new msa dash

This commit is contained in:
Tanushree Tunstall 2022-09-05 16:52:45 +01:00
parent 3037d6e3ef
commit e69c2a60aa
4 changed files with 393 additions and 98 deletions

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@ -1,17 +1,47 @@
# ***************************
# ** I M P O R T A N T **
# ***************************
# DO NOT USE OR MODIFY THIS.
# USE THE ONE IN THE 'Dashboards'
# REPO
library(shinycssloaders) library(shinycssloaders)
library(DT) library(DT)
library(NGLVieweR) library(NGLVieweR)
library(hash) library(hash)
# FIXME This is slow and should happen *once only*
#source("~/git/LSHTM_analysis/scripts/Header_TT.R")
# FIXME: these are needed but slow to load every time
# source("~/git/LSHTM_analysis/config/alr.R")
# source("~/git/LSHTM_analysis/config/gid.R")
# source("~/git/LSHTM_analysis/config/pnca.R")
# source("~/git/LSHTM_analysis/config/rpob.R")
# source("~/git/LSHTM_analysis/config/katg.R")
# TODO: this is TEMPORARY and will shortly get replaced with a target picker
# that'll reload everything when changing targets. the lapply() is *much* quicker
# than previous approaches
# system.time({
source("~/git/LSHTM_analysis/scripts/Header_TT.R")
load_target_globals=function(target){ load_target_globals=function(target){
cat(paste0("Reloading Target: ", target)) cat(paste0("Reloading Target: ", target))
source(paste0("/srv/shiny-server/git/LSHTM_analysis/config/", target, ".R")) # load per-target config file source(paste0("~/git/LSHTM_analysis/config/", target, ".R")) # load per-target config file
invisible(assign(paste0(target, "_merged_df3"), read.csv(paste0("/srv/shiny-server/git/Misc/shiny_dashboard/data/",target,"-merged_df3.csv")), envir = .GlobalEnv)) invisible(assign(paste0(target, "_merged_df3"), read.csv(paste0("~/git/Misc/shiny_dashboard/data/",target,"-merged_df3.csv")), envir = .GlobalEnv))
invisible(assign(paste0(target, "_merged_df2"), read.csv(paste0("/srv/shiny-server/git/Misc/shiny_dashboard/data/",target,"-merged_df2.csv")), envir = .GlobalEnv)) invisible(assign(paste0(target, "_merged_df2"), read.csv(paste0("~/git/Misc/shiny_dashboard/data/",target,"-merged_df2.csv")), envir = .GlobalEnv))
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)) invisible(assign(paste0(target, "_corr_df_m3_f"), read.csv(paste0("~/git/Misc/shiny_dashboard/data/",target,"-corr_df_m3_f.csv")), envir = .GlobalEnv))
invisible(assign(paste0(target, "_lin_lf"), read.csv(paste0("/srv/shiny-server/git/Misc/shiny_dashboard/data/",target,"-lin_lf.csv")), envir = .GlobalEnv)) invisible(assign(paste0(target, "_lin_lf"), read.csv(paste0("~/git/Misc/shiny_dashboard/data/",target,"-lin_lf.csv")), envir = .GlobalEnv))
invisible(assign(paste0(target, "_lin_wf"), read.csv(paste0("/srv/shiny-server/git/Misc/shiny_dashboard/data/",target,"-lin_wf.csv")), envir = .GlobalEnv)) invisible(assign(paste0(target, "_lin_wf"), read.csv(paste0("~/git/Misc/shiny_dashboard/data/",target,"-lin_wf.csv")), envir = .GlobalEnv))
lapply( lapply(
c( c(
"duet", "duet",
@ -26,12 +56,12 @@ load_target_globals=function(target){
"mcsm_ppi2"#, "mcsm_ppi2"#,
#"mcsm_na" #"mcsm_na"
), function(x){ ), function(x){
wf_filename=paste0("/srv/shiny-server/git/Misc/shiny_dashboard/data/", tolower(gene), "-wf_", x ,".csv") wf_filename=paste0("~/git/Misc/shiny_dashboard/data/", tolower(gene), "-wf_", x ,".csv")
wf_var=paste0("wf_",x) wf_var=paste0("wf_",x)
if (file.exists(wf_filename)){ if (file.exists(wf_filename)){
invisible(assign(wf_var,read.csv(wf_filename), envir = .GlobalEnv)) # FILTH invisible(assign(wf_var,read.csv(wf_filename), envir = .GlobalEnv)) # FILTH
} }
lf_filename=paste0("/srv/shiny-server/git/Misc/shiny_dashboard/data/", tolower(gene), "-lf_", x ,".csv") lf_filename=paste0("~/git/Misc/shiny_dashboard/data/", tolower(gene), "-lf_", x ,".csv")
lf_var=paste0(target, "_lf_",x) lf_var=paste0(target, "_lf_",x)
if (file.exists(lf_filename)){ if (file.exists(lf_filename)){
invisible(assign(lf_var,read.csv(lf_filename), envir = .GlobalEnv)) # FILTH invisible(assign(lf_var,read.csv(lf_filename), envir = .GlobalEnv)) # FILTH
@ -43,12 +73,12 @@ load_target_globals=function(target){
load_msa_global=function(gene){ load_msa_global=function(gene){
drug=target_map[[gene]] drug=target_map[[gene]]
in_filename_msa = paste0(tolower(gene), "_msa.csv") in_filename_msa = paste0(tolower(gene), "_msa.csv")
infile_msa = paste0("/srv/shiny-server/git/Data/", drug, "/output/", in_filename_msa) infile_msa = paste0("~/git/Data/", drug, "/output/", in_filename_msa)
print(infile_msa) print(infile_msa)
msa1 = read.csv(infile_msa, header = F) msa1 = read.csv(infile_msa, header = F)
msa_seq = msa1$V1 msa_seq = msa1$V1
infile_fasta = paste0("/srv/shiny-server/git/Data/", drug, "/input/", tolower(gene), "2_f2.fasta") infile_fasta = paste0("~/git/Data/", drug, "/input/", tolower(gene), "2_f2.fasta")
print(infile_fasta) print(infile_fasta)
msa2 = read.csv(infile_fasta, header = F) msa2 = read.csv(infile_fasta, header = F)
wt_seq = msa2$V1 wt_seq = msa2$V1
@ -59,20 +89,7 @@ load_msa_global=function(gene){
} }
#### Local Functions #### #### Local Functions ####
# Generate a conditionalPanel() for a given graph function and sidebar name combination
generate_conditionalPanel = function(tab_name, plot_function, plot_df){
# e.g.: list("lin_count_bp_diversity", "Lineage diversity count")
cond=paste0("input.sidebar == '", tab_name, "'")
conditionalPanel(condition=cond, box(
title=tab_name
, status = "info"
, width=NULL
, plotOutput(plot_function
, click = "plot_click") %>% withSpinner(color="#0dc5c1")
# , plotOutput(plot_function, click = "plot_click")
)
)
}
# FIXME: passing in the per-plot params is broken # FIXME: passing in the per-plot params is broken
lin_sc=function(x, all_lineages = F, ...){ lin_sc=function(x, all_lineages = F, ...){
@ -197,26 +214,6 @@ dm_om_methods = c("DUET ΔΔG"
, "Ligand affinity (log fold change)" , "Ligand affinity (log fold change)"
, "mCSM-NA affinity ΔΔG" , "mCSM-NA affinity ΔΔG"
, "SNAP2") , "SNAP2")
dm_om_map = hash(c(
"DUET ΔΔG"
, "Consurf"
, "Deepddg ΔΔG"
, "Dynamut2 ΔΔG"
, "FoldX ΔΔG"
, "Ligand affinity (log fold change)"
, "mCSM-NA affinity ΔΔG"
, "SNAP2"
), c(
"lf_duet"
,"lf_consurf"
,"lf_deepddg"
,"lf_dynamut2"
,"lf_foldx"
,"lf_mcsm_lig"
,"lf_mcsm_na"
,"lf_snap2"
)
)
#### target_map: handy gene/drug mapping hash #### #### target_map: handy gene/drug mapping hash ####
target_map = hash( target_map = hash(
c( c(
@ -245,57 +242,188 @@ lapply(c(
"rpob" "rpob"
),function(x){ ),function(x){
invisible(load_target_globals(x)) invisible(load_target_globals(x))
invisible(load_msa_global(x)) # turn off to speed up start time at the expense of "LogoP ED" invisible(load_msa_global(x))
} }
) )
consurf_palette1 = c("0" = "yellow2" #### Shiny UI #####
, "1" = "cyan1" if(interactive()){
, "2" = "steelblue2" ui <- dashboardPage(
, "3" = "cadetblue2" #dashboardHeader(title = paste0(gene, "/", drug)),
, "4" = "paleturquoise2" dashboardHeader(title = "Sequence Alignment"),
, "5" = "thistle3"
, "6" = "thistle2"
, "7" = "plum2"
, "8" = "maroon"
, "9" = "violetred2")
consurf_palette2 = c("0" = "yellow2" dashboardSidebar(
, "1" = "forestgreen" sidebarMenu( id = "sidebar",
, "2" = "seagreen3" selectInput(
, "3" = "palegreen1" "switch_target",
, "4" = "darkseagreen2" label="Target",
, "5" = "thistle3" choices = c(
, "6" = "lightpink1" "alr",
, "7" = "orchid3" "embb",
, "8" = "orchid4" "gid",
, "9" = "darkorchid4") "katg",
"pnca",
"rpob"
),
selected="embb"),
menuItem("LogoP ED", tabName="LogoP ED"),
# decreasing levels mess legend sliderInput(
# consurf_colours_LEVEL = c( "display_position_full_range"
# "0" = rgb(1.00,1.00,0.59) , "Display Positions"
# , "9" = rgb(0.63,0.16,0.37) , min=1, max=150, value=c(1,150)
# , "8" = rgb(0.94,0.49,0.67) ),
# , "7" = rgb(0.98,0.78,0.86)
# , "6" = rgb(0.98,0.92,0.96) conditionalPanel(
# , "5" = rgb(1.00,1.00,1.00) condition="
# , "4" = rgb(0.84,0.94,0.94) input.sidebar == 'LogoP SNP' ||
# , "3" = rgb(0.65,0.86,0.90) input.sidebar == 'LogoP OR' ||
# , "2" = rgb(0.29,0.69,0.75) input.sidebar == 'LogoP ED'",
# , "1" = rgb(0.04,0.49,0.51) selectInput(
"logoplot_colour_scheme",
label="Logo Plot Colour Scheme",
choices = logoPlotSchemes,
selected="chemistry"
)
)
)
),
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,
plotOutput("LogoPlotMSA",
click = "plot_click") %>% withSpinner(color="#0dc5c1")
)
)
)
)
#### Shiny Backend Server #####
server <- function(input, output, session) {
observeEvent(
{
input$display_position_full_range #special-purpose for MSA
input$logoplot_colour_scheme
input$switch_target
},
{
# C O M P A T I B I L I T Y
#gene=input$switch_target
#drug=target_map[[gene]]
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)
unified_msa = get(paste0(input$switch_target, '_unified_msa'))
#
# # re-sort the dataframe according to position count
sorted_df = cbind(merged_df3)
sorted_df = sorted_df %>% arrange(pos_count)
#
outdir = paste0("~/git/Data/", drug, '/output/')
indir = paste0("~/git/Data/", drug , "/input/")
#
# source("~/git/LSHTM_analysis/scripts/plotting/logo_data_msa.R") # probably unnecessary...
# source("~/git/LSHTM_analysis/scripts/plotting/get_plotting_dfs.R")
#### nasty special-purpose merged_df3 variants ####
# FIXME: SLOW
# corr_plotdf = corr_data_extract(
# merged_df3
# , gene = gene
# , drug = drug
# , extract_scaled_cols = F
# ) # )
consurf_colours = c( #input$stability_snp_param
"0" = rgb(1.00,1.00,0.59)
, "1" = rgb(0.04,0.49,0.51) updateSliderInput(
, "2" = rgb(0.29,0.69,0.75) session,
, "3" = rgb(0.65,0.86,0.90) "display_position_range",
, "4" = rgb(0.84,0.94,0.94) min = position_min,
, "5" = rgb(1.00,1.00,1.00) max = position_max
, "6" = rgb(0.98,0.92,0.96) #, value = c(position_min, position_min+150)
, "7" = rgb(0.98,0.78,0.86) )
, "8" = rgb(0.94,0.49,0.67) updateSliderInput(
, "9" = rgb(0.63,0.16,0.37) session,
"display_position_full_range",
min = 1,
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)
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$display_position_range
plot_min=display_position_range[1]
plot_max=display_position_range[2]
display_position_full_range = input$display_position_full_range
full_range_min=display_position_full_range[1]
full_range_max=display_position_full_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),]
#### LogoPlotMSA/Logo Plot ED ####
output$LogoPlotMSA = renderPlot(
LogoPlotMSA(target=input$switch_target,
plot_positions=full_range_min:full_range_max,
my_logo_col = logoplot_colour_scheme,
aa_pos_drug = aa_pos_drug,
active_aa_pos = active_aa_pos,
aa_pos_lig1 = aa_pos_lig1,
aa_pos_lig2 = aa_pos_lig2,
aa_pos_lig3 = aa_pos_lig3
)
)
}
) )
#### EOF Shiny Server ####
}
################ Running Server ##############
app <- shinyApp(ui, server)
runApp(app)
}

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@ -0,0 +1,105 @@
function(input, output, session) {
observeEvent(
{
input$display_position_full_range #special-purpose for MSA
input$logoplot_colour_scheme
input$switch_target
},
{
# C O M P A T I B I L I T Y
#gene=input$switch_target
#drug=target_map[[gene]]
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)
unified_msa = get(paste0(input$switch_target, '_unified_msa'))
#
# # re-sort the dataframe according to position count
sorted_df = cbind(merged_df3)
sorted_df = sorted_df %>% arrange(pos_count)
#
outdir = paste0("~/git/Data/", drug, '/output/')
indir = paste0("~/git/Data/", drug , "/input/")
#
# source("~/git/LSHTM_analysis/scripts/plotting/logo_data_msa.R") # probably unnecessary...
# source("~/git/LSHTM_analysis/scripts/plotting/get_plotting_dfs.R")
#### nasty special-purpose merged_df3 variants ####
# FIXME: SLOW
# corr_plotdf = corr_data_extract(
# merged_df3
# , gene = gene
# , drug = drug
# , extract_scaled_cols = F
# )
#input$stability_snp_param
updateSliderInput(
session,
"display_position_range",
min = position_min,
max = position_max
#, value = c(position_min, position_min+150)
)
updateSliderInput(
session,
"display_position_full_range",
min = 1,
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)
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$display_position_range
plot_min=display_position_range[1]
plot_max=display_position_range[2]
display_position_full_range = input$display_position_full_range
full_range_min=display_position_full_range[1]
full_range_max=display_position_full_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),]
#### LogoPlotMSA/Logo Plot ED ####
output$LogoPlotMSA = renderPlot(
LogoPlotMSA(target=input$switch_target,
plot_positions=full_range_min:full_range_max,
my_logo_col = logoplot_colour_scheme,
aa_pos_drug = aa_pos_drug,
active_aa_pos = active_aa_pos,
aa_pos_lig1 = aa_pos_lig1,
aa_pos_lig2 = aa_pos_lig2,
aa_pos_lig3 = aa_pos_lig3
)
)
}
)
}

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@ -0,0 +1,62 @@
dashboardPage(
#dashboardHeader(title = paste0(gene, "/", drug)),
dashboardHeader(title = "Sequence Alignment"),
dashboardSidebar(
sidebarMenu( id = "sidebar",
selectInput(
"switch_target",
label="Target",
choices = c(
"alr",
"embb",
"gid",
"katg",
"pnca",
"rpob"
),
selected="embb"),
menuItem("LogoP ED", tabName="LogoP ED"),
sliderInput(
"display_position_full_range"
, "Display Positions"
, min=1, max=150, value=c(1,150)
),
conditionalPanel(
condition="
input.sidebar == 'LogoP SNP' ||
input.sidebar == 'LogoP OR' ||
input.sidebar == 'LogoP ED'",
selectInput(
"logoplot_colour_scheme",
label="Logo Plot Colour Scheme",
choices = logoPlotSchemes,
selected="chemistry"
)
)
)
),
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,
plotOutput("LogoPlotMSA",
click = "plot_click") %>% withSpinner(color="#0dc5c1")
)
)
)
)