inst/HiTMaP_GUI/pipeline_ui.R
In guoguodigit/Metwork: A collection of tools for imaging MS data processing
#' HiTMaP Pipeline GUI Components
#'
#' Enhanced UI components that integrate with the new pipeline system
#' Pipeline Control Panel UI
pipeline_control_ui <- function() {
fluidRow(
column(12,
wellPanel(
h3("HiTMaP Pipeline Control", icon("play-circle")),
fluidRow(
column(3,
h4("Pipeline Status"),
div(id = "pipeline_status_display",
style = "font-family: monospace; background: #f8f9fa; padding: 10px; border-radius: 5px;",
verbatimTextOutput("pipeline_status_text")
),
br(),
actionButton("refresh_status", "Refresh Status",
icon = icon("refresh"), class = "btn-info btn-sm")
),
column(3,
h4("Quick Actions"),
actionButton("pipeline_quick_start", "Quick Start",
icon = icon("rocket"), class = "btn-success"),
br(), br(),
actionButton("pipeline_step_by_step", "Step-by-Step",
icon = icon("list"), class = "btn-primary"),
br(), br(),
actionButton("pipeline_resume", "Resume Project",
icon = icon("play"), class = "btn-warning")
),
column(3,
h4("Pipeline Templates"),
selectInput("pipeline_template", "Choose Template:",
choices = list(
"Custom" = "custom",
"High Precision" = "high_precision",
"High Throughput" = "high_throughput",
"Discovery Mode" = "discovery",
"Targeted Analysis" = "targeted"
)),
actionButton("apply_template", "Apply Template",
icon = icon("magic"), class = "btn-info")
),
column(3,
h4("Project Management"),
actionButton("save_checkpoint", "Save Checkpoint",
icon = icon("save"), class = "btn-secondary"),
br(), br(),
actionButton("load_checkpoint", "Load Checkpoint",
icon = icon("folder-open"), class = "btn-secondary"),
br(), br(),
downloadButton("export_pipeline_results", "Export Results",
class = "btn-success")
)
)
)
)
)
}
#' Pipeline Step Configuration UI
pipeline_step_ui <- function() {
tabsetPanel(
id = "pipeline_steps_tabs",
# Step 1: Project Initialization
tabPanel("1. Initialize",
icon = icon("cog"),
wellPanel(
h4("Project Initialization"),
fluidRow(
column(6,
fileInput("pipeline_data_files", "Select Data Files (.imzML)",
multiple = TRUE, accept = c(".imzML")),
textInput("pipeline_project_folder", "Project Folder:",
placeholder = "Leave empty for auto-detection"),
numericInput("pipeline_threads", "Number of Threads:",
value = 4, min = 1, max = 32, step = 1)
),
column(6,
div(style = "margin-top: 25px;",
actionButton("run_init", "Initialize Project",
icon = icon("play"), class = "btn-primary"),
br(), br(),
conditionalPanel(
condition = "output.pipeline_initialized == true",
div(class = "alert alert-success",
icon("check"), " Project initialized successfully!")
)
)
)
)
)
),
# Step 2: Candidate Generation
tabPanel("2. Candidates",
icon = icon("database"),
wellPanel(
h4("Candidate Generation"),
fluidRow(
column(4,
fileInput("pipeline_database", "Database File (.fasta)",
accept = c(".fasta", ".fa")),
selectInput("pipeline_digestion", "Digestion Site:",
choices = c("trypsin", "chymotrypsin", "pepsin", "custom"),
selected = "trypsin"),
conditionalPanel(
condition = "input.pipeline_digestion == 'custom'",
textInput("pipeline_custom_digestion", "Custom Digestion Pattern:")
),
sliderInput("pipeline_missed_cleavages", "Missed Cleavages:",
min = 0, max = 5, value = c(0, 1))
),
column(4,
selectizeInput("pipeline_adducts", "Adducts:",
choices = c("M+H", "M+Na", "M+K", "M+NH4"),
selected = "M+H", multiple = TRUE),
selectInput("pipeline_modifications", "Modification Profile:",
choices = list(
"None" = "none",
"Basic" = "basic",
"Comprehensive" = "comprehensive",
"Custom" = "custom"
)),
conditionalPanel(
condition = "input.pipeline_modifications == 'custom'",
textAreaInput("pipeline_custom_mods", "Custom Modifications (JSON):",
rows = 3)
)
),
column(4,
checkboxInput("pipeline_decoy_search", "Enable Decoy Search", TRUE),
conditionalPanel(
condition = "input.pipeline_decoy_search",
selectInput("pipeline_decoy_mode", "Decoy Mode:",
choices = c("isotope", "element", "adduct"))
),
sliderInput("pipeline_mz_range", "m/z Range:",
min = 0, max = 5000, value = c(700, 4000)),
div(style = "margin-top: 20px;",
actionButton("run_candidates", "Generate Candidates",
icon = icon("play"), class = "btn-primary")
)
)
),
conditionalPanel(
condition = "output.candidates_generated == true",
br(),
div(class = "alert alert-info",
h5("Candidates Summary:"),
textOutput("candidates_summary")
)
)
)
),
# Step 3: Preprocessing
tabPanel("3. Preprocess",
icon = icon("cogs"),
wellPanel(
h4("Data Preprocessing"),
fluidRow(
column(4,
selectInput("pipeline_preprocess_profile", "Preprocessing Profile:",
choices = list(
"Minimal" = "minimal",
"Standard" = "standard",
"Comprehensive" = "comprehensive",
"Custom" = "custom"
)),
numericInput("pipeline_ppm", "Mass Tolerance (ppm):",
value = 5, min = 0.1, max = 50, step = 0.1),
sliderInput("pipeline_preprocess_mz_range", "Processing m/z Range:",
min = 0, max = 5000, value = c(700, 4000))
),
column(4,
conditionalPanel(
condition = "input.pipeline_preprocess_profile == 'custom'",
h5("Custom Preprocessing Parameters:"),
selectInput("pipeline_smooth_method", "Smoothing:",
choices = list("Disable" = "disable", "Gaussian" = "gaussian",
"Moving Average" = "ma", "Savitzky-Golay" = "sgolay")),
selectInput("pipeline_baseline_method", "Baseline Reduction:",
choices = list("Disable" = "disable", "Local Minimum" = "locmin",
"Median" = "median")),
selectInput("pipeline_peak_method", "Peak Picking:",
choices = list("Adaptive" = "adaptive", "Simple" = "simple",
"MAD" = "mad", "Disable" = "disable"))
)
),
column(4,
checkboxInput("pipeline_force_preprocess", "Force Reprocessing", FALSE),
checkboxInput("pipeline_use_rds", "Use Preprocessed RDS", TRUE),
div(style = "margin-top: 40px;",
actionButton("run_preprocess", "Start Preprocessing",
icon = icon("play"), class = "btn-primary")
)
)
),
conditionalPanel(
condition = "output.preprocessing_complete == true",
br(),
div(class = "alert alert-success",
icon("check"), " Preprocessing completed for all files"
)
)
)
),
# Step 4: Segmentation
tabPanel("4. Segment",
icon = icon("th"),
wellPanel(
h4("Spatial Segmentation"),
fluidRow(
column(4,
numericInput("pipeline_segment_count", "Number of Segments:",
value = 4, min = 2, max = 20, step = 1),
selectInput("pipeline_segment_method", "Segmentation Method:",
choices = list(
"Spatial k-Means" = "spatialKMeans",
"Spatial Shrunken Centroids" = "spatialShrunkenCentroids",
"Manual (File-based)" = "def_file",
"None" = "none"
)),
conditionalPanel(
condition = "input.pipeline_segment_method == 'def_file'",
fileInput("pipeline_segment_def", "Segmentation Definition File:",
accept = c(".csv", ".txt"))
)
),
column(4,
sliderInput("pipeline_variance_coverage", "Variance Coverage:",
min = 0.1, max = 1.0, value = 0.8, step = 0.05),
numericInput("pipeline_smooth_range", "Smoothing Range:",
value = 1, min = 0, max = 5, step = 1),
selectInput("pipeline_segment_ncomp", "Components:",
choices = list("Auto-detect" = "auto-detect", "3" = 3, "6" = 6, "9" = 9))
),
column(4,
div(style = "margin-top: 40px;",
actionButton("run_segmentation", "Start Segmentation",
icon = icon("play"), class = "btn-primary")
),
br(), br(),
conditionalPanel(
condition = "output.segmentation_complete == true",
actionButton("preview_segmentation", "Preview Results",
icon = icon("eye"), class = "btn-info")
)
)
),
conditionalPanel(
condition = "output.segmentation_complete == true",
br(),
div(class = "alert alert-success",
textOutput("segmentation_summary")
)
)
)
),
# Step 5: PMF Search
tabPanel("5. Search",
icon = icon("search"),
wellPanel(
h4("PMF Search"),
fluidRow(
column(4,
sliderInput("pipeline_search_threshold", "Intensity Threshold:",
min = 0.0001, max = 0.1, value = 0.001, step = 0.0001),
numericInput("pipeline_search_ppm", "Search Tolerance (ppm):",
value = 5, min = 0.1, max = 50, step = 0.1),
selectInput("pipeline_score_method", "Scoring Method:",
choices = c("SQRTP", "Cosine", "Correlation"))
),
column(4,
sliderInput("pipeline_fdr_cutoff", "FDR Cutoff:",
min = 0.001, max = 0.2, value = 0.05, step = 0.001),
numericInput("pipeline_peptide_filter", "Min. Peptides per Protein:",
value = 2, min = 1, max = 10, step = 1),
checkboxInput("pipeline_plot_scores", "Generate Score Plots", FALSE)
),
column(4,
h5("Region Selection:"),
radioButtons("pipeline_region_selection", "",
choices = list(
"All Regions" = "all",
"Specific Regions" = "specific",
"Filter by Size" = "filter"
), selected = "all"),
conditionalPanel(
condition = "input.pipeline_region_selection == 'specific'",
textInput("pipeline_specific_regions", "Region Names (comma-separated):")
),
div(style = "margin-top: 20px;",
actionButton("run_pmf_search", "Start PMF Search",
icon = icon("play"), class = "btn-primary")
)
)
),
conditionalPanel(
condition = "output.pmf_search_complete == true",
br(),
div(class = "alert alert-success",
h5("PMF Search Results:"),
textOutput("pmf_search_summary")
)
)
)
),
# Step 6: Summarization
tabPanel("6. Summarize",
icon = icon("chart-bar"),
wellPanel(
h4("Generate Summaries"),
fluidRow(
column(6,
h5("Summary Options:"),
checkboxInput("pipeline_protein_summary", "Protein Summary", TRUE),
checkboxInput("pipeline_peptide_summary", "Peptide Summary", TRUE),
checkboxInput("pipeline_region_summary", "Region Feature Summary", FALSE)
),
column(6,
div(style = "margin-top: 20px;",
actionButton("run_summarize", "Generate Summaries",
icon = icon("play"), class = "btn-primary")
),
br(), br(),
conditionalPanel(
condition = "output.summarization_complete == true",
div(class = "alert alert-success",
icon("check"), " Summaries generated successfully!"
)
)
)
),
conditionalPanel(
condition = "output.summarization_complete == true",
br(),
hr(),
h5("Final Results Summary:"),
div(id = "final_results_summary",
verbatimTextOutput("final_results_text")
)
)
)
)
)
}
#' Pipeline Monitoring UI
pipeline_monitoring_ui <- function() {
fluidRow(
column(6,
wellPanel(
h4("Pipeline Progress"),
div(id = "progress_container",
htmlOutput("pipeline_progress_bar")
),
br(),
h5("Processing Log:"),
div(style = "height: 300px; overflow-y: scroll; background: #f8f9fa; padding: 10px; border-radius: 5px;",
verbatimTextOutput("pipeline_log")
)
)
),
column(6,
wellPanel(
h4("Performance Monitoring"),
conditionalPanel(
condition = "output.benchmarks_available == true",
plotOutput("benchmark_plot", height = "200px"),
br()
),
h5("Current Status:"),
tableOutput("pipeline_status_table"),
br(),
h5("Quick Actions:"),
fluidRow(
column(6,
actionButton("pipeline_pause", "Pause",
icon = icon("pause"), class = "btn-warning btn-sm"),
actionButton("pipeline_stop", "Stop",
icon = icon("stop"), class = "btn-danger btn-sm")
),
column(6,
actionButton("pipeline_debug", "Debug Mode",
icon = icon("bug"), class = "btn-info btn-sm"),
actionButton("view_detailed_status", "Detailed Status",
icon = icon("info"), class = "btn-secondary btn-sm")
)
)
)
)
)
}
#' Results Visualization UI
pipeline_results_ui <- function() {
tabsetPanel(
id = "results_tabs",
tabPanel("Summary",
icon = icon("table"),
fluidRow(
column(12,
h4("Results Overview"),
conditionalPanel(
condition = "output.has_results == true",
fluidRow(
column(3,
div(class = "info-box bg-blue",
h3(textOutput("total_peptides_count")),
p("Total Peptides")
)
),
column(3,
div(class = "info-box bg-green",
h3(textOutput("total_proteins_count")),
p("Total Proteins")
)
),
column(3,
div(class = "info-box bg-orange",
h3(textOutput("total_regions_count")),
p("Total Regions")
)
),
column(3,
div(class = "info-box bg-purple",
h3(textOutput("total_files_count")),
p("Files Processed")
)
)
),
br(),
tabsetPanel(
tabPanel("Peptides", DT::dataTableOutput("peptides_table")),
tabPanel("Proteins", DT::dataTableOutput("proteins_table")),
tabPanel("By Region", DT::dataTableOutput("regions_table"))
)
),
conditionalPanel(
condition = "output.has_results == false",
div(class = "alert alert-info",
h4("No Results Available"),
p("Complete the pipeline to view results here.")
)
)
)
)
),
tabPanel("Visualizations",
icon = icon("chart-line"),
fluidRow(
column(6,
wellPanel(
h5("Result Distribution"),
selectInput("viz_plot_type", "Plot Type:",
choices = list(
"Peptides by File" = "peptides_file",
"Peptides by Region" = "peptides_region",
"Proteins by File" = "proteins_file",
"Mass Distribution" = "mass_dist",
"Score Distribution" = "score_dist"
)),
plotOutput("results_plot")
)
),
column(6,
wellPanel(
h5("Interactive Exploration"),
conditionalPanel(
condition = "output.has_results == true",
plotlyOutput("interactive_plot")
),
conditionalPanel(
condition = "output.has_results == false",
div(class = "alert alert-info", "Complete analysis to view interactive plots")
)
)
)
)
),
tabPanel("Export",
icon = icon("download"),
wellPanel(
h4("Export Results"),
fluidRow(
column(4,
h5("Export Format:"),
radioButtons("export_format", "",
choices = list("CSV" = "csv", "Excel" = "excel", "RDS" = "rds"),
selected = "csv"),
h5("Export Types:"),
checkboxGroupInput("export_types", "",
choices = list(
"Summaries" = "summaries",
"Candidates" = "candidates",
"Raw Results" = "raw_results",
"Project Object" = "project"
),
selected = c("summaries"))
),
column(4,
h5("Export Options:"),
textInput("export_prefix", "File Prefix:", value = "hitmap_results"),
checkboxInput("export_timestamp", "Add Timestamp", TRUE),
br(),
downloadButton("download_results", "Download Results",
class = "btn-success", icon = icon("download"))
),
column(4,
h5("Quick Exports:"),
downloadButton("quick_export_peptides", "Peptides (CSV)",
class = "btn-info btn-sm"),
br(), br(),
downloadButton("quick_export_proteins", "Proteins (CSV)",
class = "btn-info btn-sm"),
br(), br(),
downloadButton("quick_export_project", "Full Project (RDS)",
class = "btn-warning btn-sm")
)
)
)
)
)
}
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#' HiTMaP Pipeline GUI Components
#'
#' Enhanced UI components that integrate with the new pipeline system
#' Pipeline Control Panel UI
pipeline_control_ui <- function() {
fluidRow(
column(12,
wellPanel(
h3("HiTMaP Pipeline Control", icon("play-circle")),
fluidRow(
column(3,
h4("Pipeline Status"),
div(id = "pipeline_status_display",
style = "font-family: monospace; background: #f8f9fa; padding: 10px; border-radius: 5px;",
verbatimTextOutput("pipeline_status_text")
),
br(),
actionButton("refresh_status", "Refresh Status",
icon = icon("refresh"), class = "btn-info btn-sm")
),
column(3,
h4("Quick Actions"),
actionButton("pipeline_quick_start", "Quick Start",
icon = icon("rocket"), class = "btn-success"),
br(), br(),
actionButton("pipeline_step_by_step", "Step-by-Step",
icon = icon("list"), class = "btn-primary"),
br(), br(),
actionButton("pipeline_resume", "Resume Project",
icon = icon("play"), class = "btn-warning")
),
column(3,
h4("Pipeline Templates"),
selectInput("pipeline_template", "Choose Template:",
choices = list(
"Custom" = "custom",
"High Precision" = "high_precision",
"High Throughput" = "high_throughput",
"Discovery Mode" = "discovery",
"Targeted Analysis" = "targeted"
)),
actionButton("apply_template", "Apply Template",
icon = icon("magic"), class = "btn-info")
),
column(3,
h4("Project Management"),
actionButton("save_checkpoint", "Save Checkpoint",
icon = icon("save"), class = "btn-secondary"),
br(), br(),
actionButton("load_checkpoint", "Load Checkpoint",
icon = icon("folder-open"), class = "btn-secondary"),
br(), br(),
downloadButton("export_pipeline_results", "Export Results",
class = "btn-success")
)
)
)
)
)
}
#' Pipeline Step Configuration UI
pipeline_step_ui <- function() {
tabsetPanel(
id = "pipeline_steps_tabs",
# Step 1: Project Initialization
tabPanel("1. Initialize",
icon = icon("cog"),
wellPanel(
h4("Project Initialization"),
fluidRow(
column(6,
fileInput("pipeline_data_files", "Select Data Files (.imzML)",
multiple = TRUE, accept = c(".imzML")),
textInput("pipeline_project_folder", "Project Folder:",
placeholder = "Leave empty for auto-detection"),
numericInput("pipeline_threads", "Number of Threads:",
value = 4, min = 1, max = 32, step = 1)
),
column(6,
div(style = "margin-top: 25px;",
actionButton("run_init", "Initialize Project",
icon = icon("play"), class = "btn-primary"),
br(), br(),
conditionalPanel(
condition = "output.pipeline_initialized == true",
div(class = "alert alert-success",
icon("check"), " Project initialized successfully!")
)
)
)
)
)
),
# Step 2: Candidate Generation
tabPanel("2. Candidates",
icon = icon("database"),
wellPanel(
h4("Candidate Generation"),
fluidRow(
column(4,
fileInput("pipeline_database", "Database File (.fasta)",
accept = c(".fasta", ".fa")),
selectInput("pipeline_digestion", "Digestion Site:",
choices = c("trypsin", "chymotrypsin", "pepsin", "custom"),
selected = "trypsin"),
conditionalPanel(
condition = "input.pipeline_digestion == 'custom'",
textInput("pipeline_custom_digestion", "Custom Digestion Pattern:")
),
sliderInput("pipeline_missed_cleavages", "Missed Cleavages:",
min = 0, max = 5, value = c(0, 1))
),
column(4,
selectizeInput("pipeline_adducts", "Adducts:",
choices = c("M+H", "M+Na", "M+K", "M+NH4"),
selected = "M+H", multiple = TRUE),
selectInput("pipeline_modifications", "Modification Profile:",
choices = list(
"None" = "none",
"Basic" = "basic",
"Comprehensive" = "comprehensive",
"Custom" = "custom"
)),
conditionalPanel(
condition = "input.pipeline_modifications == 'custom'",
textAreaInput("pipeline_custom_mods", "Custom Modifications (JSON):",
rows = 3)
)
),
column(4,
checkboxInput("pipeline_decoy_search", "Enable Decoy Search", TRUE),
conditionalPanel(
condition = "input.pipeline_decoy_search",
selectInput("pipeline_decoy_mode", "Decoy Mode:",
choices = c("isotope", "element", "adduct"))
),
sliderInput("pipeline_mz_range", "m/z Range:",
min = 0, max = 5000, value = c(700, 4000)),
div(style = "margin-top: 20px;",
actionButton("run_candidates", "Generate Candidates",
icon = icon("play"), class = "btn-primary")
)
)
),
conditionalPanel(
condition = "output.candidates_generated == true",
br(),
div(class = "alert alert-info",
h5("Candidates Summary:"),
textOutput("candidates_summary")
)
)
)
),
# Step 3: Preprocessing
tabPanel("3. Preprocess",
icon = icon("cogs"),
wellPanel(
h4("Data Preprocessing"),
fluidRow(
column(4,
selectInput("pipeline_preprocess_profile", "Preprocessing Profile:",
choices = list(
"Minimal" = "minimal",
"Standard" = "standard",
"Comprehensive" = "comprehensive",
"Custom" = "custom"
)),
numericInput("pipeline_ppm", "Mass Tolerance (ppm):",
value = 5, min = 0.1, max = 50, step = 0.1),
sliderInput("pipeline_preprocess_mz_range", "Processing m/z Range:",
min = 0, max = 5000, value = c(700, 4000))
),
column(4,
conditionalPanel(
condition = "input.pipeline_preprocess_profile == 'custom'",
h5("Custom Preprocessing Parameters:"),
selectInput("pipeline_smooth_method", "Smoothing:",
choices = list("Disable" = "disable", "Gaussian" = "gaussian",
"Moving Average" = "ma", "Savitzky-Golay" = "sgolay")),
selectInput("pipeline_baseline_method", "Baseline Reduction:",
choices = list("Disable" = "disable", "Local Minimum" = "locmin",
"Median" = "median")),
selectInput("pipeline_peak_method", "Peak Picking:",
choices = list("Adaptive" = "adaptive", "Simple" = "simple",
"MAD" = "mad", "Disable" = "disable"))
)
),
column(4,
checkboxInput("pipeline_force_preprocess", "Force Reprocessing", FALSE),
checkboxInput("pipeline_use_rds", "Use Preprocessed RDS", TRUE),
div(style = "margin-top: 40px;",
actionButton("run_preprocess", "Start Preprocessing",
icon = icon("play"), class = "btn-primary")
)
)
),
conditionalPanel(
condition = "output.preprocessing_complete == true",
br(),
div(class = "alert alert-success",
icon("check"), " Preprocessing completed for all files"
)
)
)
),
# Step 4: Segmentation
tabPanel("4. Segment",
icon = icon("th"),
wellPanel(
h4("Spatial Segmentation"),
fluidRow(
column(4,
numericInput("pipeline_segment_count", "Number of Segments:",
value = 4, min = 2, max = 20, step = 1),
selectInput("pipeline_segment_method", "Segmentation Method:",
choices = list(
"Spatial k-Means" = "spatialKMeans",
"Spatial Shrunken Centroids" = "spatialShrunkenCentroids",
"Manual (File-based)" = "def_file",
"None" = "none"
)),
conditionalPanel(
condition = "input.pipeline_segment_method == 'def_file'",
fileInput("pipeline_segment_def", "Segmentation Definition File:",
accept = c(".csv", ".txt"))
)
),
column(4,
sliderInput("pipeline_variance_coverage", "Variance Coverage:",
min = 0.1, max = 1.0, value = 0.8, step = 0.05),
numericInput("pipeline_smooth_range", "Smoothing Range:",
value = 1, min = 0, max = 5, step = 1),
selectInput("pipeline_segment_ncomp", "Components:",
choices = list("Auto-detect" = "auto-detect", "3" = 3, "6" = 6, "9" = 9))
),
column(4,
div(style = "margin-top: 40px;",
actionButton("run_segmentation", "Start Segmentation",
icon = icon("play"), class = "btn-primary")
),
br(), br(),
conditionalPanel(
condition = "output.segmentation_complete == true",
actionButton("preview_segmentation", "Preview Results",
icon = icon("eye"), class = "btn-info")
)
)
),
conditionalPanel(
condition = "output.segmentation_complete == true",
br(),
div(class = "alert alert-success",
textOutput("segmentation_summary")
)
)
)
),
# Step 5: PMF Search
tabPanel("5. Search",
icon = icon("search"),
wellPanel(
h4("PMF Search"),
fluidRow(
column(4,
sliderInput("pipeline_search_threshold", "Intensity Threshold:",
min = 0.0001, max = 0.1, value = 0.001, step = 0.0001),
numericInput("pipeline_search_ppm", "Search Tolerance (ppm):",
value = 5, min = 0.1, max = 50, step = 0.1),
selectInput("pipeline_score_method", "Scoring Method:",
choices = c("SQRTP", "Cosine", "Correlation"))
),
column(4,
sliderInput("pipeline_fdr_cutoff", "FDR Cutoff:",
min = 0.001, max = 0.2, value = 0.05, step = 0.001),
numericInput("pipeline_peptide_filter", "Min. Peptides per Protein:",
value = 2, min = 1, max = 10, step = 1),
checkboxInput("pipeline_plot_scores", "Generate Score Plots", FALSE)
),
column(4,
h5("Region Selection:"),
radioButtons("pipeline_region_selection", "",
choices = list(
"All Regions" = "all",
"Specific Regions" = "specific",
"Filter by Size" = "filter"
), selected = "all"),
conditionalPanel(
condition = "input.pipeline_region_selection == 'specific'",
textInput("pipeline_specific_regions", "Region Names (comma-separated):")
),
div(style = "margin-top: 20px;",
actionButton("run_pmf_search", "Start PMF Search",
icon = icon("play"), class = "btn-primary")
)
)
),
conditionalPanel(
condition = "output.pmf_search_complete == true",
br(),
div(class = "alert alert-success",
h5("PMF Search Results:"),
textOutput("pmf_search_summary")
)
)
)
),
# Step 6: Summarization
tabPanel("6. Summarize",
icon = icon("chart-bar"),
wellPanel(
h4("Generate Summaries"),
fluidRow(
column(6,
h5("Summary Options:"),
checkboxInput("pipeline_protein_summary", "Protein Summary", TRUE),
checkboxInput("pipeline_peptide_summary", "Peptide Summary", TRUE),
checkboxInput("pipeline_region_summary", "Region Feature Summary", FALSE)
),
column(6,
div(style = "margin-top: 20px;",
actionButton("run_summarize", "Generate Summaries",
icon = icon("play"), class = "btn-primary")
),
br(), br(),
conditionalPanel(
condition = "output.summarization_complete == true",
div(class = "alert alert-success",
icon("check"), " Summaries generated successfully!"
)
)
)
),
conditionalPanel(
condition = "output.summarization_complete == true",
br(),
hr(),
h5("Final Results Summary:"),
div(id = "final_results_summary",
verbatimTextOutput("final_results_text")
)
)
)
)
)
}
#' Pipeline Monitoring UI
pipeline_monitoring_ui <- function() {
fluidRow(
column(6,
wellPanel(
h4("Pipeline Progress"),
div(id = "progress_container",
htmlOutput("pipeline_progress_bar")
),
br(),
h5("Processing Log:"),
div(style = "height: 300px; overflow-y: scroll; background: #f8f9fa; padding: 10px; border-radius: 5px;",
verbatimTextOutput("pipeline_log")
)
)
),
column(6,
wellPanel(
h4("Performance Monitoring"),
conditionalPanel(
condition = "output.benchmarks_available == true",
plotOutput("benchmark_plot", height = "200px"),
br()
),
h5("Current Status:"),
tableOutput("pipeline_status_table"),
br(),
h5("Quick Actions:"),
fluidRow(
column(6,
actionButton("pipeline_pause", "Pause",
icon = icon("pause"), class = "btn-warning btn-sm"),
actionButton("pipeline_stop", "Stop",
icon = icon("stop"), class = "btn-danger btn-sm")
),
column(6,
actionButton("pipeline_debug", "Debug Mode",
icon = icon("bug"), class = "btn-info btn-sm"),
actionButton("view_detailed_status", "Detailed Status",
icon = icon("info"), class = "btn-secondary btn-sm")
)
)
)
)
)
}
#' Results Visualization UI
pipeline_results_ui <- function() {
tabsetPanel(
id = "results_tabs",
tabPanel("Summary",
icon = icon("table"),
fluidRow(
column(12,
h4("Results Overview"),
conditionalPanel(
condition = "output.has_results == true",
fluidRow(
column(3,
div(class = "info-box bg-blue",
h3(textOutput("total_peptides_count")),
p("Total Peptides")
)
),
column(3,
div(class = "info-box bg-green",
h3(textOutput("total_proteins_count")),
p("Total Proteins")
)
),
column(3,
div(class = "info-box bg-orange",
h3(textOutput("total_regions_count")),
p("Total Regions")
)
),
column(3,
div(class = "info-box bg-purple",
h3(textOutput("total_files_count")),
p("Files Processed")
)
)
),
br(),
tabsetPanel(
tabPanel("Peptides", DT::dataTableOutput("peptides_table")),
tabPanel("Proteins", DT::dataTableOutput("proteins_table")),
tabPanel("By Region", DT::dataTableOutput("regions_table"))
)
),
conditionalPanel(
condition = "output.has_results == false",
div(class = "alert alert-info",
h4("No Results Available"),
p("Complete the pipeline to view results here.")
)
)
)
)
),
tabPanel("Visualizations",
icon = icon("chart-line"),
fluidRow(
column(6,
wellPanel(
h5("Result Distribution"),
selectInput("viz_plot_type", "Plot Type:",
choices = list(
"Peptides by File" = "peptides_file",
"Peptides by Region" = "peptides_region",
"Proteins by File" = "proteins_file",
"Mass Distribution" = "mass_dist",
"Score Distribution" = "score_dist"
)),
plotOutput("results_plot")
)
),
column(6,
wellPanel(
h5("Interactive Exploration"),
conditionalPanel(
condition = "output.has_results == true",
plotlyOutput("interactive_plot")
),
conditionalPanel(
condition = "output.has_results == false",
div(class = "alert alert-info", "Complete analysis to view interactive plots")
)
)
)
)
),
tabPanel("Export",
icon = icon("download"),
wellPanel(
h4("Export Results"),
fluidRow(
column(4,
h5("Export Format:"),
radioButtons("export_format", "",
choices = list("CSV" = "csv", "Excel" = "excel", "RDS" = "rds"),
selected = "csv"),
h5("Export Types:"),
checkboxGroupInput("export_types", "",
choices = list(
"Summaries" = "summaries",
"Candidates" = "candidates",
"Raw Results" = "raw_results",
"Project Object" = "project"
),
selected = c("summaries"))
),
column(4,
h5("Export Options:"),
textInput("export_prefix", "File Prefix:", value = "hitmap_results"),
checkboxInput("export_timestamp", "Add Timestamp", TRUE),
br(),
downloadButton("download_results", "Download Results",
class = "btn-success", icon = icon("download"))
),
column(4,
h5("Quick Exports:"),
downloadButton("quick_export_peptides", "Peptides (CSV)",
class = "btn-info btn-sm"),
br(), br(),
downloadButton("quick_export_proteins", "Proteins (CSV)",
class = "btn-info btn-sm"),
br(), br(),
downloadButton("quick_export_project", "Full Project (RDS)",
class = "btn-warning btn-sm")
)
)
)
)
)
}
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