changes made to combining_dfs_plotting.R
This commit is contained in:
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9 changed files with 258 additions and 126 deletions
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@ -2,11 +2,12 @@
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###########################################################
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# TASK: To combine mcsm combined file and meta data.
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# This script is sourced from other .R scripts for plotting.
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# This script is sourced by other .R scripts for plotting.
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###########################################################
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# load libraries and functions
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#source("Header_TT.R")
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#==========================================================
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# combining_dfs_plotting():
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@ -34,28 +35,7 @@ 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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# #======================================
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# # 1: Read file: <gene>_meta data.csv
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# #======================================
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# cat("\nReading meta data file:", df1_mcsm_comb)
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#
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# my_df_u <- read.csv(df1_mcsm_comb
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# , stringsAsFactors = F
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# , header = T)
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# cat("\nDim:", dim(my_df_u))
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#
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# #======================================
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# # 2: Read file: <gene>_meta data.csv
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# #======================================
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# cat("\nReading meta data file:", df2_gene_metadata)
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#
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# gene_metadata <- read.csv(df2_gene_metadata
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# , stringsAsFactors = F
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# , header = T)
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# cat("\nDim:", dim(gene_metadata))
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#
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# table(gene_metadata$mutation_info)
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# counting NAs in AF, OR cols
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# or_mychisq
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if (identical(sum(is.na(my_df_u$or_mychisq))
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@ -219,7 +199,6 @@ combining_dfs_plotting <- function( my_df_u
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, "\nNA in AF:", sum(is.na(merged_df3$af_kin)))
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}
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#===================================================
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# Merge3: merged_df2_comp
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# same as merge 1 but excluding NAs from ORs, etc.
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@ -273,6 +252,13 @@ combining_dfs_plotting <- function( my_df_u
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# compare dfs: foo and merged_df3_com
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all.equal(foo, bar)
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#summary(comparedf(foo, bar))
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cat("\n------------------------"
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, "\nSummary of created dfs:"
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, "\n------------------------"
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, "\n1) Dim of merged_df2: " , nrow(merged_df2), "," , ncol(merged_df2)
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, "\n2) Dim of merged_df2_comp: " , nrow(merged_df2_comp), "," , ncol(merged_df2_comp)
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, "\n3) Dim of merged_df3: " , nrow(merged_df3), "," , ncol(merged_df3)
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, "\n4) Dim of merged_df3_comp: " , nrow(merged_df3_comp), "," , ncol(merged_df3_comp))
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#####################################################################
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# Combining: LIG
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@ -281,13 +267,35 @@ combining_dfs_plotting <- function( my_df_u
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#============
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# Merges 5-8
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#============
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df_lig = my_df_u[my_df_u[[lig_dist_colname]]<lig_dist_cutoff,]
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cat("\n=========================================="
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, "\nStarting filtering for mcsm ligand df"
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, "\n===========================================")
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merged_df2_lig = merged_df2[merged_df2$ligand_distance<lig_dist_cutoff,]
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merged_df2_comp_lig = merged_df2_comp[merged_df2_comp$ligand_distance<lig_dist_cutoff,]
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if (lig_dist_colname%in%names(my_df_u)){
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cat("\nFiltering column: ", lig_dist_colname
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, "\nCut off criteria: ", lig_dist_cutoff, "Angstroms")
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df_lig = my_df_u[my_df_u[[lig_dist_colname]] < lig_dist_cutoff,]
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merged_df3_lig = merged_df3[merged_df3$ligand_distance<lig_dist_cutoff,]
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merged_df3_comp_lig = merged_df3_comp[merged_df3_comp$ligand_distance<lig_dist_cutoff,]
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#merged_df2_lig = merged_df2[merged_df2$ligand_distance<lig_dist_cutoff,]
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merged_df2_lig = merged_df2[merged_df2[[lig_dist_colname]] < lig_dist_cutoff,]
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dim(merged_df2_lig)
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merged_df2_comp_lig = merged_df2_comp[merged_df2_comp[[lig_dist_colname]] < lig_dist_cutoff,]
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merged_df3_lig = merged_df3[merged_df3[[lig_dist_colname]] < lig_dist_cutoff,]
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merged_df3_comp_lig = merged_df3_comp[merged_df3_comp[[lig_dist_colname]] < lig_dist_cutoff,]
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cat("\n------------------------"
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, "\nSummary of created ligand dfs:"
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, "\n------------------------"
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, "\n1) Dim of merged_df2_lig: " , nrow(merged_df2_lig), "," , ncol(merged_df2_lig)
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, "\n2) Dim of merged_df2_comp_lig: " , nrow(merged_df2_comp_lig), "," , ncol(merged_df2_comp_lig)
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, "\n3) Dim of merged_df3_lig: " , nrow(merged_df3_lig), "," , ncol(merged_df3_lig)
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, "\n4) Dim of merged_df3_comp_lig: " , nrow(merged_df3_comp_lig), "," , ncol(merged_df3_comp_lig))
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} else {
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cat("\nFiltering column: ", lig_dist_colname, " not found\n")
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}
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#quit()
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# sanity check
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if (nrow(merged_df3_lig) == nrow(df_lig)){
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@ -297,7 +305,7 @@ combining_dfs_plotting <- function( my_df_u
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, "\nExpected:", nrow(df_lig)
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, "\nGot:", nrow(merged_df3_lig)))
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}
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#==============================================================
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############################################
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@ -327,8 +335,13 @@ combining_dfs_plotting <- function( my_df_u
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# , "\nNo. of cols: ", ncol(get(i)), "\n")
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#}
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return(list(merged_df2, merged_df3
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, merged_df2_comp, merged_df3_comp
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, merged_df2_lig, merged_df3_lig))
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return(list( merged_df2
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, merged_df3
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, merged_df2_comp
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, merged_df3_comp
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, merged_df2_lig
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, merged_df3_lig
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, merged_df2_comp_lig
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, merged_df3_comp_lig))
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}
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@ -16,20 +16,18 @@ library(dplyr)
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## my_df_u_lig
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## dup_muts
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#========================================================
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plotting_data <- function(infile_params, mcsm_lig_cutoff = 10) {
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plotting_data <- function(df, lig_dist_colname = 'ligand_distance', lig_dist_cutoff = 10) {
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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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dup_muts = data.frame()
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cat(paste0("\nInput file to prepare for plotting:", infile_params, "\n") )
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#===========================
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# Read file: struct params
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#===========================
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my_df = read.csv(infile_params, header = T)
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#df = read.csv(infile_params, header = T)
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cat("\nInput dimensions:", dim(my_df))
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cat("\nInput dimensions:", dim(df))
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#==================================
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# add foldx outcome category
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@ -43,17 +41,17 @@ cat("\nInput dimensions:", dim(my_df))
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# adding foldx scaled values
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# scale data b/w -1 and 1
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#------------------------------
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n = which(colnames(my_df) == "ddg"); n
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n = which(colnames(df) == "ddg"); n
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my_min = min(my_df[,n]); my_min
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my_max = max(my_df[,n]); my_max
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my_min = min(df[,n]); my_min
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my_max = max(df[,n]); my_max
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my_df$foldx_scaled = ifelse(my_df[,n] < 0
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, my_df[,n]/abs(my_min)
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, my_df[,n]/my_max)
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df$foldx_scaled = ifelse(df[,n] < 0
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, df[,n]/abs(my_min)
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, df[,n]/my_max)
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# sanity check
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my_min = min(my_df$foldx_scaled); my_min
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my_max = max(my_df$foldx_scaled); my_max
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my_min = min(df$foldx_scaled); my_min
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my_max = max(df$foldx_scaled); my_max
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if (my_min == -1 && my_max == 1){
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cat("\nPASS: foldx ddg successfully scaled b/w -1 and 1"
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@ -67,9 +65,9 @@ if (my_min == -1 && my_max == 1){
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# adding foldx outcome category
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# ddg<0 = "Stabilising" (-ve)
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#------------------------------
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c1 = table(my_df$ddg < 0)
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my_df$foldx_outcome = ifelse(my_df$ddg < 0, "Stabilising", "Destabilising")
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c2 = table(my_df$ddg < 0)
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c1 = table(df$ddg < 0)
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df$foldx_outcome = ifelse(df$ddg < 0, "Stabilising", "Destabilising")
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c2 = table(df$ddg < 0)
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if ( all(c1 == c2) ){
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cat("\nPASS: foldx outcome successfully created")
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@ -83,19 +81,19 @@ if ( all(c1 == c2) ){
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#==================================
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# check for duplicate mutations
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if ( length(unique(my_df$mutationinformation)) != length(my_df$mutationinformation)){
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if ( length(unique(df$mutationinformation)) != length(df$mutationinformation)){
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cat(paste0("\nCAUTION:", " Duplicate mutations identified"
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, "\nExtracting these...\n"))
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#cat(my_df[duplicated(my_df$mutationinformation),])
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dup_muts = my_df[duplicated(my_df$mutationinformation),]
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dup_muts = df[duplicated(df$mutationinformation),]
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dup_muts_nu = length(unique(dup_muts$mutationinformation))
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cat(paste0("\nDim of duplicate mutation df:", nrow(dup_muts)
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, "\nNo. of unique duplicate mutations:", dup_muts_nu
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, "\n\nExtracting df with unique mutations only\n"))
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my_df_u = my_df[!duplicated(my_df$mutationinformation),]
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my_df_u = df[!duplicated(df$mutationinformation),]
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}else{
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cat(paste0("\nNo duplicate mutations detected\n"))
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my_df_u = my_df
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my_df_u = df
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}
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upos = unique(my_df_u$position)
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@ -105,15 +103,14 @@ cat("\nNo. of unique mutational positions:"); cat(length(upos), "\n")
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#===============================================
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# extract mutations <10 Angstroms and symbol
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#===============================================
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table(my_df_u$ligand_distance<mcsm_lig_cutoff)
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table(my_df_u$ligand_distance<lig_dist_cutoff)
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my_df_u_lig = my_df_u[my_df_u$ligand_distance <mcsm_lig_cutoff,]
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my_df_u_lig = my_df_u[my_df_u$ligand_distance <lig_dist_cutoff,]
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cat(paste0("There are ", nrow(my_df_u_lig), " sites lying within 10\u212b of the ligand\n"))
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# return list of DFs
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#return(list(my_df, my_df_u, my_df_u_lig, dup_muts))
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my_df = df
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#df_names = c("my_df", "my_df_u", "my_df_u_lig", "dup_muts")
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all_df = list(my_df, my_df_u, my_df_u_lig, dup_muts)
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#all_df = Map(setNames, all_df, df_names)
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@ -10,21 +10,21 @@
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# input args: 'drug' and 'gene'
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# output: dir names for input and output files
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import_dirs <- function(drug, gene) {
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import_dirs <- function(drug_name, gene_name) {
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#============================
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# directories and variables
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#============================
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datadir <<- paste0("~/git/Data/")
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indir <<- paste0(datadir, drug, "/input")
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outdir <<- paste0("~/git/Data/", drug, "/output")
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plotdir <<- paste0("~/git/Data/", drug, "/output/plots")
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indir <<- paste0(datadir, drug_name, "/input")
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outdir <<- paste0("~/git/Data/", drug_name, "/output")
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plotdir <<- paste0("~/git/Data/", drug_name, "/output/plots")
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dr_muts_col <<- paste0('dr_mutations_', drug)
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other_muts_col <<- paste0('other_mutations_', drug)
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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,"_p.")
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gene_match <<- paste0(gene_name,"_p.")
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}
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@ -37,20 +37,17 @@ import_dirs <- function(drug, gene) {
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#==================
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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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#===============
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delta_symbol <<- "\u0394"; delta_symbol
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#==========
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# Colours
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#==========
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mcsm_red2 <<- "#ae301e" # most negative
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mcsm_red1 <<- "#f8766d"
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@ -58,3 +55,4 @@ mcsm_mid <<- "white" # middle
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mcsm_blue1 <<- "#00bfc4"
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mcsm_blue2 <<- "#007d85" # most positive
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#########################################################
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@ -7,6 +7,7 @@ getwd()
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#===========================================
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source("plotting_data.R")
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infile = "/home/tanu/git/Data/streptomycin/output/gid_comb_stab_struc_params.csv"
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pd_df = plotting_data(infile)
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my_df = pd_df[[1]]
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my_df_u = pd_df[[2]]
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@ -1,4 +1,4 @@
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# #!/usr/bin/env Rscript
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#!/usr/bin/env Rscript
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# working dir and loading libraries
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getwd()
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@ -33,14 +33,20 @@ source("plotting_globals.R")
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source("plotting_data.R")
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source("combining_dfs_plotting.R")
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gene = 'gid'
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drug = 'streptomycin'
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#---------------------
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# call: import_dirs()
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#---------------------
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import_dirs(drug, gene)
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gene = 'gid'
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drug = 'streptomycin'
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import_dirs(drug_name = drug, gene_name = gene)
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#-------------------------------
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# test function: plotting_data()
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#-------------------------------
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#============================
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# Input 1: plotting_data()
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#============================
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if (!exists("infile_params") && exists("gene")){
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#if (!is.character(infile_params) && exists("gene")){
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#in_filename_params = paste0(tolower(gene), "_all_params.csv")
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@ -49,6 +55,13 @@ if (!exists("infile_params") && exists("gene")){
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cat("\nInput file for mcsm comb data not specified, assuming filename: ", infile_params, "\n")
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}
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mcsm_comb_data = read.csv(infile_params, header = T)
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pd_df = plotting_data(df = mcsm_comb_data, lig_dist_cutoff = 10)
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my_df_u = pd_df[[2]]
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#======================================
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# Input 2: read <gene>_meta data.csv
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#======================================
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if (!exists("infile_metadata") && exists("gene")){
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#if (!is.character(infile_params) && exists("gene")){{
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in_filename_metadata = paste0(tolower(gene), "_metadata.csv") # part combined for gid
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@ -56,18 +69,6 @@ if (!exists("infile_metadata") && exists("gene")){
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cat("\nInput file for gene metadata not specified, assuming filename: ", infile_metadata, "\n")
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}
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#============================
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# Input 1: plotting_data()
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#============================
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#---------------------
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# call: plotting_data()
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#---------------------
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pd_df = plotting_data(infile_params)
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my_df = pd_df[[1]] # this forms one of the input for combining_dfs_plotting()
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#======================================
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# Input 2: read <gene>_meta data.csv
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#======================================
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cat("\nReading meta data file:", infile_metadata)
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gene_metadata <- read.csv(infile_metadata
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@ -79,9 +80,11 @@ all_plot_dfs = combining_dfs_plotting(my_df_u
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, lig_dist_colname = 'ligand_distance'
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, lig_dist_cutoff = 10)
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merged_df2 = all_plot_dfs[[1]]
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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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merged_df2_lig = all_plot_dfs[[5]]
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merged_df3_lig = all_plot_dfs[[6]]
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merged_df2 = all_plot_dfs[[1]]
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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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merged_df2_lig = all_plot_dfs[[5]]
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merged_df3_lig = all_plot_dfs[[6]]
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merged_df2_comp_lig = all_plot_dfs[[7]]
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merged_df3_comp_lig = all_plot_dfs[[8]]
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