separting data processing from plotting, started with basic_barplots_PS script
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2 changed files with 143 additions and 92 deletions
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@ -1,46 +1,29 @@
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#!/usr/bin/env Rscript
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#########################################################
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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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#########################################################
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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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#########################################################
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# TASK:
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#########################################################
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########################################################################
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# Installing and loading required packages and functions #
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########################################################################
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#source("Header_TT.R")
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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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library(ggplot2)
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library(data.table)
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library(dplyr)
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source("plotting_data.R")
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# should return
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#my_df
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#my_df_u
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#dup_muts
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########################################################################
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# Read file: call script for combining df for PS #
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########################################################################
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#source("../combining_two_df.R")
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#?????????????
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#########################################################
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#%% variable assignment: input and output paths & filenames
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drug = "pyrazinamide"
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gene = "pncA"
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gene_match = paste0(gene,"_p.")
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cat(gene_match)
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#=============
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# directories
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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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#======
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# input
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#======
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#in_filename = "mcsm_complex1_normalised.csv"
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in_filename_params = paste0(tolower(gene), "_all_params.csv")
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infile_params = paste0(outdir, "/", in_filename_params)
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cat(paste0("Input file 1:", infile_params) )
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#========================================================
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cat(paste0("Directories imported:"
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, "\ndatadir:", datadir
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, "\nindir:", indir
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, "\noutdir:", outdir))
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#=======
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# output
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@ -54,74 +37,34 @@ pos_count_duet = "position_count_PS.svg"
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plot_pos_count_duet = paste0(outdir, "/plots/", pos_count_duet)
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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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my_df = read.csv(infile_params
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#, stringsAsFactors = F
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, header = T)
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cat("Input dimensions:", dim(my_df))
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# clear variables
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rm(in_filename_params, infile_params)
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# quick checks
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colnames(my_df)
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str(my_df)
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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("CAUTION:", " Duplicate mutations identified"
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, "\nExtracting these..."))
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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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, "\nNo. of unique duplicate mutations:", dup_muts_nu
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, "\n\nExtracting df with unique mutations only"))
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my_df_u = my_df[!duplicated(my_df$mutationinformation),]
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}else{
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cat(paste0("No duplicate mutations detected"))
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my_df_u = my_df
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}
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upos = unique(my_df_u$position)
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cat("Dim of clean df:"); cat(dim(my_df_u))
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cat("\nNo. of unique mutational positions:"); cat(length(upos))
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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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# Data for plots
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#================
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# REASSIGNMENT as necessary
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df = my_df_u
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rm(my_df)
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rm(my_df, upos, dup_muts)
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# sanity checks
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str(df)
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library(ggplot2)
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#%%=======================================================================
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#****************
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# Plot 1:Count of stabilising and destabilsing muts
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#****************
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#svg("basic_barplots_PS.svg")
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svg(plot_basic_bp_duet)
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print(paste0("plot filename:", basic_bp_duet))
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print(paste0("plot1 filename:", basic_bp_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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theme_set(theme_grey())
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# uncomment as necessary for either directly outputting results or
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# printing on the screen
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#--------------
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# start plot 1
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#--------------
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g = ggplot(df, aes(x = duet_outcome))
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prinfFile = g + geom_bar(aes(fill = duet_outcome)
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outPlot = g + geom_bar(aes(fill = duet_outcome)
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, show.legend = TRUE) +
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geom_label(stat = "count"
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, aes(label = ..count..)
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@ -143,22 +86,36 @@ prinfFile = g + geom_bar(aes(fill = duet_outcome)
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scale_fill_discrete(name = "DUET Outcome"
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, labels = c("Destabilising", "Stabilising"))
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print(prinfFile)
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print(outPlot)
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dev.off()
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#%%=======================================================================
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#****************
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# Plot 2: frequency of positions
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#****************
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library(data.table)
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df_ncols = ncol(df)
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df_nrows = nrow(df)
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cat(paste0("original df dimensions:"
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, "\nNo. of rows:", df_nrows
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, "\nNo. of cols:", df_ncols
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, "\nNow adding column: frequency of mutational positions"))
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#setDT(df)[, .(pos_count := .N), by = .(position)]
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setDT(df)[, pos_count := .N, by = .(position)]
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rm(df_nrows, df_ncols)
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df_nrows = nrow(df)
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df_ncols = ncol(df)
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cat(paste0("revised df dimensions:"
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, "\nNo. of rows:", df_nrows
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, "\nNo. of cols:", df_ncols))
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# this is cummulative
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table(df$pos_count)
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# use group by on this
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library(dplyr)
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snpsBYpos_df <- df %>%
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group_by(position) %>%
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summarize(snpsBYpos = mean(pos_count))
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@ -178,6 +135,9 @@ foo = select(df, mutationinformation
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#write.csv(foo, "/pos_count_freq.csv")
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#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
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#--------------
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# start plot 2
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#--------------
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#svg("position_count_PS.svg")
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svg(plot_pos_count_duet)
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print(paste0("plot filename:", plot_pos_count_duet))
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@ -185,11 +145,12 @@ print(paste0("plot filename:", plot_pos_count_duet))
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my_ats = 25 # axis text size
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my_als = 22 # axis label size
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my_x = sort(unique(snpsBYpos_df$snpsBYpos))
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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$snpsBYpos))
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g = ggplot(snpsBYpos_df, aes(x = snpsBYpos))
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prinfFile = g + geom_bar(aes (alpha = 0.5)
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outPlot_pos = g + geom_bar(aes (alpha = 0.5)
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, show.legend = FALSE) +
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scale_x_continuous(breaks = unique(snpsBYpos_df$snpsBYpos)) +
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#scale_x_continuous(breaks = my_x) +
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@ -208,7 +169,7 @@ prinfFile = g + geom_bar(aes (alpha = 0.5)
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labs(x = "Number of SNPs"
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, y = "Number of Sites")
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print(prinfFile)
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print(outPlot_pos)
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dev.off()
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########################################################################
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# end of DUET barplots
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90
scripts/plotting/plotting_data.R
Normal file
90
scripts/plotting/plotting_data.R
Normal file
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@ -0,0 +1,90 @@
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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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#source("Header_TT.R")
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require("getopt", quietly = TRUE) #cmd parse arguments
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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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# "gene" , "g", 1, "character"
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#), byrow = TRUE, ncol = 4)
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#opt = getopt(spec)
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#drug = opt$druggene = opt$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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#%% variable assignment: input and output paths & filenames
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drug = "pyrazinamide"
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gene = "pncA"
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gene_match = paste0(gene,"_p.")
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cat(gene_match)
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#=============
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# directories
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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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#======
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# input
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#======
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#in_filename = "mcsm_complex1_normalised.csv"
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in_filename_params = paste0(tolower(gene), "_all_params.csv")
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infile_params = paste0(outdir, "/", in_filename_params)
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cat(paste0("Input file 1:", infile_params) )
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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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my_df = read.csv(infile_params, header = T)
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cat("Input dimensions:", dim(my_df))
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# quick checks
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#colnames(my_df)
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#str(my_df)
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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("CAUTION:", " Duplicate mutations identified"
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, "\nExtracting these..."))
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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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, "\nNo. of unique duplicate mutations:", dup_muts_nu
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, "\n\nExtracting df with unique mutations only"))
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my_df_u = my_df[!duplicated(my_df$mutationinformation),]
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}else{
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cat(paste0("No duplicate mutations detected"))
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my_df_u = my_df
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}
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upos = unique(my_df_u$position)
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cat("Dim of clean df:"); cat(dim(my_df_u))
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cat("\nNo. of unique mutational positions:"); cat(length(upos))
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########################################################################
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# end of data extraction and cleaning for plots #
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########################################################################
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# clear variables
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rm(opt, spec)
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