going through functions and script for interactive plots
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7 changed files with 83 additions and 95 deletions
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@ -21,7 +21,7 @@
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# 1) large combined df including NAs for AF, OR,etc
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# Dim: same no. of rows as gene associated meta_data_with_AFandOR
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# 2) small combined df including NAs for AF, OR, etc.
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# Dim: same as mcsm data
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# Dim: same as mcsm data or foldX
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# 3) large combined df excluding NAs
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# Dim: dim(#1) - na_count_df2
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# 4) small combined df excluding NAs
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@ -31,10 +31,13 @@
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# 6) LIGAND small combined df excluding NAs
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# Dim: dim()
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#==========================================================
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#lig_dist_colname = 'ligand_distance' or global var LigDist_colname
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#lig_dist_cutoff = 10 or global var LigDist_cutoff
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combining_dfs_plotting <- function( my_df_u
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, gene_metadata
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, lig_dist_colname = 'ligand_distance'
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, lig_dist_cutoff = 10){
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, lig_dist_colname = ''
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, lig_dist_cutoff = ''){
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# counting NAs in AF, OR cols
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# or_mychisq
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@ -16,9 +16,12 @@ library(dplyr)
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## my_df_u_lig
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## dup_muts
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#========================================================
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#lig_dist_colname = 'ligand_distance' or global var LigDist_colname
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#lig_dist_cutoff = 10 or global var LigDist_cutoff
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plotting_data <- function(df
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, lig_dist_colname = 'ligand_distance'
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, lig_dist_cutoff = 10) {
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, lig_dist_colname = ''
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, lig_dist_cutoff = '') {
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my_df = data.frame()
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my_df_u = data.frame()
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my_df_u_lig = data.frame()
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@ -23,12 +23,16 @@ import_dirs <- function(drug_name, gene_name) {
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dr_muts_col <<- paste0('dr_mutations_', drug_name)
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other_muts_col <<- paste0('other_mutations_', drug_name)
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resistance_col <<- "drtype"
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gene_match <<- paste0(gene_name,"_p.")
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}
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# other globals
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# Other globals
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#=====================
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# Resistance colname
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#=====================
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resistance_col <<- "drtype"
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#===============================
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# mcsm ligand distance cut off
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#===============================
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@ -39,7 +43,6 @@ LigDist_cutoff <<- 10
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# Angstroms symbol
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#==================
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angstroms_symbol <<- "\u212b"
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#cat(paste0("There are ", nrow(my_df_u_lig), " sites lying within 10", angstroms_symbol, " of the ligand\n"))
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#===============
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# Delta symbol
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@ -186,3 +186,7 @@ func_path = "~/git/LSHTM_analysis/scripts/functions/"
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source_files <- list.files(func_path, "\\.R$") # locate all .R files
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map(paste0(func_path, source_files), source) # source all your R scripts!
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# set plot script dir
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plot_script_path = "~/git/LSHTM_analysis/scripts/plotting/"
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@ -12,20 +12,24 @@ source("/home/tanu/git/LSHTM_analysis/scripts/plotting/Header_TT.R")
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# in from other scripts
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# to call this
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#********************
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# set drug and gene name
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#====================
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# variables for lig
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#====================
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#==========================================
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# variables for lig:
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# comes from functions/plotting_globals.R
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#==========================================
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#LigDist_colname = "ligand_distance"
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#LigDist_cutoff = 10
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cat("\nGlobal variables for Ligand:"
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, "\nligand distance colname:", LigDist_colname
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, "\nligand distance cut off:", LigDist_cutoff)
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#===========
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# input
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#===========
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#---------------------
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#--------------------------------------------
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# call: import_dirs()
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#---------------------
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# comes from functions/plotting_globals.R
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#--------------------------------------------
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import_dirs(drug, gene)
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#---------------------------
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@ -72,6 +76,8 @@ gene_metadata <- read.csv(infile_metadata
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, stringsAsFactors = F
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, header = T)
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cat("\nDim of meta data file: ", dim(gene_metadata))
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all_plot_dfs = combining_dfs_plotting(my_df_u
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, gene_metadata
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, lig_dist_colname = LigDist_colname
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@ -82,34 +88,6 @@ merged_df3 = all_plot_dfs[[2]]
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merged_df2_comp = all_plot_dfs[[3]]
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merged_df3_comp = all_plot_dfs[[4]]
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#======================================================================
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#TODO: Think! MOVE TO COMBINE or singular file for deepddg
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#============================
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# adding deepddg scaled values
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# scale data b/w -1 and 1
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#============================
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# n = which(colnames(merged_df3) == "deepddg"); n
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#
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# my_min = min(merged_df3[,n]); my_min
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# my_max = max(merged_df3[,n]); my_max
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#
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# merged_df3$deepddg_scaled = ifelse(merged_df3[,n] < 0
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# , merged_df3[,n]/abs(my_min)
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# , merged_df3[,n]/my_max)
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# # sanity check
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# my_min = min(merged_df3$deepddg_scaled); my_min
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# my_max = max(merged_df3$deepddg_scaled); my_max
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#
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# if (my_min == -1 && my_max == 1){
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# cat("\nPASS: DeepDDG successfully scaled b/w -1 and 1"
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# #, "\nProceeding with assigning deep outcome category")
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# , "\n")
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# }else{
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# cat("\nFAIL: could not scale DeepDDG ddg values"
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# , "Aborting!")
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# }
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#
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####################################################################
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# Data for combining other dfs
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####################################################################
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@ -131,7 +109,7 @@ merged_df3_comp = all_plot_dfs[[4]]
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# Data for logoplots
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####################################################################
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source("logo_data.R")
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source(paste0(plot_script_path, "logo_data.R"))
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s1 = c("\nSuccessfully sourced logo_data.R")
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cat(s1)
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@ -142,7 +120,7 @@ cat(s1)
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#source("other_plots_data.R")
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source("dm_om_data.R")
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source(paste0(plot_script_path, "dm_om_data.R"))
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s2 = c("\nSuccessfully sourced other_plots_data.R")
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cat(s2)
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@ -151,7 +129,7 @@ cat(s2)
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# Data for Lineage barplots: WF and LF dfs
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####################################################################
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source("lineage_data.R")
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source(paste0(plot_script_path, "lineage_data.R"))
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s3 = c("\nSuccessfully sourced lineage_data.R")
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cat(s3)
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@ -160,7 +138,7 @@ cat(s3)
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# Data for corr plots:
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####################################################################
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# make sure the above script works because merged_df2_combined is needed
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source("corr_data.R")
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source(paste0(plot_script_path, "corr_data.R"))
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s4 = c("\nSuccessfully sourced corr_data.R")
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cat(s4)
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@ -181,45 +159,18 @@ if ( all( length(s1), length(s2), length(s3), length(s4) ) >0 ){
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}
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########################################################################
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# clear excess variables
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rm(c1, c2, c3, c4, check1
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, curr_count, curr_total
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, cols_check
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, cols_to_select
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, cols_to_select_deepddg
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, cols_to_select_duet
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, cols_to_select_dynamut
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, cols_to_select_dynamut2
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, cols_to_select_encomddg
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, cols_to_select_encomdds
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, cols_to_select_mcsm
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, cols_to_select_mcsm_na
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, cols_to_select_sdm
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# clear excess variables: from the global enviornment
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vars0 = ls(envir = .GlobalEnv)[grepl("curr_*", ls(envir = .GlobalEnv))]
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vars1 = ls(envir = .GlobalEnv)[grepl("^cols_to*", ls(envir = .GlobalEnv))]
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vars2 = ls(envir = .GlobalEnv)[grepl("pivot_cols_*", ls(envir = .GlobalEnv))]
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vars3 = ls(envir = .GlobalEnv)[grepl("expected_*", ls(envir = .GlobalEnv))]
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rm(c1
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, fact_cols
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, infile_metadata
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, infile_params
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#, infilename_dynamut
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#, infilename_dynamut2
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#, infilename_mcsm_f_snps
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#, infilename_mcsm_na
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)
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rm(pivot_cols
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, pivot_cols_deepddg
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, pivot_cols_duet
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, pivot_cols_dynamut
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, pivot_cols_dynamut2
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, pivot_cols_encomddg
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, pivot_cols_encomdds
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, pivot_cols_foldx
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, pivot_cols_mcsm
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, pivot_cols_mcsm_na
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, pivot_cols_n
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, pivot_cols_sdm)
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rm(expected_cols
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, expected_ncols
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, expected_rows
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, expected_rows_lf
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, fact_cols)
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, vars0
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, vars1
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, vars2
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, vars3)
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@ -140,3 +140,4 @@ wide_df_or_mult = wide_df_or_mult[,-1]
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str(wide_df_or_mult)
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position_or_mult = as.numeric(colnames(wide_df_or_mult))
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@ -56,6 +56,8 @@ logo_combined_labelled = "logo_combined_labelled.svg"
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plot_logo_combined_labelled = paste0(plotdir,"/", logo_combined_labelled)
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#########################################################
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#logo_or_mult_p + theme_dark()
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#logo_or_mult_p + theme(plot.background = element_rect(fill = "black"))
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#==================================
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# Output
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@ -124,8 +126,24 @@ print(logo_logOR)
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#*****************************
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# Mutant logo plot: >1 nsSNP
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#******************************
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aa_col_choices = c('chemistry', 'hydrophobicity', 'clustalx', 'taylor')
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my_logo_col = aa_col_choices[[1]]
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if (my_logo_col == 'clustalx || taylor'){
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cat("\nSelected colour scheme:", my_logo_col
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, "\nUsing black theme\n")
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theme_bgc = "black"
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font_bgc = "white"
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} if (my_logo_col == 'chemistry || hydrophobicity') {
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cat('\nSelected colour scheme:', my_logo_col
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, "\nUsing grey theme")
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theme_bgc = "grey"
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font_bgc = "black"
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}
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p0 = ggseqlogo(tab_mt
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, method = 'custom'
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, col_scheme = my_logo_col
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, seq_type = 'aa') +
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#ylab('my custom height') +
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theme(axis.text.x = element_blank()) +
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@ -143,9 +161,12 @@ cat('\nDone: p0')
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mut_logo_p = p0 + theme(legend.position = "none"
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, legend.title = element_blank()
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, legend.text = element_text(size = 20)
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, axis.text.x = element_text(size = 14, angle = 90)
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, axis.text.y = element_blank())
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#mut_logo_p
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, axis.text.x = element_text(size = 14
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, angle = 90
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, colour = font_bgc)
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, axis.text.y = element_blank()
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, plot.background = element_rect(fill = theme_bgc))
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mut_logo_p
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cat('\nDone: p0+mut_logo_p')
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#*************************
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@ -154,9 +175,7 @@ cat('\nDone: p0+mut_logo_p')
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p2 = ggseqlogo(tab_wt
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, method = 'custom'
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, seq_type = 'aa'
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#, col_scheme = "taylor"
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#, col_scheme = chemistry2
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) +
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, col_scheme = my_logo_col) +
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#ylab('my custom height') +
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theme(text=element_text(family="FreeSans"))+
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theme(axis.text.x = element_blank()
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@ -185,7 +204,11 @@ cat('\nDone: wt_logo_p')
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#***********************
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# Logo OR >1 nsSNP
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#***********************
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logo_or_mult_p = ggseqlogo(wide_df_or_mult, method="custom", seq_type="aa") + ylab("my custom height") +
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logo_or_mult_p = ggseqlogo(wide_df_or_mult
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, method = "custom"
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, col_scheme = my_logo_col
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, seq_type="aa") +
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ylab("my custom height") +
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theme(axis.text.x = element_text(size = 14
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, angle = 90
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, hjust = 1
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