inst/shiny/ui_09_2_seuratWorkflow.R
In compbiomed/singleCellTK: Comprehensive and Interactive Analysis of Single Cell RNA-Seq Data
# User Interface for Seurat Workflow ---
shinyPanelSeurat <- fluidPage(
tags$script("Shiny.addCustomMessageHandler('close_dropDownSeuratHM', function(x){
$('html').click();
});"),
h1("Seurat"),
h5(tags$a(href = paste0(docs.artPath, "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(
"activePanelSelectSeurat",
label = "Active Panel:",
choices = c("",
"Normalize Data",
"Scale Data",
"Highly Variable Genes",
"Dimensionality Reduction",
"tSNE/UMAP",
"Clustering",
"Find Markers",
"Heatmap Plot"),
selected = ""
)
),
bsCollapse(id = "SeuratUI", open = "Normalize Data",
bsCollapsePanel("Normalize Data",
fluidRow(
column(4,
panel(heading = "Options",
selectizeInput(
inputId = "seuratSelectNormalizationAssay",
label = "Select input matrix:",
choices = NULL,
selected = NULL,
multiple = FALSE,
options = NULL),
#uiOutput("seuratSelectNormalizationAssay"),
selectInput(inputId = "normalization_method", label = "Select normalization method: ", choices = c("LogNormalize", "CLR", "RC")),
textInput(inputId = "scale_factor", label = "Set scaling factor: ", value = "10000"),
actionButton(inputId = "normalize_button", "Normalize")
)
)
),
style = "primary"
),
bsCollapsePanel("Highly Variable Genes",
fluidRow(
column(4,
fluidRow(
column(12,
panel(heading = "Compute HVG",
selectInput(inputId = "hvg_method", label = "Select HVG method: ", choices = c("vst", "mean.var.plot", "dispersion")),
textInput(inputId = "hvg_no_features", label = "Select number of features to find: ", value = "2000"),
actionButton(inputId = "find_hvg_button", "Find HVG")
)
)
),
br(),
fluidRow(
column(12,
panel(heading = "Display HVG",
numericInput(inputId = "hvg_no_features_view", label = "Select number of features to display: ", value = 10, step = 1),
verbatimTextOutput(outputId = "hvg_output", placeholder = TRUE)
)
)
)
),
column(8,
fluidRow(
column(12,
panel(heading = "Plot",
plotlyOutput(outputId = "plot_hvg")
)
)
)
)
),
style = "primary"),
# bsCollapsePanel("Scale Data",
# fluidRow(
# column(4,
# panel(heading = "Options",
# #selectInput(inputId = "model.use", label = "Select model for scaling: ", choices = c("linear", "poisson", "negbinom")),
# materialSwitch(inputId = "do.scale", label = "Scale data?", value = TRUE),
# materialSwitch(inputId = "do.center", label = "Center data?", value = TRUE),
# textInput(inputId = "scale.max", label = "Max value for scaled data: ", value = "10"),
# actionButton(inputId = "scale_button", "Scale")
# )
# )
# ),
# style = "primary"
# ),
bsCollapsePanel("Dimensionality Reduction",
tabsetPanel(type = "tabs",
tabPanel("PCA",
br(),
fluidRow(
column(4,
fluidRow(
column(12,
panel(heading = "PCA",
numericInput(inputId = "pca_no_components", label = "Select number of components to compute: ", value = 50),
materialSwitch(inputId = "pca_compute_elbow", label = "Compute ElbowPlot?", value = TRUE),
materialSwitch(inputId = "pca_compute_jackstraw", label = "Compute JackStrawPlot?", value = FALSE),
materialSwitch(inputId = "pca_compute_heatmap", label = "Compute Heatmap?", value = TRUE),
conditionalPanel(
condition = 'input.pca_compute_heatmap == true',
numericInput(inputId = "pca_compute_heatmap_nfeatures",
label = "Set number of features for heatmap:", value = 30, step = 1),
),
numericInput(inputId = "seed_PCA",
label = "Seed value for reproducibility of result:",
value = 42,
step = 1),
actionButton(inputId = "run_pca_button", "Run PCA")
),
panel(heading = "Select No. of Components",
htmlOutput(outputId = "pca_significant_pc_output", inline = FALSE),
numericInput(inputId = "pca_significant_pc_counter", label = "Select number of components for downstream analysis: ", min = 1, max = 20, value = 10)
)
)
)
),
column(8,
fluidRow(
column(12,
hidden(
tags$div(class = "seurat_pca_plots", tabsetPanel(id = "seuratPCAPlotTabset", type = "tabs"
)
))
)
)
)
)
),
tabPanel("ICA",
br(),
fluidRow(
column(4,
fluidRow(
column(12,
panel(heading = "ICA",
textInput(inputId = "ica_no_components", label = "Select number of components to compute: ", value = "20"),
materialSwitch(inputId = "ica_compute_heatmap", label = "Compute Heatmap?", value = TRUE),
conditionalPanel(
condition = 'input.ica_compute_heatmap == true',
numericInput(inputId = "ica_compute_heatmap_nfeatures",
label = "Set number of features for heatmap:", value = 30, step = 1),
),
numericInput(inputId = "seed_ICA",
label = "Seed value for reproducibility of result:",
value = 42,
step = 1),
actionButton(inputId = "run_ica_button", "Run ICA")
),
panel(heading = "Select No. of Components",
#h5("Number of components suggested by ElbowPlot: "),
#verbatimTextOutput(outputId = "ica_significant_pc_output", placeholder = TRUE),
numericInput(inputId = "ica_significant_ic_counter", label = "Select number of components for downstream analysis: ", min = 1, max = 20, value = 10)
)
)
)
),
column(8,
fluidRow(
column(12,
hidden(
tags$div(class = "seurat_ica_plots", tabsetPanel(id="seuratICAPlotTabset", type = "tabs"
))
)
)
)
)
)
)
),
style = "primary"),
bsCollapsePanel("2D-Embedding",
tabsetPanel(id = "tsneUmapTabsetSeurat", type = "tabs",
tabPanel("UMAP",
br(),
fluidRow(
column(4,
fluidRow(
column(12,
panel(heading = "UMAP",
selectInput(inputId = "reduction_umap_method", label = "Select reduction method: ", choices = c("pca", "ica")),
#textInput(inputId = "reduction_umap_count", label = "Select number of reduction components: ", value = "20"),
numericInput(inputId = "min_dist_umap", label = "Set min.dist:", value = 0.3),
numericInput(inputId = "n_neighbors_umap", label = "Set n.neighbors:", value = 30, step = 1),
numericInput(inputId = "spread_umap", label = "Set spread:", value = 1),
numericInput(inputId = "seed_UMAP",
label = "Seed value for reproducibility of result:",
value = 42,
step = 1),
htmlOutput(outputId = "display_message_umap", inline = FALSE),
actionButton(inputId = "run_umap_button", "Run UMAP")
)
)
)
),
column(8,
fluidRow(
panel(heading = "Plot",
column(12,
plotlyOutput(outputId = "plot_umap")
)
)
)
)
)
),
tabPanel("tSNE",
br(),
fluidRow(
column(4,
fluidRow(
column(12,
panel(heading = "tSNE",
selectInput(inputId = "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 = "perplexity_tsne", label = "Set perplexity:", value = 30),
numericInput(inputId = "seed_TSNE",
label = "Seed value for reproducibility of result:",
value = 1,
step = 1),
htmlOutput(outputId = "display_message_tsne", inline = FALSE),
actionButton(inputId = "run_tsne_button", "Run tSNE")
)
)
)
),
column(8,
fluidRow(
panel(heading = "Plot",
column(12,
plotlyOutput(outputId = "plot_tsne")
)
)
)
)
)
)
),
style = "primary"),
bsCollapsePanel("Clustering",
fluidRow(
column(4,
fluidRow(
column(12,
panel(heading = "Options",
selectInput(inputId = "reduction_clustering_method", label = "Select reduction method: ", choices = c("pca", "ica")),
#textInput(inputId = "reduction_clustering_count", label = "Select number of reduction components: ", value = "20"),
selectInput(inputId = "algorithm.use", label = "Select clustering algorithm: ", choices = list("Original Louvain algorithm" = "louvain",
"Louvain algorithm with multilevel refinement" = "multilevel",
"SLM algorithm" = "SLM")),
numericInput(inputId = "resolution_clustering", label = "Set resolution:", value = 0.8),
materialSwitch(inputId = "group.singletons", label = "Group singletons?", value = TRUE),
htmlOutput(outputId = "display_message_clustering", inline = FALSE),
actionButton(inputId = "find_clusters_button", "Find Clusters")
)
)
)
),
column(8,
fluidRow(
column(12,
hidden(
tags$div(class = "seurat_clustering_plots", tabsetPanel(id = "seuratClusteringPlotTabset", type = "tabs"
))
)
)
)
)
),
style = "primary"),
bsCollapsePanel("Marker Gene Plots",
fluidRow(
column(12,
fluidRow(
column(12,
panel(heading = "Options",
selectizeInput("selectGenesMarkerPlots", "Select a gene:", choices = NULL, multiple = TRUE, options = list('plugins' = list('remove_button'), server = TRUE)),
)
)
)
),
column(12,
tags$div(class = "seurat_findmarker_plots",
panel(heading = "",
HTML("<center><h5><span style='color:red; font-weight:bold; text-align:center;'>Select marker genes above to plot them below!</span></h5></br></center>"),
tabsetPanel(id = "seuratFindMarkerPlotTabset", type = "tabs"))
)
)
),
style = "primary"
),
bsCollapsePanel("Find Markers",
fluidRow(
column(4,
fluidRow(
column(12,
panel(heading = "Options",
h6("Compute marker genes that are either differentially expressed or conserved between selected groups and visualize them from the selected plots on right panel."),
radioButtons(
inputId = "seuratFindMarkerType",
label = "Select type of markers to identify:",
choices = c(
"markers between all groups" = "markerAll",
"markers differentially expressed between two selected groups" = "markerDiffExp",
"markers conserved between two selected groups" = "markerConserved"
)
),
selectInput(
inputId = "seuratFindMarkerSelectPhenotype",
label = "Select biological phenotype:",
choices = NULL
),
conditionalPanel(
condition = "input.seuratFindMarkerType == 'markerDiffExp'
|| input.seuratFindMarkerType == 'markerConserved'",
selectInput(
inputId = "seuratFindMarkerGroup1",
label = "Select first group of interest:",
choices = NULL
),
selectInput(
inputId = "seuratFindMarkerGroup2",
label = "Select second group of interest:",
choices = NULL
)
),
selectInput(
inputId = "seuratFindMarkerTest",
label = "Select test:",
choices = c("wilcox", "bimod",
"t", "negbinom",
"poisson", "LR",
"DESeq2")
),
selectInput(
inputId = "seuratFeatureUseReduction",
label = "Select reduction method for feature plot:",
choices = c("umap", "tsne",
"pca", "ica")
),
materialSwitch(
inputId = "seuratFindMarkerPosOnly",
label = "Only return positive markers?",
value = FALSE
),
actionButton(inputId = "seuratFindMarkerRun", "Find Markers")
)
)
)
),
column(8,
fluidRow(
column(12,
hidden(
tags$div(
class = "seurat_findmarker_table",
filterTableUI(id = "filterSeuratFindMarker")
)
),
br(),
hidden(
tags$div(class = "seurat_findmarker_jointHeatmap",
bsCollapse(
bsCollapsePanel(
title = "Heatmap Plot",
fluidRow(
column(12, align = "center",
panel(
numericInput("findMarkerHeatmapPlotFullNumeric", value = 10, 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("findMarkerHeatmapPlotFullNumericRun", label = "Plot"),
hr(),
shinyjqui::jqui_resizable(
plotOutput(outputId = "findMarkerHeatmapPlotFull", height = "500px")
)
)
)
)
)
)
)
),
)
)
)
),
style = "primary")
),
nonLinearWorkflowUI(id = "nlw-seurat")
)
compbiomed/singleCellTK documentation built on May 8, 2024, 6:58 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
# User Interface for Seurat Workflow ---
shinyPanelSeurat <- fluidPage(
tags$script("Shiny.addCustomMessageHandler('close_dropDownSeuratHM', function(x){
$('html').click();
});"),
h1("Seurat"),
h5(tags$a(href = paste0(docs.artPath, "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(
"activePanelSelectSeurat",
label = "Active Panel:",
choices = c("",
"Normalize Data",
"Scale Data",
"Highly Variable Genes",
"Dimensionality Reduction",
"tSNE/UMAP",
"Clustering",
"Find Markers",
"Heatmap Plot"),
selected = ""
)
),
bsCollapse(id = "SeuratUI", open = "Normalize Data",
bsCollapsePanel("Normalize Data",
fluidRow(
column(4,
panel(heading = "Options",
selectizeInput(
inputId = "seuratSelectNormalizationAssay",
label = "Select input matrix:",
choices = NULL,
selected = NULL,
multiple = FALSE,
options = NULL),
#uiOutput("seuratSelectNormalizationAssay"),
selectInput(inputId = "normalization_method", label = "Select normalization method: ", choices = c("LogNormalize", "CLR", "RC")),
textInput(inputId = "scale_factor", label = "Set scaling factor: ", value = "10000"),
actionButton(inputId = "normalize_button", "Normalize")
)
)
),
style = "primary"
),
bsCollapsePanel("Highly Variable Genes",
fluidRow(
column(4,
fluidRow(
column(12,
panel(heading = "Compute HVG",
selectInput(inputId = "hvg_method", label = "Select HVG method: ", choices = c("vst", "mean.var.plot", "dispersion")),
textInput(inputId = "hvg_no_features", label = "Select number of features to find: ", value = "2000"),
actionButton(inputId = "find_hvg_button", "Find HVG")
)
)
),
br(),
fluidRow(
column(12,
panel(heading = "Display HVG",
numericInput(inputId = "hvg_no_features_view", label = "Select number of features to display: ", value = 10, step = 1),
verbatimTextOutput(outputId = "hvg_output", placeholder = TRUE)
)
)
)
),
column(8,
fluidRow(
column(12,
panel(heading = "Plot",
plotlyOutput(outputId = "plot_hvg")
)
)
)
)
),
style = "primary"),
# bsCollapsePanel("Scale Data",
# fluidRow(
# column(4,
# panel(heading = "Options",
# #selectInput(inputId = "model.use", label = "Select model for scaling: ", choices = c("linear", "poisson", "negbinom")),
# materialSwitch(inputId = "do.scale", label = "Scale data?", value = TRUE),
# materialSwitch(inputId = "do.center", label = "Center data?", value = TRUE),
# textInput(inputId = "scale.max", label = "Max value for scaled data: ", value = "10"),
# actionButton(inputId = "scale_button", "Scale")
# )
# )
# ),
# style = "primary"
# ),
bsCollapsePanel("Dimensionality Reduction",
tabsetPanel(type = "tabs",
tabPanel("PCA",
br(),
fluidRow(
column(4,
fluidRow(
column(12,
panel(heading = "PCA",
numericInput(inputId = "pca_no_components", label = "Select number of components to compute: ", value = 50),
materialSwitch(inputId = "pca_compute_elbow", label = "Compute ElbowPlot?", value = TRUE),
materialSwitch(inputId = "pca_compute_jackstraw", label = "Compute JackStrawPlot?", value = FALSE),
materialSwitch(inputId = "pca_compute_heatmap", label = "Compute Heatmap?", value = TRUE),
conditionalPanel(
condition = 'input.pca_compute_heatmap == true',
numericInput(inputId = "pca_compute_heatmap_nfeatures",
label = "Set number of features for heatmap:", value = 30, step = 1),
),
numericInput(inputId = "seed_PCA",
label = "Seed value for reproducibility of result:",
value = 42,
step = 1),
actionButton(inputId = "run_pca_button", "Run PCA")
),
panel(heading = "Select No. of Components",
htmlOutput(outputId = "pca_significant_pc_output", inline = FALSE),
numericInput(inputId = "pca_significant_pc_counter", label = "Select number of components for downstream analysis: ", min = 1, max = 20, value = 10)
)
)
)
),
column(8,
fluidRow(
column(12,
hidden(
tags$div(class = "seurat_pca_plots", tabsetPanel(id = "seuratPCAPlotTabset", type = "tabs"
)
))
)
)
)
)
),
tabPanel("ICA",
br(),
fluidRow(
column(4,
fluidRow(
column(12,
panel(heading = "ICA",
textInput(inputId = "ica_no_components", label = "Select number of components to compute: ", value = "20"),
materialSwitch(inputId = "ica_compute_heatmap", label = "Compute Heatmap?", value = TRUE),
conditionalPanel(
condition = 'input.ica_compute_heatmap == true',
numericInput(inputId = "ica_compute_heatmap_nfeatures",
label = "Set number of features for heatmap:", value = 30, step = 1),
),
numericInput(inputId = "seed_ICA",
label = "Seed value for reproducibility of result:",
value = 42,
step = 1),
actionButton(inputId = "run_ica_button", "Run ICA")
),
panel(heading = "Select No. of Components",
#h5("Number of components suggested by ElbowPlot: "),
#verbatimTextOutput(outputId = "ica_significant_pc_output", placeholder = TRUE),
numericInput(inputId = "ica_significant_ic_counter", label = "Select number of components for downstream analysis: ", min = 1, max = 20, value = 10)
)
)
)
),
column(8,
fluidRow(
column(12,
hidden(
tags$div(class = "seurat_ica_plots", tabsetPanel(id="seuratICAPlotTabset", type = "tabs"
))
)
)
)
)
)
)
),
style = "primary"),
bsCollapsePanel("2D-Embedding",
tabsetPanel(id = "tsneUmapTabsetSeurat", type = "tabs",
tabPanel("UMAP",
br(),
fluidRow(
column(4,
fluidRow(
column(12,
panel(heading = "UMAP",
selectInput(inputId = "reduction_umap_method", label = "Select reduction method: ", choices = c("pca", "ica")),
#textInput(inputId = "reduction_umap_count", label = "Select number of reduction components: ", value = "20"),
numericInput(inputId = "min_dist_umap", label = "Set min.dist:", value = 0.3),
numericInput(inputId = "n_neighbors_umap", label = "Set n.neighbors:", value = 30, step = 1),
numericInput(inputId = "spread_umap", label = "Set spread:", value = 1),
numericInput(inputId = "seed_UMAP",
label = "Seed value for reproducibility of result:",
value = 42,
step = 1),
htmlOutput(outputId = "display_message_umap", inline = FALSE),
actionButton(inputId = "run_umap_button", "Run UMAP")
)
)
)
),
column(8,
fluidRow(
panel(heading = "Plot",
column(12,
plotlyOutput(outputId = "plot_umap")
)
)
)
)
)
),
tabPanel("tSNE",
br(),
fluidRow(
column(4,
fluidRow(
column(12,
panel(heading = "tSNE",
selectInput(inputId = "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 = "perplexity_tsne", label = "Set perplexity:", value = 30),
numericInput(inputId = "seed_TSNE",
label = "Seed value for reproducibility of result:",
value = 1,
step = 1),
htmlOutput(outputId = "display_message_tsne", inline = FALSE),
actionButton(inputId = "run_tsne_button", "Run tSNE")
)
)
)
),
column(8,
fluidRow(
panel(heading = "Plot",
column(12,
plotlyOutput(outputId = "plot_tsne")
)
)
)
)
)
)
),
style = "primary"),
bsCollapsePanel("Clustering",
fluidRow(
column(4,
fluidRow(
column(12,
panel(heading = "Options",
selectInput(inputId = "reduction_clustering_method", label = "Select reduction method: ", choices = c("pca", "ica")),
#textInput(inputId = "reduction_clustering_count", label = "Select number of reduction components: ", value = "20"),
selectInput(inputId = "algorithm.use", label = "Select clustering algorithm: ", choices = list("Original Louvain algorithm" = "louvain",
"Louvain algorithm with multilevel refinement" = "multilevel",
"SLM algorithm" = "SLM")),
numericInput(inputId = "resolution_clustering", label = "Set resolution:", value = 0.8),
materialSwitch(inputId = "group.singletons", label = "Group singletons?", value = TRUE),
htmlOutput(outputId = "display_message_clustering", inline = FALSE),
actionButton(inputId = "find_clusters_button", "Find Clusters")
)
)
)
),
column(8,
fluidRow(
column(12,
hidden(
tags$div(class = "seurat_clustering_plots", tabsetPanel(id = "seuratClusteringPlotTabset", type = "tabs"
))
)
)
)
)
),
style = "primary"),
bsCollapsePanel("Marker Gene Plots",
fluidRow(
column(12,
fluidRow(
column(12,
panel(heading = "Options",
selectizeInput("selectGenesMarkerPlots", "Select a gene:", choices = NULL, multiple = TRUE, options = list('plugins' = list('remove_button'), server = TRUE)),
)
)
)
),
column(12,
tags$div(class = "seurat_findmarker_plots",
panel(heading = "",
HTML("<center><h5><span style='color:red; font-weight:bold; text-align:center;'>Select marker genes above to plot them below!</span></h5></br></center>"),
tabsetPanel(id = "seuratFindMarkerPlotTabset", type = "tabs"))
)
)
),
style = "primary"
),
bsCollapsePanel("Find Markers",
fluidRow(
column(4,
fluidRow(
column(12,
panel(heading = "Options",
h6("Compute marker genes that are either differentially expressed or conserved between selected groups and visualize them from the selected plots on right panel."),
radioButtons(
inputId = "seuratFindMarkerType",
label = "Select type of markers to identify:",
choices = c(
"markers between all groups" = "markerAll",
"markers differentially expressed between two selected groups" = "markerDiffExp",
"markers conserved between two selected groups" = "markerConserved"
)
),
selectInput(
inputId = "seuratFindMarkerSelectPhenotype",
label = "Select biological phenotype:",
choices = NULL
),
conditionalPanel(
condition = "input.seuratFindMarkerType == 'markerDiffExp'
|| input.seuratFindMarkerType == 'markerConserved'",
selectInput(
inputId = "seuratFindMarkerGroup1",
label = "Select first group of interest:",
choices = NULL
),
selectInput(
inputId = "seuratFindMarkerGroup2",
label = "Select second group of interest:",
choices = NULL
)
),
selectInput(
inputId = "seuratFindMarkerTest",
label = "Select test:",
choices = c("wilcox", "bimod",
"t", "negbinom",
"poisson", "LR",
"DESeq2")
),
selectInput(
inputId = "seuratFeatureUseReduction",
label = "Select reduction method for feature plot:",
choices = c("umap", "tsne",
"pca", "ica")
),
materialSwitch(
inputId = "seuratFindMarkerPosOnly",
label = "Only return positive markers?",
value = FALSE
),
actionButton(inputId = "seuratFindMarkerRun", "Find Markers")
)
)
)
),
column(8,
fluidRow(
column(12,
hidden(
tags$div(
class = "seurat_findmarker_table",
filterTableUI(id = "filterSeuratFindMarker")
)
),
br(),
hidden(
tags$div(class = "seurat_findmarker_jointHeatmap",
bsCollapse(
bsCollapsePanel(
title = "Heatmap Plot",
fluidRow(
column(12, align = "center",
panel(
numericInput("findMarkerHeatmapPlotFullNumeric", value = 10, 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("findMarkerHeatmapPlotFullNumericRun", label = "Plot"),
hr(),
shinyjqui::jqui_resizable(
plotOutput(outputId = "findMarkerHeatmapPlotFull", height = "500px")
)
)
)
)
)
)
)
),
)
)
)
),
style = "primary")
),
nonLinearWorkflowUI(id = "nlw-seurat")
)
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.