isolating observeevent()s
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1 changed files with 421 additions and 421 deletions
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@ -651,425 +651,425 @@ if (interactive()){
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),
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)
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)
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server <- function(input, output, session) {
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#output$LogoPlotSnps = renderPlot(LogoPlotSnps(mutable_df3))
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output$lin_sc = renderPlot(
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lin_sc(
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input$switch_target,
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all_lineages = input$all_lineages,
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my_xats = 12, # x axis text size
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my_yats = 12, # y axis text size
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my_xals = 12, # x axis label size
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my_yals = 12, # y axis label size
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my_lls = 12, # legend label size
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d_lab_size = 4
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)
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)
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#### lineage_distP ####
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output$lineage_distP = renderPlot(
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lineage_distP(
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get(paste0(input$switch_target, '_merged_df2')),
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all_lineages = input$all_lineages,
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x_lab = "Average Stability",
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x_axis = "avg_stability_scaled",
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fill_categ_cols = c("red", "blue")
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)
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)
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#### observeEvent() Fun(tm) ####
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observeEvent(input$clear_ngl, {
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NGLVieweR_proxy("structure") %>%
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removeSelection("Pos")
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})
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# Button to test adding a position
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observeEvent(input$test_ngl, {
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NGLVieweR_proxy("structure") %>%
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addSelection('ball+stick'
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, param = list(
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name = "Pos"
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, sele = "35"
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, color = "green")
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)
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})
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observeEvent(
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{
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input$display_position_range
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input$stability_snp_param
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input$logoplot_colour_scheme
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input$omit_snp_count
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input$switch_target
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input$snp_ligand_dist
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input$snp_nca_dist
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input$snp_interface_dist
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},
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{
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print("entering main observeEvent()")
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# C O M P A T I B I L I T Y
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#gene=input$switch_target
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#drug=target_map[[gene]]
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target_gene = input$switch_target
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merged_df3 = cbind(get(paste0(input$switch_target, '_merged_df3')))
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position_max=max(merged_df3[['position']])
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position_min=min(merged_df3[['position']])
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min_ligand_distance=min(merged_df3$ligand_distance)
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max_ligand_distance=max(merged_df3$ligand_distance)
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# FIXME: these are IMPORTANT
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# # add "pos_count" position count column
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# merged_df3=merged_df3 %>% dplyr::add_count(position)
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# merged_df3$pos_count=merged_df3$n
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# merged_df3$n=NULL
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# server <- function(input, output, session) {
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#
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mutable_df3 = cbind(merged_df3)
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#
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# # re-sort the dataframe according to position count
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sorted_df = cbind(merged_df3)
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sorted_df = sorted_df %>% arrange(pos_count)
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#
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outdir = paste0(load_dir, "Data/", drug, '/output/')
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indir = paste0(load_dir, "Data/", drug , "/input/")
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#### nasty special-purpose merged_df3 variants ####
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# FIXME: SLOW
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# corr_plotdf = corr_data_extract(
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# merged_df3
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# , gene = gene
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# , drug = drug
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# , extract_scaled_cols = F
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# #output$LogoPlotSnps = renderPlot(LogoPlotSnps(mutable_df3))
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# output$lin_sc = renderPlot(
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# lin_sc(
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# input$switch_target,
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# all_lineages = input$all_lineages,
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# my_xats = 12, # x axis text size
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# my_yats = 12, # y axis text size
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# my_xals = 12, # x axis label size
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# my_yals = 12, # y axis label size
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# my_lls = 12, # legend label size
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# d_lab_size = 4
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# )
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#input$stability_snp_param
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updateCheckboxGroupInput(
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session,
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"corr_selected",
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choiceNames = colnames(get(paste0(input$switch_target,"_corr_df_m3_f"))),
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choiceValues = colnames(get(paste0(input$switch_target,"_corr_df_m3_f"))),
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selected = c("FoldX", "DeepDDG", "mCSM.DUET")
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)
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updateSliderInput(
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session,
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"display_position_range",
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min = position_min,
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max = position_max
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#, value = c(position_min, position_min+150)
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)
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updateNumericInput(session, "selected_logop_snp_position", min = position_min, max = position_max, value = position_min)
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updateNumericInput(session, "selected_logop_ed_position", min = position_min, max = position_max, value = position_min)
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updateNumericInput(session, "corr_lig_dist", min = min_ligand_distance, max = max_ligand_distance, value = min_ligand_distance)
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updateNumericInput(session, "snp_ligand_dist", min = min(merged_df3$ligand_distance), max = max(merged_df3$ligand_distance))
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updateNumericInput(session, "snp_interface_dist", min = min(merged_df3$interface_dist), max = max(merged_df3$interface_dist))
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updateNumericInput(session, "snp_nca_dist", min = min(merged_df3$nca_distance), max = max(merged_df3$nca_distance))
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# different data ranges required for SNP distances
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snp_ligand_dist_df3 = merged_df3[merged_df3[['ligand_distance']]<input$snp_ligand_dist,]
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if ("interface_dist" %in% colnames(merged_df3)){
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snp_interface_dist_df3 = merged_df3[merged_df3[['interface_dist']]<input$snp_interface_dist,]
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} else {
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snp_interface_dist_df3 = NULL
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}
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if ("nca_distance" %in% colnames(merged_df3)){
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snp_nca_dist_df3 = merged_df3[merged_df3[['nca_distance']]<input$snp_nca_dist,]
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} else {
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snp_nca_dist_df3 = NULL
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}
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stability_colname = stability_boxes_df[stability_boxes_df$stability_type==input$stability_snp_param,"stability_colname"]
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outcome_colname = stability_boxes_df[stability_boxes_df$stability_type==input$stability_snp_param,"outcome_colname"]
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display_position_range = input$display_position_range
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plot_min=display_position_range[1]
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plot_max=display_position_range[2]
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logoplot_colour_scheme = input$logoplot_colour_scheme
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omit_snp_count = input$omit_snp_count
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print(paste0('Plotting positions between: ', plot_min, ' and ', plot_max))
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subset_mutable_df3=mutable_df3[(mutable_df3$position>=plot_min & mutable_df3$position <=plot_max),]
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subset_mutable_df3=mutable_df3[(mutable_df3$position>=plot_min & mutable_df3$position <=plot_max),]
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subset_sorted_df=sorted_df[(sorted_df$position>=plot_min & sorted_df$position <=plot_max),]
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#### LogoPlotSnps ####
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output$LogoPlotSnps = renderPlot(
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LogoPlotSnps(subset_mutable_df3,
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aa_pos_drug = get(paste0(target_gene,"_aa_pos_drug")),
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active_aa_pos = get(paste0(target_gene,"_active_aa_pos")),
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aa_pos_lig1 = get(paste0(target_gene,"_aa_pos_lig1")),
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aa_pos_lig2 = get(paste0(target_gene,"_aa_pos_lig2")),
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aa_pos_lig3 = get(paste0(target_gene,"_aa_pos_lig3")),
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my_logo_col = logoplot_colour_scheme,
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omit_snp_count = omit_snp_count
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)
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)
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### NGLViewer ####
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# Structure Viewer WebGL/NGLViewR window
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output$structure <- renderNGLVieweR({
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#ngl_gene=isolate(input$switch_target)
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ngl_gene=input$switch_target
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ngl_drug=target_map[[ngl_gene]]
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ngl_pdb_file=paste0(load_dir, "Data/", ngl_drug, '/output/depth/', ngl_gene, '_complex.pdb')
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print(ngl_pdb_file)
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NGLVieweR(ngl_pdb_file) %>%
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addRepresentation("cartoon",
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param = list(name = "cartoon",
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color="tan"
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#, colorScheme = "chainid"
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)
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) %>%
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stageParameters(backgroundColor = "lightgrey") %>%
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setQuality("high") %>%
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setFocus(0) %>%
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setSpin(FALSE)
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})
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#### Shared dataTable() ####
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output$table = DT::renderDataTable(
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datatable(subset_sorted_df[,table_columns],
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filter="top",
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selection = "single"
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)
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)
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#### bp_stability_hmap ####
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# red/blue tiles wala "Stability SNP by Site"
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output$bp_stability_hmap = renderPlot(
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bp_stability_hmap(
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subset_sorted_df,
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reorder_position = input$reorder_custom_h,
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p_title = NULL,
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yvar_colname = stability_colname,
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stability_colname = stability_colname,
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stability_outcome_colname = outcome_colname,
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my_ylab = NULL,
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y_max_override = max(sorted_df$pos_count),
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aa_pos_drug = get(paste0("embb","_aa_pos_drug")),
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active_aa_pos = get(paste0("embb","_active_aa_pos")),
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aa_pos_lig1 = get(paste0("embb","_aa_pos_lig1")),
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aa_pos_lig2 = get(paste0("embb","_aa_pos_lig2")),
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aa_pos_lig3 = get(paste0("embb","_aa_pos_lig3"))
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)
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)
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#### LogoPlotCustomH ####
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output$LogoPlotCustomH = renderPlot(
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LogoPlotCustomH(
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subset_sorted_df,
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my_logo_col = logoplot_colour_scheme,
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aa_pos_drug = get(paste0(target_gene,"_aa_pos_drug")),
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active_aa_pos = get(paste0(target_gene,"_active_aa_pos")),
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aa_pos_lig1 = get(paste0(target_gene,"_aa_pos_lig1")),
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aa_pos_lig2 = get(paste0(target_gene,"_aa_pos_lig2")),
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aa_pos_lig3 = get(paste0(target_gene,"_aa_pos_lig3"))
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)
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)
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#### wideP_consurf3 ####
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output$wideP_consurf3 = renderPlot(
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wideP_consurf3(
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subset_sorted_df,
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point_colours = consurf_colours,
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aa_pos_drug = get(paste0(target_gene,"_aa_pos_drug")),
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active_aa_pos = get(paste0(target_gene,"_active_aa_pos")),
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aa_pos_lig1 = get(paste0(target_gene,"_aa_pos_lig1")),
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aa_pos_lig2 = get(paste0(target_gene,"_aa_pos_lig2")),
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aa_pos_lig3 = get(paste0(target_gene,"_aa_pos_lig3"))
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)
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)
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#### site_snp_count_bp ####
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#mutable_df3[(mutable_df3$position>=plot_min & mutable_df3$position <=plot_max),]
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# ligand_distance
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# interface_dist
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# nca_distance
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# change to: multiple plots, all use site_snp_count_bp
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# 4 x plots side by side, one normal (no dist. filter), 2/3 filtered by distance columns above
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# use "subtitle text" from pos_count_bp_i.R
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output$site_snp_count_bp = renderPlot(
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site_snp_count_bp(
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mutable_df3,
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title_colour = 'black',
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subtitle_colour = "black",
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leg_text_size = 12,
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axis_label_size = 12,
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geom_ls = 4
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)
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)
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output$site_snp_count_bp_ligand = renderPlot(
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site_snp_count_bp(
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snp_ligand_dist_df3,
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title_colour = 'black',
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subtitle_colour = "black",
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leg_text_size = 12,
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axis_label_size = 12,
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geom_ls = 4
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)
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)
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# if ("interface_dist" %in% colnames(input$switch_target)) {
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output$site_snp_count_interface = renderPlot(
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site_snp_count_bp(
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snp_interface_dist_df3,
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title_colour = 'black',
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subtitle_colour = "black",
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leg_text_size = 12,
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axis_label_size = 12,
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geom_ls = 4
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)
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)
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# } #else {
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# output$site_snp_count_interface = renderPlot(
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# ggplot() + annotate(x=1,y=1,"text", label="No interface data for this target")+theme_void()
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# )
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# }
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output$site_snp_count_nca = renderPlot( #{
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#if ("nca_distance" %in% colnames(input$switch_target)) {
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site_snp_count_bp(
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snp_nca_dist_df3,
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title_colour = 'black',
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subtitle_colour = "black",
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leg_text_size = 12,
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axis_label_size = 12,
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geom_ls = 4
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)
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# } else {
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# ggplot() + annotate(x=1,y=1,"text", label="No RNA data for this target")+theme_void()
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# }
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# }
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)
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#### DM OM Plots ####
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#dm_om_param
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# order needs to be:
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# embb_lf_duet, embb_lf_foldx, embb_lf_deepddg, embb_lf_dynamut2, embb_lf_dist_gen,
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# embb_lf_consurf, embb_lf_provean, embb_lf_snap2, embb_lf_mcsm_lig, embb_lf_mmcsm_lig,
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# embb_lf_mcsm_ppi2, SOMETHING NA
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# embb_lf_mmcsm_lig SOMETHING NA,
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#dm_om_selection=input$dm_om_param
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#dm_om_df = dm_om_map[[dm_om_selection]]
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#output$lf_bp2 = renderPlot(lf_bp2(get(paste0(input$switch_target, '_', dm_om_df))))
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output$lf_bp2 = renderPlot(
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cowplot::plot_grid(
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plotlist = lapply(
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ls(name=.GlobalEnv,
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pattern=paste0(
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target_gene,
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'_lf_'
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)
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),
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function(x){
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lf_bp2(get(x))
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}
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)#, nrow=3
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), height=800
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)
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}
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)
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# FIXME: Doesn't add selected table rows correctly
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observeEvent(
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{
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input$table_rows_selected
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},
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{
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# having to duplicate this is a bit annoying :-(
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ngl_merged_df3=cbind(get(paste0(input$switch_target, '_merged_df3')))
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ngl_sorted_df = cbind(ngl_merged_df3)
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ngl_sorted_df = ngl_sorted_df %>% arrange(pos_count)
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position_max=max(ngl_merged_df3[['position']])
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position_min=min(ngl_merged_df3[['position']])
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display_position_range = input$display_position_range
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plot_min=display_position_range[1]
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plot_max=display_position_range[2]
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#ngl_subset_df=ngl_merged_df3[(ngl_merged_df3$position>=plot_min & ngl_merged_df3$position <=plot_max),]
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ngl_subset_df=ngl_sorted_df[(ngl_sorted_df$position>=plot_min & ngl_sorted_df$position <=plot_max),]
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#table_rows_selected = isolate(input$table_rows_selected)
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table_rows_selected = input$table_rows_selected
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class(table_rows_selected)
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#cat(paste0("Target: ", as.character(input$switch_target), "\nTable Rows for NGLViewR: ", as.character(table_rows_selected)))
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struct_pos=(as.character(ngl_subset_df[table_rows_selected,"position"]))
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cat(paste0('Table Index: ', table_rows_selected, "position: ", struct_pos))
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NGLVieweR_proxy("structure") %>%
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# #### lineage_distP ####
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# output$lineage_distP = renderPlot(
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# lineage_distP(
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# get(paste0(input$switch_target, '_merged_df2')),
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# all_lineages = input$all_lineages,
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# x_lab = "Average Stability",
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# x_axis = "avg_stability_scaled",
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# fill_categ_cols = c("red", "blue")
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# )
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# )
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#
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#
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# #### observeEvent() Fun(tm) ####
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# observeEvent(input$clear_ngl, {
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# NGLVieweR_proxy("structure") %>%
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# removeSelection("Pos")
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# })
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# # Button to test adding a position
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# observeEvent(input$test_ngl, {
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# NGLVieweR_proxy("structure") %>%
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# addSelection('ball+stick'
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addSelection('hyperball'
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, param = list(
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name = "Pos"
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, sele = struct_pos
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#, color = "#00ff00"
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, colorValue="00ff00"
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, colorScheme="element"
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)
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)
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#cat(paste0('Done NGLViewR addSelection for: ', positions_to_add))
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}
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)
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#### Correlation observeEvent ####
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# Yet another special-case observeEvent to handle the correlation pair plot
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observeEvent(
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{
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input$corr_selected
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input$corr_method
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input$corr_lig_dist
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},
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{
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dist_cutoff_user = input$corr_lig_dist
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target_gene=input$switch_target
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plot_title=paste0(target_map[[target_gene]],"/",target_gene)
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|
||||
corr_plot_df = get(
|
||||
paste0(
|
||||
input$switch_target,"_corr_df_m3_f"
|
||||
)
|
||||
)[,c(input$corr_selected, "dst_mode")]
|
||||
|
||||
#if ( dist_cutoff_user >= 2) {
|
||||
#corr_plotdf_subset = corr_plot_df[corr_plot_df[['Lig.Dist']] < dist_cutoff_user,]
|
||||
# , param = list(
|
||||
# name = "Pos"
|
||||
# , sele = "35"
|
||||
# , color = "green")
|
||||
# )
|
||||
# })
|
||||
#
|
||||
# observeEvent(
|
||||
# {
|
||||
# input$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']]<input$snp_ligand_dist,]
|
||||
# if ("interface_dist" %in% colnames(merged_df3)){
|
||||
# snp_interface_dist_df3 = merged_df3[merged_df3[['interface_dist']]<input$snp_interface_dist,]
|
||||
# } else {
|
||||
# snp_interface_dist_df3 = NULL
|
||||
# }
|
||||
# else {
|
||||
# corr_plotdf_subset = corr_plot_df
|
||||
#
|
||||
# if ("nca_distance" %in% colnames(merged_df3)){
|
||||
# snp_nca_dist_df3 = merged_df3[merged_df3[['nca_distance']]<input$snp_nca_dist,]
|
||||
# } else {
|
||||
# snp_nca_dist_df3 = NULL
|
||||
# }
|
||||
|
||||
#### Correlation using ggpairs() ####
|
||||
output$my_corr_pairs = renderPlot(
|
||||
dashboard_ggpairs(
|
||||
corr_plot_df,
|
||||
plot_title = plot_title,
|
||||
method = input$corr_method,
|
||||
tt_args_size = 7,
|
||||
gp_args_size = 7
|
||||
), height = 900
|
||||
)
|
||||
}
|
||||
)
|
||||
}
|
||||
|
||||
|
||||
app <- shinyApp(ui, server)
|
||||
runApp(app)
|
||||
#
|
||||
# stability_colname = stability_boxes_df[stability_boxes_df$stability_type==input$stability_snp_param,"stability_colname"]
|
||||
# outcome_colname = stability_boxes_df[stability_boxes_df$stability_type==input$stability_snp_param,"outcome_colname"]
|
||||
#
|
||||
# display_position_range = input$display_position_range
|
||||
# plot_min=display_position_range[1]
|
||||
# plot_max=display_position_range[2]
|
||||
#
|
||||
# logoplot_colour_scheme = input$logoplot_colour_scheme
|
||||
# omit_snp_count = input$omit_snp_count
|
||||
#
|
||||
# print(paste0('Plotting positions between: ', plot_min, ' and ', plot_max))
|
||||
#
|
||||
# subset_mutable_df3=mutable_df3[(mutable_df3$position>=plot_min & mutable_df3$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)
|
||||
}
|
||||
|
|
|
|||
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