inst/shiny/ui_09_4_scanpyWorkflow.R
In compbiomed/singleCellTK: Comprehensive and Interactive Analysis of Single Cell RNA-Seq Data
# User Interface for Scanpy Workflow ---
shinyPanelScanpy <- fluidPage(
tags$script("Shiny.addCustomMessageHandler('close_dropDownScanpy', function(x){
$('html').click();
});"), # close_dropDownSeuratHM
h1("Scanpy"),
h5(tags$a(href = paste0(docs.artPath, "cnsl_scanpy_curated_workflow.html"), #cnsl_seurat_curated_workflow.html
"(help)", target = "_blank")),
inlineCSS(list(".panel-danger>.panel-heading" = "background-color:#dcdcdc; color:#000000", ".panel-primary>.panel-heading" = "background-color:#f5f5f5; color:#000000; border-color:#dddddd", ".panel-primary" = "border-color:#dddddd;", ".panel-primary>.panel-heading+.panel-collapse>.panel-body" = "border-color:#dddddd;")),
conditionalPanel(
condition = "false",
selectInput(
"activePanelSelectScanpy", #activePanelSelectSeurat
label = "Active Panel:",
choices = c("",
"Normalize Data",
"Highly Variable Genes",
"Dimensionality Reduction",
"tSNE/UMAP",
"Clustering",
"Find Markers",
"Heatmap Plot"),
selected = ""
)
),
bsCollapse(id = "ScanpyUI", open = "scanpy QC & Filtering", # ScanPy
bsCollapsePanel("Normalize Data",
fluidRow(
column(4,
panel(heading = "Options",
selectizeInput(
inputId = "scanpySelectNormalizationAssay", # seuratSelectNormalizationAssay
label = "Select input matrix:",
choices = NULL,
selected = NULL,
multiple = FALSE,
options = NULL),
numericInput(inputId = "scanpy_targetSum", label = "Specify targetSum:", value = 10000),
numericInput(inputId = "scanpy_maxFraction", label = "Specify maxFraction:", value = 0.05),
actionButton(inputId = "scanpy_normalize_button", "Normalize") # normalize_button
)
)
),
style = "primary"
),
bsCollapsePanel("Highly Variable Genes",
fluidRow(
column(4,
fluidRow(
column(12,
panel(heading = "Compute HVG",
selectInput(inputId = "scanpy_hvg_method", label = "Select HVG method: ", choices = c("seurat", "cell_ranger", "seurat_v3")), # hvg_method
numericInput(inputId = "scanpy_hvg_no_features", label = "Select number of features to find: ", value = "2000"), # hvg_no_features
numericInput(inputId = "scanpy_minMean", label = "Specify minMean: ", value = "0.0125"),
numericInput(inputId = "scanpy_maxMean", label = "Specify maxMean: ", value = "3"),
numericInput(inputId = "scanpy_minDisp", label = "Specify minDisp: ", value = "0.5"),
checkboxInput(inputId = "scanpy_maxDisp_Inf", label = "Set maxDisp to infinite?", value = TRUE),
conditionalPanel(
condition = 'input.scanpy_maxDisp_Inf == false',
numericInput(inputId = "scanpy_maxDisp", label = "Specify maxDisp: ", value = 3)
),
actionButton(inputId = "scanpy_find_hvg_button", "Find HVG") # find_hvg_button
)
)
)
# ,
# br(),
# fluidRow(
# column(12,
# panel(heading = "Display HVG",
# numericInput(inputId = "scanpy_hvg_no_features_view", label = "Select number of features to display: ", value = 10, step = 1), # hvg_no_features_view
# verbatimTextOutput(outputId = "scanpy_hvg_output", placeholder = TRUE) # hvg_output
# )
# )
# )
),
column(8,
fluidRow(
column(12,
panel(heading = "Plot",
plotOutput(outputId = "scanpy_plot_hvg") # plot_hvg
)
)
)
)
),
style = "primary"
),
bsCollapsePanel("Dimensionality Reduction",
fluidRow(
column(4,
fluidRow(
column(12,
panel(heading = "scanpy PCA",
selectInput(inputId = "scanpy_pca_method", label = "Select PCA method: ", choices = c("arpack", "randomized", "auto", "lobpcg")),
numericInput(inputId = "scanpy_pca_no_components", label = "Select number of components to compute: ", value = 50), # pca_no_components
actionButton(inputId = "scanpy_run_pca_button", "Run PCA")
),
panel(heading = "Select No. of Components",
htmlOutput(outputId = "scanpy_pca_significant_pc_output", inline = FALSE),
numericInput(inputId = "scanpy_pca_significant_pc_counter", label = "Select number of components for downstream analysis: ", min = 1, max = 50, value = 40)
)
)
)
),
column(8,
fluidRow(
column(12,
hidden(
tags$div(class = "scanpy_pca_plots", tabsetPanel(id = "scanpyPCAPlotTabset", type = "tabs"
)
))
)
)
)
),
style = "primary"
),
bsCollapsePanel("2D-Embedding",
tabsetPanel(id = "tsneUmapTabsetScanpy", type = "tabs",
tabPanel("UMAP",
br(),
fluidRow(
column(4,
fluidRow(
column(12,
panel(heading = "UMAP",
#selectInput(inputId = "scanpy_reduction_umap_method", label = "Select reduction method: ", choices = c("pca", "ica")),
#numericInput(inputId = "scanpy_reduction_umap_count", label = "Select number of reduction components: ", value = 50),
numericInput(inputId = "scanpy_min_dist_umap", label = "Set min.dist:", value = 0.5),
numericInput(inputId = "scanpy_n_neighbors_umap", label = "Set n.neighbors:", value = 10, step = 1),
numericInput(inputId = "scanpy_spread_umap", label = "Set spread:", value = 1),
numericInput(inputId = "scanpy_spread_alpha", label = "Set alpha:", value = 1),
numericInput(inputId = "scanpy_spread_gamma", label = "Set gamma:", value = 1),
# numericInput(inputId = "scanpy_seed_UMAP",
# label = "Seed value for reproducibility of result:",
# value = 42,
# step = 1),
# htmlOutput(outputId = "scanpy_display_message_umap", inline = FALSE),
actionButton(inputId = "scanpy_run_umap_button", "Run UMAP")
)
)
)
),
column(8,
fluidRow(
panel(heading = "Plot",
column(12,
plotOutput(outputId = "scanpy_plot_umap")
)
)
)
)
)
),
tabPanel("tSNE",
br(),
fluidRow(
column(4,
fluidRow(
column(12,
panel(heading = "tSNE",
selectInput(inputId = "scanpy_reduction_tsne_method", label = "Select reduction method: ", choices = c("pca", "ica")),
#textInput(inputId = "reduction_tsne_count", label = "Select number of reduction components: ", value = "20"),
numericInput(inputId = "scanpy_perplexity_tsne", label = "Set perplexity:", value = 15),
# numericInput(inputId = "scanpy_seed_TSNE",
# label = "Seed value for reproducibility of result:",
# value = 1,
# step = 1),
# htmlOutput(outputId = "scanpy_display_message_tsne", inline = FALSE),
actionButton(inputId = "scanpy_run_tsne_button", "Run tSNE")
)
)
)
),
column(8,
fluidRow(
panel(heading = "Plot",
column(12,
plotOutput(outputId = "scanpy_plot_tsne")
)
)
)
)
)
)
),
style = "primary"
),
bsCollapsePanel("Clustering",
fluidRow(
column(4,
fluidRow(
column(12,
panel(heading = "Options",
#selectInput(inputId = "scanpy_reduction_clustering_method", label = "Select reduction method: ", choices = c("pca", "ica")),
#numericInput(inputId = "scanpy_reduction_clustering_count", label = "Select number of reduction components: ", value = 40),
selectInput(inputId = "scanpy_algorithm.use", label = "Select clustering algorithm: ", choices = list("leiden algorithm" = "leiden",
"louvain algorithm" = "louvain")),
numericInput(inputId = "scanpy_resolution_clustering", label = "Set resolution:", value = 1),
numericInput(inputId = "scanpy_nNeighbors", label = "Set no. of neigbours:", value = 10),
# materialSwitch(inputId = "scanpy_group.singletons", label = "Group singletons?", value = TRUE),
# htmlOutput(outputId = "scanpy_display_message_clustering", inline = FALSE),
selectInput(inputId = "scanpy_corr_method", label = "Select correlation metric: ", choices = list("pearson" = "pearson",
"kendall" = "kendall",
"spearman" = "spearman")),
actionButton(inputId = "scanpy_find_clusters_button", "Find Clusters")
)
)
)
),
column(8,
fluidRow(
column(12,
hidden(
tags$div(class = "scanpy_clustering_plots", tabsetPanel(id = "scanpyClusteringPlotTabset", type = "tabs"
))
)
)
)
)
),
style = "primary"),
bsCollapsePanel("Find Markers",
fluidRow(
column(4,
fluidRow(
column(12,
panel(heading = "Options",
selectInput(
inputId = "scanpyFindMarkerSelectPhenotype",
label = "Select biological phenotype:",
choices = NULL
),
selectInput(
inputId = "scanpyFindMarkerTest",
label = "Select test:",
choices = c("wilcoxon", "t-test", "t-test_overestim_var", "logreg")
),
selectInput(
inputId = "scanpyFindMarkerCorrMethod",
label = "Multiple testing correction method:",
choices = c("benjamini-hochberg", "bonferroni")
),
actionButton(inputId = "scanpyFindMarkerRun", "Find Markers")
)
)
)
),
column(8,
fluidRow(
column(12,
hidden(
tags$div(
class = "scanpy_findmarker_table",
filterTableUI(id = "filterScanpyFindMarker")
)
),
br(),
hidden(
tags$div(class = "scanpy_findmarker_jointHeatmap",
bsCollapse(
bsCollapsePanel(
title = "Heatmap Plot",
fluidRow(
column(12, align = "center",
panel(
numericInput("scanpy_findMarkerHeatmapPlotFullNumeric", value = 2, max = 2000, min = 2, step = 1, label = "Select number of top genes from each cluster/group to visualize in the heatmap below based on highest average log fold change value:"),
actionButton("scanpy_findMarkerHeatmapPlotFullNumericRun", label = "Plot"),
hr(),
shinyjqui::jqui_resizable(
plotOutput(outputId = "scanpy_findMarkerHeatmapPlotFull", height = "500px")
)
)
)
)
)
)
)
),
br(),
hidden(
tags$div(class = "scanpy_findmarker_plots",
panel(heading = "Marker Gene Plots",
HTML("<center><h5><span style='color:red; font-weight:bold; text-align:center;'>Click on the rows of the table above to plot the selected marker genes below!</span></h5></br></center>"),
tabsetPanel(id = "scanpyFindMarkerPlotTabset", type = "tabs"))
)
)
)
)
)
),
style = "primary")
),
nonLinearWorkflowUI(id = "nlw-scanpy") #nlw-seurat
)
compbiomed/singleCellTK documentation built on May 8, 2024, 6:58 p.m.
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# User Interface for Scanpy Workflow ---
shinyPanelScanpy <- fluidPage(
tags$script("Shiny.addCustomMessageHandler('close_dropDownScanpy', function(x){
$('html').click();
});"), # close_dropDownSeuratHM
h1("Scanpy"),
h5(tags$a(href = paste0(docs.artPath, "cnsl_scanpy_curated_workflow.html"), #cnsl_seurat_curated_workflow.html
"(help)", target = "_blank")),
inlineCSS(list(".panel-danger>.panel-heading" = "background-color:#dcdcdc; color:#000000", ".panel-primary>.panel-heading" = "background-color:#f5f5f5; color:#000000; border-color:#dddddd", ".panel-primary" = "border-color:#dddddd;", ".panel-primary>.panel-heading+.panel-collapse>.panel-body" = "border-color:#dddddd;")),
conditionalPanel(
condition = "false",
selectInput(
"activePanelSelectScanpy", #activePanelSelectSeurat
label = "Active Panel:",
choices = c("",
"Normalize Data",
"Highly Variable Genes",
"Dimensionality Reduction",
"tSNE/UMAP",
"Clustering",
"Find Markers",
"Heatmap Plot"),
selected = ""
)
),
bsCollapse(id = "ScanpyUI", open = "scanpy QC & Filtering", # ScanPy
bsCollapsePanel("Normalize Data",
fluidRow(
column(4,
panel(heading = "Options",
selectizeInput(
inputId = "scanpySelectNormalizationAssay", # seuratSelectNormalizationAssay
label = "Select input matrix:",
choices = NULL,
selected = NULL,
multiple = FALSE,
options = NULL),
numericInput(inputId = "scanpy_targetSum", label = "Specify targetSum:", value = 10000),
numericInput(inputId = "scanpy_maxFraction", label = "Specify maxFraction:", value = 0.05),
actionButton(inputId = "scanpy_normalize_button", "Normalize") # normalize_button
)
)
),
style = "primary"
),
bsCollapsePanel("Highly Variable Genes",
fluidRow(
column(4,
fluidRow(
column(12,
panel(heading = "Compute HVG",
selectInput(inputId = "scanpy_hvg_method", label = "Select HVG method: ", choices = c("seurat", "cell_ranger", "seurat_v3")), # hvg_method
numericInput(inputId = "scanpy_hvg_no_features", label = "Select number of features to find: ", value = "2000"), # hvg_no_features
numericInput(inputId = "scanpy_minMean", label = "Specify minMean: ", value = "0.0125"),
numericInput(inputId = "scanpy_maxMean", label = "Specify maxMean: ", value = "3"),
numericInput(inputId = "scanpy_minDisp", label = "Specify minDisp: ", value = "0.5"),
checkboxInput(inputId = "scanpy_maxDisp_Inf", label = "Set maxDisp to infinite?", value = TRUE),
conditionalPanel(
condition = 'input.scanpy_maxDisp_Inf == false',
numericInput(inputId = "scanpy_maxDisp", label = "Specify maxDisp: ", value = 3)
),
actionButton(inputId = "scanpy_find_hvg_button", "Find HVG") # find_hvg_button
)
)
)
# ,
# br(),
# fluidRow(
# column(12,
# panel(heading = "Display HVG",
# numericInput(inputId = "scanpy_hvg_no_features_view", label = "Select number of features to display: ", value = 10, step = 1), # hvg_no_features_view
# verbatimTextOutput(outputId = "scanpy_hvg_output", placeholder = TRUE) # hvg_output
# )
# )
# )
),
column(8,
fluidRow(
column(12,
panel(heading = "Plot",
plotOutput(outputId = "scanpy_plot_hvg") # plot_hvg
)
)
)
)
),
style = "primary"
),
bsCollapsePanel("Dimensionality Reduction",
fluidRow(
column(4,
fluidRow(
column(12,
panel(heading = "scanpy PCA",
selectInput(inputId = "scanpy_pca_method", label = "Select PCA method: ", choices = c("arpack", "randomized", "auto", "lobpcg")),
numericInput(inputId = "scanpy_pca_no_components", label = "Select number of components to compute: ", value = 50), # pca_no_components
actionButton(inputId = "scanpy_run_pca_button", "Run PCA")
),
panel(heading = "Select No. of Components",
htmlOutput(outputId = "scanpy_pca_significant_pc_output", inline = FALSE),
numericInput(inputId = "scanpy_pca_significant_pc_counter", label = "Select number of components for downstream analysis: ", min = 1, max = 50, value = 40)
)
)
)
),
column(8,
fluidRow(
column(12,
hidden(
tags$div(class = "scanpy_pca_plots", tabsetPanel(id = "scanpyPCAPlotTabset", type = "tabs"
)
))
)
)
)
),
style = "primary"
),
bsCollapsePanel("2D-Embedding",
tabsetPanel(id = "tsneUmapTabsetScanpy", type = "tabs",
tabPanel("UMAP",
br(),
fluidRow(
column(4,
fluidRow(
column(12,
panel(heading = "UMAP",
#selectInput(inputId = "scanpy_reduction_umap_method", label = "Select reduction method: ", choices = c("pca", "ica")),
#numericInput(inputId = "scanpy_reduction_umap_count", label = "Select number of reduction components: ", value = 50),
numericInput(inputId = "scanpy_min_dist_umap", label = "Set min.dist:", value = 0.5),
numericInput(inputId = "scanpy_n_neighbors_umap", label = "Set n.neighbors:", value = 10, step = 1),
numericInput(inputId = "scanpy_spread_umap", label = "Set spread:", value = 1),
numericInput(inputId = "scanpy_spread_alpha", label = "Set alpha:", value = 1),
numericInput(inputId = "scanpy_spread_gamma", label = "Set gamma:", value = 1),
# numericInput(inputId = "scanpy_seed_UMAP",
# label = "Seed value for reproducibility of result:",
# value = 42,
# step = 1),
# htmlOutput(outputId = "scanpy_display_message_umap", inline = FALSE),
actionButton(inputId = "scanpy_run_umap_button", "Run UMAP")
)
)
)
),
column(8,
fluidRow(
panel(heading = "Plot",
column(12,
plotOutput(outputId = "scanpy_plot_umap")
)
)
)
)
)
),
tabPanel("tSNE",
br(),
fluidRow(
column(4,
fluidRow(
column(12,
panel(heading = "tSNE",
selectInput(inputId = "scanpy_reduction_tsne_method", label = "Select reduction method: ", choices = c("pca", "ica")),
#textInput(inputId = "reduction_tsne_count", label = "Select number of reduction components: ", value = "20"),
numericInput(inputId = "scanpy_perplexity_tsne", label = "Set perplexity:", value = 15),
# numericInput(inputId = "scanpy_seed_TSNE",
# label = "Seed value for reproducibility of result:",
# value = 1,
# step = 1),
# htmlOutput(outputId = "scanpy_display_message_tsne", inline = FALSE),
actionButton(inputId = "scanpy_run_tsne_button", "Run tSNE")
)
)
)
),
column(8,
fluidRow(
panel(heading = "Plot",
column(12,
plotOutput(outputId = "scanpy_plot_tsne")
)
)
)
)
)
)
),
style = "primary"
),
bsCollapsePanel("Clustering",
fluidRow(
column(4,
fluidRow(
column(12,
panel(heading = "Options",
#selectInput(inputId = "scanpy_reduction_clustering_method", label = "Select reduction method: ", choices = c("pca", "ica")),
#numericInput(inputId = "scanpy_reduction_clustering_count", label = "Select number of reduction components: ", value = 40),
selectInput(inputId = "scanpy_algorithm.use", label = "Select clustering algorithm: ", choices = list("leiden algorithm" = "leiden",
"louvain algorithm" = "louvain")),
numericInput(inputId = "scanpy_resolution_clustering", label = "Set resolution:", value = 1),
numericInput(inputId = "scanpy_nNeighbors", label = "Set no. of neigbours:", value = 10),
# materialSwitch(inputId = "scanpy_group.singletons", label = "Group singletons?", value = TRUE),
# htmlOutput(outputId = "scanpy_display_message_clustering", inline = FALSE),
selectInput(inputId = "scanpy_corr_method", label = "Select correlation metric: ", choices = list("pearson" = "pearson",
"kendall" = "kendall",
"spearman" = "spearman")),
actionButton(inputId = "scanpy_find_clusters_button", "Find Clusters")
)
)
)
),
column(8,
fluidRow(
column(12,
hidden(
tags$div(class = "scanpy_clustering_plots", tabsetPanel(id = "scanpyClusteringPlotTabset", type = "tabs"
))
)
)
)
)
),
style = "primary"),
bsCollapsePanel("Find Markers",
fluidRow(
column(4,
fluidRow(
column(12,
panel(heading = "Options",
selectInput(
inputId = "scanpyFindMarkerSelectPhenotype",
label = "Select biological phenotype:",
choices = NULL
),
selectInput(
inputId = "scanpyFindMarkerTest",
label = "Select test:",
choices = c("wilcoxon", "t-test", "t-test_overestim_var", "logreg")
),
selectInput(
inputId = "scanpyFindMarkerCorrMethod",
label = "Multiple testing correction method:",
choices = c("benjamini-hochberg", "bonferroni")
),
actionButton(inputId = "scanpyFindMarkerRun", "Find Markers")
)
)
)
),
column(8,
fluidRow(
column(12,
hidden(
tags$div(
class = "scanpy_findmarker_table",
filterTableUI(id = "filterScanpyFindMarker")
)
),
br(),
hidden(
tags$div(class = "scanpy_findmarker_jointHeatmap",
bsCollapse(
bsCollapsePanel(
title = "Heatmap Plot",
fluidRow(
column(12, align = "center",
panel(
numericInput("scanpy_findMarkerHeatmapPlotFullNumeric", value = 2, max = 2000, min = 2, step = 1, label = "Select number of top genes from each cluster/group to visualize in the heatmap below based on highest average log fold change value:"),
actionButton("scanpy_findMarkerHeatmapPlotFullNumericRun", label = "Plot"),
hr(),
shinyjqui::jqui_resizable(
plotOutput(outputId = "scanpy_findMarkerHeatmapPlotFull", height = "500px")
)
)
)
)
)
)
)
),
br(),
hidden(
tags$div(class = "scanpy_findmarker_plots",
panel(heading = "Marker Gene Plots",
HTML("<center><h5><span style='color:red; font-weight:bold; text-align:center;'>Click on the rows of the table above to plot the selected marker genes below!</span></h5></br></center>"),
tabsetPanel(id = "scanpyFindMarkerPlotTabset", type = "tabs"))
)
)
)
)
)
),
style = "primary")
),
nonLinearWorkflowUI(id = "nlw-scanpy") #nlw-seurat
)
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