main dashboard
This commit is contained in:
parent
877e128ee1
commit
b1c65863c6
3 changed files with 1471 additions and 677 deletions
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@ -4,32 +4,40 @@ 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("/srv/shiny-server/git/LSHTM_analysis/scripts/Header_TT.R")
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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("/srv/shiny-server/git/LSHTM_analysis/config/alr.R")
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# source("/srv/shiny-server/git/LSHTM_analysis/config/gid.R")
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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("/srv/shiny-server/git/LSHTM_analysis/config/pnca.R")
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# source("/srv/shiny-server/git/LSHTM_analysis/config/rpob.R")
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# source("/srv/shiny-server/git/LSHTM_analysis/config/katg.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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#source("/srv/shiny-server/git/LSHTM_analysis/scripts/Header_TT.R")
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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("/srv/shiny-server/git/LSHTM_analysis/config/", target, ".R")) # load per-target config file
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invisible(assign(paste0(target, "_merged_df3"), read.csv(paste0("/srv/shiny-server/git/Misc/shiny_dashboard/data/",target,"-merged_df3.csv")), envir = .GlobalEnv))
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invisible(assign(paste0(target, "_merged_df2"), read.csv(paste0("/srv/shiny-server/git/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("/srv/shiny-server/git/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("/srv/shiny-server/git/Misc/shiny_dashboard/data/",target,"-lin_lf.csv")), envir = .GlobalEnv))
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invisible(assign(paste0(target, "_lin_wf"), read.csv(paste0("/srv/shiny-server/git/Misc/shiny_dashboard/data/",target,"-lin_wf.csv")), envir = .GlobalEnv))
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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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@ -41,15 +49,18 @@ load_target_globals=function(target){
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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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"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("/srv/shiny-server/git/Misc/shiny_dashboard/data/", tolower(gene), "-wf_", x ,".csv")
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wf_var=paste0("wf_",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("/srv/shiny-server/git/Misc/shiny_dashboard/data/", tolower(gene), "-lf_", x ,".csv")
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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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@ -57,24 +68,6 @@ load_target_globals=function(target){
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}
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)
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}
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# populate target-specific *_unified_msa vars
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load_msa_global=function(gene){
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drug=target_map[[gene]]
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in_filename_msa = paste0(tolower(gene), "_msa.csv")
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infile_msa = paste0("/srv/shiny-server/git/Data/", drug, "/output/", in_filename_msa)
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print(infile_msa)
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msa1 = read.csv(infile_msa, header = F)
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msa_seq = msa1$V1
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infile_fasta = paste0("/srv/shiny-server/git/Data/", drug, "/input/", tolower(gene), "2_f2.fasta")
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print(infile_fasta)
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msa2 = read.csv(infile_fasta, header = F)
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wt_seq = msa2$V1
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target_name=paste0(gene, '_unified_msa')
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#print(target_name)
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invisible(assign(target_name, list(msa_seq = msa_seq, wt_seq = wt_seq), envir = .GlobalEnv))
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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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@ -114,39 +107,36 @@ options(DT.options = list(scrollX = TRUE))
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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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#"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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#"Lineage Distribution",
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"Consurf",
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"LogoP OR",
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"LogoP ED"
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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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#"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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#"lineage_distP",
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"wideP_consurf3",
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"LogoPlotCustomH",
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"LogoPlotMSA"
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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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#"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_df2",
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"merged_df3",
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"merged_df2",
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"unified_msa"
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"merged_df2"
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)
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)
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@ -262,8 +252,7 @@ lapply(c(
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"pnca",
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"rpob"
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),function(x){
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invisible(load_target_globals(x))
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invisible(load_msa_global(x)) # turn off to speed up start time at the expense of "LogoP ED"
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load_target_globals(x)
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}
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)
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@ -316,14 +305,15 @@ consurf_colours = c(
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, "9" = rgb(0.63,0.16,0.37)
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)
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if (interactive()){
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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=dashboardPage(skin="purple",
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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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@ -341,16 +331,16 @@ if (interactive()){
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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("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("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("LogoP ED", tabName="LogoP ED"),
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menuItem('Stability count', tabName='Stability count'),
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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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@ -374,23 +364,13 @@ if (interactive()){
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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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input.sidebar == 'Site SNP count'",
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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="input.sidebar == 'LogoP ED'",
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sliderInput(
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"display_position_full_range"
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, "Display Positions"
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, min=1, max=150, value=c(1,150)
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)
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),
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conditionalPanel(
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condition="
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@ -404,13 +384,6 @@ if (interactive()){
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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 == 'LogoP SNP' || input.sidebar == 'LogoP ED'|| input.sidebar == 'Consurf'",
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# numericInput(
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# "table_position"
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# , "Table Position", value=1
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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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@ -429,6 +402,27 @@ if (interactive()){
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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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@ -440,33 +434,30 @@ if (interactive()){
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"Dynamut2",
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"FoldX",
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"ConSurf"#,
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#"dst_mode"
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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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#"dst_mode"
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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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#"dst_mode"
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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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# 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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@ -493,8 +484,7 @@ if (interactive()){
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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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input.sidebar =='LogoP ED'",
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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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@ -502,8 +492,7 @@ if (interactive()){
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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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input.sidebar =='LogoP ED'",
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input.sidebar =='LogoP OR'",
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actionButton("test_ngl",
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"Test NGLViewR")
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)#,
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@ -518,6 +507,7 @@ if (interactive()){
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)
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),
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#### body ####
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body <- dashboardBody(
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tabItems(
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@ -549,6 +539,92 @@ if (interactive()){
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)
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)
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),
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# Explicit fluidRow() for Lineage plots together
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fluidRow(
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column(conditionalPanel(
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condition="input.sidebar.match(/^Lineage.*/)", box(
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title="Lineage Distribution"
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, status = "info"
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, width=NULL
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, plotOutput("lineage_distP", height="700px") %>% withSpinner(color="#0dc5c1"),
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height=800
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)
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), width=6
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),
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column(conditionalPanel(
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condition="input.sidebar.match(/^Lineage.*/)", box(
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title="Lineage SNP Diversity"
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, status = "info"
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, width=NULL
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, plotOutput("lin_sc", height="700px") %>% withSpinner(color="#0dc5c1"),
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height=800
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)
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), width=6
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)
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),
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# Explicit fluidRow() for Site SNP Count
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fluidRow(
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column(conditionalPanel(
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condition="input.sidebar == 'Site SNP count'",
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box(
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title="Site SNP count"
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, status = "info"
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, width=NULL
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, plotOutput("site_snp_count_bp") %>% withSpinner(color="#0dc5c1")
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)
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), width=6
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),
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column(conditionalPanel(
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condition="input.sidebar == 'Site SNP count'",
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box(
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title="Ligand Distance"
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, status = "info"
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, width=NULL
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, plotOutput("site_snp_count_bp_ligand") %>% withSpinner(color="#0dc5c1")
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)
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), width=6
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),
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column(conditionalPanel(
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condition="input.sidebar == 'Site SNP count'",
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box(
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title="Interface Distance"
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, status = "info"
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, width=NULL
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, plotOutput("site_snp_count_interface") %>% withSpinner(color="#0dc5c1")
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)
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), width=6
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),
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column(conditionalPanel(
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condition="input.sidebar == 'Site SNP count'",
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box(
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title="NCA Distance"
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, status = "info"
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, width=NULL
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, plotOutput("site_snp_count_nca") %>% withSpinner(color="#0dc5c1")
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)
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), width=6
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)
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),
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# # Explicit fluidRow() for Stability Count
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# fluidRow(
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# column(
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# conditionalPanel(
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# condition="input.sidebar.match(/^Lineage.*/)",
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# lapply(
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||||
# # FIXME: using a hardcoded target DF for this IS WRONG AND WILL BREAK
|
||||
# stability_boxes_df[stability_boxes_df$outcome_colname %in% colnames(embb_merged_df3),"outcome_colname"],
|
||||
# function(x){
|
||||
# print(paste0("outcome_colname: ",x))
|
||||
# box(plotOutput(x), width=4)
|
||||
# }
|
||||
# ),
|
||||
# width=12
|
||||
# )
|
||||
# )
|
||||
# ),
|
||||
|
||||
#### fluidRow()s for "Stability Count" in the sidebar ####
|
||||
fluidRow(
|
||||
conditionalPanel(
|
||||
|
|
@ -556,8 +632,7 @@ if (interactive()){
|
|||
input.sidebar == 'LogoP SNP' ||
|
||||
input.sidebar =='Stability SNP by site' ||
|
||||
input.sidebar =='Consurf' ||
|
||||
input.sidebar =='LogoP OR' ||
|
||||
input.sidebar =='LogoP ED'",
|
||||
input.sidebar =='LogoP OR'",
|
||||
column(NGLVieweROutput("structure"),
|
||||
width=3
|
||||
)
|
||||
|
|
@ -568,92 +643,410 @@ if (interactive()){
|
|||
input.sidebar == 'Stability SNP by site' ||
|
||||
input.sidebar == 'Site SNP count' ||
|
||||
input.sidebar == 'Consurf' ||
|
||||
input.sidebar == 'LogoP OR' ||
|
||||
input.sidebar == 'LogoP ED'",
|
||||
input.sidebar == 'LogoP OR'",
|
||||
column(
|
||||
DT::dataTableOutput('table'),
|
||||
width=9
|
||||
)
|
||||
)
|
||||
)
|
||||
),
|
||||
)
|
||||
)
|
||||
|
||||
server = function(input, output) {
|
||||
observeEvent({
|
||||
input$combined_model
|
||||
input$combined_data
|
||||
input$combined_training_genes
|
||||
input$score_dropdown
|
||||
input$resample_dropdown
|
||||
input$drug_dropdown
|
||||
input$split_dropdown
|
||||
server <- function(input, output, session) {
|
||||
|
||||
},{
|
||||
combined_model = input$combined_model
|
||||
selection = input$score_dropdown
|
||||
resampler = input$resample_dropdown
|
||||
selected_drug = input$drug_dropdown
|
||||
selected_split = input$split_dropdown
|
||||
combined_data = input$combined_data
|
||||
combined_training_genes = input$combined_training_genes
|
||||
|
||||
selected_gene = combo[combo$drug == selected_drug,'gene']
|
||||
|
||||
# hide stuff if selected
|
||||
if(combined_model == "combined") {
|
||||
#if(combined_model == TRUE) {
|
||||
|
||||
hide("split_dropdown")
|
||||
hide("resample_dropdown")
|
||||
show("combined_data")
|
||||
show("combined_training_genes")
|
||||
filedata = paste0(combined_training_genes,
|
||||
'genes_logo_skf_BT_',
|
||||
selected_gene,
|
||||
'_',
|
||||
combined_data
|
||||
)
|
||||
print(filedata)
|
||||
|
||||
print('doing COMBINED plot')
|
||||
output$plot <- renderPlot(makeplot(loaded_files[[filedata]],
|
||||
selection,
|
||||
"none", # always 'none' for combined plot
|
||||
gene = combo[drug==selected_drug,"gene"],
|
||||
combined_training_genes = combined_training_genes,
|
||||
display_combined = TRUE,
|
||||
#output$LogoPlotSnps = renderPlot(LogoPlotSnps(mutable_df3))
|
||||
output$lin_sc = renderPlot(
|
||||
lin_sc(
|
||||
input$switch_target,
|
||||
all_lineages = input$all_lineages,
|
||||
my_xats = 12, # x axis text size
|
||||
my_yats = 12, # y axis text size
|
||||
my_xals = 12, # x axis label size
|
||||
my_yals = 12, # y axis label size
|
||||
my_lls = 12, # legend label size
|
||||
d_lab_size = 4
|
||||
)
|
||||
)
|
||||
# e.g.
|
||||
# makeplot(loaded_files$`5genes_logo_skf_BT_pnca_actual`, "MCC", "none" , gene = 'foo', combined_training_genes = '1234', display_combined = TRUE)
|
||||
} else {
|
||||
show("split_dropdown")
|
||||
show("resample_dropdown")
|
||||
hide("combined_data")
|
||||
hide("combined_training_genes")
|
||||
filedata = paste0(combo[drug==selected_drug,"gene"],
|
||||
'_baselineC_',
|
||||
selected_split
|
||||
)
|
||||
print(filedata)
|
||||
print("doing GENE plot")
|
||||
output$plot <- renderPlot(makeplot(loaded_files[[filedata]],
|
||||
selection,
|
||||
resampler,
|
||||
gene = combo[drug==selected_drug,"gene"],
|
||||
display_combined = FALSE,
|
||||
#### lineage_distP ####
|
||||
output$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")
|
||||
)
|
||||
)
|
||||
|
||||
|
||||
}
|
||||
# 6genes_logo_skf_BT_gid_complete
|
||||
|
||||
# filedata example for combined: 6genes_logo_skf_BT_embb_actual
|
||||
# 6genes_logo_skf_BT_embb_combined
|
||||
#### 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)
|
||||
}
|
||||
|
|
@ -4,78 +4,395 @@ library(DT)
|
|||
library(NGLVieweR)
|
||||
library(hash)
|
||||
|
||||
server <- function(input, output) {
|
||||
observeEvent({
|
||||
input$combined_model
|
||||
input$combined_data
|
||||
input$combined_training_genes
|
||||
input$score_dropdown
|
||||
input$resample_dropdown
|
||||
input$drug_dropdown
|
||||
input$split_dropdown
|
||||
function(input, output, session) {
|
||||
|
||||
},{
|
||||
combined_model = input$combined_model
|
||||
selection = input$score_dropdown
|
||||
resampler = input$resample_dropdown
|
||||
selected_drug = input$drug_dropdown
|
||||
selected_split = input$split_dropdown
|
||||
combined_data = input$combined_data
|
||||
combined_training_genes = input$combined_training_genes
|
||||
|
||||
selected_gene = combo[combo$drug == selected_drug,'gene']
|
||||
|
||||
# hide stuff if selected
|
||||
if(combined_model == "combined") {
|
||||
#if(combined_model == TRUE) {
|
||||
|
||||
hide("split_dropdown")
|
||||
hide("resample_dropdown")
|
||||
show("combined_data")
|
||||
show("combined_training_genes")
|
||||
filedata = paste0(combined_training_genes,
|
||||
'genes_logo_skf_BT_',
|
||||
selected_gene,
|
||||
'_',
|
||||
combined_data
|
||||
)
|
||||
print(filedata)
|
||||
|
||||
print('doing COMBINED plot')
|
||||
output$plot <- renderPlot(makeplot(loaded_files[[filedata]],
|
||||
selection,
|
||||
"none", # always 'none' for combined plot
|
||||
gene = combo[drug==selected_drug,"gene"],
|
||||
combined_training_genes = combined_training_genes,
|
||||
display_combined = TRUE,
|
||||
#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
|
||||
)
|
||||
)
|
||||
# e.g.
|
||||
# makeplot(loaded_files$`5genes_logo_skf_BT_pnca_actual`, "MCC", "none" , gene = 'foo', combined_training_genes = '1234', display_combined = TRUE)
|
||||
} else {
|
||||
show("split_dropdown")
|
||||
show("resample_dropdown")
|
||||
hide("combined_data")
|
||||
hide("combined_training_genes")
|
||||
filedata = paste0(combo[drug==selected_drug,"gene"],
|
||||
'_baselineC_',
|
||||
selected_split
|
||||
)
|
||||
print(filedata)
|
||||
print("doing GENE plot")
|
||||
output$plot <- renderPlot(makeplot(loaded_files[[filedata]],
|
||||
selection,
|
||||
resampler,
|
||||
gene = combo[drug==selected_drug,"gene"],
|
||||
display_combined = FALSE,
|
||||
#### lineage_distP ####
|
||||
output$lineage_distP = renderPlot(
|
||||
lineage_distP(
|
||||
get(paste0(input$switch_target, '_merged_df2')),
|
||||
all_lineages = input$all_lineages,
|
||||
x_lab = "Average Stability",
|
||||
x_axis = "avg_stability_scaled",
|
||||
fill_categ_cols = c("red", "blue")
|
||||
)
|
||||
)
|
||||
|
||||
|
||||
}
|
||||
# 6genes_logo_skf_BT_gid_complete
|
||||
|
||||
# filedata example for combined: 6genes_logo_skf_BT_embb_actual
|
||||
# 6genes_logo_skf_BT_embb_combined
|
||||
#### observeEvent() Fun(tm) ####
|
||||
observeEvent(input$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
|
||||
)
|
||||
}
|
||||
)
|
||||
}
|
||||
178
drug-target/ui.R
178
drug-target/ui.R
|
|
@ -7,11 +7,10 @@ library(hash)
|
|||
|
||||
#### Shiny UI #####
|
||||
|
||||
#dashboardHeader(title = paste0(gene, "/", drug)),
|
||||
dashboardPage(skin="purple",
|
||||
dashboardHeader(title = "Drug/Target Explorer"),
|
||||
dashboardHeader(title = "Drug/Target Explorer"),
|
||||
|
||||
dashboardSidebar(
|
||||
dashboardSidebar(
|
||||
sidebarMenu( id = "sidebar",
|
||||
selectInput(
|
||||
"switch_target",
|
||||
|
|
@ -26,16 +25,16 @@ dashboardSidebar(
|
|||
),
|
||||
selected="embb"),
|
||||
menuItem("LogoP SNP", tabName="LogoP SNP"),
|
||||
menuItem("Lineage Sample Count", tabName="Lineage Sample Count"),
|
||||
#menuItem("Lineage Sample Count", tabName="Lineage Sample Count"),
|
||||
menuItem("Site SNP count", tabName="Site SNP count"),
|
||||
menuItem("Stability SNP by site", tabName="Stability SNP by site"),
|
||||
menuItem("DM OM Plots", tabName="DM OM Plots"),
|
||||
menuItem("Correlation", tabName="Correlation"),
|
||||
menuItem("Lineage Distribution", tabName="Lineage Distribution"),
|
||||
#menuItem("Lineage Distribution", tabName="Lineage Distribution"),
|
||||
menuItem("Consurf", tabName="Consurf"),
|
||||
menuItem("LogoP OR", tabName="LogoP OR"),
|
||||
menuItem("LogoP ED", tabName="LogoP ED"),
|
||||
menuItem('Stability count', tabName='Stability count'),
|
||||
menuItem("Lineage", tabName="Lineage"),
|
||||
#menuItem('Stability count', tabName='Stability count'),
|
||||
|
||||
# These conditionalPanel()s make extra settings appear in the sidebar when needed
|
||||
conditionalPanel(
|
||||
|
|
@ -59,23 +58,13 @@ dashboardSidebar(
|
|||
input.sidebar == 'LogoP SNP'||
|
||||
input.sidebar == 'Stability SNP by site' ||
|
||||
input.sidebar == 'Consurf' ||
|
||||
input.sidebar == 'LogoP OR' ||
|
||||
input.sidebar == 'Site SNP count'",
|
||||
input.sidebar == 'LogoP OR'",
|
||||
sliderInput(
|
||||
"display_position_range"
|
||||
, "Display Positions"
|
||||
, min=1, max=150, value=c(1,150) # 150 is just a little less than the smallest pos_count
|
||||
)
|
||||
),
|
||||
conditionalPanel(
|
||||
condition="input.sidebar == 'LogoP ED'",
|
||||
sliderInput(
|
||||
"display_position_full_range"
|
||||
, "Display Positions"
|
||||
, min=1, max=150, value=c(1,150)
|
||||
)
|
||||
),
|
||||
|
||||
|
||||
conditionalPanel(
|
||||
condition="
|
||||
|
|
@ -89,13 +78,6 @@ dashboardSidebar(
|
|||
selected="chemistry"
|
||||
)
|
||||
),
|
||||
#conditionalPanel(
|
||||
# condition="input.sidebar == 'LogoP SNP' || input.sidebar == 'LogoP ED'|| input.sidebar == 'Consurf'",
|
||||
# numericInput(
|
||||
# "table_position"
|
||||
# , "Table Position", value=1
|
||||
# )
|
||||
#),
|
||||
conditionalPanel(
|
||||
condition="input.sidebar == 'Correlation'",
|
||||
selectInput(
|
||||
|
|
@ -114,6 +96,27 @@ dashboardSidebar(
|
|||
, "Ligand Distance Cutoff (Å)", value=1
|
||||
)
|
||||
),
|
||||
conditionalPanel(
|
||||
condition="input.sidebar == 'Site SNP count'",
|
||||
numericInput(
|
||||
"snp_ligand_dist"
|
||||
, "Ligand Distance Cutoff (Å)", value=10
|
||||
)
|
||||
),
|
||||
conditionalPanel(
|
||||
condition="input.sidebar == 'Site SNP count'",
|
||||
numericInput(
|
||||
"snp_interface_dist"
|
||||
, "Interface Distance Cutoff (Å)", value=10
|
||||
)
|
||||
),
|
||||
conditionalPanel(
|
||||
condition="input.sidebar == 'Site SNP count'",
|
||||
numericInput(
|
||||
"snp_nca_dist"
|
||||
, "NCA Distance Cutoff (Å)", value=10
|
||||
)
|
||||
),
|
||||
|
||||
conditionalPanel(
|
||||
condition="input.sidebar == 'Correlation'",
|
||||
|
|
@ -125,33 +128,30 @@ dashboardSidebar(
|
|||
"Dynamut2",
|
||||
"FoldX",
|
||||
"ConSurf"#,
|
||||
#"dst_mode"
|
||||
),
|
||||
choiceValues = c(
|
||||
"DeepDDG",
|
||||
"Dynamut2",
|
||||
"FoldX",
|
||||
"ConSurf"#,
|
||||
#"dst_mode"
|
||||
),
|
||||
selected = c(
|
||||
"DeepDDG",
|
||||
"Dynamut2",
|
||||
"FoldX",
|
||||
"ConSurf"#,
|
||||
#"dst_mode"
|
||||
)
|
||||
)
|
||||
),
|
||||
|
||||
conditionalPanel(
|
||||
condition="input.sidebar == 'DM OM Plots'",
|
||||
selectInput(
|
||||
"dm_om_param",
|
||||
label="Stability Parameter",
|
||||
choices = keys(dm_om_map),
|
||||
selected="SNAP2")
|
||||
),
|
||||
# conditionalPanel(
|
||||
# condition="input.sidebar == 'DM OM Plots'",
|
||||
# selectInput(
|
||||
# "dm_om_param",
|
||||
# label="Stability Parameter",
|
||||
# choices = keys(dm_om_map),
|
||||
# selected="SNAP2")
|
||||
# ),
|
||||
# colour_categ
|
||||
conditionalPanel(
|
||||
condition="input.sidebar == 'Stability SNP by site'",
|
||||
|
|
@ -178,8 +178,7 @@ dashboardSidebar(
|
|||
condition="input.sidebar == 'LogoP SNP' ||
|
||||
input.sidebar =='Stability SNP by site' ||
|
||||
input.sidebar =='Consurf' ||
|
||||
input.sidebar =='LogoP OR' ||
|
||||
input.sidebar =='LogoP ED'",
|
||||
input.sidebar =='LogoP OR'",
|
||||
actionButton("clear_ngl",
|
||||
"Clear Structure")
|
||||
),
|
||||
|
|
@ -187,8 +186,7 @@ dashboardSidebar(
|
|||
condition="input.sidebar == 'LogoP SNP' ||
|
||||
input.sidebar =='Stability SNP by site' ||
|
||||
input.sidebar =='Consurf' ||
|
||||
input.sidebar =='LogoP OR' ||
|
||||
input.sidebar =='LogoP ED'",
|
||||
input.sidebar =='LogoP OR'",
|
||||
actionButton("test_ngl",
|
||||
"Test NGLViewR")
|
||||
)#,
|
||||
|
|
@ -202,8 +200,9 @@ dashboardSidebar(
|
|||
# )
|
||||
|
||||
)
|
||||
),
|
||||
body <- dashboardBody(
|
||||
),
|
||||
#### body ####
|
||||
body <- dashboardBody(
|
||||
|
||||
tabItems(
|
||||
tabItem(tabName = "dashboard",
|
||||
|
|
@ -234,6 +233,92 @@ body <- dashboardBody(
|
|||
)
|
||||
)
|
||||
),
|
||||
# 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(
|
||||
|
|
@ -241,8 +326,7 @@ body <- dashboardBody(
|
|||
input.sidebar == 'LogoP SNP' ||
|
||||
input.sidebar =='Stability SNP by site' ||
|
||||
input.sidebar =='Consurf' ||
|
||||
input.sidebar =='LogoP OR' ||
|
||||
input.sidebar =='LogoP ED'",
|
||||
input.sidebar =='LogoP OR'",
|
||||
column(NGLVieweROutput("structure"),
|
||||
width=3
|
||||
)
|
||||
|
|
@ -253,13 +337,13 @@ body <- dashboardBody(
|
|||
input.sidebar == 'Stability SNP by site' ||
|
||||
input.sidebar == 'Site SNP count' ||
|
||||
input.sidebar == 'Consurf' ||
|
||||
input.sidebar == 'LogoP OR' ||
|
||||
input.sidebar == 'LogoP ED'",
|
||||
input.sidebar == 'LogoP OR'",
|
||||
column(
|
||||
DT::dataTableOutput('table'),
|
||||
width=9
|
||||
)
|
||||
)
|
||||
),
|
||||
)
|
||||
)
|
||||
)
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue