main dashboard
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3 changed files with 1471 additions and 677 deletions
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@ -4,78 +4,395 @@ library(DT)
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library(NGLVieweR)
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library(hash)
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server <- function(input, output) {
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observeEvent({
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input$combined_model
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input$combined_data
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input$combined_training_genes
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input$score_dropdown
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input$resample_dropdown
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input$drug_dropdown
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input$split_dropdown
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},{
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combined_model = input$combined_model
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selection = input$score_dropdown
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resampler = input$resample_dropdown
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selected_drug = input$drug_dropdown
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selected_split = input$split_dropdown
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combined_data = input$combined_data
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combined_training_genes = input$combined_training_genes
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selected_gene = combo[combo$drug == selected_drug,'gene']
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# hide stuff if selected
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if(combined_model == "combined") {
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#if(combined_model == TRUE) {
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hide("split_dropdown")
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hide("resample_dropdown")
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show("combined_data")
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show("combined_training_genes")
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filedata = paste0(combined_training_genes,
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'genes_logo_skf_BT_',
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selected_gene,
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'_',
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combined_data
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)
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print(filedata)
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print('doing COMBINED plot')
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output$plot <- renderPlot(makeplot(loaded_files[[filedata]],
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selection,
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"none", # always 'none' for combined plot
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gene = combo[drug==selected_drug,"gene"],
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combined_training_genes = combined_training_genes,
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display_combined = TRUE,
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)
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)
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# e.g.
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# makeplot(loaded_files$`5genes_logo_skf_BT_pnca_actual`, "MCC", "none" , gene = 'foo', combined_training_genes = '1234', display_combined = TRUE)
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} else {
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show("split_dropdown")
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show("resample_dropdown")
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hide("combined_data")
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hide("combined_training_genes")
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filedata = paste0(combo[drug==selected_drug,"gene"],
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'_baselineC_',
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selected_split
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)
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print(filedata)
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print("doing GENE plot")
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output$plot <- renderPlot(makeplot(loaded_files[[filedata]],
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selection,
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resampler,
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gene = combo[drug==selected_drug,"gene"],
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display_combined = FALSE,
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)
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)
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}
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# 6genes_logo_skf_BT_gid_complete
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# filedata example for combined: 6genes_logo_skf_BT_embb_actual
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# 6genes_logo_skf_BT_embb_combined
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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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}
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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$n=NULL
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#
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mutable_df3 = cbind(merged_df3)
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#
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# # re-sort the dataframe according to position count
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sorted_df = cbind(merged_df3)
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sorted_df = sorted_df %>% arrange(pos_count)
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#
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outdir = paste0(load_dir, "Data/", drug, '/output/')
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indir = paste0(load_dir, "Data/", drug , "/input/")
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#### nasty special-purpose merged_df3 variants ####
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# FIXME: SLOW
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# corr_plotdf = corr_data_extract(
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# merged_df3
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# , gene = gene
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# , drug = drug
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# , extract_scaled_cols = F
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# )
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#input$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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snp_interface_dist_df3 = merged_df3[merged_df3$interface_dist<input$snp_interface_dist,]
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snp_nca_dist_df3 = merged_df3[merged_df3$nca_distance<input$snp_nca_dist,]
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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_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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output$site_snp_count_interface = renderPlot(
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site_snp_count_bp(
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snp_interface_dist_df3,
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title_colour = 'black',
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subtitle_colour = "black",
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leg_text_size = 12,
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axis_label_size = 12,
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geom_ls = 4
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)
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)
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output$site_snp_count_nca = renderPlot(
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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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)
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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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#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$corr_selected
|
||||
input$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
|
||||
)
|
||||
}
|
||||
)
|
||||
}
|
||||
Loading…
Add table
Add a link
Reference in a new issue