1052 lines
42 KiB
R
1052 lines
42 KiB
R
library(shinycssloaders)
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library(DT)
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library(NGLVieweR)
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library(hash)
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# FIXME This is slow and should happen *once only*
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#source(load_dir, "git/LSHTM_analysis/scripts/Header_TT.R")
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# FIXME: these are needed but slow to load every time
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# source(load_dir, "git/LSHTM_analysis/config/alr.R")
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# source(load_dir, "git/LSHTM_analysis/config/gid.R")
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# source(load_dir, "git/LSHTM_analysis/config/pnca.R")
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# source(load_dir, "git/LSHTM_analysis/config/rpob.R")
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# source(load_dir, "git/LSHTM_analysis/config/katg.R")
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# TODO: this is TEMPORARY and will shortly get replaced with a target picker
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# that'll reload everything when changing targets. the lapply() is *much* quicker
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# than previous approaches
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# system.time({
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load_dir="~/git/"
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#load_dir="/srv/shiny-server/git/"
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source(paste0(load_dir, "LSHTM_analysis/scripts/Header_TT.R"))
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load_target_globals=function(target){
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cat(paste0("Reloading Target: ", target))
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source(paste0(load_dir, "LSHTM_analysis/config/", target, ".R")) # load per-target config file
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invisible(assign(paste0(target, "_aa_pos_drug"), aa_pos_drug, envir = .GlobalEnv))
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invisible(assign(paste0(target, "_active_aa_pos"), active_aa_pos, envir = .GlobalEnv))
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invisible(assign(paste0(target, "_aa_pos_lig1"), aa_pos_lig1, envir = .GlobalEnv))
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invisible(assign(paste0(target, "_aa_pos_lig2"), aa_pos_lig2, envir = .GlobalEnv))
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invisible(assign(paste0(target, "_aa_pos_lig3"), aa_pos_lig3, envir = .GlobalEnv))
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invisible(assign(paste0(target, "_merged_df3"), read.csv(paste0(load_dir, "Misc/shiny_dashboard/data/",target,"-merged_df3.csv")), envir = .GlobalEnv))
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#invisible(assign(paste0(target, "_merged_df2"), read.csv(paste0(load_dir, "Misc/shiny_dashboard/data/",target,"-merged_df2.csv")), envir = .GlobalEnv))
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invisible(assign(paste0(target, "_corr_df_m3_f"), read.csv(paste0(load_dir, "Misc/shiny_dashboard/data/",target,"-corr_df_m3_f.csv")), envir = .GlobalEnv))
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invisible(assign(paste0(target, "_lin_lf"), read.csv(paste0(load_dir, "Misc/shiny_dashboard/data/",target,"-lin_lf.csv")), envir = .GlobalEnv))
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invisible(assign(paste0(target, "_lin_wf"), read.csv(paste0(load_dir, "Misc/shiny_dashboard/data/",target,"-lin_wf.csv")), envir = .GlobalEnv))
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lapply(
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c(
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"duet",
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"mcsm_lig",
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"foldx",
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"deepddg",
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"dynamut2",
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"consurf",
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"snap2",
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"provean",
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"dist_gen",
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"mcsm_ppi2",
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"mmcsm_lig",
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"mcsm_na"
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#,
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#"mcsm_na"
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), function(x){
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wf_filename=paste0(load_dir, "Misc/shiny_dashboard/data/", tolower(gene), "-wf_", x ,".csv")
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wf_var=paste0(target, "wf_",x)
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if (file.exists(wf_filename)){
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invisible(assign(wf_var,read.csv(wf_filename), envir = .GlobalEnv)) # FILTH
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}
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lf_filename=paste0(load_dir, "Misc/shiny_dashboard/data/", tolower(gene), "-lf_", x ,".csv")
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lf_var=paste0(target, "_lf_",x)
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if (file.exists(lf_filename)){
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invisible(assign(lf_var,read.csv(lf_filename), envir = .GlobalEnv)) # FILTH
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}
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}
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)
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}
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#### Local Functions ####
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# Generate a conditionalPanel() for a given graph function and sidebar name combination
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generate_conditionalPanel = function(tab_name, plot_function, plot_df){
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# e.g.: list("lin_count_bp_diversity", "Lineage diversity count")
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cond=paste0("input.sidebar == '", tab_name, "'")
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conditionalPanel(condition=cond, box(
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title=tab_name
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, status = "info"
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, width=NULL
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, plotOutput(plot_function
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, click = "plot_click") %>% withSpinner(color="#0dc5c1")
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# , plotOutput(plot_function, click = "plot_click")
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)
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)
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}
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# FIXME: passing in the per-plot params is broken
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lin_sc=function(x, all_lineages = F, ...){
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lf_var = get(paste0(x,"_lin_lf"))
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wf_var = get(paste0(x,"_lin_wf"))
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cowplot::plot_grid(lin_count_bp_diversity(wf_var, all_lineages, ...), lin_count_bp(lf_var, all_lineages, ...))
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}
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options(shiny.port = 8000)
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options(shiny.host = '0.0.0.0') # This means "listen to all addresses on all interfaces"
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options(shiny.launch.browser = FALSE)
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options(width=120)
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options(DT.options = list(scrollX = TRUE))
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################ STATIC GLOBALS ONLY ##############
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# never quite sure where "outdir" gets set :-|
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# using dataframes instead of lists lets us avoid use of map()
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plot_functions_df=data.frame(
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tab_name=c(
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"LogoP SNP",
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#"Lineage Sample Count",
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#"Site SNP count",
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"Stability SNP by site",
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"DM OM Plots",
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"Correlation",
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#"Lineage Distribution",
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"Consurf",
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"LogoP OR"
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),
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plot_function=c(
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"LogoPlotSnps",
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#"lin_sc",
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#"site_snp_count_bp",
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"bp_stability_hmap",
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"lf_bp2",
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"my_corr_pairs",
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#"lineage_distP",
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"wideP_consurf3",
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"LogoPlotCustomH"
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),
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plot_df=c(
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"mutable_df3" ,
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#"lin_lf",
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#"mutable_df3",
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"merged_df3" ,
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"lf_duet" ,
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"corr_df_m3_f",
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#"merged_df2",
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"merged_df3",
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"merged_df2"
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)
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)
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stability_boxes_df=data.frame(
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outcome_colname=c("duet_outcome",
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"foldx_outcome",
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"deepddg_outcome",
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"ddg_dynamut2_outcome",
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"mcsm_na_outcome",
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"mcsm_ppi2_outcome",
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"snap2_outcome",
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"consurf_outcome",
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"avg_stability_outcome"),
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stability_type=c(
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"DUET",
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"FoldX",
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"DeepDDG",
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"Dynamut2",
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"mCSM-NA",
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"mCSM-ppi2",
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"SNAP2",
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"Consurf",
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"Average"
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),
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stability_colname=c(
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"duet_scaled",
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"foldx_scaled",
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"deepddg_scaled",
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"ddg_dynamut2_scaled",
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"mcsm_na_scaled",
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"mcsm_ppi2_scaled",
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"snap2_scaled",
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"consurf_scaled",
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"avg_stability_scaled"
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)
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)
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table_columns = c(
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"position",
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"mutationinformation",
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"sensitivity",
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"ligand_distance",
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"avg_lig_affinity",
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"avg_lig_affinity_outcome",
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"avg_stability",
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"avg_stability_outcome",
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"or_mychisq",
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"maf",
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"snap2_outcome",
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"consurf_outcome",
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"provean_outcome",
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"rsa",
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"kd_values" ,
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"rd_values"
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)
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logoPlotSchemes <- list("chemistry"
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, "taylor"
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, "hydrophobicity"
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, "clustalx")
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dm_om_methods = c("DUET ΔΔG"
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, "Consurf"
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, "Deepddg ΔΔG"
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, "Dynamut2 ΔΔG"
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, "FoldX ΔΔG"
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, "Ligand affinity (log fold change)"
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, "mCSM-NA affinity ΔΔG"
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, "SNAP2")
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dm_om_map = hash(c(
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"DUET ΔΔG"
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, "Consurf"
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, "Deepddg ΔΔG"
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, "Dynamut2 ΔΔG"
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, "FoldX ΔΔG"
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, "Ligand affinity (log fold change)"
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, "mCSM-NA affinity ΔΔG"
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, "SNAP2"
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), c(
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"lf_duet"
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,"lf_consurf"
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,"lf_deepddg"
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,"lf_dynamut2"
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,"lf_foldx"
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,"lf_mcsm_lig"
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,"lf_mcsm_na"
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,"lf_snap2"
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)
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)
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#### target_map: handy gene/drug mapping hash ####
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target_map = hash(
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c(
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"alr",
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"gid",
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"embb",
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"pnca",
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"rpob",
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"katg"),
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c(
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"cycloserine",
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"streptomycin",
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"ethambutol",
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"pyrazinamide",
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"rifampicin",
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"isoniazid")
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)
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# load E V E R Y T H I N G
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lapply(c(
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"alr",
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"embb",
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"gid",
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"katg",
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"pnca",
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"rpob"
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),function(x){
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load_target_globals(x)
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}
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)
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consurf_palette1 = c("0" = "yellow2"
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, "1" = "cyan1"
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, "2" = "steelblue2"
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, "3" = "cadetblue2"
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, "4" = "paleturquoise2"
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, "5" = "thistle3"
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, "6" = "thistle2"
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, "7" = "plum2"
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, "8" = "maroon"
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, "9" = "violetred2")
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consurf_palette2 = c("0" = "yellow2"
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, "1" = "forestgreen"
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, "2" = "seagreen3"
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, "3" = "palegreen1"
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, "4" = "darkseagreen2"
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, "5" = "thistle3"
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, "6" = "lightpink1"
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, "7" = "orchid3"
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, "8" = "orchid4"
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, "9" = "darkorchid4")
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# decreasing levels mess legend
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# consurf_colours_LEVEL = c(
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# "0" = rgb(1.00,1.00,0.59)
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# , "9" = rgb(0.63,0.16,0.37)
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# , "8" = rgb(0.94,0.49,0.67)
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# , "7" = rgb(0.98,0.78,0.86)
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# , "6" = rgb(0.98,0.92,0.96)
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# , "5" = rgb(1.00,1.00,1.00)
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# , "4" = rgb(0.84,0.94,0.94)
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# , "3" = rgb(0.65,0.86,0.90)
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# , "2" = rgb(0.29,0.69,0.75)
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# , "1" = rgb(0.04,0.49,0.51)
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# )
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consurf_colours = c(
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"0" = rgb(1.00,1.00,0.59)
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, "1" = rgb(0.04,0.49,0.51)
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, "2" = rgb(0.29,0.69,0.75)
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, "3" = rgb(0.65,0.86,0.90)
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, "4" = rgb(0.84,0.94,0.94)
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, "5" = rgb(1.00,1.00,1.00)
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, "6" = rgb(0.98,0.92,0.96)
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, "7" = rgb(0.98,0.78,0.86)
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, "8" = rgb(0.94,0.49,0.67)
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, "9" = rgb(0.63,0.16,0.37)
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)
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# if (true()){
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if (interactive()){
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options(shiny.launch.browser = FALSE) # i am a big girl and can tie my own laces
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options(shiny.port = 8000) # don't change the port every time
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options(shiny.host = '0.0.0.0') # This means "listen to all addresses on all interfaces"
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options(width=120)
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options(DT.options = list(scrollX = TRUE))
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#### UI ####
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ui <- dashboardPage(skin="purple",
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dashboardHeader(title = "Drug/Target Explorer"),
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dashboardSidebar(
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sidebarMenu( id = "sidebar",
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selectInput(
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"switch_target",
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label="Switch to New Target",
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choices = c(
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"alr",
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"embb",
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"gid",
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"katg",
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"pnca",
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"rpob"
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),
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selected="embb"),
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menuItem("LogoP SNP", tabName="LogoP SNP"),
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#menuItem("Lineage Sample Count", tabName="Lineage Sample Count"),
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menuItem("Site SNP count", tabName="Site SNP count"),
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menuItem("Stability SNP by site", tabName="Stability SNP by site"),
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menuItem("DM OM Plots", tabName="DM OM Plots"),
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menuItem("Correlation", tabName="Correlation"),
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#menuItem("Lineage Distribution", tabName="Lineage Distribution"),
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menuItem("Consurf", tabName="Consurf"),
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menuItem("LogoP OR", tabName="LogoP OR"),
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menuItem("Lineage", tabName="Lineage"),
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#menuItem('Stability count', tabName='Stability count'),
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# These conditionalPanel()s make extra settings appear in the sidebar when needed
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conditionalPanel(
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condition="input.sidebar == 'LogoP SNP'",
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textInput(
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"omit_snp_count",
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"Omit SNPs",
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value = c(0),
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placeholder = "1,3,6"
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)
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),
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# NOTE:
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# I *think* we can cheat here slightly and use the min/max from
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# merged_df3[['position']] for everything because the various
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# dataframes for a given gene/drug combination have the
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# same range of positions. May need fixing, especially
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# if we get/shrink the imported data files to something
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# more reasonable.
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conditionalPanel(
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condition="
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input.sidebar == 'LogoP SNP'||
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input.sidebar == 'Stability SNP by site' ||
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input.sidebar == 'Consurf' ||
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input.sidebar == 'LogoP OR'",
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sliderInput(
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"display_position_range"
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, "Display Positions"
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, min=1, max=150, value=c(1,150) # 150 is just a little less than the smallest pos_count
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)
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),
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conditionalPanel(
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condition="
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input.sidebar == 'LogoP SNP' ||
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input.sidebar == 'LogoP OR' ||
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input.sidebar == 'LogoP ED'",
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selectInput(
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"logoplot_colour_scheme",
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label="Logo Plot Colour Scheme",
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choices = logoPlotSchemes,
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selected="chemistry"
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)
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),
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conditionalPanel(
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condition="input.sidebar == 'Correlation'",
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selectInput(
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"corr_method",
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label="Correlation Method",
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choices = list("spearman",
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"pearson",
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"kendall"),
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selected="spearman"
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)
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),
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conditionalPanel(
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condition="input.sidebar == 'Correlation'",
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numericInput(
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"corr_lig_dist"
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, "Ligand Distance Cutoff (Å)", value=1
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)
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),
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conditionalPanel(
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condition="input.sidebar == 'Site SNP count'",
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numericInput(
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"snp_ligand_dist"
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, "Ligand Distance Cutoff (Å)", value=10
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)
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),
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conditionalPanel(
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condition="input.sidebar == 'Site SNP count'",
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numericInput(
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"snp_interface_dist"
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, "Interface Distance Cutoff (Å)", value=10
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)
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),
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conditionalPanel(
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condition="input.sidebar == 'Site SNP count'",
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numericInput(
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"snp_nca_dist"
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, "NCA Distance Cutoff (Å)", value=10
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)
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),
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conditionalPanel(
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condition="input.sidebar == 'Correlation'",
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checkboxGroupInput(
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"corr_selected",
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"Parameters",
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choiceNames = c(
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"DeepDDG",
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"Dynamut2",
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"FoldX",
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"ConSurf"#,
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),
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choiceValues = c(
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"DeepDDG",
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"Dynamut2",
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"FoldX",
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"ConSurf"#,
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),
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selected = c(
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"DeepDDG",
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"Dynamut2",
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"FoldX",
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"ConSurf"#,
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)
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)
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),
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# conditionalPanel(
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# condition="input.sidebar == 'DM OM Plots'",
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# selectInput(
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# "dm_om_param",
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# label="Stability Parameter",
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# choices = keys(dm_om_map),
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# selected="SNAP2")
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# ),
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# colour_categ
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conditionalPanel(
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condition="input.sidebar == 'Stability SNP by site'",
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selectInput(
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"stability_snp_param",
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label="Stability Parameter",
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choices = stability_boxes_df$stability_type,
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selected="Average")
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),
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conditionalPanel(
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condition="input.sidebar == 'Stability SNP by site'",
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checkboxInput("reorder_custom_h",
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label="Reorder by SNP count",
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FALSE)
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),
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conditionalPanel(
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condition="input.sidebar.match(/^Lineage.*/)",
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checkboxInput("all_lineages",
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label="All Lineages",
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FALSE)
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),
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# an example of how you can match multiple things in frontend JS
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|
conditionalPanel(
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condition="input.sidebar == 'LogoP SNP' ||
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input.sidebar =='Stability SNP by site' ||
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input.sidebar =='Consurf' ||
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input.sidebar =='LogoP OR'",
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actionButton("clear_ngl",
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"Clear Structure")
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),
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conditionalPanel(
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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="NCA Distance"
|
|
, status = "info"
|
|
, width=NULL
|
|
, plotOutput("site_snp_count_nca") %>% withSpinner(color="#0dc5c1")
|
|
)
|
|
), width=6
|
|
)
|
|
),
|
|
|
|
# # Explicit fluidRow() for Stability Count
|
|
# fluidRow(
|
|
# column(
|
|
# conditionalPanel(
|
|
# condition="input.sidebar.match(/^Lineage.*/)",
|
|
# lapply(
|
|
# # FIXME: using a hardcoded target DF for this IS WRONG AND WILL BREAK
|
|
# stability_boxes_df[stability_boxes_df$outcome_colname %in% colnames(embb_merged_df3),"outcome_colname"],
|
|
# function(x){
|
|
# print(paste0("outcome_colname: ",x))
|
|
# box(plotOutput(x), width=4)
|
|
# }
|
|
# ),
|
|
# width=12
|
|
# )
|
|
# )
|
|
# ),
|
|
|
|
#### fluidRow()s for "Stability Count" in the sidebar ####
|
|
fluidRow(
|
|
conditionalPanel(
|
|
condition="
|
|
input.sidebar == 'LogoP SNP' ||
|
|
input.sidebar =='Stability SNP by site' ||
|
|
input.sidebar =='Consurf' ||
|
|
input.sidebar =='LogoP OR'",
|
|
column(NGLVieweROutput("structure"),
|
|
width=3
|
|
)
|
|
),
|
|
conditionalPanel(
|
|
condition="
|
|
input.sidebar == 'LogoP SNP' ||
|
|
input.sidebar == 'Stability SNP by site' ||
|
|
input.sidebar == 'Site SNP count' ||
|
|
input.sidebar == 'Consurf' ||
|
|
input.sidebar == 'LogoP OR'",
|
|
column(
|
|
DT::dataTableOutput('table'),
|
|
width=9
|
|
)
|
|
)
|
|
),
|
|
)
|
|
)
|
|
|
|
server <- function(input, output, session) {
|
|
|
|
#output$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<input$snp_ligand_dist,]
|
|
snp_interface_dist_df3 = merged_df3[merged_df3$interface_dist<input$snp_interface_dist,]
|
|
snp_nca_dist_df3 = merged_df3[merged_df3$nca_distance<input$snp_nca_dist,]
|
|
|
|
stability_colname = stability_boxes_df[stability_boxes_df$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$switch_target)
|
|
ngl_drug=target_map[[ngl_gene]]
|
|
ngl_pdb_file=paste0(load_dir, "Data/", ngl_drug, '/output/depth/', ngl_gene, '_complex.pdb')
|
|
print(ngl_pdb_file)
|
|
NGLVieweR(ngl_pdb_file) %>%
|
|
addRepresentation("cartoon",
|
|
param = list(name = "cartoon",
|
|
color="tan"
|
|
#, colorScheme = "chainid"
|
|
)
|
|
) %>%
|
|
stageParameters(backgroundColor = "lightgrey") %>%
|
|
setQuality("high") %>%
|
|
setFocus(0) %>%
|
|
setSpin(FALSE)
|
|
})
|
|
|
|
|
|
#### Shared dataTable() ####
|
|
output$table = DT::renderDataTable(
|
|
datatable(subset_sorted_df[,table_columns],
|
|
filter="top",
|
|
selection = "single"
|
|
)
|
|
)
|
|
|
|
#### bp_stability_hmap ####
|
|
# red/blue tiles wala "Stability SNP by Site"
|
|
output$bp_stability_hmap = renderPlot(
|
|
bp_stability_hmap(
|
|
subset_sorted_df,
|
|
reorder_position = input$reorder_custom_h,
|
|
p_title = NULL,
|
|
yvar_colname = stability_colname,
|
|
stability_colname = stability_colname,
|
|
stability_outcome_colname = outcome_colname,
|
|
my_ylab = NULL,
|
|
y_max_override = max(sorted_df$pos_count),
|
|
aa_pos_drug = get(paste0("embb","_aa_pos_drug")),
|
|
active_aa_pos = get(paste0("embb","_active_aa_pos")),
|
|
aa_pos_lig1 = get(paste0("embb","_aa_pos_lig1")),
|
|
aa_pos_lig2 = get(paste0("embb","_aa_pos_lig2")),
|
|
aa_pos_lig3 = get(paste0("embb","_aa_pos_lig3"))
|
|
)
|
|
)
|
|
#### LogoPlotCustomH ####
|
|
output$LogoPlotCustomH = renderPlot(
|
|
LogoPlotCustomH(
|
|
subset_sorted_df,
|
|
my_logo_col = logoplot_colour_scheme,
|
|
aa_pos_drug = get(paste0(target_gene,"_aa_pos_drug")),
|
|
active_aa_pos = get(paste0(target_gene,"_active_aa_pos")),
|
|
aa_pos_lig1 = get(paste0(target_gene,"_aa_pos_lig1")),
|
|
aa_pos_lig2 = get(paste0(target_gene,"_aa_pos_lig2")),
|
|
aa_pos_lig3 = get(paste0(target_gene,"_aa_pos_lig3"))
|
|
)
|
|
)
|
|
|
|
#### wideP_consurf3 ####
|
|
output$wideP_consurf3 = renderPlot(
|
|
wideP_consurf3(
|
|
subset_sorted_df,
|
|
point_colours = consurf_colours,
|
|
aa_pos_drug = get(paste0(target_gene,"_aa_pos_drug")),
|
|
active_aa_pos = get(paste0(target_gene,"_active_aa_pos")),
|
|
aa_pos_lig1 = get(paste0(target_gene,"_aa_pos_lig1")),
|
|
aa_pos_lig2 = get(paste0(target_gene,"_aa_pos_lig2")),
|
|
aa_pos_lig3 = get(paste0(target_gene,"_aa_pos_lig3"))
|
|
)
|
|
)
|
|
|
|
#### site_snp_count_bp ####
|
|
#mutable_df3[(mutable_df3$position>=plot_min & mutable_df3$position <=plot_max),]
|
|
# ligand_distance
|
|
# interface_dist
|
|
# nca_distance
|
|
# change to: multiple plots, all use site_snp_count_bp
|
|
# 4 x plots side by side, one normal (no dist. filter), 2/3 filtered by distance columns above
|
|
# use "subtitle text" from pos_count_bp_i.R
|
|
|
|
output$site_snp_count_bp = renderPlot(
|
|
site_snp_count_bp(
|
|
mutable_df3,
|
|
title_colour = 'black',
|
|
subtitle_colour = "black",
|
|
leg_text_size = 12,
|
|
axis_label_size = 12,
|
|
geom_ls = 4
|
|
)
|
|
)
|
|
output$site_snp_count_bp_ligand = renderPlot(
|
|
site_snp_count_bp(
|
|
snp_ligand_dist_df3,
|
|
title_colour = 'black',
|
|
subtitle_colour = "black",
|
|
leg_text_size = 12,
|
|
axis_label_size = 12,
|
|
geom_ls = 4
|
|
)
|
|
)
|
|
output$site_snp_count_interface = renderPlot(
|
|
site_snp_count_bp(
|
|
snp_interface_dist_df3,
|
|
title_colour = 'black',
|
|
subtitle_colour = "black",
|
|
leg_text_size = 12,
|
|
axis_label_size = 12,
|
|
geom_ls = 4
|
|
)
|
|
)
|
|
output$site_snp_count_nca = renderPlot(
|
|
site_snp_count_bp(
|
|
snp_nca_dist_df3,
|
|
title_colour = 'black',
|
|
subtitle_colour = "black",
|
|
leg_text_size = 12,
|
|
axis_label_size = 12,
|
|
geom_ls = 4
|
|
)
|
|
)
|
|
|
|
#### DM OM Plots ####
|
|
#dm_om_param
|
|
# order needs to be:
|
|
# embb_lf_duet, embb_lf_foldx, embb_lf_deepddg, embb_lf_dynamut2, embb_lf_dist_gen,
|
|
# embb_lf_consurf, embb_lf_provean, embb_lf_snap2, embb_lf_mcsm_lig, embb_lf_mmcsm_lig,
|
|
# embb_lf_mcsm_ppi2, SOMETHING NA
|
|
|
|
# embb_lf_mmcsm_lig SOMETHING NA,
|
|
#dm_om_selection=input$dm_om_param
|
|
#dm_om_df = dm_om_map[[dm_om_selection]]
|
|
#output$lf_bp2 = renderPlot(lf_bp2(get(paste0(input$switch_target, '_', dm_om_df))))
|
|
|
|
output$lf_bp2 = renderPlot(
|
|
cowplot::plot_grid(
|
|
plotlist = lapply(
|
|
ls(name=.GlobalEnv,
|
|
pattern=paste0(
|
|
target_gene,
|
|
'_lf_'
|
|
)
|
|
),
|
|
function(x){
|
|
lf_bp2(get(x))
|
|
}
|
|
)#, nrow=3
|
|
), height=800
|
|
)
|
|
}
|
|
)
|
|
|
|
|
|
# FIXME: Doesn't add selected table rows correctly
|
|
observeEvent(
|
|
{
|
|
input$table_rows_selected
|
|
},
|
|
{
|
|
# having to duplicate this is a bit annoying :-(
|
|
ngl_merged_df3=cbind(get(paste0(input$switch_target, '_merged_df3')))
|
|
ngl_sorted_df = cbind(ngl_merged_df3)
|
|
ngl_sorted_df = ngl_sorted_df %>% arrange(pos_count)
|
|
|
|
position_max=max(ngl_merged_df3[['position']])
|
|
position_min=min(ngl_merged_df3[['position']])
|
|
display_position_range = input$display_position_range
|
|
plot_min=display_position_range[1]
|
|
plot_max=display_position_range[2]
|
|
#ngl_subset_df=ngl_merged_df3[(ngl_merged_df3$position>=plot_min & ngl_merged_df3$position <=plot_max),]
|
|
ngl_subset_df=ngl_sorted_df[(ngl_sorted_df$position>=plot_min & ngl_sorted_df$position <=plot_max),]
|
|
|
|
|
|
#table_rows_selected = isolate(input$table_rows_selected)
|
|
table_rows_selected = input$table_rows_selected
|
|
class(table_rows_selected)
|
|
#cat(paste0("Target: ", as.character(input$switch_target), "\nTable Rows for NGLViewR: ", as.character(table_rows_selected)))
|
|
|
|
struct_pos=(as.character(ngl_subset_df[table_rows_selected,"position"]))
|
|
cat(paste0('Table Index: ', table_rows_selected, "position: ", struct_pos))
|
|
|
|
NGLVieweR_proxy("structure") %>%
|
|
#addSelection('ball+stick'
|
|
addSelection('hyperball'
|
|
, param = list(
|
|
name = "Pos"
|
|
, sele = struct_pos
|
|
#, color = "#00ff00"
|
|
, colorValue="00ff00"
|
|
, colorScheme="element"
|
|
)
|
|
)
|
|
#cat(paste0('Done NGLViewR addSelection for: ', positions_to_add))
|
|
}
|
|
)
|
|
#### Correlation observeEvent ####
|
|
# Yet another special-case observeEvent to handle the correlation pair plot
|
|
|
|
observeEvent(
|
|
{
|
|
input$corr_selected
|
|
input$corr_method
|
|
input$corr_lig_dist
|
|
},
|
|
{
|
|
dist_cutoff_user = input$corr_lig_dist
|
|
target_gene=input$switch_target
|
|
plot_title=paste0(target_map[[target_gene]],"/",target_gene)
|
|
|
|
corr_plot_df = get(
|
|
paste0(
|
|
input$switch_target,"_corr_df_m3_f"
|
|
)
|
|
)[,c(input$corr_selected, "dst_mode")]
|
|
|
|
#if ( dist_cutoff_user >= 2) {
|
|
#corr_plotdf_subset = corr_plot_df[corr_plot_df[['Lig.Dist']] < dist_cutoff_user,]
|
|
#}
|
|
# else {
|
|
# corr_plotdf_subset = corr_plot_df
|
|
# }
|
|
|
|
#### Correlation using ggpairs() ####
|
|
output$my_corr_pairs = renderPlot(
|
|
dashboard_ggpairs(
|
|
corr_plot_df,
|
|
plot_title = plot_title,
|
|
method = input$corr_method,
|
|
tt_args_size = 7,
|
|
gp_args_size = 7
|
|
), height = 900
|
|
)
|
|
}
|
|
)
|
|
}
|
|
|
|
|
|
app <- shinyApp(ui, server)
|
|
runApp(app)
|
|
}
|