tidied plotting_data.R as a function returning a lits of dfs
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3 changed files with 134 additions and 81 deletions
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@ -3,40 +3,27 @@
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# TASK: producing barplots
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# basic barplots with count of mutations
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# basic barplots with frequency of count of mutations
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# Depends on
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## plotting_globals.R (previously dir.R)
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## plotting_data.R
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#########################################################
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#=======================================================================
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# working dir and loading libraries
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# working dir
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getwd()
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setwd("~/git/LSHTM_analysis/scripts/plotting")
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getwd()
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# load libraries
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#source("Header_TT.R")
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library(ggplot2)
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library(data.table)
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library(dplyr)
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require("getopt", quietly = TRUE) # cmd parse arguments
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# Set globals:
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# load functions
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source("plotting_globals.R")
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# pretent cli
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drug = "streptomycin"
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gene = "gid"
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infile = "merged_df3_short.csv"
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import_dirs(drug, gene)
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source("plotting_data.R")
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plotting_data("merged_df3_short.csv")
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if (!exists("infile") && exists("gene")){
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#in_filename_params = paste0(tolower(gene), "_all_params.csv")
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in_filename_params = paste0(tolower(gene), "_comb_stab_struc_params.csv") # part combined for gid
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infile = paste0(outdir, "/", in_filename_params)
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}
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plotting_data(infile)
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#=======================================================================
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#########################################################
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# command line args
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spec = matrix(c(
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"drug" , "d", 1, "character",
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@ -56,9 +43,29 @@ gene = opt$gene
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if(is.null(drug)|is.null(gene)) {
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stop("Missing arguments: --drug and --gene must both be specified (case-sensitive)")
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}
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#=======================================================================
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#########################################################
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# call functions with relevant args
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drug = "streptomycin"
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gene = "gid"
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import_dirs(drug, gene)
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# should return the following dfs, directories and variables
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if (!exists("infile") && exists("gene")){
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#in_filename_params = paste0(tolower(gene), "_all_params.csv")
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in_filename_params = paste0(tolower(gene), "_comb_stab_struc_params.csv") # part combined for gid
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infile = paste0(outdir, "/", in_filename_params)
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}
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#infile = "/home/tanu/git/Data/streptomycin/output/gid_comb_stab_struc_params.csv"
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#infile = ""
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# Get the DFs out of plotting_data()
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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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my_df_u_lig = pd_df[[3]]
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dup_muts = pd_df[[4]]
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#########################################################
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# This script: should return the following dfs, directories and variables
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# my_df
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# my_df_u
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# my_df_u_lig
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@ -85,11 +92,11 @@ rm(my_df, upos, dup_muts, my_df_u_lig)
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# output
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#=======
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# plot 1
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basic_bp_duet = "basic_barplot_PS.svg"
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basic_bp_duet = paste0(tolower(gene), "_basic_barplot_PS.svg")
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plot_basic_bp_duet = paste0(plotdir,"/", basic_bp_duet)
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# plot 2
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pos_count_duet = "position_count_PS.svg"
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pos_count_duet = paste0(tolower(gene), "_position_count_PS.svg")
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plot_pos_count_duet = paste0(plotdir, "/", pos_count_duet)
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#=======================================================================
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@ -194,11 +201,37 @@ foo = select(df, mutationinformation
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#--------------
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# start plot 2
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#--------------
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svg(plot_pos_count_duet)
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print(paste0("plot filename:", plot_pos_count_duet))
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my_ats = 25 # axis text size
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my_als = 22 # axis label size
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# to make x axis display all positions
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# not sure if to use with sort or directly
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my_x = sort(unique(snpsBYpos_df$snpsBYpos))
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g = ggplot(snpsBYpos_df, aes(x = snpsBYpos))
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OutPlot_pos_count = g + geom_bar(aes (alpha = 0.5)
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, show.legend = FALSE) +
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scale_x_continuous(breaks = unique(snpsBYpos_df$snpsBYpos)) +
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#scale_x_continuous(breaks = my_x) +
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geom_label(stat = "count", aes(label = ..count..)
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, color = "black"
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, size = 10) +
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theme(axis.text.x = element_text(size = my_ats
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, angle = 0)
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, axis.text.y = element_text(size = my_ats
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, angle = 0
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, hjust = 1)
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, axis.title.x = element_text(size = my_als)
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, axis.title.y = element_text(size = my_als)
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, plot.title = element_blank()) +
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labs(x = "Number of nsSNPs"
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, y = "Number of Sites")
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print(OutPlot_pos_count)
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source("dirs.R")
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dev.off()
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########################################################################
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# end of PS barplots
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@ -1,51 +1,56 @@
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#!/usr/bin/env Rscript
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#########################################################
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# TASK: formatting data that will be used for various plots
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# useful links
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#https://stackoverflow.com/questions/38851592/r-append-column-in-a-dataframe-with-frequency-count-based-on-two-columns
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#########################################################
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# working dir and loading libraries
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#getwd()
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#setwd("~/git/LSHTM_analysis/scripts/plotting")
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#getwd()
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# load libraries and functions
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library(data.table)
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library(dplyr)
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#########################################################
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# FIXME (not urgent!): Dirty function return nothing, but creates global dfs
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# plotting_data(): formatting data for plots
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# input args:
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## input csv file
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## lig cut off dist, default = 10 Ang
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# output: None
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# Side effects: global dfs (formatted and added columns)
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## my_df
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## my_df_u
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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) {
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plotting_data <- function(infile_params, mcsm_lig_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("Input file 1:", infile_params, '\n') )
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# These globals are created by import_dirs()
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cat('columns based on variables:\n'
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, drug
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, '\n'
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, dr_muts_col
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, '\n'
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, other_muts_col
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, "\n"
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, resistance_col
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, '\n===============================================================')
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#cat('columns based on variables:\n'
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# , drug
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# , '\n'
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# , dr_muts_col
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# , '\n'
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# , other_muts_col
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# , "\n"
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# , resistance_col
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# , '\n===============================================================')
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#%%===============================================================
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###########################
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#===========================
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# Read file: struct params
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###########################
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#cat("Reading struct params including mcsm:", in_filename_params)
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#===========================
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my_df = read.csv(infile_params, header = T)
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cat("\nInput dimensions:", dim(my_df))
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###########################
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#==================================
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# add foldx outcome category
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# and foldx scaled values
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# This will enable to always have these variables available
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# when calling for plots
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###########################
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#==================================
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#------------------------------
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# adding foldx scaled values
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@ -86,14 +91,15 @@ if ( all(c1 == c2) ){
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exit()
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}
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###########################
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#==================================
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# extract unique mutation entries
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###########################
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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$mutationinformation)){
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cat(paste0("\nCAUTION:", " Duplicate mutations identified"
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, "\nExtracting these..."))
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#cat(my_df[duplicated(my_df$mutationinformation),])
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dup_muts = my_df[duplicated(my_df$mutationinformation),]
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dup_muts_nu = length(unique(dup_muts$mutationinformation))
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cat(paste0("\nDim of duplicate mutation df:", nrow(dup_muts)
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@ -109,18 +115,25 @@ upos = unique(my_df_u$position)
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cat("\nDim of clean df:"); cat(dim(my_df_u))
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cat("\nNo. of unique mutational positions:"); cat(length(upos), "\n")
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###########################
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# extract mutations <10Angstroms and symbols
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###########################
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#===============================================
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# extract mutations <10 Angstroms and symbol
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#===============================================
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table(my_df_u$ligand_distance<mcsm_lig_cutoff)
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my_df_u_lig = my_df_u[my_df_u$ligand_distance <mcsm_lig_cutoff,]
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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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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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#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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return(all_df)
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}
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########################################################################
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# end of data extraction and cleaning for plots #
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########################################################################
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}
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@ -1,17 +1,23 @@
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#!/usr/bin/env Rscript
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#########################################################
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# TASK: importing dir str
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# create a function that takes 'drug' and 'gene' as args,
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# This script is sourced by plotting.R to import dir str
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# for various plots, etc.
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# TASK: importing global variable for plotting
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# import_dirs()
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# other global variables
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#########################################################
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# import_dirs(): similar to mkdirs from python script in repo.
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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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gene_match = paste0(gene,"_p.")
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cat(gene_match)
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#=============
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#============================
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# directories and variables
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#=============
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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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@ -23,10 +29,11 @@ import_dirs <- function(drug, gene) {
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}
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#==================
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# other globals
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#===============================
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# mcsm ligand distance cut off
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#==================
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mcsm_lig_cutoff <<- 10
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#===============================
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#mcsm_lig_cutoff <<- 10
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#==================
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# Angstroms symbol
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@ -35,15 +42,15 @@ mcsm_lig_cutoff <<- 10
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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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#===============
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# Delta symbol
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#==================
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#===============
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delta_symbol <<- "\u0394"; delta_symbol
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###########################
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# variables for my cols
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###########################
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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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