diff --git a/drug-target/global.R b/drug-target/global.R index ea08946..a294e03 100644 --- a/drug-target/global.R +++ b/drug-target/global.R @@ -4,32 +4,40 @@ library(NGLVieweR) library(hash) # FIXME This is slow and should happen *once only* -#source("/srv/shiny-server/git/LSHTM_analysis/scripts/Header_TT.R") +#source(load_dir, "git/LSHTM_analysis/scripts/Header_TT.R") # FIXME: these are needed but slow to load every time -# source("/srv/shiny-server/git/LSHTM_analysis/config/alr.R") -# source("/srv/shiny-server/git/LSHTM_analysis/config/gid.R") +# source(load_dir, "git/LSHTM_analysis/config/alr.R") +# source(load_dir, "git/LSHTM_analysis/config/gid.R") -# source("/srv/shiny-server/git/LSHTM_analysis/config/pnca.R") -# source("/srv/shiny-server/git/LSHTM_analysis/config/rpob.R") -# source("/srv/shiny-server/git/LSHTM_analysis/config/katg.R") +# source(load_dir, "git/LSHTM_analysis/config/pnca.R") +# source(load_dir, "git/LSHTM_analysis/config/rpob.R") +# source(load_dir, "git/LSHTM_analysis/config/katg.R") # TODO: this is TEMPORARY and will shortly get replaced with a target picker # that'll reload everything when changing targets. the lapply() is *much* quicker # than previous approaches # system.time({ -#source("/srv/shiny-server/git/LSHTM_analysis/scripts/Header_TT.R") + +load_dir="~/git/" +#load_dir="/srv/shiny-server/git/" + +source(paste0(load_dir, "LSHTM_analysis/scripts/Header_TT.R")) load_target_globals=function(target){ cat(paste0("Reloading Target: ", target)) - source(paste0("/srv/shiny-server/git/LSHTM_analysis/config/", target, ".R")) # load per-target config file - - invisible(assign(paste0(target, "_merged_df3"), read.csv(paste0("/srv/shiny-server/git/Misc/shiny_dashboard/data/",target,"-merged_df3.csv")), envir = .GlobalEnv)) - invisible(assign(paste0(target, "_merged_df2"), read.csv(paste0("/srv/shiny-server/git/Misc/shiny_dashboard/data/",target,"-merged_df2.csv")), envir = .GlobalEnv)) - invisible(assign(paste0(target, "_corr_df_m3_f"), read.csv(paste0("/srv/shiny-server/git/Misc/shiny_dashboard/data/",target,"-corr_df_m3_f.csv")), envir = .GlobalEnv)) - invisible(assign(paste0(target, "_lin_lf"), read.csv(paste0("/srv/shiny-server/git/Misc/shiny_dashboard/data/",target,"-lin_lf.csv")), envir = .GlobalEnv)) - invisible(assign(paste0(target, "_lin_wf"), read.csv(paste0("/srv/shiny-server/git/Misc/shiny_dashboard/data/",target,"-lin_wf.csv")), envir = .GlobalEnv)) + source(paste0(load_dir, "LSHTM_analysis/config/", target, ".R")) # load per-target config file + invisible(assign(paste0(target, "_aa_pos_drug"), aa_pos_drug, envir = .GlobalEnv)) + invisible(assign(paste0(target, "_active_aa_pos"), active_aa_pos, envir = .GlobalEnv)) + invisible(assign(paste0(target, "_aa_pos_lig1"), aa_pos_lig1, envir = .GlobalEnv)) + invisible(assign(paste0(target, "_aa_pos_lig2"), aa_pos_lig2, envir = .GlobalEnv)) + invisible(assign(paste0(target, "_aa_pos_lig3"), aa_pos_lig3, envir = .GlobalEnv)) + invisible(assign(paste0(target, "_merged_df3"), read.csv(paste0(load_dir, "Misc/shiny_dashboard/data/",target,"-merged_df3.csv")), envir = .GlobalEnv)) + invisible(assign(paste0(target, "_merged_df2"), read.csv(paste0(load_dir, "Misc/shiny_dashboard/data/",target,"-merged_df2.csv")), envir = .GlobalEnv)) + invisible(assign(paste0(target, "_corr_df_m3_f"), read.csv(paste0(load_dir, "Misc/shiny_dashboard/data/",target,"-corr_df_m3_f.csv")), envir = .GlobalEnv)) + invisible(assign(paste0(target, "_lin_lf"), read.csv(paste0(load_dir, "Misc/shiny_dashboard/data/",target,"-lin_lf.csv")), envir = .GlobalEnv)) + invisible(assign(paste0(target, "_lin_wf"), read.csv(paste0(load_dir, "Misc/shiny_dashboard/data/",target,"-lin_wf.csv")), envir = .GlobalEnv)) lapply( c( "duet", @@ -41,15 +49,18 @@ load_target_globals=function(target){ "snap2", "provean", "dist_gen", - "mcsm_ppi2"#, + "mcsm_ppi2", + "mmcsm_lig", + "mcsm_na" + #, #"mcsm_na" ), function(x){ - wf_filename=paste0("/srv/shiny-server/git/Misc/shiny_dashboard/data/", tolower(gene), "-wf_", x ,".csv") - wf_var=paste0("wf_",x) + wf_filename=paste0(load_dir, "Misc/shiny_dashboard/data/", tolower(gene), "-wf_", x ,".csv") + wf_var=paste0(target, "wf_",x) if (file.exists(wf_filename)){ invisible(assign(wf_var,read.csv(wf_filename), envir = .GlobalEnv)) # FILTH } - lf_filename=paste0("/srv/shiny-server/git/Misc/shiny_dashboard/data/", tolower(gene), "-lf_", x ,".csv") + lf_filename=paste0(load_dir, "Misc/shiny_dashboard/data/", tolower(gene), "-lf_", x ,".csv") lf_var=paste0(target, "_lf_",x) if (file.exists(lf_filename)){ invisible(assign(lf_var,read.csv(lf_filename), envir = .GlobalEnv)) # FILTH @@ -57,24 +68,6 @@ load_target_globals=function(target){ } ) } -# populate target-specific *_unified_msa vars -load_msa_global=function(gene){ - drug=target_map[[gene]] - in_filename_msa = paste0(tolower(gene), "_msa.csv") - infile_msa = paste0("/srv/shiny-server/git/Data/", drug, "/output/", in_filename_msa) - print(infile_msa) - msa1 = read.csv(infile_msa, header = F) - msa_seq = msa1$V1 - - infile_fasta = paste0("/srv/shiny-server/git/Data/", drug, "/input/", tolower(gene), "2_f2.fasta") - print(infile_fasta) - msa2 = read.csv(infile_fasta, header = F) - wt_seq = msa2$V1 - - target_name=paste0(gene, '_unified_msa') - #print(target_name) - invisible(assign(target_name, list(msa_seq = msa_seq, wt_seq = wt_seq), envir = .GlobalEnv)) -} #### Local Functions #### # Generate a conditionalPanel() for a given graph function and sidebar name combination @@ -114,39 +107,36 @@ options(DT.options = list(scrollX = TRUE)) plot_functions_df=data.frame( tab_name=c( "LogoP SNP", - "Lineage Sample Count", - "Site SNP count", + #"Lineage Sample Count", + #"Site SNP count", "Stability SNP by site", "DM OM Plots", "Correlation", - "Lineage Distribution", + #"Lineage Distribution", "Consurf", - "LogoP OR", - "LogoP ED" + "LogoP OR" ), plot_function=c( "LogoPlotSnps", - "lin_sc", - "site_snp_count_bp", + #"lin_sc", + #"site_snp_count_bp", "bp_stability_hmap", "lf_bp2", "my_corr_pairs", - "lineage_distP", + #"lineage_distP", "wideP_consurf3", - "LogoPlotCustomH", - "LogoPlotMSA" + "LogoPlotCustomH" ), plot_df=c( "mutable_df3" , - "lin_lf", - "mutable_df3", + #"lin_lf", + #"mutable_df3", "merged_df3" , "lf_duet" , "corr_df_m3_f", - "merged_df2", + #"merged_df2", "merged_df3", - "merged_df2", - "unified_msa" + "merged_df2" ) ) @@ -262,8 +252,7 @@ lapply(c( "pnca", "rpob" ),function(x){ - invisible(load_target_globals(x)) - invisible(load_msa_global(x)) # turn off to speed up start time at the expense of "LogoP ED" + load_target_globals(x) } ) @@ -316,344 +305,748 @@ consurf_colours = c( , "9" = rgb(0.63,0.16,0.37) ) -if (interactive()){ +if (true()){ + #if (interactive()){ options(shiny.launch.browser = FALSE) # i am a big girl and can tie my own laces options(shiny.port = 8000) # don't change the port every time options(shiny.host = '0.0.0.0') # This means "listen to all addresses on all interfaces" options(width=120) options(DT.options = list(scrollX = TRUE)) - - ui=dashboardPage(skin="purple", - dashboardHeader(title = "Drug/Target Explorer"), - - dashboardSidebar( - sidebarMenu( id = "sidebar", - selectInput( - "switch_target", - label="Switch to New Target", - choices = c( - "alr", - "embb", - "gid", - "katg", - "pnca", - "rpob" - ), - selected="embb"), - menuItem("LogoP SNP", tabName="LogoP SNP"), - menuItem("Lineage Sample Count", tabName="Lineage Sample Count"), - menuItem("Site SNP count", tabName="Site SNP count"), - menuItem("Stability SNP by site", tabName="Stability SNP by site"), - menuItem("DM OM Plots", tabName="DM OM Plots"), - menuItem("Correlation", tabName="Correlation"), - menuItem("Lineage Distribution", tabName="Lineage Distribution"), - menuItem("Consurf", tabName="Consurf"), - menuItem("LogoP OR", tabName="LogoP OR"), - menuItem("LogoP ED", tabName="LogoP ED"), - menuItem('Stability count', tabName='Stability count'), - - # These conditionalPanel()s make extra settings appear in the sidebar when needed - conditionalPanel( - condition="input.sidebar == 'LogoP SNP'", - textInput( - "omit_snp_count", - "Omit SNPs", - value = c(0), - placeholder = "1,3,6" - ) - ), - # NOTE: - # I *think* we can cheat here slightly and use the min/max from - # merged_df3[['position']] for everything because the various - # dataframes for a given gene/drug combination have the - # same range of positions. May need fixing, especially - # if we get/shrink the imported data files to something - # more reasonable. - conditionalPanel( - condition=" + #### UI #### + ui <- dashboardPage(skin="purple", + dashboardHeader(title = "Drug/Target Explorer"), + + dashboardSidebar( + sidebarMenu( id = "sidebar", + selectInput( + "switch_target", + label="Switch to New Target", + choices = c( + "alr", + "embb", + "gid", + "katg", + "pnca", + "rpob" + ), + selected="embb"), + menuItem("LogoP SNP", tabName="LogoP SNP"), + #menuItem("Lineage Sample Count", tabName="Lineage Sample Count"), + menuItem("Site SNP count", tabName="Site SNP count"), + menuItem("Stability SNP by site", tabName="Stability SNP by site"), + menuItem("DM OM Plots", tabName="DM OM Plots"), + menuItem("Correlation", tabName="Correlation"), + #menuItem("Lineage Distribution", tabName="Lineage Distribution"), + menuItem("Consurf", tabName="Consurf"), + menuItem("LogoP OR", tabName="LogoP OR"), + menuItem("Lineage", tabName="Lineage"), + #menuItem('Stability count', tabName='Stability count'), + + # These conditionalPanel()s make extra settings appear in the sidebar when needed + conditionalPanel( + condition="input.sidebar == 'LogoP SNP'", + textInput( + "omit_snp_count", + "Omit SNPs", + value = c(0), + placeholder = "1,3,6" + ) + ), + # NOTE: + # I *think* we can cheat here slightly and use the min/max from + # merged_df3[['position']] for everything because the various + # dataframes for a given gene/drug combination have the + # same range of positions. May need fixing, especially + # if we get/shrink the imported data files to something + # more reasonable. + conditionalPanel( + condition=" input.sidebar == 'LogoP SNP'|| input.sidebar == 'Stability SNP by site' || input.sidebar == 'Consurf' || - input.sidebar == 'LogoP OR' || - input.sidebar == 'Site SNP count'", - sliderInput( - "display_position_range" - , "Display Positions" - , min=1, max=150, value=c(1,150) # 150 is just a little less than the smallest pos_count - ) - ), - conditionalPanel( - condition="input.sidebar == 'LogoP ED'", - sliderInput( - "display_position_full_range" - , "Display Positions" - , min=1, max=150, value=c(1,150) - ) - ), - - - conditionalPanel( - condition=" + input.sidebar == 'LogoP OR'", + sliderInput( + "display_position_range" + , "Display Positions" + , min=1, max=150, value=c(1,150) # 150 is just a little less than the smallest pos_count + ) + ), + + conditionalPanel( + condition=" input.sidebar == 'LogoP SNP' || input.sidebar == 'LogoP OR' || input.sidebar == 'LogoP ED'", - selectInput( - "logoplot_colour_scheme", - label="Logo Plot Colour Scheme", - choices = logoPlotSchemes, - selected="chemistry" - ) - ), - #conditionalPanel( - # condition="input.sidebar == 'LogoP SNP' || input.sidebar == 'LogoP ED'|| input.sidebar == 'Consurf'", - # numericInput( - # "table_position" - # , "Table Position", value=1 - # ) - #), - conditionalPanel( - condition="input.sidebar == 'Correlation'", - selectInput( - "corr_method", - label="Correlation Method", - choices = list("spearman", - "pearson", - "kendall"), - selected="spearman" - ) - ), - conditionalPanel( - condition="input.sidebar == 'Correlation'", - numericInput( - "corr_lig_dist" - , "Ligand Distance Cutoff (Å)", value=1 - ) - ), - - conditionalPanel( - condition="input.sidebar == 'Correlation'", - checkboxGroupInput( - "corr_selected", - "Parameters", - choiceNames = c( - "DeepDDG", - "Dynamut2", - "FoldX", - "ConSurf"#, - #"dst_mode" - ), - choiceValues = c( - "DeepDDG", - "Dynamut2", - "FoldX", - "ConSurf"#, - #"dst_mode" - ), - selected = c( - "DeepDDG", - "Dynamut2", - "FoldX", - "ConSurf"#, - #"dst_mode" - ) - ) - ), - - conditionalPanel( - condition="input.sidebar == 'DM OM Plots'", - selectInput( - "dm_om_param", - label="Stability Parameter", - choices = keys(dm_om_map), - selected="SNAP2") - ), - # colour_categ - conditionalPanel( - condition="input.sidebar == 'Stability SNP by site'", - selectInput( - "stability_snp_param", - label="Stability Parameter", - choices = stability_boxes_df$stability_type, - selected="Average") - ), - conditionalPanel( - condition="input.sidebar == 'Stability SNP by site'", - checkboxInput("reorder_custom_h", - label="Reorder by SNP count", - FALSE) - ), - conditionalPanel( - condition="input.sidebar.match(/^Lineage.*/)", - checkboxInput("all_lineages", - label="All Lineages", - FALSE) - ), - # an example of how you can match multiple things in frontend JS - conditionalPanel( - condition="input.sidebar == 'LogoP SNP' || + selectInput( + "logoplot_colour_scheme", + label="Logo Plot Colour Scheme", + choices = logoPlotSchemes, + selected="chemistry" + ) + ), + conditionalPanel( + condition="input.sidebar == 'Correlation'", + selectInput( + "corr_method", + label="Correlation Method", + choices = list("spearman", + "pearson", + "kendall"), + selected="spearman" + ) + ), + conditionalPanel( + condition="input.sidebar == 'Correlation'", + numericInput( + "corr_lig_dist" + , "Ligand Distance Cutoff (Å)", value=1 + ) + ), + conditionalPanel( + condition="input.sidebar == 'Site SNP count'", + numericInput( + "snp_ligand_dist" + , "Ligand Distance Cutoff (Å)", value=10 + ) + ), + conditionalPanel( + condition="input.sidebar == 'Site SNP count'", + numericInput( + "snp_interface_dist" + , "Interface Distance Cutoff (Å)", value=10 + ) + ), + conditionalPanel( + condition="input.sidebar == 'Site SNP count'", + numericInput( + "snp_nca_dist" + , "NCA Distance Cutoff (Å)", value=10 + ) + ), + + conditionalPanel( + condition="input.sidebar == 'Correlation'", + checkboxGroupInput( + "corr_selected", + "Parameters", + choiceNames = c( + "DeepDDG", + "Dynamut2", + "FoldX", + "ConSurf"#, + ), + choiceValues = c( + "DeepDDG", + "Dynamut2", + "FoldX", + "ConSurf"#, + ), + selected = c( + "DeepDDG", + "Dynamut2", + "FoldX", + "ConSurf"#, + ) + ) + ), + + # conditionalPanel( + # condition="input.sidebar == 'DM OM Plots'", + # selectInput( + # "dm_om_param", + # label="Stability Parameter", + # choices = keys(dm_om_map), + # selected="SNAP2") + # ), + # colour_categ + conditionalPanel( + condition="input.sidebar == 'Stability SNP by site'", + selectInput( + "stability_snp_param", + label="Stability Parameter", + choices = stability_boxes_df$stability_type, + selected="Average") + ), + conditionalPanel( + condition="input.sidebar == 'Stability SNP by site'", + checkboxInput("reorder_custom_h", + label="Reorder by SNP count", + FALSE) + ), + conditionalPanel( + condition="input.sidebar.match(/^Lineage.*/)", + checkboxInput("all_lineages", + label="All Lineages", + FALSE) + ), + # an example of how you can match multiple things in frontend JS + conditionalPanel( + condition="input.sidebar == 'LogoP SNP' || input.sidebar =='Stability SNP by site' || input.sidebar =='Consurf' || - input.sidebar =='LogoP OR' || - input.sidebar =='LogoP ED'", - actionButton("clear_ngl", - "Clear Structure") - ), - conditionalPanel( - condition="input.sidebar == 'LogoP SNP' || + input.sidebar =='LogoP OR'", + actionButton("clear_ngl", + "Clear Structure") + ), + conditionalPanel( + condition="input.sidebar == 'LogoP SNP' || input.sidebar =='Stability SNP by site' || input.sidebar =='Consurf' || - input.sidebar =='LogoP OR' || - input.sidebar =='LogoP ED'", - actionButton("test_ngl", - "Test NGLViewR") - )#, - - # downloadButton("save", - # "Download Plot" - # ) - # actionButton( - # "reload_target", - # label="Reload Target\nData (slow!)" - # ) - - ) - ), - body <- dashboardBody( - - tabItems( - tabItem(tabName = "dashboard", - h2("Dashboard tab content") - ), - - tabItem(tabName = "widgets", - h2("Widgets tab content") - ) - ), - # creates a 'Conditional Panel' containing a plot object from each of our - # ggplot plot functions (and its associated data frame) - fluidRow(column(width=12, - lapply(plot_functions_df$tab_name, - function(x){ - - plot_function=plot_functions_df[ - plot_functions_df$tab_name==x, - "plot_function"] - - plot_df=plot_functions_df[ - plot_functions_df$tab_name==x, - "plot_df"] - cat(paste0('\nCreating output: ', x)) - generate_conditionalPanel(x, plot_function, plot_df) - - } - ) - ) - ), - #### fluidRow()s for "Stability Count" in the sidebar #### - fluidRow( - conditionalPanel( - condition=" + input.sidebar =='LogoP OR'", + actionButton("test_ngl", + "Test NGLViewR") + )#, + + # downloadButton("save", + # "Download Plot" + # ) + # actionButton( + # "reload_target", + # label="Reload Target\nData (slow!)" + # ) + + ) + ), + #### body #### + body <- dashboardBody( + + tabItems( + tabItem(tabName = "dashboard", + h2("Dashboard tab content") + ), + + tabItem(tabName = "widgets", + h2("Widgets tab content") + ) + ), + # creates a 'Conditional Panel' containing a plot object from each of our + # ggplot plot functions (and its associated data frame) + fluidRow(column(width=12, + lapply(plot_functions_df$tab_name, + function(x){ + + plot_function=plot_functions_df[ + plot_functions_df$tab_name==x, + "plot_function"] + + plot_df=plot_functions_df[ + plot_functions_df$tab_name==x, + "plot_df"] + cat(paste0('\nCreating output: ', x)) + generate_conditionalPanel(x, plot_function, plot_df) + + } + ) + ) + ), + # Explicit fluidRow() for Lineage plots together + fluidRow( + column(conditionalPanel( + condition="input.sidebar.match(/^Lineage.*/)", box( + title="Lineage Distribution" + , status = "info" + , width=NULL + , plotOutput("lineage_distP", height="700px") %>% withSpinner(color="#0dc5c1"), + height=800 + ) + ), width=6 + ), + column(conditionalPanel( + condition="input.sidebar.match(/^Lineage.*/)", box( + title="Lineage SNP Diversity" + , status = "info" + , width=NULL + , plotOutput("lin_sc", height="700px") %>% withSpinner(color="#0dc5c1"), + height=800 + ) + ), width=6 + ) + + ), + # Explicit fluidRow() for Site SNP Count + fluidRow( + column(conditionalPanel( + condition="input.sidebar == 'Site SNP count'", + box( + title="Site SNP count" + , status = "info" + , width=NULL + , plotOutput("site_snp_count_bp") %>% withSpinner(color="#0dc5c1") + ) + ), width=6 + ), + column(conditionalPanel( + condition="input.sidebar == 'Site SNP count'", + box( + title="Ligand Distance" + , status = "info" + , width=NULL + , plotOutput("site_snp_count_bp_ligand") %>% withSpinner(color="#0dc5c1") + ) + ), width=6 + ), + column(conditionalPanel( + condition="input.sidebar == 'Site SNP count'", + box( + title="Interface Distance" + , status = "info" + , width=NULL + , plotOutput("site_snp_count_interface") %>% withSpinner(color="#0dc5c1") + ) + ), width=6 + ), + column(conditionalPanel( + condition="input.sidebar == 'Site SNP count'", + box( + title="NCA Distance" + , status = "info" + , width=NULL + , plotOutput("site_snp_count_nca") %>% withSpinner(color="#0dc5c1") + ) + ), width=6 + ) + ), + + # # Explicit fluidRow() for Stability Count + # fluidRow( + # column( + # conditionalPanel( + # condition="input.sidebar.match(/^Lineage.*/)", + # lapply( + # # FIXME: using a hardcoded target DF for this IS WRONG AND WILL BREAK + # stability_boxes_df[stability_boxes_df$outcome_colname %in% colnames(embb_merged_df3),"outcome_colname"], + # function(x){ + # print(paste0("outcome_colname: ",x)) + # box(plotOutput(x), width=4) + # } + # ), + # width=12 + # ) + # ) + # ), + + #### fluidRow()s for "Stability Count" in the sidebar #### + fluidRow( + conditionalPanel( + condition=" input.sidebar == 'LogoP SNP' || input.sidebar =='Stability SNP by site' || input.sidebar =='Consurf' || - input.sidebar =='LogoP OR' || - input.sidebar =='LogoP ED'", - column(NGLVieweROutput("structure"), - width=3 - ) - ), - conditionalPanel( - condition=" + input.sidebar =='LogoP OR'", + column(NGLVieweROutput("structure"), + width=3 + ) + ), + conditionalPanel( + condition=" input.sidebar == 'LogoP SNP' || input.sidebar == 'Stability SNP by site' || input.sidebar == 'Site SNP count' || input.sidebar == 'Consurf' || - input.sidebar == 'LogoP OR' || - input.sidebar == 'LogoP ED'", - column( - DT::dataTableOutput('table'), - width=9 - ) - ) - ) - ) + input.sidebar == 'LogoP OR'", + column( + DT::dataTableOutput('table'), + width=9 + ) + ) + ), + ) ) - server = function(input, output) { - observeEvent({ - input$combined_model - input$combined_data - input$combined_training_genes - input$score_dropdown - input$resample_dropdown - input$drug_dropdown - input$split_dropdown - - },{ - combined_model = input$combined_model - selection = input$score_dropdown - resampler = input$resample_dropdown - selected_drug = input$drug_dropdown - selected_split = input$split_dropdown - combined_data = input$combined_data - combined_training_genes = input$combined_training_genes - - selected_gene = combo[combo$drug == selected_drug,'gene'] - - # hide stuff if selected - if(combined_model == "combined") { - #if(combined_model == TRUE) { - - hide("split_dropdown") - hide("resample_dropdown") - show("combined_data") - show("combined_training_genes") - filedata = paste0(combined_training_genes, - 'genes_logo_skf_BT_', - selected_gene, - '_', - combined_data - ) - print(filedata) - - print('doing COMBINED plot') - output$plot <- renderPlot(makeplot(loaded_files[[filedata]], - selection, - "none", # always 'none' for combined plot - gene = combo[drug==selected_drug,"gene"], - combined_training_genes = combined_training_genes, - display_combined = TRUE, - ) - ) - # e.g. - # makeplot(loaded_files$`5genes_logo_skf_BT_pnca_actual`, "MCC", "none" , gene = 'foo', combined_training_genes = '1234', display_combined = TRUE) - } else { - show("split_dropdown") - show("resample_dropdown") - hide("combined_data") - hide("combined_training_genes") - filedata = paste0(combo[drug==selected_drug,"gene"], - '_baselineC_', - selected_split - ) - print(filedata) - print("doing GENE plot") - output$plot <- renderPlot(makeplot(loaded_files[[filedata]], - selection, - resampler, - gene = combo[drug==selected_drug,"gene"], - display_combined = FALSE, - ) - ) - - - } - # 6genes_logo_skf_BT_gid_complete - - # filedata example for combined: 6genes_logo_skf_BT_embb_actual - # 6genes_logo_skf_BT_embb_combined + server <- function(input, output, session) { + + #output$LogoPlotSnps = renderPlot(LogoPlotSnps(mutable_df3)) + output$lin_sc = renderPlot( + lin_sc( + input$switch_target, + all_lineages = input$all_lineages, + my_xats = 12, # x axis text size + my_yats = 12, # y axis text size + my_xals = 12, # x axis label size + my_yals = 12, # y axis label size + my_lls = 12, # legend label size + d_lab_size = 4 + ) + ) + #### lineage_distP #### + output$lineage_distP = renderPlot( + lineage_distP( + get(paste0(input$switch_target, '_merged_df2')), + all_lineages = input$all_lineages, + x_lab = "Average Stability", + x_axis = "avg_stability_scaled", + fill_categ_cols = c("red", "blue") + ) + ) + + + #### observeEvent() Fun(tm) #### + observeEvent(input$clear_ngl, { + NGLVieweR_proxy("structure") %>% + removeSelection("Pos") }) + # Button to test adding a position + observeEvent(input$test_ngl, { + NGLVieweR_proxy("structure") %>% + addSelection('ball+stick' + , param = list( + name = "Pos" + , sele = "35" + , color = "green") + ) + }) + + observeEvent( + { + input$display_position_range + input$stability_snp_param + input$logoplot_colour_scheme + input$omit_snp_count + input$switch_target + input$snp_ligand_dist + input$snp_nca_dist + input$snp_interface_dist + }, + { + print("entering main observeEvent()") + # C O M P A T I B I L I T Y + #gene=input$switch_target + #drug=target_map[[gene]] + target_gene = input$switch_target + merged_df3 = cbind(get(paste0(input$switch_target, '_merged_df3'))) + + position_max=max(merged_df3[['position']]) + position_min=min(merged_df3[['position']]) + min_ligand_distance=min(merged_df3$ligand_distance) + max_ligand_distance=max(merged_df3$ligand_distance) + # FIXME: these are IMPORTANT + # # add "pos_count" position count column + # merged_df3=merged_df3 %>% dplyr::add_count(position) + # merged_df3$pos_count=merged_df3$n + # merged_df3$n=NULL + # + mutable_df3 = cbind(merged_df3) + # + # # re-sort the dataframe according to position count + sorted_df = cbind(merged_df3) + sorted_df = sorted_df %>% arrange(pos_count) + + # + outdir = paste0(load_dir, "Data/", drug, '/output/') + indir = paste0(load_dir, "Data/", drug , "/input/") + + + #### nasty special-purpose merged_df3 variants #### + # FIXME: SLOW + # corr_plotdf = corr_data_extract( + # merged_df3 + # , gene = gene + # , drug = drug + # , extract_scaled_cols = F + # ) + + #input$stability_snp_param + + updateCheckboxGroupInput( + session, + "corr_selected", + choiceNames = colnames(get(paste0(input$switch_target,"_corr_df_m3_f"))), + choiceValues = colnames(get(paste0(input$switch_target,"_corr_df_m3_f"))), + selected = c("FoldX", "DeepDDG", "mCSM.DUET") + ) + + updateSliderInput( + session, + "display_position_range", + min = position_min, + max = position_max + #, value = c(position_min, position_min+150) + ) + + updateNumericInput(session, "selected_logop_snp_position", min = position_min, max = position_max, value = position_min) + updateNumericInput(session, "selected_logop_ed_position", min = position_min, max = position_max, value = position_min) + updateNumericInput(session, "corr_lig_dist", min = min_ligand_distance, max = max_ligand_distance, value = min_ligand_distance) + + updateNumericInput(session, "snp_ligand_dist", min = min(merged_df3$ligand_distance), max = max(merged_df3$ligand_distance)) + updateNumericInput(session, "snp_interface_dist", min = min(merged_df3$interface_dist), max = max(merged_df3$interface_dist)) + updateNumericInput(session, "snp_nca_dist", min = min(merged_df3$nca_distance), max = max(merged_df3$nca_distance)) + + + # different data ranges required for SNP distances + snp_ligand_dist_df3 = merged_df3[merged_df3$ligand_distance=plot_min & mutable_df3$position <=plot_max),] + + subset_mutable_df3=mutable_df3[(mutable_df3$position>=plot_min & mutable_df3$position <=plot_max),] + subset_sorted_df=sorted_df[(sorted_df$position>=plot_min & sorted_df$position <=plot_max),] + + #### LogoPlotSnps #### + output$LogoPlotSnps = renderPlot( + LogoPlotSnps(subset_mutable_df3, + aa_pos_drug = get(paste0(target_gene,"_aa_pos_drug")), + active_aa_pos = get(paste0(target_gene,"_active_aa_pos")), + aa_pos_lig1 = get(paste0(target_gene,"_aa_pos_lig1")), + aa_pos_lig2 = get(paste0(target_gene,"_aa_pos_lig2")), + aa_pos_lig3 = get(paste0(target_gene,"_aa_pos_lig3")), + my_logo_col = logoplot_colour_scheme, + omit_snp_count = omit_snp_count + + ) + ) + + ### NGLViewer #### + # Structure Viewer WebGL/NGLViewR window + output$structure <- renderNGLVieweR({ + ngl_gene=isolate(input$switch_target) + ngl_drug=target_map[[ngl_gene]] + ngl_pdb_file=paste0(load_dir, "Data/", ngl_drug, '/output/depth/', ngl_gene, '_complex.pdb') + print(ngl_pdb_file) + NGLVieweR(ngl_pdb_file) %>% + addRepresentation("cartoon", + param = list(name = "cartoon", + color="tan" + #, colorScheme = "chainid" + ) + ) %>% + stageParameters(backgroundColor = "lightgrey") %>% + setQuality("high") %>% + setFocus(0) %>% + setSpin(FALSE) + }) + + + #### Shared dataTable() #### + output$table = DT::renderDataTable( + datatable(subset_sorted_df[,table_columns], + filter="top", + selection = "single" + ) + ) + + #### bp_stability_hmap #### + # red/blue tiles wala "Stability SNP by Site" + output$bp_stability_hmap = renderPlot( + bp_stability_hmap( + subset_sorted_df, + reorder_position = input$reorder_custom_h, + p_title = NULL, + yvar_colname = stability_colname, + stability_colname = stability_colname, + stability_outcome_colname = outcome_colname, + my_ylab = NULL, + y_max_override = max(sorted_df$pos_count), + aa_pos_drug = get(paste0("embb","_aa_pos_drug")), + active_aa_pos = get(paste0("embb","_active_aa_pos")), + aa_pos_lig1 = get(paste0("embb","_aa_pos_lig1")), + aa_pos_lig2 = get(paste0("embb","_aa_pos_lig2")), + aa_pos_lig3 = get(paste0("embb","_aa_pos_lig3")) + ) + ) + #### LogoPlotCustomH #### + output$LogoPlotCustomH = renderPlot( + LogoPlotCustomH( + subset_sorted_df, + my_logo_col = logoplot_colour_scheme, + aa_pos_drug = get(paste0(target_gene,"_aa_pos_drug")), + active_aa_pos = get(paste0(target_gene,"_active_aa_pos")), + aa_pos_lig1 = get(paste0(target_gene,"_aa_pos_lig1")), + aa_pos_lig2 = get(paste0(target_gene,"_aa_pos_lig2")), + aa_pos_lig3 = get(paste0(target_gene,"_aa_pos_lig3")) + ) + ) + + #### wideP_consurf3 #### + output$wideP_consurf3 = renderPlot( + wideP_consurf3( + subset_sorted_df, + point_colours = consurf_colours, + aa_pos_drug = get(paste0(target_gene,"_aa_pos_drug")), + active_aa_pos = get(paste0(target_gene,"_active_aa_pos")), + aa_pos_lig1 = get(paste0(target_gene,"_aa_pos_lig1")), + aa_pos_lig2 = get(paste0(target_gene,"_aa_pos_lig2")), + aa_pos_lig3 = get(paste0(target_gene,"_aa_pos_lig3")) + ) + ) + + #### site_snp_count_bp #### + #mutable_df3[(mutable_df3$position>=plot_min & mutable_df3$position <=plot_max),] + # ligand_distance + # interface_dist + # nca_distance + # change to: multiple plots, all use site_snp_count_bp + # 4 x plots side by side, one normal (no dist. filter), 2/3 filtered by distance columns above + # use "subtitle text" from pos_count_bp_i.R + + output$site_snp_count_bp = renderPlot( + site_snp_count_bp( + mutable_df3, + title_colour = 'black', + subtitle_colour = "black", + leg_text_size = 12, + axis_label_size = 12, + geom_ls = 4 + ) + ) + output$site_snp_count_bp_ligand = renderPlot( + site_snp_count_bp( + snp_ligand_dist_df3, + title_colour = 'black', + subtitle_colour = "black", + leg_text_size = 12, + axis_label_size = 12, + geom_ls = 4 + ) + ) + output$site_snp_count_interface = renderPlot( + site_snp_count_bp( + snp_interface_dist_df3, + title_colour = 'black', + subtitle_colour = "black", + leg_text_size = 12, + axis_label_size = 12, + geom_ls = 4 + ) + ) + output$site_snp_count_nca = renderPlot( + site_snp_count_bp( + snp_nca_dist_df3, + title_colour = 'black', + subtitle_colour = "black", + leg_text_size = 12, + axis_label_size = 12, + geom_ls = 4 + ) + ) + + #### DM OM Plots #### + #dm_om_param + # order needs to be: + # embb_lf_duet, embb_lf_foldx, embb_lf_deepddg, embb_lf_dynamut2, embb_lf_dist_gen, + # embb_lf_consurf, embb_lf_provean, embb_lf_snap2, embb_lf_mcsm_lig, embb_lf_mmcsm_lig, + # embb_lf_mcsm_ppi2, SOMETHING NA + + # embb_lf_mmcsm_lig SOMETHING NA, + #dm_om_selection=input$dm_om_param + #dm_om_df = dm_om_map[[dm_om_selection]] + #output$lf_bp2 = renderPlot(lf_bp2(get(paste0(input$switch_target, '_', dm_om_df)))) + + output$lf_bp2 = renderPlot( + cowplot::plot_grid( + plotlist = lapply( + ls(name=.GlobalEnv, + pattern=paste0( + target_gene, + '_lf_' + ) + ), + function(x){ + lf_bp2(get(x)) + } + )#, nrow=3 + ), height=800 + ) + } + ) + + + # FIXME: Doesn't add selected table rows correctly + observeEvent( + { + input$table_rows_selected + }, + { + # having to duplicate this is a bit annoying :-( + ngl_merged_df3=cbind(get(paste0(input$switch_target, '_merged_df3'))) + ngl_sorted_df = cbind(ngl_merged_df3) + ngl_sorted_df = ngl_sorted_df %>% arrange(pos_count) + + position_max=max(ngl_merged_df3[['position']]) + position_min=min(ngl_merged_df3[['position']]) + display_position_range = input$display_position_range + plot_min=display_position_range[1] + plot_max=display_position_range[2] + #ngl_subset_df=ngl_merged_df3[(ngl_merged_df3$position>=plot_min & ngl_merged_df3$position <=plot_max),] + ngl_subset_df=ngl_sorted_df[(ngl_sorted_df$position>=plot_min & ngl_sorted_df$position <=plot_max),] + + + #table_rows_selected = isolate(input$table_rows_selected) + table_rows_selected = input$table_rows_selected + class(table_rows_selected) + #cat(paste0("Target: ", as.character(input$switch_target), "\nTable Rows for NGLViewR: ", as.character(table_rows_selected))) + + struct_pos=(as.character(ngl_subset_df[table_rows_selected,"position"])) + cat(paste0('Table Index: ', table_rows_selected, "position: ", struct_pos)) + + NGLVieweR_proxy("structure") %>% + #addSelection('ball+stick' + addSelection('hyperball' + , param = list( + name = "Pos" + , sele = struct_pos + #, color = "#00ff00" + , colorValue="00ff00" + , colorScheme="element" + ) + ) + #cat(paste0('Done NGLViewR addSelection for: ', positions_to_add)) + } + ) + #### Correlation observeEvent #### + # Yet another special-case observeEvent to handle the correlation pair plot + + observeEvent( + { + input$corr_selected + input$corr_method + input$corr_lig_dist + }, + { + dist_cutoff_user = input$corr_lig_dist + target_gene=input$switch_target + plot_title=paste0(target_map[[target_gene]],"/",target_gene) + + corr_plot_df = get( + paste0( + input$switch_target,"_corr_df_m3_f" + ) + )[,c(input$corr_selected, "dst_mode")] + + #if ( dist_cutoff_user >= 2) { + #corr_plotdf_subset = corr_plot_df[corr_plot_df[['Lig.Dist']] < dist_cutoff_user,] + #} + # else { + # corr_plotdf_subset = corr_plot_df + # } + + #### Correlation using ggpairs() #### + output$my_corr_pairs = renderPlot( + dashboard_ggpairs( + corr_plot_df, + plot_title = plot_title, + method = input$corr_method, + tt_args_size = 7, + gp_args_size = 7 + ), height = 900 + ) + } + ) } + + app <- shinyApp(ui, server) runApp(app) -} \ No newline at end of file +} diff --git a/drug-target/server.R b/drug-target/server.R index a38f6c3..37cff79 100644 --- a/drug-target/server.R +++ b/drug-target/server.R @@ -4,78 +4,395 @@ library(DT) library(NGLVieweR) library(hash) -server <- function(input, output) { - observeEvent({ - input$combined_model - input$combined_data - input$combined_training_genes - input$score_dropdown - input$resample_dropdown - input$drug_dropdown - input$split_dropdown - - },{ - combined_model = input$combined_model - selection = input$score_dropdown - resampler = input$resample_dropdown - selected_drug = input$drug_dropdown - selected_split = input$split_dropdown - combined_data = input$combined_data - combined_training_genes = input$combined_training_genes - - selected_gene = combo[combo$drug == selected_drug,'gene'] - - # hide stuff if selected - if(combined_model == "combined") { - #if(combined_model == TRUE) { - - hide("split_dropdown") - hide("resample_dropdown") - show("combined_data") - show("combined_training_genes") - filedata = paste0(combined_training_genes, - 'genes_logo_skf_BT_', - selected_gene, - '_', - combined_data - ) - print(filedata) - - print('doing COMBINED plot') - output$plot <- renderPlot(makeplot(loaded_files[[filedata]], - selection, - "none", # always 'none' for combined plot - gene = combo[drug==selected_drug,"gene"], - combined_training_genes = combined_training_genes, - display_combined = TRUE, - ) - ) - # e.g. - # makeplot(loaded_files$`5genes_logo_skf_BT_pnca_actual`, "MCC", "none" , gene = 'foo', combined_training_genes = '1234', display_combined = TRUE) - } else { - show("split_dropdown") - show("resample_dropdown") - hide("combined_data") - hide("combined_training_genes") - filedata = paste0(combo[drug==selected_drug,"gene"], - '_baselineC_', - selected_split - ) - print(filedata) - print("doing GENE plot") - output$plot <- renderPlot(makeplot(loaded_files[[filedata]], - selection, - resampler, - gene = combo[drug==selected_drug,"gene"], - display_combined = FALSE, - ) - ) - - - } - # 6genes_logo_skf_BT_gid_complete - - # filedata example for combined: 6genes_logo_skf_BT_embb_actual - # 6genes_logo_skf_BT_embb_combined +function(input, output, session) { + + #output$LogoPlotSnps = renderPlot(LogoPlotSnps(mutable_df3)) + output$lin_sc = renderPlot( + lin_sc( + input$switch_target, + all_lineages = input$all_lineages, + my_xats = 12, # x axis text size + my_yats = 12, # y axis text size + my_xals = 12, # x axis label size + my_yals = 12, # y axis label size + my_lls = 12, # legend label size + d_lab_size = 4 + ) + ) + #### lineage_distP #### + output$lineage_distP = renderPlot( + lineage_distP( + get(paste0(input$switch_target, '_merged_df2')), + all_lineages = input$all_lineages, + x_lab = "Average Stability", + x_axis = "avg_stability_scaled", + fill_categ_cols = c("red", "blue") + ) + ) + + + #### observeEvent() Fun(tm) #### + observeEvent(input$clear_ngl, { + NGLVieweR_proxy("structure") %>% + removeSelection("Pos") }) -} + # Button to test adding a position + observeEvent(input$test_ngl, { + NGLVieweR_proxy("structure") %>% + addSelection('ball+stick' + , param = list( + name = "Pos" + , sele = "35" + , color = "green") + ) + }) + + observeEvent( + { + input$display_position_range + input$stability_snp_param + input$logoplot_colour_scheme + input$omit_snp_count + input$switch_target + input$snp_ligand_dist + input$snp_nca_dist + input$snp_interface_dist + }, + { + print("entering main observeEvent()") + # C O M P A T I B I L I T Y + #gene=input$switch_target + #drug=target_map[[gene]] + target_gene = input$switch_target + merged_df3 = cbind(get(paste0(input$switch_target, '_merged_df3'))) + + position_max=max(merged_df3[['position']]) + position_min=min(merged_df3[['position']]) + min_ligand_distance=min(merged_df3$ligand_distance) + max_ligand_distance=max(merged_df3$ligand_distance) + # FIXME: these are IMPORTANT + # # add "pos_count" position count column + # merged_df3=merged_df3 %>% dplyr::add_count(position) + # merged_df3$pos_count=merged_df3$n + # merged_df3$n=NULL + # + mutable_df3 = cbind(merged_df3) + # + # # re-sort the dataframe according to position count + sorted_df = cbind(merged_df3) + sorted_df = sorted_df %>% arrange(pos_count) + + # + outdir = paste0(load_dir, "Data/", drug, '/output/') + indir = paste0(load_dir, "Data/", drug , "/input/") + + + #### nasty special-purpose merged_df3 variants #### + # FIXME: SLOW + # corr_plotdf = corr_data_extract( + # merged_df3 + # , gene = gene + # , drug = drug + # , extract_scaled_cols = F + # ) + + #input$stability_snp_param + + updateCheckboxGroupInput( + session, + "corr_selected", + choiceNames = colnames(get(paste0(input$switch_target,"_corr_df_m3_f"))), + choiceValues = colnames(get(paste0(input$switch_target,"_corr_df_m3_f"))), + selected = c("FoldX", "DeepDDG", "mCSM.DUET") + ) + + updateSliderInput( + session, + "display_position_range", + min = position_min, + max = position_max + #, value = c(position_min, position_min+150) + ) + + updateNumericInput(session, "selected_logop_snp_position", min = position_min, max = position_max, value = position_min) + updateNumericInput(session, "selected_logop_ed_position", min = position_min, max = position_max, value = position_min) + updateNumericInput(session, "corr_lig_dist", min = min_ligand_distance, max = max_ligand_distance, value = min_ligand_distance) + + updateNumericInput(session, "snp_ligand_dist", min = min(merged_df3$ligand_distance), max = max(merged_df3$ligand_distance)) + updateNumericInput(session, "snp_interface_dist", min = min(merged_df3$interface_dist), max = max(merged_df3$interface_dist)) + updateNumericInput(session, "snp_nca_dist", min = min(merged_df3$nca_distance), max = max(merged_df3$nca_distance)) + + + # different data ranges required for SNP distances + snp_ligand_dist_df3 = merged_df3[merged_df3$ligand_distance=plot_min & mutable_df3$position <=plot_max),] + + subset_mutable_df3=mutable_df3[(mutable_df3$position>=plot_min & mutable_df3$position <=plot_max),] + subset_sorted_df=sorted_df[(sorted_df$position>=plot_min & sorted_df$position <=plot_max),] + + #### LogoPlotSnps #### + output$LogoPlotSnps = renderPlot( + LogoPlotSnps(subset_mutable_df3, + aa_pos_drug = get(paste0(target_gene,"_aa_pos_drug")), + active_aa_pos = get(paste0(target_gene,"_active_aa_pos")), + aa_pos_lig1 = get(paste0(target_gene,"_aa_pos_lig1")), + aa_pos_lig2 = get(paste0(target_gene,"_aa_pos_lig2")), + aa_pos_lig3 = get(paste0(target_gene,"_aa_pos_lig3")), + my_logo_col = logoplot_colour_scheme, + omit_snp_count = omit_snp_count + + ) + ) + + ### NGLViewer #### + # Structure Viewer WebGL/NGLViewR window + output$structure <- renderNGLVieweR({ + ngl_gene=isolate(input$switch_target) + ngl_drug=target_map[[ngl_gene]] + ngl_pdb_file=paste0(load_dir, "Data/", ngl_drug, '/output/depth/', ngl_gene, '_complex.pdb') + print(ngl_pdb_file) + NGLVieweR(ngl_pdb_file) %>% + addRepresentation("cartoon", + param = list(name = "cartoon", + color="tan" + #, colorScheme = "chainid" + ) + ) %>% + stageParameters(backgroundColor = "lightgrey") %>% + setQuality("high") %>% + setFocus(0) %>% + setSpin(FALSE) + }) + + + #### Shared dataTable() #### + output$table = DT::renderDataTable( + datatable(subset_sorted_df[,table_columns], + filter="top", + selection = "single" + ) + ) + + #### bp_stability_hmap #### + # red/blue tiles wala "Stability SNP by Site" + output$bp_stability_hmap = renderPlot( + bp_stability_hmap( + subset_sorted_df, + reorder_position = input$reorder_custom_h, + p_title = NULL, + yvar_colname = stability_colname, + stability_colname = stability_colname, + stability_outcome_colname = outcome_colname, + my_ylab = NULL, + y_max_override = max(sorted_df$pos_count), + aa_pos_drug = get(paste0("embb","_aa_pos_drug")), + active_aa_pos = get(paste0("embb","_active_aa_pos")), + aa_pos_lig1 = get(paste0("embb","_aa_pos_lig1")), + aa_pos_lig2 = get(paste0("embb","_aa_pos_lig2")), + aa_pos_lig3 = get(paste0("embb","_aa_pos_lig3")) + ) + ) + #### LogoPlotCustomH #### + output$LogoPlotCustomH = renderPlot( + LogoPlotCustomH( + subset_sorted_df, + my_logo_col = logoplot_colour_scheme, + aa_pos_drug = get(paste0(target_gene,"_aa_pos_drug")), + active_aa_pos = get(paste0(target_gene,"_active_aa_pos")), + aa_pos_lig1 = get(paste0(target_gene,"_aa_pos_lig1")), + aa_pos_lig2 = get(paste0(target_gene,"_aa_pos_lig2")), + aa_pos_lig3 = get(paste0(target_gene,"_aa_pos_lig3")) + ) + ) + + #### wideP_consurf3 #### + output$wideP_consurf3 = renderPlot( + wideP_consurf3( + subset_sorted_df, + point_colours = consurf_colours, + aa_pos_drug = get(paste0(target_gene,"_aa_pos_drug")), + active_aa_pos = get(paste0(target_gene,"_active_aa_pos")), + aa_pos_lig1 = get(paste0(target_gene,"_aa_pos_lig1")), + aa_pos_lig2 = get(paste0(target_gene,"_aa_pos_lig2")), + aa_pos_lig3 = get(paste0(target_gene,"_aa_pos_lig3")) + ) + ) + + #### site_snp_count_bp #### + #mutable_df3[(mutable_df3$position>=plot_min & mutable_df3$position <=plot_max),] + # ligand_distance + # interface_dist + # nca_distance + # change to: multiple plots, all use site_snp_count_bp + # 4 x plots side by side, one normal (no dist. filter), 2/3 filtered by distance columns above + # use "subtitle text" from pos_count_bp_i.R + + output$site_snp_count_bp = renderPlot( + site_snp_count_bp( + mutable_df3, + title_colour = 'black', + subtitle_colour = "black", + leg_text_size = 12, + axis_label_size = 12, + geom_ls = 4 + ) + ) + output$site_snp_count_bp_ligand = renderPlot( + site_snp_count_bp( + snp_ligand_dist_df3, + title_colour = 'black', + subtitle_colour = "black", + leg_text_size = 12, + axis_label_size = 12, + geom_ls = 4 + ) + ) + output$site_snp_count_interface = renderPlot( + site_snp_count_bp( + snp_interface_dist_df3, + title_colour = 'black', + subtitle_colour = "black", + leg_text_size = 12, + axis_label_size = 12, + geom_ls = 4 + ) + ) + output$site_snp_count_nca = renderPlot( + site_snp_count_bp( + snp_nca_dist_df3, + title_colour = 'black', + subtitle_colour = "black", + leg_text_size = 12, + axis_label_size = 12, + geom_ls = 4 + ) + ) + + #### DM OM Plots #### + #dm_om_param + # order needs to be: + # embb_lf_duet, embb_lf_foldx, embb_lf_deepddg, embb_lf_dynamut2, embb_lf_dist_gen, + # embb_lf_consurf, embb_lf_provean, embb_lf_snap2, embb_lf_mcsm_lig, embb_lf_mmcsm_lig, + # embb_lf_mcsm_ppi2, SOMETHING NA + + # embb_lf_mmcsm_lig SOMETHING NA, + #dm_om_selection=input$dm_om_param + #dm_om_df = dm_om_map[[dm_om_selection]] + #output$lf_bp2 = renderPlot(lf_bp2(get(paste0(input$switch_target, '_', dm_om_df)))) + + output$lf_bp2 = renderPlot( + cowplot::plot_grid( + plotlist = lapply( + ls(name=.GlobalEnv, + pattern=paste0( + target_gene, + '_lf_' + ) + ), + function(x){ + lf_bp2(get(x)) + } + )#, nrow=3 + ), height=800 + ) + } + ) + + + # FIXME: Doesn't add selected table rows correctly + observeEvent( + { + input$table_rows_selected + }, + { + # having to duplicate this is a bit annoying :-( + ngl_merged_df3=cbind(get(paste0(input$switch_target, '_merged_df3'))) + ngl_sorted_df = cbind(ngl_merged_df3) + ngl_sorted_df = ngl_sorted_df %>% arrange(pos_count) + + position_max=max(ngl_merged_df3[['position']]) + position_min=min(ngl_merged_df3[['position']]) + display_position_range = input$display_position_range + plot_min=display_position_range[1] + plot_max=display_position_range[2] + #ngl_subset_df=ngl_merged_df3[(ngl_merged_df3$position>=plot_min & ngl_merged_df3$position <=plot_max),] + ngl_subset_df=ngl_sorted_df[(ngl_sorted_df$position>=plot_min & ngl_sorted_df$position <=plot_max),] + + + #table_rows_selected = isolate(input$table_rows_selected) + table_rows_selected = input$table_rows_selected + class(table_rows_selected) + #cat(paste0("Target: ", as.character(input$switch_target), "\nTable Rows for NGLViewR: ", as.character(table_rows_selected))) + + struct_pos=(as.character(ngl_subset_df[table_rows_selected,"position"])) + cat(paste0('Table Index: ', table_rows_selected, "position: ", struct_pos)) + + NGLVieweR_proxy("structure") %>% + #addSelection('ball+stick' + addSelection('hyperball' + , param = list( + name = "Pos" + , sele = struct_pos + #, color = "#00ff00" + , colorValue="00ff00" + , colorScheme="element" + ) + ) + #cat(paste0('Done NGLViewR addSelection for: ', positions_to_add)) + } + ) + #### Correlation observeEvent #### + # Yet another special-case observeEvent to handle the correlation pair plot + + observeEvent( + { + input$corr_selected + input$corr_method + input$corr_lig_dist + }, + { + dist_cutoff_user = input$corr_lig_dist + target_gene=input$switch_target + plot_title=paste0(target_map[[target_gene]],"/",target_gene) + + corr_plot_df = get( + paste0( + input$switch_target,"_corr_df_m3_f" + ) + )[,c(input$corr_selected, "dst_mode")] + + #if ( dist_cutoff_user >= 2) { + #corr_plotdf_subset = corr_plot_df[corr_plot_df[['Lig.Dist']] < dist_cutoff_user,] + #} + # else { + # corr_plotdf_subset = corr_plot_df + # } + + #### Correlation using ggpairs() #### + output$my_corr_pairs = renderPlot( + dashboard_ggpairs( + corr_plot_df, + plot_title = plot_title, + method = input$corr_method, + tt_args_size = 7, + gp_args_size = 7 + ), height = 900 + ) + } + ) +} \ No newline at end of file diff --git a/drug-target/ui.R b/drug-target/ui.R index 825a416..3c8ed17 100644 --- a/drug-target/ui.R +++ b/drug-target/ui.R @@ -7,259 +7,343 @@ library(hash) #### Shiny UI ##### -#dashboardHeader(title = paste0(gene, "/", drug)), dashboardPage(skin="purple", -dashboardHeader(title = "Drug/Target Explorer"), - -dashboardSidebar( - sidebarMenu( id = "sidebar", - selectInput( - "switch_target", - label="Switch to New Target", - choices = c( - "alr", - "embb", - "gid", - "katg", - "pnca", - "rpob" - ), - selected="embb"), - menuItem("LogoP SNP", tabName="LogoP SNP"), - menuItem("Lineage Sample Count", tabName="Lineage Sample Count"), - menuItem("Site SNP count", tabName="Site SNP count"), - menuItem("Stability SNP by site", tabName="Stability SNP by site"), - menuItem("DM OM Plots", tabName="DM OM Plots"), - menuItem("Correlation", tabName="Correlation"), - menuItem("Lineage Distribution", tabName="Lineage Distribution"), - menuItem("Consurf", tabName="Consurf"), - menuItem("LogoP OR", tabName="LogoP OR"), - menuItem("LogoP ED", tabName="LogoP ED"), - menuItem('Stability count', tabName='Stability count'), - - # These conditionalPanel()s make extra settings appear in the sidebar when needed - conditionalPanel( - condition="input.sidebar == 'LogoP SNP'", - textInput( - "omit_snp_count", - "Omit SNPs", - value = c(0), - placeholder = "1,3,6" - ) - ), - # NOTE: - # I *think* we can cheat here slightly and use the min/max from - # merged_df3[['position']] for everything because the various - # dataframes for a given gene/drug combination have the - # same range of positions. May need fixing, especially - # if we get/shrink the imported data files to something - # more reasonable. - conditionalPanel( - condition=" + dashboardHeader(title = "Drug/Target Explorer"), + + dashboardSidebar( + sidebarMenu( id = "sidebar", + selectInput( + "switch_target", + label="Switch to New Target", + choices = c( + "alr", + "embb", + "gid", + "katg", + "pnca", + "rpob" + ), + selected="embb"), + menuItem("LogoP SNP", tabName="LogoP SNP"), + #menuItem("Lineage Sample Count", tabName="Lineage Sample Count"), + menuItem("Site SNP count", tabName="Site SNP count"), + menuItem("Stability SNP by site", tabName="Stability SNP by site"), + menuItem("DM OM Plots", tabName="DM OM Plots"), + menuItem("Correlation", tabName="Correlation"), + #menuItem("Lineage Distribution", tabName="Lineage Distribution"), + menuItem("Consurf", tabName="Consurf"), + menuItem("LogoP OR", tabName="LogoP OR"), + menuItem("Lineage", tabName="Lineage"), + #menuItem('Stability count', tabName='Stability count'), + + # These conditionalPanel()s make extra settings appear in the sidebar when needed + conditionalPanel( + condition="input.sidebar == 'LogoP SNP'", + textInput( + "omit_snp_count", + "Omit SNPs", + value = c(0), + placeholder = "1,3,6" + ) + ), + # NOTE: + # I *think* we can cheat here slightly and use the min/max from + # merged_df3[['position']] for everything because the various + # dataframes for a given gene/drug combination have the + # same range of positions. May need fixing, especially + # if we get/shrink the imported data files to something + # more reasonable. + conditionalPanel( + condition=" input.sidebar == 'LogoP SNP'|| input.sidebar == 'Stability SNP by site' || input.sidebar == 'Consurf' || - input.sidebar == 'LogoP OR' || - input.sidebar == 'Site SNP count'", - sliderInput( - "display_position_range" - , "Display Positions" - , min=1, max=150, value=c(1,150) # 150 is just a little less than the smallest pos_count - ) - ), - conditionalPanel( - condition="input.sidebar == 'LogoP ED'", - sliderInput( - "display_position_full_range" - , "Display Positions" - , min=1, max=150, value=c(1,150) - ) - ), - - - conditionalPanel( - condition=" + input.sidebar == 'LogoP OR'", + sliderInput( + "display_position_range" + , "Display Positions" + , min=1, max=150, value=c(1,150) # 150 is just a little less than the smallest pos_count + ) + ), + + conditionalPanel( + condition=" input.sidebar == 'LogoP SNP' || input.sidebar == 'LogoP OR' || input.sidebar == 'LogoP ED'", - selectInput( - "logoplot_colour_scheme", - label="Logo Plot Colour Scheme", - choices = logoPlotSchemes, - selected="chemistry" - ) - ), - #conditionalPanel( - # condition="input.sidebar == 'LogoP SNP' || input.sidebar == 'LogoP ED'|| input.sidebar == 'Consurf'", - # numericInput( - # "table_position" - # , "Table Position", value=1 - # ) - #), - conditionalPanel( - condition="input.sidebar == 'Correlation'", - selectInput( - "corr_method", - label="Correlation Method", - choices = list("spearman", - "pearson", - "kendall"), - selected="spearman" - ) - ), - conditionalPanel( - condition="input.sidebar == 'Correlation'", - numericInput( - "corr_lig_dist" - , "Ligand Distance Cutoff (Å)", value=1 - ) - ), - - conditionalPanel( - condition="input.sidebar == 'Correlation'", - checkboxGroupInput( - "corr_selected", - "Parameters", - choiceNames = c( - "DeepDDG", - "Dynamut2", - "FoldX", - "ConSurf"#, - #"dst_mode" - ), - choiceValues = c( - "DeepDDG", - "Dynamut2", - "FoldX", - "ConSurf"#, - #"dst_mode" - ), - selected = c( - "DeepDDG", - "Dynamut2", - "FoldX", - "ConSurf"#, - #"dst_mode" - ) - ) - ), - - conditionalPanel( - condition="input.sidebar == 'DM OM Plots'", - selectInput( - "dm_om_param", - label="Stability Parameter", - choices = keys(dm_om_map), - selected="SNAP2") - ), - # colour_categ - conditionalPanel( - condition="input.sidebar == 'Stability SNP by site'", - selectInput( - "stability_snp_param", - label="Stability Parameter", - choices = stability_boxes_df$stability_type, - selected="Average") - ), - conditionalPanel( - condition="input.sidebar == 'Stability SNP by site'", - checkboxInput("reorder_custom_h", - label="Reorder by SNP count", - FALSE) - ), - conditionalPanel( - condition="input.sidebar.match(/^Lineage.*/)", - checkboxInput("all_lineages", - label="All Lineages", - FALSE) - ), - # an example of how you can match multiple things in frontend JS - conditionalPanel( - condition="input.sidebar == 'LogoP SNP' || + selectInput( + "logoplot_colour_scheme", + label="Logo Plot Colour Scheme", + choices = logoPlotSchemes, + selected="chemistry" + ) + ), + conditionalPanel( + condition="input.sidebar == 'Correlation'", + selectInput( + "corr_method", + label="Correlation Method", + choices = list("spearman", + "pearson", + "kendall"), + selected="spearman" + ) + ), + conditionalPanel( + condition="input.sidebar == 'Correlation'", + numericInput( + "corr_lig_dist" + , "Ligand Distance Cutoff (Å)", value=1 + ) + ), + conditionalPanel( + condition="input.sidebar == 'Site SNP count'", + numericInput( + "snp_ligand_dist" + , "Ligand Distance Cutoff (Å)", value=10 + ) + ), + conditionalPanel( + condition="input.sidebar == 'Site SNP count'", + numericInput( + "snp_interface_dist" + , "Interface Distance Cutoff (Å)", value=10 + ) + ), + conditionalPanel( + condition="input.sidebar == 'Site SNP count'", + numericInput( + "snp_nca_dist" + , "NCA Distance Cutoff (Å)", value=10 + ) + ), + + conditionalPanel( + condition="input.sidebar == 'Correlation'", + checkboxGroupInput( + "corr_selected", + "Parameters", + choiceNames = c( + "DeepDDG", + "Dynamut2", + "FoldX", + "ConSurf"#, + ), + choiceValues = c( + "DeepDDG", + "Dynamut2", + "FoldX", + "ConSurf"#, + ), + selected = c( + "DeepDDG", + "Dynamut2", + "FoldX", + "ConSurf"#, + ) + ) + ), + + # conditionalPanel( + # condition="input.sidebar == 'DM OM Plots'", + # selectInput( + # "dm_om_param", + # label="Stability Parameter", + # choices = keys(dm_om_map), + # selected="SNAP2") + # ), + # colour_categ + conditionalPanel( + condition="input.sidebar == 'Stability SNP by site'", + selectInput( + "stability_snp_param", + label="Stability Parameter", + choices = stability_boxes_df$stability_type, + selected="Average") + ), + conditionalPanel( + condition="input.sidebar == 'Stability SNP by site'", + checkboxInput("reorder_custom_h", + label="Reorder by SNP count", + FALSE) + ), + conditionalPanel( + condition="input.sidebar.match(/^Lineage.*/)", + checkboxInput("all_lineages", + label="All Lineages", + FALSE) + ), + # an example of how you can match multiple things in frontend JS + conditionalPanel( + condition="input.sidebar == 'LogoP SNP' || input.sidebar =='Stability SNP by site' || input.sidebar =='Consurf' || - input.sidebar =='LogoP OR' || - input.sidebar =='LogoP ED'", - actionButton("clear_ngl", - "Clear Structure") - ), - conditionalPanel( - condition="input.sidebar == 'LogoP SNP' || + input.sidebar =='LogoP OR'", + actionButton("clear_ngl", + "Clear Structure") + ), + conditionalPanel( + condition="input.sidebar == 'LogoP SNP' || input.sidebar =='Stability SNP by site' || input.sidebar =='Consurf' || - input.sidebar =='LogoP OR' || - input.sidebar =='LogoP ED'", - actionButton("test_ngl", - "Test NGLViewR") - )#, - - # downloadButton("save", - # "Download Plot" - # ) - # actionButton( - # "reload_target", - # label="Reload Target\nData (slow!)" - # ) - - ) -), -body <- dashboardBody( - - tabItems( - tabItem(tabName = "dashboard", - h2("Dashboard tab content") - ), - - tabItem(tabName = "widgets", - h2("Widgets tab content") - ) - ), - # creates a 'Conditional Panel' containing a plot object from each of our - # ggplot plot functions (and its associated data frame) - fluidRow(column(width=12, - lapply(plot_functions_df$tab_name, - function(x){ - - plot_function=plot_functions_df[ - plot_functions_df$tab_name==x, - "plot_function"] - - plot_df=plot_functions_df[ - plot_functions_df$tab_name==x, - "plot_df"] - cat(paste0('\nCreating output: ', x)) - generate_conditionalPanel(x, plot_function, plot_df) - - } - ) - ) - ), - #### fluidRow()s for "Stability Count" in the sidebar #### - fluidRow( - conditionalPanel( - condition=" + input.sidebar =='LogoP OR'", + actionButton("test_ngl", + "Test NGLViewR") + )#, + + # downloadButton("save", + # "Download Plot" + # ) + # actionButton( + # "reload_target", + # label="Reload Target\nData (slow!)" + # ) + + ) + ), + #### body #### + body <- dashboardBody( + + tabItems( + tabItem(tabName = "dashboard", + h2("Dashboard tab content") + ), + + tabItem(tabName = "widgets", + h2("Widgets tab content") + ) + ), + # creates a 'Conditional Panel' containing a plot object from each of our + # ggplot plot functions (and its associated data frame) + fluidRow(column(width=12, + lapply(plot_functions_df$tab_name, + function(x){ + + plot_function=plot_functions_df[ + plot_functions_df$tab_name==x, + "plot_function"] + + plot_df=plot_functions_df[ + plot_functions_df$tab_name==x, + "plot_df"] + cat(paste0('\nCreating output: ', x)) + generate_conditionalPanel(x, plot_function, plot_df) + + } + ) + ) + ), + # Explicit fluidRow() for Lineage plots together + fluidRow( + column(conditionalPanel( + condition="input.sidebar.match(/^Lineage.*/)", box( + title="Lineage Distribution" + , status = "info" + , width=NULL + , plotOutput("lineage_distP", height="700px") %>% withSpinner(color="#0dc5c1"), + height=800 + ) + ), width=6 + ), + column(conditionalPanel( + condition="input.sidebar.match(/^Lineage.*/)", box( + title="Lineage SNP Diversity" + , status = "info" + , width=NULL + , plotOutput("lin_sc", height="700px") %>% withSpinner(color="#0dc5c1"), + height=800 + ) + ), width=6 + ) + + ), + # Explicit fluidRow() for Site SNP Count + fluidRow( + column(conditionalPanel( + condition="input.sidebar == 'Site SNP count'", + box( + title="Site SNP count" + , status = "info" + , width=NULL + , plotOutput("site_snp_count_bp") %>% withSpinner(color="#0dc5c1") + ) + ), width=6 + ), + column(conditionalPanel( + condition="input.sidebar == 'Site SNP count'", + box( + title="Ligand Distance" + , status = "info" + , width=NULL + , plotOutput("site_snp_count_bp_ligand") %>% withSpinner(color="#0dc5c1") + ) + ), width=6 + ), + column(conditionalPanel( + condition="input.sidebar == 'Site SNP count'", + box( + title="Interface Distance" + , status = "info" + , width=NULL + , plotOutput("site_snp_count_interface") %>% withSpinner(color="#0dc5c1") + ) + ), width=6 + ), + column(conditionalPanel( + condition="input.sidebar == 'Site SNP count'", + box( + title="NCA Distance" + , status = "info" + , width=NULL + , plotOutput("site_snp_count_nca") %>% withSpinner(color="#0dc5c1") + ) + ), width=6 + ) + ), + + # # Explicit fluidRow() for Stability Count + # fluidRow( + # column( + # conditionalPanel( + # condition="input.sidebar.match(/^Lineage.*/)", + # lapply( + # # FIXME: using a hardcoded target DF for this IS WRONG AND WILL BREAK + # stability_boxes_df[stability_boxes_df$outcome_colname %in% colnames(embb_merged_df3),"outcome_colname"], + # function(x){ + # print(paste0("outcome_colname: ",x)) + # box(plotOutput(x), width=4) + # } + # ), + # width=12 + # ) + # ) + # ), + + #### fluidRow()s for "Stability Count" in the sidebar #### + fluidRow( + conditionalPanel( + condition=" input.sidebar == 'LogoP SNP' || input.sidebar =='Stability SNP by site' || input.sidebar =='Consurf' || - input.sidebar =='LogoP OR' || - input.sidebar =='LogoP ED'", - column(NGLVieweROutput("structure"), - width=3 - ) - ), - conditionalPanel( - condition=" + input.sidebar =='LogoP OR'", + column(NGLVieweROutput("structure"), + width=3 + ) + ), + conditionalPanel( + condition=" input.sidebar == 'LogoP SNP' || input.sidebar == 'Stability SNP by site' || input.sidebar == 'Site SNP count' || input.sidebar == 'Consurf' || - input.sidebar == 'LogoP OR' || - input.sidebar == 'LogoP ED'", - column( - DT::dataTableOutput('table'), - width=9 - ) - ) - ) -) + input.sidebar == 'LogoP OR'", + column( + DT::dataTableOutput('table'), + width=9 + ) + ) + ), + ) ) +