TCGA-THCA",
# "TCGA-THYM","TCGA-UCEC","TCGA-UCS","TCGA-UVM")
#
# projects.tcga.sub <- c("TCGA-BLCA","TCGA-BRCA","TCGA-CESC","TCGA-CHOL",
# "TCGA-COAD","TCGA-ESCA","TCGA-HNSC","TCGA-KICH",
# "TCGA-KIRC","TCGA-KIRP","TCGA-LIHC","TCGA-LUAD",
# "TCGA-LUSC","TCGA-PAAD","TCGA-PCPG","TCGA-PRAD",
# "TCGA-READ","TCGA-SKCM","TCGA-STAD","TCGA-THCA",
# "TCGA-THYM","TCGA-UCEC")
# ### CCMA Datasets
# ccma.datasets <- readRDS('data/miRNomes_Datasets.RDS')
# ccma.primary <- readRDS('data/miRNomes_Datasets_Primary.RDS')
#
#
# ### TCGA Data
# meta.tcga <- readRDS('data/Metadata_TCGA.RDS')
# mir.tcga <- readRDS('data/miRNA_Expression_TCGA.RDS')
# rna.tcga <- readRDS('data/RNAseq_Expression_TCGA.miRTarBase.RDS')
#
#
# ### TCGA Data Analysis
# expr.high.tcga <- readRDS(file='data/Highly.Expressed.miRNAs.TCGA.RDS')
#
# km.tcga <- readRDS('data/Survival.KM.TCGA.RDS')
# coxph.tcga <- readRDS('data/Survival.CoxPH.TCGA.RDS')
#
# lasso.tcga <- readRDS('data/Survival.Lasso.Feature.TCGA.RDS')
# lasso.plot.tcga <- readRDS('data/Survival.Lasso.Plot.TCGA.RDS')
#
# risk.km.plot.tcga <- readRDS(file='data/Survival.KM.Risk.Plot.TCGA.RDS')
#
# tcga.feature.table <- readRDS('data/Lasso.Feature.Table.RDS')
# tcga.feature.plot <- readRDS('data/Lasso.Feature.Plot.RDS')
#
# roc.tcga <- readRDS('data/ROC.Analysis.TCGA.RDS')
# surv.roc.plot.tcga <- readRDS(file='data/Survival.ROC.Risk.Plot.TCGA.RDS')
#
# pca.tcga <- readRDS(file='data/PCA.Analysis.TCGA.RDS')
#
# ### Correlation/Functional Analysis
# cor.table <- readRDS('data/Correlation.miRTarBase.RDS')
# enrichment.table <- readRDS('data/Enrichment.miRTarBase.RDS')
#
#
#
# ### CCMA Data Analysis
# expr.high.ccma <- readRDS(file='data/Highly.Expressed.miRNAs.CCMA.RDS')
#
# expr.ccma <- readRDS('data/miRNomes_Expression.RDS')
# meta.ccma <- readRDS('data/miRNomes_Metadata.RDS')
#
# pca.ccma <- readRDS(file='data/PCA.Analysis.CCMA.RDS')
################################## Input #####################################
###### miRNA
mir.default <- 'MIMAT0000062' # hsa-let-7a-5p
#mir.annotation <- readRDS('data/miRBase_10.0_22.RDS')
mir.id <- selectizeInput(inputId = "mir.id", label=h4(strong('Search a miRNA')), #list(h4('Search a miRNA:'), icon('search', 'fa-1.5x')),# h4(strong('miRNA'))
choices = NULL, selected = mir.default, #mir.default,
multiple = FALSE, width = 400,
options = list(placeholder = 'e.g. hsa-miR-7a-5p', # or MIMAT0000062
server = TRUE, selectOnTab=TRUE,
searchField = c('Name', 'ID', 'Previous_ID'),
labelField = "Name",
valueField = "ID",
#maxOptions = 5,
render = I("{option: function(item, escape)
{var gene = '<div>' + '<strong>' + escape(item.Name) + '</strong>' + '<ul>';
gene = gene + '<li>' + 'Previous IDs: ' + item.Previous_ID + '</li>';
gene = gene + '<li>' + 'Accession: ' + item.ID + '</li>' + '</ul>' + '</div>';
return gene
}
}")
))
###### TCGA Project
project.default <- 'TCGA-BLCA'
project.id <- selectizeInput(inputId = "project.id", label=h5(strong('TCGA Project:')),# h4(strong('miRNA'))
choices = NULL, selected = project.default,
multiple = FALSE, width = 150,
options = list(placeholder = 'Select a project',
server = TRUE, selectOnTab=TRUE
))
project.id.cor <- selectizeInput(inputId = "project.id.cor", label=h5(strong('TCGA Project:')),# h4(strong('miRNA'))
choices = NULL, selected = project.default,
multiple = FALSE, width = 150,
options = list(placeholder = 'Select a project',
server = TRUE, selectOnTab=TRUE
))
###### Gene Sets
gene.sets <- c('Kyoto Encyclopedia of Genes and Genomes (KEGG)' = 'KEGG',
'REACTOME' = 'REACTOME',
'Disease Ontology (DO)' = 'DO',
'Network of Cancer Gene (NCG)' = 'NCG',
'DisGeNET' = 'DGN',
'Gene Ontology - Biological Process (GO-BP)' = 'GOBP',
'Gene Ontology - Cellular Component (GO-CC)' = 'GOCC',
'Gene Ontology - Molecular Function (GO-MF)' = 'GOMF',
'MSigDB - H:HALLMARK' = 'MSigDBHALLMARK',
'MSigDB - C4:Cancer Gene Neighborhoods' = 'MSigDBC4CGN',
'MSigDB - C4:Cancer Modules' = 'MSigDBC4CM',
'MSigDB - C6:Oncogenic Signatures' = 'MSigDBC6',
'MSigDB - C7:Immunologic Signatures' = 'MSigDBC7')
geneset.id.default <- gene.sets[1]
geneset.id <- selectizeInput(inputId = "geneset.id", label=h5(strong('Gene Sets:')),# h4(strong('miRNA'))
choices = NULL, selected = geneset.id.default,
multiple = FALSE, width = 410,
options = list(placeholder = 'Select a gene set',
server = TRUE, selectOnTab=TRUE
))
################################## UI #####################################
###### Home
tab_home <- dashboardPage(
dashboardHeader(disable = T),
dashboardSidebar(disable = T),
dashboardBody(fluidRow(
box(
title = NULL, status = "primary", solidHeader = FALSE, collapsible = FALSE,
width = 12,
h3(strong("Welcome to CancerMIRNome, a web server for cancer miRNome interactive analysis"), align='center')#,
#hr(),
#tags$hr(style="border-top: 2px solid #A9A9A9"),
# column(12,
# h3(strong('The Cancer Genome Atlas (TCGA) miRNome')),
#
# valueBox(value = tags$p(strong("33"), style = "font-size: 90%;"), color = 'aqua', width = 3,
# subtitle = tags$p(strong("Cancer types"), style = "font-size: 160%;"), icon = icon("dna fa-0.5x")),
# # valueBox(value = '88', color = 'teal', width = 3,
# # subtitle = tags$p(strong("Studies"), style = "font-size: 200%;"), icon = icon("database")),
# valueBox(value = tags$p(strong("10,998"), style = "font-size: 90%;"), color = 'aqua', width = 3,
# subtitle = tags$p(strong("Samples"), style = "font-size: 160%;"), icon = icon("user-circle"))
# ),
#
# br(),
#
# column(12,
# h3(strong('Cancer Circulating miRNome')),
#
# valueBox(value = tags$p(strong("31"), style = "font-size: 90%;"), color = 'teal', width = 3,
# subtitle = tags$p(strong("Cancer types"), style = "font-size: 160%;"), icon = icon("dna")),
# valueBox(value = tags$p(strong("40"), style = "font-size: 90%;"), color = 'teal', width = 3,
# subtitle = tags$p(strong("Studies"), style = "font-size: 160%;"), icon = icon("database")),
# valueBox(value = tags$p(strong("21,993"), style = "font-size: 90%;"), color = 'teal', width = 3,
# subtitle = tags$p(strong("Samples"), style = "font-size: 160%;"), icon = icon("user-circle"))
#
# )
),
box(
title = NULL, solidHeader = TRUE, collapsible = FALSE,
width = 12, # solidHeader=TRUE can remove the top boarder
column(12,
h3(strong('The Cancer Genome Atlas (TCGA) miRNome')),
valueBox(value = tags$p(strong("33"), style = "font-size: 90%;"), color = 'aqua', width = 3,
subtitle = tags$p(strong("Cancer types"), style = "font-size: 160%;"), icon = icon("dna fa-0.5x")),
# valueBox(value = '88', color = 'teal', width = 3,
# subtitle = tags$p(strong("Studies"), style = "font-size: 200%;"), icon = icon("database")),
valueBox(value = tags$p(strong("10,998"), style = "font-size: 90%;"), color = 'aqua', width = 3,
subtitle = tags$p(strong("Samples"), style = "font-size: 160%;"), icon = icon("user-circle"))
),
br(),
column(12,
h3(strong('Cancer Circulating miRNome')),
valueBox(value = tags$p(strong("32"), style = "font-size: 90%;"), color = 'teal', width = 3,
subtitle = tags$p(strong("Cancer types"), style = "font-size: 160%;"), icon = icon("dna")),
valueBox(value = tags$p(strong("40"), style = "font-size: 90%;"), color = 'teal', width = 3,
subtitle = tags$p(strong("Studies"), style = "font-size: 160%;"), icon = icon("database")),
valueBox(value = tags$p(strong("21,993"), style = "font-size: 90%;"), color = 'teal', width = 3,
subtitle = tags$p(strong("Samples"), style = "font-size: 160%;"), icon = icon("user-circle"))
)
),
box(
title = NULL, solidHeader = TRUE, collapsible = FALSE,
width = 12, # solidHeader=TRUE can remove the top boarder
h2(strong("Introduction")),
h3(strong("miRNAs in cancer")),
tags$p("MicroRNAs (miRNAs) are a class of small endogenous non-coding RNAs of
~22nt in length that negatively regulate the expression of their target
protein-coding genes. miRNAs play critical roles in many biological processes,
such as cell proliferation, differentiation, and apoptosis. miRNAs may
function as oncogenes or tumor suppressors. Mounting evidences have
demonstrated that the expression of miRNAs is dysregulated in various
types of human cancers, which makes them potential biomarkers for cancer
diagnosis and prognosis.",
style = "font-size: 150%;"),
#br(),
tags$hr(style="border-top: 1px dashed #A9A9A9"),
h3(strong("Circulating miRNAs as promising diagnostic biomarkers")),
tags$p("miRNAs can be released into extracellular body fluids, including blood.
Circulating miRNAs are incorporated in extracellular vesicles (EVs) such as
shed microvesicles (sMVs) and exosomes, apoptotic bodies, or form complexes
with RNA binding proteins such as Argonates (AGOs). The protected circulating
miRNAs are remarkably stable in the extracellular environment, thus have
tremendous potential as non-invasive diagnostic biomarkers for early cancer
detection.",
style = "font-size: 150%;"),
#tags$img(src='img/mirna.jpg', width=550), # in www
#tags$img(src='img/fluid.jpg', width=550),
#br(),
tags$hr(style="border-top: 1px dashed #A9A9A9"),
h3(strong("About CancerMIRNome")),
tags$p('CancerMIRNome is a web server for cancer miRNome interactive analysis and visualization
based on the huamn miRNome data of 33 cancer types from
The Cancer Genome Atlas (TCGA), and 40 public cancer circulating miRNome
profiling datasets from NCBI Gene Expression Omnibus (GEO) and ArrayExpress.', style = "font-size: 150%;"),
tags$p('CancerMIRNome provides a user-friendly interface for miRNA analysis both at
a single miRNA level to explore the expression and function of a miRNA in
many different cancers and at a dataset level to identify diagnostic and
prognostic biomarkers for a specific cancer type.', style = "font-size: 150%;"),
#tags$img(src='img/query.jpg', width=550), # in www
#tags$img(src='img/mirnome.jpg', width=550),
tags$img(src='img/both.jpg', width=1100),
br(),
tags$hr(style="border-top: 1px dashed #A9A9A9"),
h3(strong("Cite")),
tags$p('Please cite the following publication:
Li, R., et al., CancerMIRNome: a web server for cancer miRNome interactive analysis and visualization', style = "font-size: 150%;")
)
)
)
)
###### Query
tab_query <- dashboardPage(
dashboardHeader(disable = T),
dashboardSidebar(disable = T),
dashboardBody(fluidRow(
box(
title = NULL, status = "primary", solidHeader = FALSE, collapsible = FALSE,
width = 12,
column(3),
column(3,
tags$div(id='div_mir',
class='mir',
mir.id)
), #br(),
column(6,
strong(uiOutput("mir.name")),
h5(strong(textOutput("mir.preid"))),
h5(strong(textOutput("mir.info"))),
h5(strong(textOutput("mir.seq"))),
h5(strong(uiOutput("mir.targets")))
)
),
#tabBox(id = 'query', width = 12,
box(
title = NULL, status = "success", solidHeader = FALSE, collapsible = FALSE,
width = 12,
#navlistPanel
tabsetPanel(id = 'query', type='pills', #widths = c(2,10),
tabPanel(strong("Overview in TCGA"),
br(),
hr(),
column(12,
column(1),
column(11,
h5("miRNA Expression in Tumor and Normal Samples in TCGA", align = 'center'),
h6("(Wilcoxon rank-sum test, ***: P < 0.001; **: P < 0.01; *: P < 0.05; ns: P > 0.05)", align = 'center'),
br(),
withSpinner(plotOutput('tcga_boxplot',width = 900, height = 400), # 1100 * 500
type = 1)
),
column(10),
column(2,
downloadButton(outputId='tcga.box.summ.downbttn.csv', label = "CSV"),
#downloadButton(outputId='tcga.box.summ.downbttn.png', label = "PNG"),
downloadButton(outputId='tcga.box.summ.downbttn.pdf', label = "PDF")
)
),
column(12,
#br(),
tags$hr(style="border-top: 1px dashed #A9A9A9"),
column(1),
column(11,
h5("ROC Analysis Between Tumor and Normal Samples in TCGA", align = 'center'),
br(),
withSpinner(plotOutput('tcga_rocplot_forest',width = 950, height = 800), # 1100 * 500
type = 1)
),
column(10),
column(2,
downloadButton(outputId='tcga.roc.forest.downbttn.csv', label = "CSV"),
#downloadButton(outputId='tcga.roc.forest.downbttn.png', label = "PNG"),
downloadButton(outputId='tcga.roc.forest.downbttn.pdf', label = "PDF")
)
),
column(12,
#br(),
tags$hr(style="border-top: 1px dashed #A9A9A9"),
column(1),
column(11,
h5("Kaplan Meier Survival Analysis of Overall Survival in TCGA", align = 'center'),
h6("(Low- and high-expression groups were separated by median values)", align = 'center'),
br(),
withSpinner(plotOutput('tcga_km_forest',width = 950, height = 950), # 1100 * 600
type = 1)
),
column(10),
column(2,
downloadButton(outputId='tcga.km.forest.downbttn.csv', label = "CSV"),
#downloadButton(outputId='tcga.km.forest.downbttn.png', label = "PNG"),
downloadButton(outputId='tcga.km.forest.downbttn.pdf', label = "PDF")
)
)
),
tabPanel(strong('miRNA in TCGA'),
column(12,
br(),
project.id,
hr(),
column(4,
h5('Box Plot of miRNA Expression', align='center'),
plotOutput('tcga_violinplot',width = 350, height = 350),
br(),
column(7),
column(5,
downloadButton(outputId='tcga.box.downbttn.csv', label = "CSV"),
#downloadButton(outputId='tcga.box.downbttn.png', label = "PNG"),
downloadButton(outputId='tcga.box.downbttn.pdf', label = "PDF")
)
),
column(4,
h5('ROC Analysis (Tumor vs. Normal)', align='center'),
plotOutput('tcga_rocplot',width = 350, height = 350),
br(),
column(7),
column(5,
downloadButton(outputId='tcga.roc.downbttn.csv', label = "CSV"),
#downloadButton(outputId='tcga.roc.downbttn.png', label = "PNG"),
downloadButton(outputId='tcga.roc.downbttn.pdf', label = "PDF")
)
),
column(4,
h5('Kaplan Meier Survival Analysis', align='center'),
plotOutput('tcga_km_plot',width = 350, height = 350),
br(),
column(7),
column(5,
downloadButton(outputId='tcga.km.downbttn.csv', label = "CSV"),
#downloadButton(outputId='tcga.km.downbttn.png', label = "PNG"),
downloadButton(outputId='tcga.km.downbttn.pdf', label = "PDF")
)
)
)
),
tabPanel(strong("miRNA-Target Correlation"),
column(12,
br(),
#hr(),
project.id.cor,
hr(),
h5('Pearson Correlation Analysis of the miRNA and its Targets (miRTarBase 2020)', align='center'),
br(),
column(12, DT::dataTableOutput("correlation"),
hr()
),
column(3),
column(6,
h5('miRNA-Target Correlation Plot', align='center'),
#br(),
withSpinner(plotOutput('cor_plot',width = 500, height = 400), type=1)),
column(7),
column(5,
downloadButton(outputId='tcga.cor.downbttn.csv', label = "CSV"),
#downloadButton(outputId='tcga.cor.downbttn.png', label = "PNG"),
downloadButton(outputId='tcga.cor.downbttn.pdf', label = "PDF")
),
column(12,
tags$hr(style="border-top: 1px dashed #A9A9A9"),
h5('miRNA-Target Correlation Across All TCGA Projects', align='center'),
h6("(Pearson Correlation, ***: P < 0.001; **: P < 0.01; *: P < 0.05)", align = 'center'),
#br(),
plotlyOutput('cor_heatmap', height='125px')#, #, width='100%',
#tags$hr(style="border-top: 1px dashed #A9A9A9")
)
)
),
tabPanel(strong('Functional Enrcichment'),
br(),
geneset.id,
hr(),
column(12,
h5('Functional Enrichment Analysis of the Targets (miRTarBase 2020)', align='center'),
br(),
DT::dataTableOutput("enrichment")
),
column(12,
hr(),
column(1),
column(10,
h5('Bar Plot of the Top 30 Enriched Pathways', align='center'),
plotOutput('enrichment_bar_plot',width = 800, height = 500)
),
column(8),
column(4,
downloadButton(outputId='enrich.bar.downbttn.csv', label = "CSV"),
#downloadButton(outputId='enrich.bar.downbttn.png', label = "PNG"),
downloadButton(outputId='enrich.bar.downbttn.pdf', label = "PDF")
)
),
column(12,
br(),
tags$hr(style="border-top: 1px dashed #A9A9A9"),
column(1),
column(10,
h5('Bubble Plot of the Top 30 Enriched Pathways', align='center'),
plotOutput('enrichment_bubble_plot',width = 800, height = 500)
),
column(8),
column(4,
downloadButton(outputId='enrich.bubble.downbttn.csv', label = "CSV"),
#downloadButton(outputId='enrich.bubble.downbttn.png', label = "PNG"),
downloadButton(outputId='enrich.bubble.downbttn.pdf', label = "PDF")
)
)
),
tabPanel(strong('Circulating miRNA Expression'),
br(),
br(),
DT::dataTableOutput("browser_datasets"),
hr(),
column(12,
h5('Expression of the miRNA in the Selected Circulating miRNome Datasets', align='center'),
br(),
withSpinner(uiOutput("multi_plot_ui"),type=1),
column(4),
column(6,
downloadButton(outputId='circ.expr.downbttn.csv', label = "CSV"),
#downloadButton(outputId='circ.expr.downbttn.png', label = "PNG"),
downloadButton(outputId='circ.expr.downbttn.pdf', label = "PDF")
)
)
)
)
)
)
)
)
###### TCGA miRNomes
tab_tcga <- dashboardPage(
dashboardHeader(disable = T),
dashboardSidebar(disable = T),
dashboardBody(fluidRow(
box(
title = NULL, status = "primary", solidHeader = FALSE, collapsible = FALSE,
width = 12,
h4(strong('Comprehensive miRNome Interactive Analysis in The Cancer Genome Atlas (TCGA)'), align='center')
),
box(
title = 'Select a TCGA miRNome Dataset', status = "primary", solidHeader = TRUE, collapsible = FALSE,
width = 12,
DT::dataTableOutput("tcga_datasets")
),
box(
title = NULL, status = "success", solidHeader = FALSE, collapsible = FALSE,
width = 12,
#tabBox
tabsetPanel(#id = 'degbox.tcga',#width = 12,
tabPanel(strong("Summary"),
br(),
div(h4(strong(textOutput("dataset_summary_tcga"))), style = "color:black", align='center'),
br(),
box(title = 'Sample Type',
status = "info", solidHeader = TRUE, collapsible = FALSE,
width = 4,
height = 375,
plotlyOutput('pie_sample_type_tcga', width='100%', height='300px')
),
box(title = 'Pathological Stage',
status = "info", solidHeader = TRUE, collapsible = FALSE,
width = 4,
height = 375,
plotlyOutput('pie_pstage_tcga', width='100%', height='300px')
),
box(title = 'Clinical Stage',
status = "info", solidHeader = TRUE, collapsible = FALSE,
width = 4,
height = 375,
plotlyOutput('pie_cstage_tcga', width='100%', height='300px')
),
box(title = 'Age at Diagnosis',
status = "info", solidHeader = TRUE, collapsible = FALSE,
width = 4,
height = 375,
plotlyOutput('histogram_age_tcga', width='100%', height='300px')
),
box(title = 'Overall Survival Status',
status = "info", solidHeader = TRUE, collapsible = FALSE,
width = 4,
height = 375,
plotlyOutput('pie_os_status_tcga', width='100%', height='300px')
),
box(title = 'Overall Survival',
status = "info", solidHeader = TRUE, collapsible = FALSE,
width = 4,
height = 375,
plotlyOutput('km_os_time_tcga', width='100%', height='300px')
)
),
tabPanel(strong("Highly Expressed miRNA"),
column(12,
br(),
h5('Highly Expressed miRNAs', align='center'),
h6('(CPM > 1 in more than 50% of the samples)', align='center'),
DT::dataTableOutput('high.expr.table.tcga'),
br(),
tags$hr(style="border-top: 1px dashed #A9A9A9"),
h5('Bar Plot of the Top 50 Highly Expressed miRNAs', align='center'),
plotOutput('high.expr.barplot.tcga'),
column(10),
column(2,
downloadButton(outputId='high.expr.tcga.downbttn.csv', label = "CSV"),
#downloadButton(outputId='circ.expr.downbttn.png', label = "PNG"),
downloadButton(outputId='high.expr.tcga.downbttn.pdf', label = "PDF")
)
)
),
tabPanel(strong("Differential miRNA"),
value = 'differential.tcga',
column(12,
br()
),
column(12,
column(6,
selectInput("tcga_metadata", h5(strong("Metadata:")), width = 300,
choices = list('General'=c('Sample Type' = 'sample_type',
'Age' = "age_at_initial_pathologic_diagnosis",
'Gender' = 'gender',
'Ethnicity' = 'ethnicity',
'Race' = 'race',
'Clinical Stage' = "clinical_stage",
'Clinical T Stage' = "clinical_T",
'Clinical N Stage' = "clinical_N",
'Clinical M Stage' = "clinical_M",
'Pathological Stage' = 'pathologic_stage',
'Pathological T Stage' = 'pathologic_T',
'Pathological N Stage' = 'pathologic_N',
'Pathological M Stage' = 'pathologic_M'),
'TCGA-PRAD'=c('PSA'='preop_psa',
'Gleason Score'='gleason_score'))
),
# conditionalPanel(condition="input.tcga_metadata=='preop_psa' && input.tcga_datasets_rows_selected==23",
# numericInput("psa", strong("Cutoff:"), 4, min = 0, max = 100, width = 250)
# ),
#
# conditionalPanel(condition="input.tcga_metadata=='age_at_initial_pathologic_diagnosis'",# || input$tcga_metadata=='age_at_initial_pathologic_diagnosis'",
# numericInput("age", strong("Cutoff:"), 60, min = 0, max = 100, width = 250)
# ),
DT::dataTableOutput("groups.tcga.diy")
),
column(6,
# column(12, selectInput("deg.test.tcga", h5(strong("Method:")), width = 300,
# c("Limma" = "limma",
# "Wilcoxon Rank-Sum Test" = "wilcox"
# ))),
column(12, radioButtons(inputId = "deg.test.tcga", label = h5(strong("Method:")),
c('Limma', 'Wilcoxon Rank Sum Test'),
inline = FALSE)),
column(4, numericInput(inputId = "foldchange.tcga", label = strong('Fold Change:'),
value = 2, min = 0, max = 10, step = 0.1, width = 150)),
column(8, numericInput(inputId = "fdr.tcga", label = strong('BH Adjusted P Value:'),
value = 0.01, min = 0, max = 1, step = 0.01, width = 150)),
column(1),
column(6, actionButton(inputId = 'deg.submit.tcga', label = strong('Submit'), icon=icon("check"),
style="color: #fff; background-color: #4095c9; border-color: #368dc2; border-width: 2px; font-size: 12px;",
class = 'btn-sm', width = 200))
# column(6, shinyWidgets::actionBttn(inputId = 'deg.submit.tcga', label = 'Submit', icon=icon("check"),
# style = 'fill', color = 'default', size = 'sm', block=TRUE))
)
),
column(12,
br(),
tags$hr(style="border-top: 1px dashed #A9A9A9"),
h5('Volcano Plot of Differentially Expressed miRNAs', align='center'),
column(3),
column(6,
plotOutput('volcano_sample_type_tcga', height = 500)
),
column(7),
column(5,
downloadButton(outputId='volcano.tcga.downbttn.csv', label = "CSV"),
#downloadButton(outputId='circ.expr.downbttn.png', label = "PNG"),
downloadButton(outputId='volcano.tcga.downbttn.pdf', label = "PDF")
)
),
column(12,
br(),
tags$hr(style="border-top: 1px dashed #A9A9A9"),
h5('Differential Expression Analysis of Highly Expressed miRNAs', align='center'),
DT::dataTableOutput("table_sample_type_tcga")
)
),
tabPanel(strong("ROC Analysis"),
column(12,
br(),
h5('ROC Analysis Between Tumor and Normal Samples', align = 'center'),
DT::dataTableOutput('tcga_roc_analysis_table')
)
),
tabPanel(strong("Feature Selection"),
column(12,
br(),
h5('Selection of Features for the Classification of Tumor and Normal Samples using LASSO', align='center'),
column(6,
br(),
plotOutput('tcga.feature.plot1')
),
column(6,
br(),
DT::dataTableOutput('tcga.feature.table')
)
)
),
tabPanel(strong("PCA"),
column(6,
br(),
h5('2D Principal Component Analysis using Highly Expressed miRNAs', align='center'),
withSpinner(plotlyOutput('pca.tcga.2d'),
type = 1)
),
column(6,
br(),
h5('3D Principal Component Analysis using Highly Expressed miRNAs', align='center'),
withSpinner(plotlyOutput("pca.tcga.3d"),
type = 1)
)
),
tabPanel(strong("Survival Analysis"),
column(12,
br(),
h5('Kaplan Meier (KM) Survival Analysis of the Highly Expressed miRNAs', align='center'),
h6("(Low- and high-expression groups were separated by median values)", align = 'center'),
DT::dataTableOutput('table_km_tcga')
),
column(12,
br(),
tags$hr(style="border-top: 1px dashed #A9A9A9"),
h5('Cox Proportional-Hazards (CoxPH) Survival Analysis of the Highly Expressed miRNAs', align='center'),
DT::dataTableOutput('table_coxph_tcga')
),
column(12,
br(),
tags$hr(style="border-top: 1px dashed #A9A9A9"),
h5('Construction of a Prognostic Signature using LASSO', align='center')
),
column(6,
br(),
plotOutput('lasso_plot_tcga')
),
column(6,
br(),
DT::dataTableOutput('table_lasso_tcga')
),
column(12,
br(),
tags$hr(style="border-top: 1px dashed #A9A9A9"),
column(6,
h5('Kaplan Meier Survival Analysis of the Prognostic Signature', align='center'),
h6("(Low- and high-risk groups were separated by median values)", align = 'center'),
plotOutput('risk_plot_tcga'),
column(9),
column(3,
downloadButton(outputId='risk.tcga.downbttn.csv', label = "CSV"),
#downloadButton(outputId='circ.expr.downbttn.png', label = "PNG"),
downloadButton(outputId='risk.tcga.downbttn.pdf', label = "PDF")
)
),
column(6,
h5('Time-dependent ROC Analysis of the Prognostic Signature', align='center'),
h6("(NNE method, span = 0.01)", align = 'center'),
plotOutput('surv_roc_plot_tcga'),
column(9),
column(3,
downloadButton(outputId='surv.roc.downbttn.csv', label = "CSV"),
#downloadButton(outputId='circ.expr.downbttn.png', label = "PNG"),
downloadButton(outputId='surv.roc.downbttn.pdf', label = "PDF")
)
)
)
)
)
)
)
)
)
###### CCMA miRNomes
tab_circulating <- dashboardPage(
dashboardHeader(disable = T),
dashboardSidebar(disable = T),
dashboardBody(fluidRow(
box(
title = NULL, status = "primary", solidHeader = FALSE, collapsible = FALSE,
width = 12,
h4(strong('Comprehensive miRNome Data Analysis in Cancer Circulating miRNome Datasets'), align='center')
),
box(
title = 'Select a Circulating miRNome Dataset', status = "primary", solidHeader = TRUE, collapsible = FALSE,
width = 12,
DT::dataTableOutput("ccma_datasets")
),
box(
title = NULL, status = "success", solidHeader = FALSE, collapsible = FALSE,
width = 12,
tabsetPanel(id = 'ccma.tabset',
tabPanel(strong("Summary"),
br(),
div(h4(strong(textOutput("dataset_summary"))), style = "color:black", align='center'),
br(),
box(title = 'Disease Status',
status = "info", solidHeader = TRUE, collapsible = FALSE,
width = 6,
height = 500,
plotlyOutput('pie_disease_status')
),
box(title = 'Subgroups',
status = "info", solidHeader = TRUE, collapsible = FALSE,
width = 6,
height = 500,
plotlyOutput('pie_group')
),
box(title = NULL,
status = "info", solidHeader = FALSE, collapsible = TRUE,
width = 12,
height = 600,
htmlOutput("gse")
)
),
tabPanel(strong("Highly Expressed miRNA"),
column(12,
br(),
h5('Top 500 Highly Expressed miRNAs', align='center'),
#h6('(CPM > 1 in more than 50% of the samples)', align='center'),
DT::dataTableOutput('high.expr.table.ccma'),
br(),
tags$hr(style="border-top: 1px dashed #A9A9A9"),
h5('Bar Plot of the Top 50 Highly Expressed miRNAs', align='center'),
plotOutput('high.expr.barplot.ccma'),
column(10),
column(2,
downloadButton(outputId='high.expr.ccma.downbttn.csv', label = "CSV"),
#downloadButton(outputId='circ.expr.downbttn.png', label = "PNG"),
downloadButton(outputId='high.expr.ccma.downbttn.pdf', label = "PDF")
)
)
),
tabPanel(strong("Differential miRNA"),
column(12,
br()
),
column(12,
column(8,
DT::dataTableOutput("groups.ccma")
),
column(4,
column(12, radioButtons(inputId = "deg.test", label = h5(strong("Method:")),
c('Limma', 'Wilcoxon Rank Sum Test'),
inline = FALSE)),
column(6, numericInput(inputId = "foldchange", label = strong('Fold Change:'),
value = 2, min = 0, max = 10, step = 0.1, width = 150)),
column(6, numericInput(inputId = "fdr", label = strong('BH Adjusted P Value:'),
value = 0.01, min = 0, max = 1, step = 0.01, width = 150)),
#column(1),
column(6, actionButton(inputId = 'deg.submit', label = strong('Submit'), icon=icon("check"),
style="color: #fff; background-color: #4095c9; border-color: #368dc2; border-width: 2px; font-size: 12px;",
class = 'btn-sm', width = 200))
)
),
column(12,
br(),
tags$hr(style="border-top: 1px dashed #A9A9A9"),
h5('Volcano Plot of Differentially Expressed miRNAs', align='center'),
column(3),
column(6,
plotOutput('volcano_sample_type', height = 500)
),
column(7),
column(5,
downloadButton(outputId='volcano.ccma.downbttn.csv', label = "CSV"),
#downloadButton(outputId='circ.expr.downbttn.png', label = "PNG"),
downloadButton(outputId='volcano.ccma.downbttn.pdf', label = "PDF")
)
),
column(12,
br(),