R/ui.R
In AlexanderKononov/cytofCore: Detail analysis and visualisation CyTOF data
Defines functions
cytofBrowserGUI
Documented in
cytofBrowserGUI
#' The main function to run cutofCore graphic interface
#' @description Run graphical user interface for cytofBrowser.
#' The function runs the Shiny App in browser. The current package
#' was created with assumption that it will be run with GUI mode
#' as Shiny App, That is why the function is the most appropriate
#' way to use the cytofBrowser.
#'
#' @return The function runs the Shiny App in browser.
#' @import shiny shinyFiles visNetwork d3heatmap shinydashboard shinyWidgets
#' @export
#'
#' @examples
#' \dontrun{
#' cytofBrowserGUI()
#' }
cytofBrowserGUI <-function(){
cytofBrowser_ui <- dashboardPage(
dashboardHeader(title = "cytofBrowser"),
dashboardSidebar(
sidebarMenu(
menuItem("Data", tabName = 'data_processing', icon = icon("bars")),
menuItem("Gating", tabName = 'gating', icon = icon("door-open")),
menuItem("Exploration", tabName = 'data_exploration', icon = icon("chart-area")),
menuItem("Correlation", tabName = 'data_correlation', icon = icon("braille")),
menuItem("Cross-panel", tabName = 'data_crosspanel', icon = icon("clone"))
)
),
dashboardBody(
tabItems(
# First tab content
tabItem(tabName = 'data_processing',
fluidRow(
infoBoxOutput('iBox_upload_dproc'),
infoBoxOutput('iBox_preproc_dproc'),
infoBoxOutput('iBox_clust_dproc')
),
fluidRow(
tabBox(
tabPanel("Uploading",
shinyFiles::shinyFilesButton('choose_fcs_dp', label='Select FCS files', title='Please select FCS files', multiple=TRUE),
hr(),
materialSwitch(inputId = 'extr_clust_dproc', label = h4("extract cluster info")),
conditionalPanel(
condition = "input.extr_clust_dproc == true",
textInput("extr_clust_pattern_dproc",
label = h5("full or part column name with clusters info (for cytofBrowser and cytofkit : <cluster>)"), value = "cluster")
),
materialSwitch(inputId = 'test_data_upload_dproc', label = h4("build-in dataset"), value = FALSE),
conditionalPanel(
condition = "input.test_data_upload_dproc == true",
selectInput('test_data_dproc', label = NULL, choices = c("Test data" = 'test_data'), selected = "Test data")
),
hr(),
actionButton('butt_upload_dproc', label = "Upload")
),
tabPanel("Transforming",
checkboxGroupInput("transformation_list", label = h4("Transformations"),
choices = list("asinh" = 'asinh', "outlier by quartile" = 'outlier_by_quantile',
"extract cluster info" = 'extract_cluster_info'),
selected = c("asinh", "outlier_by_quantile")),
conditionalPanel(
condition = "input.transformation_list.includes('asinh')",
numericInput("cofactor", label = h4("Cofactor for dividing"), value = 5)
),
conditionalPanel(
condition = "input.transformation_list.includes('outlier_by_quantile')",
numericInput("quantile", label = h4("Quantile for outlier removing"), value = 0.01)
),
hr(),
actionButton('butt_trans_dproc', label = "Transform")
),
tabPanel("Markers",
uiOutput('mk_subset_dp_ui')
),
tabPanel("Clustering",
radioButtons("mode_k_choice", label = h4("Choose of number of clusters"),
choices = list("Automatically detect optimum" = 1, "Manually choose" = 2),
selected = 1),
conditionalPanel(
condition = "input.mode_k_choice == 1",
numericInput("rate_var_explan", label = h4("Rate of explained variance"), value = 0.9),
numericInput("maxK", label = h4("Max number of clusters"), value = 20)
),
conditionalPanel(
condition = "input.mode_k_choice == 2",
numericInput("k", label = h4("Choose number of clusters"), value = 8)
),
uiOutput("mk_subset_clusterisation_ui"),
actionButton('start_clusterization', label = "Clustering")
),
tabPanel("Cluster management",
uiOutput("mergeing_clusterisation_ui"),
hr(),
uiOutput("rename_clusterisation_ui")
),
tabPanel("Save",
h5("Saving the data for samples as FCS files with cluster-info"),
shinyDirButton('choose_panel_clust', "Folder choose", "Select a folder to save panel samples"),
hr(),
h5("Saved set of files can be used as panel data in a cross-panel analysiso"),
textInput("panel_name_clust", label = h4("Panel name"), value = "Panel1"),
hr(),
actionButton('dwn_panel_clust', label = "Save panel")
)
),
uiOutput('scatter_plot_dp_ui'),
tabBox(
tabPanel("Cell number",
fluidRow(
column(2,
dropdownButton(
selectInput('dwn_smpl_hist_dp_ext', label = NULL,
choices = list('pdf' = "pdf", 'jpeg' = "jpeg", 'png' = "png",
'tiff' = "tiff", 'svg' = "svg", 'bmp' = "bmp")),
downloadButton('dwn_smpl_hist_dp', ""),
icon = icon("save"), status = "primary", tooltip = tooltipOptions(title = "save plot")
)
)
),
plotOutput("smpl_hist_preparation")
),
tabPanel("Expression",
fluidRow(
column(2,
dropdownButton(
selectInput('dwn_mk_hist_dp_ext', label = NULL,
choices = list('pdf' = "pdf", 'jpeg' = "jpeg", 'png' = "png",
'tiff' = "tiff", 'svg' = "svg", 'bmp' = "bmp")),
downloadButton('dwn_mk_hist_dp', ""),
icon = icon("save"), status = "primary", tooltip = tooltipOptions(title = "save plot")
)
)
),
plotOutput('mk_density_plot_dp'),
uiOutput('mk_density_dp_ui')
),
tabPanel("Markers",
h4("Analysed markers"),
verbatimTextOutput('mk_rested_dp'),
h4("Excluded markers"),
verbatimTextOutput('mk_excluded_dp')
),
tabPanel("Abundance",
fluidRow(
column(2,
dropdownButton(
selectInput('dwn_abundance_clust_ext', label = NULL,
choices = list('pdf' = "pdf", 'jpeg' = "jpeg", 'png' = "png",
'tiff' = "tiff", 'svg' = "svg", 'bmp' = "bmp")),
downloadButton('dwn_abundance_clust', ""),
icon = icon("save"), status = "primary", tooltip = tooltipOptions(title = "save plot")
)
)
),
fluidRow(
plotOutput('abundance_clust')
)
)
),
uiOutput('network_clust_ui')
)
),
# Second tab content
tabItem(tabName = 'gating',
fluidRow(
tabBox(
tabPanel("Gating",
uiOutput('gating_api_ui')
),
tabPanel("Gate management",
uiOutput("mergeing_gates_ui"),
hr(),
uiOutput("rename_gates_ui")
),
tabPanel("Cells management",
uiOutput('extract_cell_annotation_ui'),
hr(),
#uiOutput('convert_cells_annotation_ui')
),
tabPanel("Save",
uiOutput('save_cell_annaotation')
)
),
box(uiOutput('plot_for_gating_ui'),
fluidRow(
column(6,
textInput('new_gate_name', label = h4("Name of new gated cells"), value = "marker1+/marker2+")
),
column(6,
actionButton("gete_chosen_cells", label = "Gate chosen cells")
)
)
)
),
fluidRow(
box(
fluidRow(
column(1),
column(10,
verbatimTextOutput('gating_text')
)
)
),
box(
fluidRow(
column(1),
column(10,
#plotOutput('gate_antology_graph')
visNetworkOutput('gate_antology_graph')
)
)
)
)
),
# Third tab content
tabItem(tabName = 'data_exploration',
fluidRow(
box(
fluidRow(
column(1, actionBttn(inputId = "redraw_expression", style = "material-circle", color = "default" ,icon = icon("redo"))),
column(1,
dropdownButton(
tags$h4("Options of plotting"),
selectInput("method_summarize_expression", label = h4("summarise method"),
choices = list('median' = "median", 'mean' = "mean"),
selected = 'median'),
icon = icon("edit"), status = "primary", tooltip = tooltipOptions(title = "plot setting")
)
),
column(1,
dropdownButton(
selectInput('dwn_drawn_cluster_hm_expr_ext', label = NULL,
choices = list('pdf' = "pdf", 'jpeg' = "jpeg", 'png' = "png")),
downloadButton('dwn_drawn_cluster_hm_expr', ""),
icon = icon("save"), status = "primary", tooltip = tooltipOptions(title = "save plot")
)
)
),
d3heatmapOutput('cluster_heatmap'),
width = 12
),
box(
fluidRow(
column(2,
dropdownButton(
selectInput('dwn_deconvol_expr_ext', label = NULL,
choices = list('pdf' = "pdf", 'jpeg' = "jpeg", 'png' = "png",
'tiff' = "tiff", 'svg' = "svg", 'bmp' = "bmp")),
downloadButton('dwn_deconvol_expr', ""),
icon = icon("save"), status = "primary", tooltip = tooltipOptions(title = "save plot")
)
)
),
plotOutput("deconvol_expr"),
uiOutput('mk_deconvol_gene_expression_ui')
)
)
),
# Fourth tab content
tabItem(tabName = 'data_correlation',
fluidRow(
infoBoxOutput('iBox_abund_corr'),
infoBoxOutput('iBox_mk_corr')
),
fluidRow(
box(
sliderInput('edges_threshold_abund_corr', "Edge weight threshold for graph",
min =0, max = 1, value = 0.5, step = 0.01),
sliderInput('gravity_abund_corr', "Gravity for graph",
min = -100, max = 0, value = -40, step = 1),
visNetworkOutput('network_corr')
),
box(
fluidRow(
column(2,
dropdownButton(
selectInput('dwn_abund_corr_ext', label = NULL,
choices = list('pdf' = "pdf", 'jpeg' = "jpeg", 'png' = "png")),
downloadButton('dwn_abund_corr', ""),
icon = icon("save"), status = "primary", tooltip = tooltipOptions(title = "save plot")
)
)
),
plotOutput("abun_cor_plot", click = "abun_cor_click")
),
tabBox(
tabPanel("Abundance correlations",
fluidRow(
column(1, actionBttn(inputId = "corr_analysis", style = "material-circle", color = "default" ,icon = icon("play"))),
column(1,
dropdownButton(
tags$h4("Advanced settings"),
selectInput("method", "Select correlation method:", c("spearman" = "Spearman", "pearson" = "Pearson")),
numericInput("corr_pValue", "p-Value filter (alpha):", min = 0, max = 1, value = 0.01, step = 0.001),
numericInput("corr_threshold", "Correlation coefficient threshold:", min = -100, max = 100, value = 0.1, step = 0.1),
numericInput("corr_bg_anova_alpha", "Background anova filter (alpha):", min = 0, max = 1, value = 0.01, step = 0.001),
numericInput("corr_bg_ctCor_alpha", "Background correlation filter (alpha):", min = 0, max = 1, value = 0.01, step = 0.001),
numericInput("corr_gene_ctCor_alpha", "Marker correlation filter (alpha):", min = 0, max = 1, value = 0.01, step = 0.001),
icon = icon("gear"), status = "primary", tooltip = tooltipOptions(title = "Correlation settings")
)
),
column(1,
dropdownButton(
tags$h4("Neo4j export"),
h5("Check that neo4j database is created and activated"),
textInput('user_neo4j', label = h4("User name"), value = "neo4j"),
textInput('password_neo4j', label = h4("Password"), value = "password"),
actionButton('neo4j_export', "Export"),
icon = icon("share-alt"), status = "primary", tooltip = tooltipOptions(title = "export database to neo4j")
)
),
column(1,
dropdownButton(
selectInput('dwn_gene_cor_acorr_ext', label = NULL,
choices = list("chosen correlations" = 'focus_corr_data',
"correlations within clusters" = 'signals_in_cluster',
"correlations between clusters" = 'signals_between_clusters',
"all correlations" = 'signals')),
downloadButton('dwn_gene_cor_acorr', ""),
icon = icon("save"), status = "primary", tooltip = tooltipOptions(title = "save plot")
)
)
),
DT::dataTableOutput("gene_cor_table")
),
tabPanel("Marker correlations",
fluidRow(
column(1, actionBttn(inputId = "mk_corr_analysis", style = "material-circle", color = "default" ,icon = icon("play"))),
column(1,
dropdownButton(
tags$h4("Advanced settings"),
selectInput("mk_corr_method", "Select correlation method:", c("spearman" = "Spearman", "pearson" = "Pearson")),
numericInput("mk_corr_pValue", "p-Value filter (alpha):", min = 0, max = 1, value = 0.1, step = 0.001),
numericInput("mk_corr_threshold", "Correlation coefficient threshold:", min = 0, max = 100, value = 0, step = 0.1),
numericInput("mk_corr_bg_anova_alpha", "Background anova filter (alpha):", min = 0, max = 1, value = 0.01, step = 0.001),
numericInput("mk_corr_bg_ctCor_alpha", "Background correlation filter (alpha):", min = 0, max = 1, value = 0.01, step = 0.001),
numericInput("mk_corr_gene_ctCor_alpha", "Marker correlation filter (alpha):", min = 0, max = 1, value = 0.01, step = 0.001),
icon = icon("gear"), status = "primary", tooltip = tooltipOptions(title = "Correlation settings")
)
),
column(1,
dropdownButton(
selectInput('dwn_marker_table_mk_corr_ext', label = NULL,
choices = list("chosen correlations" = 'focus_corr_data',
"correlations within clusters" = 'signals_in_cluster',
"correlations between clusters" = 'signals_between_clusters',
"all correlations" = 'signals')),
downloadButton('dwn_marker_table_mk_corr', ""),
icon = icon("save"), status = "primary", tooltip = tooltipOptions(title = "save plot")
)
)
),
DT::dataTableOutput('marker_mk_corr_table')
),
width = 12
)
)
),
# Fifth tab content
tabItem(tabName = 'data_crosspanel',
fluidRow(
tabBox(
tabPanel("Upload panel",
h4("FCS files"),
h5("Choose FSC files to upload them as one panel"),
shinyFilesButton('choose_fcs_cross_p', label= h5("Select FCS files"),
title='Please select clustered FCS files to upload as panel', multiple=TRUE),
hr(),
h4("Name the panel"),
textInput("panel_name_cross_p", label = h5("You can name the new panel (try to use a short name or even one or few letters)")),
hr(),
textInput("extr_clust_pattern_cross_p",
label = h5("full or part column name with clusters info (for cytofBrowser and cytofkit : <cluster>)"), value = "cluster"),
hr(),
actionButton('butt_upload_cross_p', label = "Upload")
),
tabPanel("Remove panel",
uiOutput('remove_panel_cross_p_ui')
)
),
tabBox(
tabPanel("Samples content",
plotOutput('content_cross_p')
),
tabPanel("Markers content",
plotOutput('mk_content_cross_p')
)
),
tabBox(
tabPanel("Abundance cross-panel correlation",
fluidRow(
column(2, actionBttn(inputId = "abund_corr_cross_p", style = "material-circle", color = "default" ,icon = icon("play"))),
column(2,
dropdownButton(
tags$h4("Advanced options"),
selectInput('abund_corr_method_cross_p', "Select correlation method:", c("spearman" = 'spearman', "pearson" = 'pearson')),
selectInput('abund_corr_p_mode_cross_p', "p-value", c("p-value" = "pval", "adjusted p-value" = "padj"), selected = 'padj'),
uiOutput('abund_corr_padj_method_cross_p_ui'),
icon = icon("gear"), status = "primary", tooltip = tooltipOptions(title = "Correlation test options")
)
),
column(2,
dropdownButton(
selectInput('dwn_abund_corr_cross_p_ext', label = NULL,
choices = list('pdf' = "pdf", 'jpeg' = "jpeg", 'png' = "png")),
downloadButton('dwn_abund_corr_cross_p', ""),
hr(),
selectInput('dwn_table_abund_corr_cross_p_ext', label = NULL,
choices = list("abundance data" = 'abund_data', "correlation data" = 'corr_data'),
selected = 'corr_data'),
downloadButton('dwn_table_abund_corr_cross_p', ""),
icon = icon("save"), status = "primary", tooltip = tooltipOptions(title = "save plot")
)
)
),
plotOutput('plot_abund_corr_cross_p')
),
tabPanel("Expressing cell fraction Correlation",
fluidRow(
column(2, actionBttn(inputId = "exp_cell_f_corr_cross_p", style = "material-circle", color = "default" ,icon = icon("play"))),
column(4, uiOutput('mk_exp_cell_f_cross_p_ui')),
column(2,
dropdownButton(
tags$h4("Settings"),
selectInput("exp_cell_f_corr_method", label = "Choose the method to get expression cells fraction",
choices =c("clustering" = 'clustering', "threshold" = 'threshold'), selected = 'clustering'),
uiOutput('threshold_exp_cell_f_cross_p_ui'),
icon = icon("edit"), status = "primary", tooltip = tooltipOptions(title = "Cell fraction allocation methods")
)
),
column(2,
dropdownButton(
tags$h4("Advanced options"),
selectInput('exp_cell_f_corr_method_cross_p', "Select correlation method:", c("spearman" = 'spearman', "pearson" = 'pearson')),
selectInput('exp_cell_f_corr_p_mode_cross_p', "p-value", c("p-value" = "pval", "adjusted p-value" = "padj"), selected = 'padj'),
uiOutput('exp_cell_f_corr_padj_method_cross_p_ui'),
numericInput('exp_cell_f_corr_min_cell_cross_p', "Min expressing cell number ", min = 0, value = 5, step = 1),
icon = icon("gear"), status = "primary", tooltip = tooltipOptions(title = "Correlation test options")
)
),
column(2,
dropdownButton(
selectInput('dwn_exp_cell_f_corr_cross_p_ext', label = NULL,
choices = list('pdf' = "pdf", 'jpeg' = "jpeg", 'png' = "png")),
downloadButton('dwn_exp_cell_f_corr_cross_p', ""),
hr(),
selectInput('dwn_table_exp_cell_f_corr_cross_p_ext', label = NULL,
choices = list("expressing cell fractions" = 'exp_cell_f_data', "correlation data" = 'corr_data'),
selected = 'corr_data'),
downloadButton('dwn_table_exp_cell_f_corr_cross_p', ""),
icon = icon("save"), status = "primary", tooltip = tooltipOptions(title = "save plot")
)
)
),
fluidRow(
column(7, plotOutput('plot_exp_cell_f_corr_cross_p')),
column(5, plotOutput('mk_density_plot_cross_p'))
)
),
width = 12
)
)
)
)
)
)
shinyApp(ui = cytofBrowser_ui, server = cytofBrowser_server)
}
AlexanderKononov/cytofCore documentation built on Aug. 30, 2020, 5:23 a.m.
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Defines functions cytofBrowserGUI
Documented in cytofBrowserGUI
#' The main function to run cutofCore graphic interface
#' @description Run graphical user interface for cytofBrowser.
#' The function runs the Shiny App in browser. The current package
#' was created with assumption that it will be run with GUI mode
#' as Shiny App, That is why the function is the most appropriate
#' way to use the cytofBrowser.
#'
#' @return The function runs the Shiny App in browser.
#' @import shiny shinyFiles visNetwork d3heatmap shinydashboard shinyWidgets
#' @export
#'
#' @examples
#' \dontrun{
#' cytofBrowserGUI()
#' }
cytofBrowserGUI <-function(){
cytofBrowser_ui <- dashboardPage(
dashboardHeader(title = "cytofBrowser"),
dashboardSidebar(
sidebarMenu(
menuItem("Data", tabName = 'data_processing', icon = icon("bars")),
menuItem("Gating", tabName = 'gating', icon = icon("door-open")),
menuItem("Exploration", tabName = 'data_exploration', icon = icon("chart-area")),
menuItem("Correlation", tabName = 'data_correlation', icon = icon("braille")),
menuItem("Cross-panel", tabName = 'data_crosspanel', icon = icon("clone"))
)
),
dashboardBody(
tabItems(
# First tab content
tabItem(tabName = 'data_processing',
fluidRow(
infoBoxOutput('iBox_upload_dproc'),
infoBoxOutput('iBox_preproc_dproc'),
infoBoxOutput('iBox_clust_dproc')
),
fluidRow(
tabBox(
tabPanel("Uploading",
shinyFiles::shinyFilesButton('choose_fcs_dp', label='Select FCS files', title='Please select FCS files', multiple=TRUE),
hr(),
materialSwitch(inputId = 'extr_clust_dproc', label = h4("extract cluster info")),
conditionalPanel(
condition = "input.extr_clust_dproc == true",
textInput("extr_clust_pattern_dproc",
label = h5("full or part column name with clusters info (for cytofBrowser and cytofkit : <cluster>)"), value = "cluster")
),
materialSwitch(inputId = 'test_data_upload_dproc', label = h4("build-in dataset"), value = FALSE),
conditionalPanel(
condition = "input.test_data_upload_dproc == true",
selectInput('test_data_dproc', label = NULL, choices = c("Test data" = 'test_data'), selected = "Test data")
),
hr(),
actionButton('butt_upload_dproc', label = "Upload")
),
tabPanel("Transforming",
checkboxGroupInput("transformation_list", label = h4("Transformations"),
choices = list("asinh" = 'asinh', "outlier by quartile" = 'outlier_by_quantile',
"extract cluster info" = 'extract_cluster_info'),
selected = c("asinh", "outlier_by_quantile")),
conditionalPanel(
condition = "input.transformation_list.includes('asinh')",
numericInput("cofactor", label = h4("Cofactor for dividing"), value = 5)
),
conditionalPanel(
condition = "input.transformation_list.includes('outlier_by_quantile')",
numericInput("quantile", label = h4("Quantile for outlier removing"), value = 0.01)
),
hr(),
actionButton('butt_trans_dproc', label = "Transform")
),
tabPanel("Markers",
uiOutput('mk_subset_dp_ui')
),
tabPanel("Clustering",
radioButtons("mode_k_choice", label = h4("Choose of number of clusters"),
choices = list("Automatically detect optimum" = 1, "Manually choose" = 2),
selected = 1),
conditionalPanel(
condition = "input.mode_k_choice == 1",
numericInput("rate_var_explan", label = h4("Rate of explained variance"), value = 0.9),
numericInput("maxK", label = h4("Max number of clusters"), value = 20)
),
conditionalPanel(
condition = "input.mode_k_choice == 2",
numericInput("k", label = h4("Choose number of clusters"), value = 8)
),
uiOutput("mk_subset_clusterisation_ui"),
actionButton('start_clusterization', label = "Clustering")
),
tabPanel("Cluster management",
uiOutput("mergeing_clusterisation_ui"),
hr(),
uiOutput("rename_clusterisation_ui")
),
tabPanel("Save",
h5("Saving the data for samples as FCS files with cluster-info"),
shinyDirButton('choose_panel_clust', "Folder choose", "Select a folder to save panel samples"),
hr(),
h5("Saved set of files can be used as panel data in a cross-panel analysiso"),
textInput("panel_name_clust", label = h4("Panel name"), value = "Panel1"),
hr(),
actionButton('dwn_panel_clust', label = "Save panel")
)
),
uiOutput('scatter_plot_dp_ui'),
tabBox(
tabPanel("Cell number",
fluidRow(
column(2,
dropdownButton(
selectInput('dwn_smpl_hist_dp_ext', label = NULL,
choices = list('pdf' = "pdf", 'jpeg' = "jpeg", 'png' = "png",
'tiff' = "tiff", 'svg' = "svg", 'bmp' = "bmp")),
downloadButton('dwn_smpl_hist_dp', ""),
icon = icon("save"), status = "primary", tooltip = tooltipOptions(title = "save plot")
)
)
),
plotOutput("smpl_hist_preparation")
),
tabPanel("Expression",
fluidRow(
column(2,
dropdownButton(
selectInput('dwn_mk_hist_dp_ext', label = NULL,
choices = list('pdf' = "pdf", 'jpeg' = "jpeg", 'png' = "png",
'tiff' = "tiff", 'svg' = "svg", 'bmp' = "bmp")),
downloadButton('dwn_mk_hist_dp', ""),
icon = icon("save"), status = "primary", tooltip = tooltipOptions(title = "save plot")
)
)
),
plotOutput('mk_density_plot_dp'),
uiOutput('mk_density_dp_ui')
),
tabPanel("Markers",
h4("Analysed markers"),
verbatimTextOutput('mk_rested_dp'),
h4("Excluded markers"),
verbatimTextOutput('mk_excluded_dp')
),
tabPanel("Abundance",
fluidRow(
column(2,
dropdownButton(
selectInput('dwn_abundance_clust_ext', label = NULL,
choices = list('pdf' = "pdf", 'jpeg' = "jpeg", 'png' = "png",
'tiff' = "tiff", 'svg' = "svg", 'bmp' = "bmp")),
downloadButton('dwn_abundance_clust', ""),
icon = icon("save"), status = "primary", tooltip = tooltipOptions(title = "save plot")
)
)
),
fluidRow(
plotOutput('abundance_clust')
)
)
),
uiOutput('network_clust_ui')
)
),
# Second tab content
tabItem(tabName = 'gating',
fluidRow(
tabBox(
tabPanel("Gating",
uiOutput('gating_api_ui')
),
tabPanel("Gate management",
uiOutput("mergeing_gates_ui"),
hr(),
uiOutput("rename_gates_ui")
),
tabPanel("Cells management",
uiOutput('extract_cell_annotation_ui'),
hr(),
#uiOutput('convert_cells_annotation_ui')
),
tabPanel("Save",
uiOutput('save_cell_annaotation')
)
),
box(uiOutput('plot_for_gating_ui'),
fluidRow(
column(6,
textInput('new_gate_name', label = h4("Name of new gated cells"), value = "marker1+/marker2+")
),
column(6,
actionButton("gete_chosen_cells", label = "Gate chosen cells")
)
)
)
),
fluidRow(
box(
fluidRow(
column(1),
column(10,
verbatimTextOutput('gating_text')
)
)
),
box(
fluidRow(
column(1),
column(10,
#plotOutput('gate_antology_graph')
visNetworkOutput('gate_antology_graph')
)
)
)
)
),
# Third tab content
tabItem(tabName = 'data_exploration',
fluidRow(
box(
fluidRow(
column(1, actionBttn(inputId = "redraw_expression", style = "material-circle", color = "default" ,icon = icon("redo"))),
column(1,
dropdownButton(
tags$h4("Options of plotting"),
selectInput("method_summarize_expression", label = h4("summarise method"),
choices = list('median' = "median", 'mean' = "mean"),
selected = 'median'),
icon = icon("edit"), status = "primary", tooltip = tooltipOptions(title = "plot setting")
)
),
column(1,
dropdownButton(
selectInput('dwn_drawn_cluster_hm_expr_ext', label = NULL,
choices = list('pdf' = "pdf", 'jpeg' = "jpeg", 'png' = "png")),
downloadButton('dwn_drawn_cluster_hm_expr', ""),
icon = icon("save"), status = "primary", tooltip = tooltipOptions(title = "save plot")
)
)
),
d3heatmapOutput('cluster_heatmap'),
width = 12
),
box(
fluidRow(
column(2,
dropdownButton(
selectInput('dwn_deconvol_expr_ext', label = NULL,
choices = list('pdf' = "pdf", 'jpeg' = "jpeg", 'png' = "png",
'tiff' = "tiff", 'svg' = "svg", 'bmp' = "bmp")),
downloadButton('dwn_deconvol_expr', ""),
icon = icon("save"), status = "primary", tooltip = tooltipOptions(title = "save plot")
)
)
),
plotOutput("deconvol_expr"),
uiOutput('mk_deconvol_gene_expression_ui')
)
)
),
# Fourth tab content
tabItem(tabName = 'data_correlation',
fluidRow(
infoBoxOutput('iBox_abund_corr'),
infoBoxOutput('iBox_mk_corr')
),
fluidRow(
box(
sliderInput('edges_threshold_abund_corr', "Edge weight threshold for graph",
min =0, max = 1, value = 0.5, step = 0.01),
sliderInput('gravity_abund_corr', "Gravity for graph",
min = -100, max = 0, value = -40, step = 1),
visNetworkOutput('network_corr')
),
box(
fluidRow(
column(2,
dropdownButton(
selectInput('dwn_abund_corr_ext', label = NULL,
choices = list('pdf' = "pdf", 'jpeg' = "jpeg", 'png' = "png")),
downloadButton('dwn_abund_corr', ""),
icon = icon("save"), status = "primary", tooltip = tooltipOptions(title = "save plot")
)
)
),
plotOutput("abun_cor_plot", click = "abun_cor_click")
),
tabBox(
tabPanel("Abundance correlations",
fluidRow(
column(1, actionBttn(inputId = "corr_analysis", style = "material-circle", color = "default" ,icon = icon("play"))),
column(1,
dropdownButton(
tags$h4("Advanced settings"),
selectInput("method", "Select correlation method:", c("spearman" = "Spearman", "pearson" = "Pearson")),
numericInput("corr_pValue", "p-Value filter (alpha):", min = 0, max = 1, value = 0.01, step = 0.001),
numericInput("corr_threshold", "Correlation coefficient threshold:", min = -100, max = 100, value = 0.1, step = 0.1),
numericInput("corr_bg_anova_alpha", "Background anova filter (alpha):", min = 0, max = 1, value = 0.01, step = 0.001),
numericInput("corr_bg_ctCor_alpha", "Background correlation filter (alpha):", min = 0, max = 1, value = 0.01, step = 0.001),
numericInput("corr_gene_ctCor_alpha", "Marker correlation filter (alpha):", min = 0, max = 1, value = 0.01, step = 0.001),
icon = icon("gear"), status = "primary", tooltip = tooltipOptions(title = "Correlation settings")
)
),
column(1,
dropdownButton(
tags$h4("Neo4j export"),
h5("Check that neo4j database is created and activated"),
textInput('user_neo4j', label = h4("User name"), value = "neo4j"),
textInput('password_neo4j', label = h4("Password"), value = "password"),
actionButton('neo4j_export', "Export"),
icon = icon("share-alt"), status = "primary", tooltip = tooltipOptions(title = "export database to neo4j")
)
),
column(1,
dropdownButton(
selectInput('dwn_gene_cor_acorr_ext', label = NULL,
choices = list("chosen correlations" = 'focus_corr_data',
"correlations within clusters" = 'signals_in_cluster',
"correlations between clusters" = 'signals_between_clusters',
"all correlations" = 'signals')),
downloadButton('dwn_gene_cor_acorr', ""),
icon = icon("save"), status = "primary", tooltip = tooltipOptions(title = "save plot")
)
)
),
DT::dataTableOutput("gene_cor_table")
),
tabPanel("Marker correlations",
fluidRow(
column(1, actionBttn(inputId = "mk_corr_analysis", style = "material-circle", color = "default" ,icon = icon("play"))),
column(1,
dropdownButton(
tags$h4("Advanced settings"),
selectInput("mk_corr_method", "Select correlation method:", c("spearman" = "Spearman", "pearson" = "Pearson")),
numericInput("mk_corr_pValue", "p-Value filter (alpha):", min = 0, max = 1, value = 0.1, step = 0.001),
numericInput("mk_corr_threshold", "Correlation coefficient threshold:", min = 0, max = 100, value = 0, step = 0.1),
numericInput("mk_corr_bg_anova_alpha", "Background anova filter (alpha):", min = 0, max = 1, value = 0.01, step = 0.001),
numericInput("mk_corr_bg_ctCor_alpha", "Background correlation filter (alpha):", min = 0, max = 1, value = 0.01, step = 0.001),
numericInput("mk_corr_gene_ctCor_alpha", "Marker correlation filter (alpha):", min = 0, max = 1, value = 0.01, step = 0.001),
icon = icon("gear"), status = "primary", tooltip = tooltipOptions(title = "Correlation settings")
)
),
column(1,
dropdownButton(
selectInput('dwn_marker_table_mk_corr_ext', label = NULL,
choices = list("chosen correlations" = 'focus_corr_data',
"correlations within clusters" = 'signals_in_cluster',
"correlations between clusters" = 'signals_between_clusters',
"all correlations" = 'signals')),
downloadButton('dwn_marker_table_mk_corr', ""),
icon = icon("save"), status = "primary", tooltip = tooltipOptions(title = "save plot")
)
)
),
DT::dataTableOutput('marker_mk_corr_table')
),
width = 12
)
)
),
# Fifth tab content
tabItem(tabName = 'data_crosspanel',
fluidRow(
tabBox(
tabPanel("Upload panel",
h4("FCS files"),
h5("Choose FSC files to upload them as one panel"),
shinyFilesButton('choose_fcs_cross_p', label= h5("Select FCS files"),
title='Please select clustered FCS files to upload as panel', multiple=TRUE),
hr(),
h4("Name the panel"),
textInput("panel_name_cross_p", label = h5("You can name the new panel (try to use a short name or even one or few letters)")),
hr(),
textInput("extr_clust_pattern_cross_p",
label = h5("full or part column name with clusters info (for cytofBrowser and cytofkit : <cluster>)"), value = "cluster"),
hr(),
actionButton('butt_upload_cross_p', label = "Upload")
),
tabPanel("Remove panel",
uiOutput('remove_panel_cross_p_ui')
)
),
tabBox(
tabPanel("Samples content",
plotOutput('content_cross_p')
),
tabPanel("Markers content",
plotOutput('mk_content_cross_p')
)
),
tabBox(
tabPanel("Abundance cross-panel correlation",
fluidRow(
column(2, actionBttn(inputId = "abund_corr_cross_p", style = "material-circle", color = "default" ,icon = icon("play"))),
column(2,
dropdownButton(
tags$h4("Advanced options"),
selectInput('abund_corr_method_cross_p', "Select correlation method:", c("spearman" = 'spearman', "pearson" = 'pearson')),
selectInput('abund_corr_p_mode_cross_p', "p-value", c("p-value" = "pval", "adjusted p-value" = "padj"), selected = 'padj'),
uiOutput('abund_corr_padj_method_cross_p_ui'),
icon = icon("gear"), status = "primary", tooltip = tooltipOptions(title = "Correlation test options")
)
),
column(2,
dropdownButton(
selectInput('dwn_abund_corr_cross_p_ext', label = NULL,
choices = list('pdf' = "pdf", 'jpeg' = "jpeg", 'png' = "png")),
downloadButton('dwn_abund_corr_cross_p', ""),
hr(),
selectInput('dwn_table_abund_corr_cross_p_ext', label = NULL,
choices = list("abundance data" = 'abund_data', "correlation data" = 'corr_data'),
selected = 'corr_data'),
downloadButton('dwn_table_abund_corr_cross_p', ""),
icon = icon("save"), status = "primary", tooltip = tooltipOptions(title = "save plot")
)
)
),
plotOutput('plot_abund_corr_cross_p')
),
tabPanel("Expressing cell fraction Correlation",
fluidRow(
column(2, actionBttn(inputId = "exp_cell_f_corr_cross_p", style = "material-circle", color = "default" ,icon = icon("play"))),
column(4, uiOutput('mk_exp_cell_f_cross_p_ui')),
column(2,
dropdownButton(
tags$h4("Settings"),
selectInput("exp_cell_f_corr_method", label = "Choose the method to get expression cells fraction",
choices =c("clustering" = 'clustering', "threshold" = 'threshold'), selected = 'clustering'),
uiOutput('threshold_exp_cell_f_cross_p_ui'),
icon = icon("edit"), status = "primary", tooltip = tooltipOptions(title = "Cell fraction allocation methods")
)
),
column(2,
dropdownButton(
tags$h4("Advanced options"),
selectInput('exp_cell_f_corr_method_cross_p', "Select correlation method:", c("spearman" = 'spearman', "pearson" = 'pearson')),
selectInput('exp_cell_f_corr_p_mode_cross_p', "p-value", c("p-value" = "pval", "adjusted p-value" = "padj"), selected = 'padj'),
uiOutput('exp_cell_f_corr_padj_method_cross_p_ui'),
numericInput('exp_cell_f_corr_min_cell_cross_p', "Min expressing cell number ", min = 0, value = 5, step = 1),
icon = icon("gear"), status = "primary", tooltip = tooltipOptions(title = "Correlation test options")
)
),
column(2,
dropdownButton(
selectInput('dwn_exp_cell_f_corr_cross_p_ext', label = NULL,
choices = list('pdf' = "pdf", 'jpeg' = "jpeg", 'png' = "png")),
downloadButton('dwn_exp_cell_f_corr_cross_p', ""),
hr(),
selectInput('dwn_table_exp_cell_f_corr_cross_p_ext', label = NULL,
choices = list("expressing cell fractions" = 'exp_cell_f_data', "correlation data" = 'corr_data'),
selected = 'corr_data'),
downloadButton('dwn_table_exp_cell_f_corr_cross_p', ""),
icon = icon("save"), status = "primary", tooltip = tooltipOptions(title = "save plot")
)
)
),
fluidRow(
column(7, plotOutput('plot_exp_cell_f_corr_cross_p')),
column(5, plotOutput('mk_density_plot_cross_p'))
)
),
width = 12
)
)
)
)
)
)
shinyApp(ui = cytofBrowser_ui, server = cytofBrowser_server)
}
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