library(shinycssloaders) library(DT) library(NGLVieweR) library(hash) library(feather) library(cowplot) # FIXME This is slow and should happen *once only* #source(load_dir, "git/LSHTM_analysis/scripts/Header_TT.R") # FIXME: these are needed but slow to load every time # source(load_dir, "git/LSHTM_analysis/config/alr.R") # source(load_dir, "git/LSHTM_analysis/config/gid.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({ load_dir="~/git/" #load_dir="/srv/shiny-server/git/" set_null_device("cairo") # makes Unicode errors go away source(paste0(load_dir, "LSHTM_analysis/scripts/Header_TT.R")) load_target_globals=function(target){ cat(paste0("Reloading Target: ", target)) 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_feather(paste0(load_dir, "Misc/shiny_dashboard/data/",target,"-merged_df3.feather")), envir = .GlobalEnv)) invisible(assign(paste0(target, "_merged_df2"), read_feather(paste0(load_dir, "Misc/shiny_dashboard/data/",target,"-merged_df2.feather")), envir = .GlobalEnv)) invisible(assign(paste0(target, "_corr_df_m3_f"), read_feather(paste0(load_dir, "Misc/shiny_dashboard/data/",target,"-corr_df_m3_f.feather")), envir = .GlobalEnv)) invisible(assign(paste0(target, "_lin_lf"), read_feather(paste0(load_dir, "Misc/shiny_dashboard/data/",target,"-lin_lf.feather")), envir = .GlobalEnv)) invisible(assign(paste0(target, "_lin_wf"), read_feather(paste0(load_dir, "Misc/shiny_dashboard/data/",target,"-lin_wf.feather")), envir = .GlobalEnv)) lapply( c( "duet", "mcsm_lig", "foldx", "deepddg", "dynamut2", "consurf", "snap2", "provean", "dist_gen", "mcsm_ppi2", "mmcsm_lig", "mcsm_na" #, #"mcsm_na" ), function(x){ lf_filename=paste0(load_dir, "Misc/shiny_dashboard/data/", tolower(gene), "-lf_", x ,".feather") lf_var=paste0(target, "_lf_",x) if (file.exists(lf_filename)){ invisible(assign(lf_var,read_feather(lf_filename), envir = .GlobalEnv)) # FILTH } } ) } #### Local Functions #### # Generate a conditionalPanel() for a given graph function and sidebar name combination generate_conditionalPanel = function(tab_name, plot_function, plot_df){ # e.g.: list("lin_count_bp_diversity", "Lineage diversity count") cond=paste0("input.sidebar == '", tab_name, "'") conditionalPanel(condition=cond, box( title=tab_name , status = "info" , width=NULL , plotOutput(plot_function , click = "plot_click") %>% withSpinner(color="#0dc5c1") # , plotOutput(plot_function, click = "plot_click") ) ) } # FIXME: passing in the per-plot params is broken lin_sc=function(x, all_lineages = F, ...){ lf_var = get(paste0(x,"_lin_lf")) wf_var = get(paste0(x,"_lin_wf")) cowplot::plot_grid(lin_count_bp_diversity(wf_var, all_lineages, ...), lin_count_bp(lf_var, all_lineages, ...)) } options(shiny.port = 8000) options(shiny.host = '0.0.0.0') # This means "listen to all addresses on all interfaces" options(shiny.launch.browser = FALSE) options(width=120) options(DT.options = list(scrollX = TRUE)) ################ STATIC GLOBALS ONLY ############## # never quite sure where "outdir" gets set :-| # using dataframes instead of lists lets us avoid use of map() plot_functions_df=data.frame( tab_name=c( "LogoP SNP", #"Lineage Sample Count", #"Site SNP count", "Stability SNP by site", "DM OM Plots", "Correlation", #"Lineage Distribution", "Consurf", "LogoP OR" ), plot_function=c( "LogoPlotSnps", #"lin_sc", #"site_snp_count_bp", "bp_stability_hmap", "lf_bp2", "my_corr_pairs", #"lineage_distP", "wideP_consurf3", "LogoPlotCustomH" ), plot_df=c( "mutable_df3" , #"lin_lf", #"mutable_df3", "merged_df3" , "lf_duet" , "corr_df_m3_f", #"merged_df2", "merged_df3", "merged_df2" ) ) stability_boxes_df=data.frame( outcome_colname=c("duet_outcome", "foldx_outcome", "deepddg_outcome", "ddg_dynamut2_outcome", "mcsm_na_outcome", "mcsm_ppi2_outcome", "snap2_outcome", "consurf_outcome", "avg_stability_outcome"), stability_type=c( "DUET", "FoldX", "DeepDDG", "Dynamut2", "mCSM-NA", "mCSM-ppi2", "SNAP2", "Consurf", "Average" ), stability_colname=c( "duet_scaled", "foldx_scaled", "deepddg_scaled", "ddg_dynamut2_scaled", "mcsm_na_scaled", "mcsm_ppi2_scaled", "snap2_scaled", "consurf_scaled", "avg_stability_scaled" ) ) table_columns = c( "position", "mutationinformation", "sensitivity", "ligand_distance", "avg_lig_affinity", "avg_lig_affinity_outcome", "avg_stability", "avg_stability_outcome", "or_mychisq", "maf", "snap2_outcome", "consurf_outcome", "provean_outcome", "rsa", "kd_values" , "rd_values" ) logoPlotSchemes <- list("chemistry" , "taylor" , "hydrophobicity" , "clustalx") dm_om_methods = c("DUET ΔΔG" , "Consurf" , "Deepddg ΔΔG" , "Dynamut2 ΔΔG" , "FoldX ΔΔG" , "Ligand affinity (log fold change)" , "mCSM-NA affinity ΔΔG" , "SNAP2") dm_om_map = hash(c( "DUET ΔΔG" , "Consurf" , "Deepddg ΔΔG" , "Dynamut2 ΔΔG" , "FoldX ΔΔG" , "Ligand affinity (log fold change)" , "mCSM-NA affinity ΔΔG" , "SNAP2" ), c( "lf_duet" ,"lf_consurf" ,"lf_deepddg" ,"lf_dynamut2" ,"lf_foldx" ,"lf_mcsm_lig" ,"lf_mcsm_na" ,"lf_snap2" ) ) #### target_map: handy gene/drug mapping hash #### target_map = hash( c( "alr", "gid", "embb", "pnca", "rpob", "katg"), c( "cycloserine", "streptomycin", "ethambutol", "pyrazinamide", "rifampicin", "isoniazid") ) # load E V E R Y T H I N G lapply(c( "alr", "embb", "gid", "katg", "pnca", "rpob" ),function(x){ load_target_globals(x) } ) consurf_palette1 = c("0" = "yellow2" , "1" = "cyan1" , "2" = "steelblue2" , "3" = "cadetblue2" , "4" = "paleturquoise2" , "5" = "thistle3" , "6" = "thistle2" , "7" = "plum2" , "8" = "maroon" , "9" = "violetred2") consurf_palette2 = c("0" = "yellow2" , "1" = "forestgreen" , "2" = "seagreen3" , "3" = "palegreen1" , "4" = "darkseagreen2" , "5" = "thistle3" , "6" = "lightpink1" , "7" = "orchid3" , "8" = "orchid4" , "9" = "darkorchid4") # decreasing levels mess legend # consurf_colours_LEVEL = c( # "0" = rgb(1.00,1.00,0.59) # , "9" = rgb(0.63,0.16,0.37) # , "8" = rgb(0.94,0.49,0.67) # , "7" = rgb(0.98,0.78,0.86) # , "6" = rgb(0.98,0.92,0.96) # , "5" = rgb(1.00,1.00,1.00) # , "4" = rgb(0.84,0.94,0.94) # , "3" = rgb(0.65,0.86,0.90) # , "2" = rgb(0.29,0.69,0.75) # , "1" = rgb(0.04,0.49,0.51) # ) consurf_colours = c( "0" = rgb(1.00,1.00,0.59) , "1" = rgb(0.04,0.49,0.51) , "2" = rgb(0.29,0.69,0.75) , "3" = rgb(0.65,0.86,0.90) , "4" = rgb(0.84,0.94,0.94) , "5" = rgb(1.00,1.00,1.00) , "6" = rgb(0.98,0.92,0.96) , "7" = rgb(0.98,0.78,0.86) , "8" = rgb(0.94,0.49,0.67) , "9" = rgb(0.63,0.16,0.37) ) # 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 #### 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'", 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 == '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$stability_type, selected="Average") ), conditionalPanel( condition="input.sidebar == 'Stability SNP by site'", checkboxInput("reorder_custom_h", label="Reorder by SNP count", FALSE) ), conditionalPanel( condition="input.sidebar.match(/^Lineage.*/)", checkboxInput("all_lineages", label="All Lineages", FALSE) ), # an example of how you can match multiple things in frontend JS conditionalPanel( condition="input.sidebar == 'LogoP SNP' || input.sidebar =='Stability SNP by site' || input.sidebar =='Consurf' || input.sidebar =='LogoP OR'", actionButton("clear_ngl", "Clear Structure") ), conditionalPanel( condition="input.sidebar == 'LogoP SNP' || input.sidebar =='Stability SNP by site' || input.sidebar =='Consurf' || input.sidebar =='LogoP OR'", actionButton("test_ngl", "Test NGLViewR") )#, # downloadButton("save", # "Download Plot" # ) # actionButton( # "reload_target", # label="Reload Target\nData (slow!)" # ) ) ), #### body #### body <- dashboardBody( tabItems( tabItem(tabName = "dashboard", h2("Dashboard tab content") ), tabItem(tabName = "widgets", h2("Widgets tab content") ) ), # creates a 'Conditional Panel' containing a plot object from each of our # ggplot plot functions (and its associated data frame) fluidRow(column(width=12, lapply(plot_functions_df$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="RNA 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$outcome_colname %in% colnames(embb_merged_df3),"outcome_colname"], # function(x){ # print(paste0("outcome_colname: ",x)) # box(plotOutput(x), width=4) # } # ), # width=12 # ) # ) # ), #### fluidRow()s for "Stability Count" in the sidebar #### fluidRow( conditionalPanel( condition=" input.sidebar == 'LogoP SNP' || input.sidebar =='Stability SNP by site' || input.sidebar =='Consurf' || input.sidebar =='LogoP OR'", column(NGLVieweROutput("structure"), width=3 ) ), conditionalPanel( condition=" input.sidebar == 'LogoP SNP' || input.sidebar == 'Stability SNP by site' || input.sidebar == 'Site SNP count' || input.sidebar == 'Consurf' || input.sidebar == 'LogoP OR'", column( DT::dataTableOutput('table'), width=9 ) ) ), ) ) # server <- function(input, output, session) { # # #output$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$switch_target) # ngl_gene=input$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 # ) # ) # # # if ("interface_dist" %in% colnames(input$switch_target)) { # output$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 # ) # ) # # } #else { # # output$site_snp_count_interface = renderPlot( # # ggplot() + annotate(x=1,y=1,"text", label="No interface data for this target")+theme_void() # # ) # # } # # output$site_snp_count_nca = renderPlot( #{ # #if ("nca_distance" %in% colnames(input$switch_target)) { # site_snp_count_bp( # snp_nca_dist_df3, # title_colour = 'black', # subtitle_colour = "black", # leg_text_size = 12, # axis_label_size = 12, # geom_ls = 4 # ) # # } else { # # ggplot() + annotate(x=1,y=1,"text", label="No RNA data for this target")+theme_void() # # } # # } # ) # # # # #### DM OM Plots #### # #dm_om_param # # order needs to be: # # embb_lf_duet, embb_lf_foldx, embb_lf_deepddg, embb_lf_dynamut2, embb_lf_dist_gen, # # embb_lf_consurf, embb_lf_provean, embb_lf_snap2, embb_lf_mcsm_lig, embb_lf_mmcsm_lig, # # embb_lf_mcsm_ppi2, SOMETHING NA # # # embb_lf_mmcsm_lig SOMETHING NA, # #dm_om_selection=input$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) }