R/ui.R
In EdwardAgboraw/FrankenSeq: A user friendly and modular machine learning platform for scRNA-seq analysis
ui = fluidPage(theme = shinytheme("spacelab"),
navbarPage(
"FrankenSeq",
tabPanel("Data Processing", # Data Processing Tab
sidebarPanel(
radioButtons(inputId = "Help_QC", label = "Select an Option", choices = c("Help", "Run Tab"), selected = "Help"),
selectInput(inputId = "FileType", label = "Select Input File Type", choices = c("RDS (Existing Seurat Object)", "CSV (Gene x Cell Table)", "SingleCellExperiment Object (RDS)", "Read Count Data (CSV)", "PBMC3k Test Data"), selected = "RDS (Existing Seurat Object)"),
conditionalPanel(
condition = "input.FileType != 'PBMC3k Test Data'",
fileInput(inputId = "rdata", label = "Select an input file", accept = ".rds", buttonLabel = "Browse...", placeholder = "No file selected..."),
),
radioButtons(inputId = "DPOptions", label = "Select an Analysis", choices = c("Quality Control", "Data Filtration")),
#Conditional Panel
conditionalPanel(
condition = "input.DPOptions == 'Data Filtration'",
numericInput(inputId = "Max_Features", label = "Maximum Number of Features", max = 5000, min = 1, value = 3000, step = 1),
numericInput(inputId = "Min_Features", label = " Minimum Number of Features", max = 5000, min = 1, value = 200, step = 1),
numericInput(inputId = "Mitochondria", label = " Max % Mitochondrial DNA", max = 100, min = 1, value = 5, step = 1),
),
),
mainPanel(
conditionalPanel( #QC Help Text
condition = "input.Help_QC == 'Help'",
textOutput("QC_Help_Text")
),
plotOutput("dgraph") %>% withSpinner(color="#0dc5c1", hide.ui = FALSE),
)
),
tabPanel("Feature Selection", #Feature Selection Tab
sidebarPanel(
radioButtons(inputId = "Help_FS", label = "Select an Option", choices = c("Help", "Run Tab"), selected = "Help"),
selectInput(inputId = "FS", label = "Choose a Feature Selection method", choices = c("HVG - Seurat (vst)", "HVG - Seurat (mvp)", "HVG - Seurat (Dispersion)", "Drop-Out Based Feature Selection - M3Drop", "Gene-Gene Correlation Feature Selection (DubStepR)", "Feature Selection by Deviance (Scry)","Filter by Gene Expression"), selected = "HVG - Seurat (vst)"),
#conditional Feature Selection Panels
conditionalPanel(
condition = "input.FS == 'HVG - Seurat (vst)' || input.FS == 'HVG - Seurat (Dispersion)' || input.FS == 'Feature Selection by Deviance (Scry)' || input.FS == 'Filter by Gene Expression' || input.FS == 'Drop-Out Based Feature Selection - M3Drop'",
numericInput("nFeatures", "Number of Top Features", min = 1, value = 1556, step = 1)
),
),
mainPanel(
conditionalPanel( #DL Help Text
condition = "input.Help_FS == 'Help'",
textOutput("FS_Help_Text")
),
plotOutput("FSPlot") %>% withSpinner(color="#0dc5c1", hide.ui = FALSE),
)
),
tabPanel("Dimension Reduction", #Dimension Reduction Tab
sidebarPanel(
radioButtons(inputId = "Help_DR", label = "Select an Option", choices = c("Help", "Run Tab"), selected = "Help"),
selectInput(inputId = "dr", label = "Choose a Dimension Reduction method", choices = c("PCA (Seurat)", "GLM PCA (Residuals)", "GLM PCA"), selected = "PCA (Seurat)"),
conditionalPanel(
condition = "input.dr == 'GLM PCA'",
numericInput("L_value", "Choose the number of dimensions to return: ", min = 2, value = 10, step = 1),
),
),
mainPanel(
conditionalPanel(
condition = "input.dr == 'PCA (Seurat)' || input.dr == 'GLM PCA (Residuals)' ",
selectInput("DRG_options", "Select a visualition option", choices = c("Elbow Plot", "PCA"), selected = "PCA"),
),
conditionalPanel( #DR Help Text
condition = "input.Help_DR == 'Help'",
textOutput("DR_Help_Text")
),
plotOutput("drPlot") %>% withSpinner(color="#0dc5c1", hide.ui = FALSE),
)
),
tabPanel("Cluster Validation", #Cluster Validation Tab
sidebarPanel(
radioButtons(inputId = "Help_CV", label = "Select an Option", choices = c("Help", "Run Tab"), selected = "Help"),
radioButtons(inputId = "CVOptions", label = "Select a Plot", choices = c("Estimate K with SC3","Elbow Plot", "Silhouette Plot")),
numericInput(inputId = "maxK", label = "Select maxinum number of clusters to evaluate;", value = 15, step = 1)
),
mainPanel(
conditionalPanel( #CV Help Text
condition = "input.Help_CV == 'Help'",
textOutput("CV_Help_Text")
),
textOutput("sc3_estimated_k_value") %>% withSpinner(color="#0dc5c1", hide.ui = FALSE),
plotOutput("cvPlot") %>% withSpinner(color="#0dc5c1", hide.ui = FALSE),
),
),
tabPanel("Cluster Analysis", #Cluster Analysis Tab
sidebarPanel(
radioButtons(inputId = "Help_CA", label = "Select an Option", choices = c("Help", "Run Tab"), selected = "Help"),
selectInput(inputId = "CM", label = "Choose an Unsupervised Clustering Algorithm", choices = c("K-Nearest Neighbor (Seurat)", "Graph Based Hierarchical Clustering (HGC)", "Hierarchical Clustering", "K-means Clustering", "Density Peak Clustering (Monocle)", "Consensus Clustering (SC3)")),
numericInput(inputId = "dimensions", label = "Dimensionality: ", value = 10, max = 30, min = 2, step = 1),
#Conditional Panels
conditionalPanel(
condition = "input.CM == 'K-Nearest Neighbor (Seurat)'",
numericInput(inputId = "resolution", label = "Resolution Parameter", value = 0.5, max = 1.5, step = 0.05),
),
conditionalPanel(
condition = "input.CM == 'Graph Based Hierarchical Clustering (HGC)' || input.CM == 'K-means Clustering' || input.CM == 'Consensus Clustering (SC3)' || input.CM == 'Density Peak Clustering (Monocle)' || input.CM == 'Hierarchical Clustering'",
numericInput(inputId = "k_value", label = "Desired Number of Clusters", value = 4, min = 1, step = 1),
),
conditionalPanel(
condition = "input.CM == 'Hierarchical Clustering'",
selectInput(inputId = "LM", label = "Choose a Linkage Method", choices = c("ward.D", "ward.D2", "complete", "mcquitty", "average", "median", "centroid"), selected = "ward.D2"),
radioButtons(inputId = "HCoptions", label = "Select a methodology", choices = c("Use determined Cluster Number","Use dynamic tree cut")),
),
),
mainPanel(
selectInput(inputId = "cv", label = "T-SNE or UMAP Cluster Visualization", choices = c("umap", "tsne"), selected = "umap"),
conditionalPanel( #CA Help Text
condition = "input.Help_CA == 'Help'",
textOutput("CA_Help_Text")
),
plotOutput("SClusters") %>% withSpinner(color="#0dc5c1", hide.ui = FALSE)
),
),
tabPanel("Results", #Clustering Results Tab
sidebarPanel(
radioButtons(inputId = "Help_R", label = "Select an Option", choices = c("Help", "Run Tab"), selected = "Help"),
radioButtons(inputId = "TableOptions", label = "Select a Table", choices = c("Summary Report", "Full Data Table")),
helpText("Current Pipeline"),
htmlOutput("Pipeline"),
helpText("Click here to download the Table as a CSV"),
downloadButton("dlb", "Download Table"),
helpText("Click here to download a List of the Current Selected Features"),
downloadButton("FSdlb", "Download Features"),
helpText("Click here to download the Current Seurat Object as an RDS"),
downloadButton("SOdlb", "Download Seurat Object"),
),
mainPanel(
conditionalPanel( #DL Help Text
condition = "input.Help_R == 'Help'",
textOutput("R_Help_Text")
),
tableOutput("table"),
),
),
tabPanel("DEG Analysis", #DEG ANALYSIS Tab
sidebarPanel(
radioButtons(inputId = "Help_DA", label = "Select an Option", choices = c("Help", "Run Tab"), selected = "Help"),
radioButtons(inputId = "DEG_Options", label = "Select an Option", choices = c("Heatmap","Table")),
numericInput(inputId = "LFC", label = "Select a Log Fold Change Threshold;", value = 0.25, step = 0.05),
numericInput(inputId = "minPC", label = "Select a minimum percent expression value; ", value = 0.25, step = 0.05),
conditionalPanel(
condition = "input.DEG_Options == 'Table'",
helpText("Click here to download the Table as a CSV"),
downloadButton("deg_biomarkers", "Download Table"),
),
),
mainPanel(
conditionalPanel( #DA Help Text
condition = "input.Help_DA == 'Help'",
textOutput("DA_Help_Text")
),
conditionalPanel(
condition = "input.DEG_Options == 'Heatmap'",
plotOutput('degPlot', width = "100%") %>% withSpinner(color="#0dc5c1", hide.ui = FALSE),
),
conditionalPanel(
condition = "input.DEG_Options == 'Table'",
tableOutput('degTable') %>% withSpinner(color="#0dc5c1", hide.ui = FALSE),
),
),
),
tabPanel("Deep Learning", #Deep Learning Tab
sidebarPanel(
radioButtons(inputId = "Help_DL", label = "Select an Option", choices = c("Help", "Run Tab"), selected = "Help"),
radioButtons(inputId = "DL_Options", label = "Select an Option", choices = c("Cluster Analysis","Cluster Data Table", "Heatmap", "Cluster Biomarker Table"), selected = "Cluster Analysis"),
conditionalPanel(
condition = "input.DL_Options == 'Cluster Analysis' || input$Help_DL == 'Help'",
numericInput(inputId = "DL_Knumber", label = "Select the desired number of clusters;", value = 8, step = 1, min = 1),
numericInput(inputId = "DL_dimensions", label = "Dimensionality: ", value = 10, max = 30, min = 2, step = 1),
numericInput(inputId = "DL_ncore", label = "Choose the number of cores to use in the analysis", value = 4, min = 1, step = 1),
selectInput(inputId = "DL_cv", label = "T-SNE, or UMAP Cluster Visualization", choices = c("umap", "tsne"), selected = "umap"),
helpText("Click here to download the complete Seurat Object as an RDS file"),
downloadButton("dl_seurat", "Download Object"),
),
conditionalPanel(
condition = "input.DL_Options == 'Cluster Data Table'",
radioButtons(inputId = "DL_TableOptions", label = "Select a Table", choices = c("Summary Report", "Full Data Table")),
helpText("Click here to download the current Table Option as a CSV"),
downloadButton("dl_table_download", "Download Table"),
),
conditionalPanel(
condition = "input.DL_Options == 'Heatmap' || input.DL_Options == 'Cluster Biomarker Table'",
numericInput(inputId = "DL_LFC", label = "Select a Log Fold Change Threshold;", value = 0.25, step = 0.05),
numericInput(inputId = "DL_minPC", label = "Select a minimum percent expression value; ", value = 0.25, step = 0.05),
),
conditionalPanel(
condition = "input.DL_Options == 'Cluster Biomarker Table'",
helpText("Click here to download the Cluster Biomarker Table as a PNG"),
downloadButton("dl_bmTable", "Download Table"),
),
),
mainPanel(
conditionalPanel( #DL Help Text
condition = "input.Help_DL == 'Help'",
textOutput("DL_Help_Text")
),
conditionalPanel( #DL Cluster Plot
condition = "input.DL_Options == 'Cluster Analysis'",
plotOutput('DL_Plot', width = "100%") %>% withSpinner(color="#0dc5c1", hide.ui = FALSE),
),
conditionalPanel( #DL Data Tables
condition = "input.DL_Options == 'Cluster Data Table'",
tableOutput('DL_Table') %>% withSpinner(color="#0dc5c1", hide.ui = FALSE),
),
conditionalPanel( #DL Heatmap
condition = "input.DL_Options == 'Heatmap'",
plotOutput('DL_Heatmap', width = "100%") %>% withSpinner(color="#0dc5c1", hide.ui = FALSE),
),
conditionalPanel( #DL Biomarker Table
condition = "input.DL_Options == 'Cluster Biomarker Table'",
tableOutput('DL_Biomarkers') %>% withSpinner(color="#0dc5c1", hide.ui = FALSE),
),
),
),
)
)
EdwardAgboraw/FrankenSeq documentation built on May 3, 2024, 8:26 a.m.
R Package Documentation
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We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
ui = fluidPage(theme = shinytheme("spacelab"),
navbarPage(
"FrankenSeq",
tabPanel("Data Processing", # Data Processing Tab
sidebarPanel(
radioButtons(inputId = "Help_QC", label = "Select an Option", choices = c("Help", "Run Tab"), selected = "Help"),
selectInput(inputId = "FileType", label = "Select Input File Type", choices = c("RDS (Existing Seurat Object)", "CSV (Gene x Cell Table)", "SingleCellExperiment Object (RDS)", "Read Count Data (CSV)", "PBMC3k Test Data"), selected = "RDS (Existing Seurat Object)"),
conditionalPanel(
condition = "input.FileType != 'PBMC3k Test Data'",
fileInput(inputId = "rdata", label = "Select an input file", accept = ".rds", buttonLabel = "Browse...", placeholder = "No file selected..."),
),
radioButtons(inputId = "DPOptions", label = "Select an Analysis", choices = c("Quality Control", "Data Filtration")),
#Conditional Panel
conditionalPanel(
condition = "input.DPOptions == 'Data Filtration'",
numericInput(inputId = "Max_Features", label = "Maximum Number of Features", max = 5000, min = 1, value = 3000, step = 1),
numericInput(inputId = "Min_Features", label = " Minimum Number of Features", max = 5000, min = 1, value = 200, step = 1),
numericInput(inputId = "Mitochondria", label = " Max % Mitochondrial DNA", max = 100, min = 1, value = 5, step = 1),
),
),
mainPanel(
conditionalPanel( #QC Help Text
condition = "input.Help_QC == 'Help'",
textOutput("QC_Help_Text")
),
plotOutput("dgraph") %>% withSpinner(color="#0dc5c1", hide.ui = FALSE),
)
),
tabPanel("Feature Selection", #Feature Selection Tab
sidebarPanel(
radioButtons(inputId = "Help_FS", label = "Select an Option", choices = c("Help", "Run Tab"), selected = "Help"),
selectInput(inputId = "FS", label = "Choose a Feature Selection method", choices = c("HVG - Seurat (vst)", "HVG - Seurat (mvp)", "HVG - Seurat (Dispersion)", "Drop-Out Based Feature Selection - M3Drop", "Gene-Gene Correlation Feature Selection (DubStepR)", "Feature Selection by Deviance (Scry)","Filter by Gene Expression"), selected = "HVG - Seurat (vst)"),
#conditional Feature Selection Panels
conditionalPanel(
condition = "input.FS == 'HVG - Seurat (vst)' || input.FS == 'HVG - Seurat (Dispersion)' || input.FS == 'Feature Selection by Deviance (Scry)' || input.FS == 'Filter by Gene Expression' || input.FS == 'Drop-Out Based Feature Selection - M3Drop'",
numericInput("nFeatures", "Number of Top Features", min = 1, value = 1556, step = 1)
),
),
mainPanel(
conditionalPanel( #DL Help Text
condition = "input.Help_FS == 'Help'",
textOutput("FS_Help_Text")
),
plotOutput("FSPlot") %>% withSpinner(color="#0dc5c1", hide.ui = FALSE),
)
),
tabPanel("Dimension Reduction", #Dimension Reduction Tab
sidebarPanel(
radioButtons(inputId = "Help_DR", label = "Select an Option", choices = c("Help", "Run Tab"), selected = "Help"),
selectInput(inputId = "dr", label = "Choose a Dimension Reduction method", choices = c("PCA (Seurat)", "GLM PCA (Residuals)", "GLM PCA"), selected = "PCA (Seurat)"),
conditionalPanel(
condition = "input.dr == 'GLM PCA'",
numericInput("L_value", "Choose the number of dimensions to return: ", min = 2, value = 10, step = 1),
),
),
mainPanel(
conditionalPanel(
condition = "input.dr == 'PCA (Seurat)' || input.dr == 'GLM PCA (Residuals)' ",
selectInput("DRG_options", "Select a visualition option", choices = c("Elbow Plot", "PCA"), selected = "PCA"),
),
conditionalPanel( #DR Help Text
condition = "input.Help_DR == 'Help'",
textOutput("DR_Help_Text")
),
plotOutput("drPlot") %>% withSpinner(color="#0dc5c1", hide.ui = FALSE),
)
),
tabPanel("Cluster Validation", #Cluster Validation Tab
sidebarPanel(
radioButtons(inputId = "Help_CV", label = "Select an Option", choices = c("Help", "Run Tab"), selected = "Help"),
radioButtons(inputId = "CVOptions", label = "Select a Plot", choices = c("Estimate K with SC3","Elbow Plot", "Silhouette Plot")),
numericInput(inputId = "maxK", label = "Select maxinum number of clusters to evaluate;", value = 15, step = 1)
),
mainPanel(
conditionalPanel( #CV Help Text
condition = "input.Help_CV == 'Help'",
textOutput("CV_Help_Text")
),
textOutput("sc3_estimated_k_value") %>% withSpinner(color="#0dc5c1", hide.ui = FALSE),
plotOutput("cvPlot") %>% withSpinner(color="#0dc5c1", hide.ui = FALSE),
),
),
tabPanel("Cluster Analysis", #Cluster Analysis Tab
sidebarPanel(
radioButtons(inputId = "Help_CA", label = "Select an Option", choices = c("Help", "Run Tab"), selected = "Help"),
selectInput(inputId = "CM", label = "Choose an Unsupervised Clustering Algorithm", choices = c("K-Nearest Neighbor (Seurat)", "Graph Based Hierarchical Clustering (HGC)", "Hierarchical Clustering", "K-means Clustering", "Density Peak Clustering (Monocle)", "Consensus Clustering (SC3)")),
numericInput(inputId = "dimensions", label = "Dimensionality: ", value = 10, max = 30, min = 2, step = 1),
#Conditional Panels
conditionalPanel(
condition = "input.CM == 'K-Nearest Neighbor (Seurat)'",
numericInput(inputId = "resolution", label = "Resolution Parameter", value = 0.5, max = 1.5, step = 0.05),
),
conditionalPanel(
condition = "input.CM == 'Graph Based Hierarchical Clustering (HGC)' || input.CM == 'K-means Clustering' || input.CM == 'Consensus Clustering (SC3)' || input.CM == 'Density Peak Clustering (Monocle)' || input.CM == 'Hierarchical Clustering'",
numericInput(inputId = "k_value", label = "Desired Number of Clusters", value = 4, min = 1, step = 1),
),
conditionalPanel(
condition = "input.CM == 'Hierarchical Clustering'",
selectInput(inputId = "LM", label = "Choose a Linkage Method", choices = c("ward.D", "ward.D2", "complete", "mcquitty", "average", "median", "centroid"), selected = "ward.D2"),
radioButtons(inputId = "HCoptions", label = "Select a methodology", choices = c("Use determined Cluster Number","Use dynamic tree cut")),
),
),
mainPanel(
selectInput(inputId = "cv", label = "T-SNE or UMAP Cluster Visualization", choices = c("umap", "tsne"), selected = "umap"),
conditionalPanel( #CA Help Text
condition = "input.Help_CA == 'Help'",
textOutput("CA_Help_Text")
),
plotOutput("SClusters") %>% withSpinner(color="#0dc5c1", hide.ui = FALSE)
),
),
tabPanel("Results", #Clustering Results Tab
sidebarPanel(
radioButtons(inputId = "Help_R", label = "Select an Option", choices = c("Help", "Run Tab"), selected = "Help"),
radioButtons(inputId = "TableOptions", label = "Select a Table", choices = c("Summary Report", "Full Data Table")),
helpText("Current Pipeline"),
htmlOutput("Pipeline"),
helpText("Click here to download the Table as a CSV"),
downloadButton("dlb", "Download Table"),
helpText("Click here to download a List of the Current Selected Features"),
downloadButton("FSdlb", "Download Features"),
helpText("Click here to download the Current Seurat Object as an RDS"),
downloadButton("SOdlb", "Download Seurat Object"),
),
mainPanel(
conditionalPanel( #DL Help Text
condition = "input.Help_R == 'Help'",
textOutput("R_Help_Text")
),
tableOutput("table"),
),
),
tabPanel("DEG Analysis", #DEG ANALYSIS Tab
sidebarPanel(
radioButtons(inputId = "Help_DA", label = "Select an Option", choices = c("Help", "Run Tab"), selected = "Help"),
radioButtons(inputId = "DEG_Options", label = "Select an Option", choices = c("Heatmap","Table")),
numericInput(inputId = "LFC", label = "Select a Log Fold Change Threshold;", value = 0.25, step = 0.05),
numericInput(inputId = "minPC", label = "Select a minimum percent expression value; ", value = 0.25, step = 0.05),
conditionalPanel(
condition = "input.DEG_Options == 'Table'",
helpText("Click here to download the Table as a CSV"),
downloadButton("deg_biomarkers", "Download Table"),
),
),
mainPanel(
conditionalPanel( #DA Help Text
condition = "input.Help_DA == 'Help'",
textOutput("DA_Help_Text")
),
conditionalPanel(
condition = "input.DEG_Options == 'Heatmap'",
plotOutput('degPlot', width = "100%") %>% withSpinner(color="#0dc5c1", hide.ui = FALSE),
),
conditionalPanel(
condition = "input.DEG_Options == 'Table'",
tableOutput('degTable') %>% withSpinner(color="#0dc5c1", hide.ui = FALSE),
),
),
),
tabPanel("Deep Learning", #Deep Learning Tab
sidebarPanel(
radioButtons(inputId = "Help_DL", label = "Select an Option", choices = c("Help", "Run Tab"), selected = "Help"),
radioButtons(inputId = "DL_Options", label = "Select an Option", choices = c("Cluster Analysis","Cluster Data Table", "Heatmap", "Cluster Biomarker Table"), selected = "Cluster Analysis"),
conditionalPanel(
condition = "input.DL_Options == 'Cluster Analysis' || input$Help_DL == 'Help'",
numericInput(inputId = "DL_Knumber", label = "Select the desired number of clusters;", value = 8, step = 1, min = 1),
numericInput(inputId = "DL_dimensions", label = "Dimensionality: ", value = 10, max = 30, min = 2, step = 1),
numericInput(inputId = "DL_ncore", label = "Choose the number of cores to use in the analysis", value = 4, min = 1, step = 1),
selectInput(inputId = "DL_cv", label = "T-SNE, or UMAP Cluster Visualization", choices = c("umap", "tsne"), selected = "umap"),
helpText("Click here to download the complete Seurat Object as an RDS file"),
downloadButton("dl_seurat", "Download Object"),
),
conditionalPanel(
condition = "input.DL_Options == 'Cluster Data Table'",
radioButtons(inputId = "DL_TableOptions", label = "Select a Table", choices = c("Summary Report", "Full Data Table")),
helpText("Click here to download the current Table Option as a CSV"),
downloadButton("dl_table_download", "Download Table"),
),
conditionalPanel(
condition = "input.DL_Options == 'Heatmap' || input.DL_Options == 'Cluster Biomarker Table'",
numericInput(inputId = "DL_LFC", label = "Select a Log Fold Change Threshold;", value = 0.25, step = 0.05),
numericInput(inputId = "DL_minPC", label = "Select a minimum percent expression value; ", value = 0.25, step = 0.05),
),
conditionalPanel(
condition = "input.DL_Options == 'Cluster Biomarker Table'",
helpText("Click here to download the Cluster Biomarker Table as a PNG"),
downloadButton("dl_bmTable", "Download Table"),
),
),
mainPanel(
conditionalPanel( #DL Help Text
condition = "input.Help_DL == 'Help'",
textOutput("DL_Help_Text")
),
conditionalPanel( #DL Cluster Plot
condition = "input.DL_Options == 'Cluster Analysis'",
plotOutput('DL_Plot', width = "100%") %>% withSpinner(color="#0dc5c1", hide.ui = FALSE),
),
conditionalPanel( #DL Data Tables
condition = "input.DL_Options == 'Cluster Data Table'",
tableOutput('DL_Table') %>% withSpinner(color="#0dc5c1", hide.ui = FALSE),
),
conditionalPanel( #DL Heatmap
condition = "input.DL_Options == 'Heatmap'",
plotOutput('DL_Heatmap', width = "100%") %>% withSpinner(color="#0dc5c1", hide.ui = FALSE),
),
conditionalPanel( #DL Biomarker Table
condition = "input.DL_Options == 'Cluster Biomarker Table'",
tableOutput('DL_Biomarkers') %>% withSpinner(color="#0dc5c1", hide.ui = FALSE),
),
),
),
)
)
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We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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