sourcing plotting_data for subcols_axis_PS
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4 changed files with 110 additions and 90 deletions
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@ -3,52 +3,92 @@ setwd("~/git/LSHTM_analysis/scripts/plotting")
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getwd()
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#########################################################
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# TASK:
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# TASK: output barplot by position with each bar coloured by
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# its stability value and active site positions indicated
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# according to colour specified in "subcols_axis_PS.R"
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#########################################################
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#=======================================================================
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############################################################
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# 1: Installing and loading required packages and functions
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############################################################
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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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source("barplot_colour_function.R")
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############################################################
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# 2: Read file: struct params data with columns containing
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# colours for axis labels
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############################################################
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#source("subcols_axis.R")
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source("subcols_axis_PS.R")
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# this should return
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# should return the following dfs, directories and variables
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# mut_pos_cols
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# my_df
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# my_df_u: df with unique mutations
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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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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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, "\nplotdir:", plotdir))
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cat(paste0("Variables imported:"
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, "\ndrug:", drug
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, "\ngene:", gene
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, "\ngene_match:", gene_match
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, "\nLength of upos:", length(upos)
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, "\nAngstrom symbol:", angstroms_symbol))
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# clear excess variable
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# "mut_pos_cols" is just for inspection in case you need to cross check
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rm(my_df, upos, dup_muts, my_df_u_lig)
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#=======================================================================
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#================
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# Inspecting mut_pos_cols
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# position numbers and colours
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# open file from deskptop ("sample_axis_cols") for cross checking
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# open file from desktop ("sample_axis_cols") for cross checking
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#================
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table(mut_pos_cols$lab_bg)
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sum( table(mut_pos_cols$lab_bg) ) == nrow(mut_pos_cols) # should be True
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check_lab_bg = sum( table(mut_pos_cols$lab_bg) ) == nrow(mut_pos_cols) # should be True
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check_lab_bg
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table(mut_pos_cols$lab_bg2)
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sum( table(mut_pos_cols$lab_bg2) ) == nrow(mut_pos_cols) # should be True
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check_lab_bg2 = sum( table(mut_pos_cols$lab_bg2) ) == nrow(mut_pos_cols) # should be True
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check_lab_bg2
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table(mut_pos_cols$lab_fg)
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sum( table(mut_pos_cols$lab_fg) ) == nrow(mut_pos_cols) # should be True
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check_lab_fg = sum( table(mut_pos_cols$lab_fg) ) == nrow(mut_pos_cols) # should be True
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check_lab_fg
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# sanity checks:
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if (check_lab_bg && check_lab_bg2 && check_lab_fg) {
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print("PASS: No. of assigned colours match length")
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}else{
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print("FAIL: length of assigned colours mismatch")
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quit()
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}
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# very important!
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my_axis_colours = mut_pos_cols$lab_fg
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# now clear mut_pos_cols
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rm(mut_pos_cols)
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rm(mut_pos_cols)
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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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# sanity checks
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str(df)
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###########################
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# 2: Plot: DUET scores
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###########################
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#==========================
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# Plot 2: Barplot with scores (unordered)
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# corresponds to duet_outcome
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@ -62,20 +102,12 @@ rm(mut_pos_cols)
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# will require generating the colour scale separately.
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#============================
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# sanity checks
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upos = unique(my_df$position)
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upos = unique(df$position)
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table(my_df$duet_outcome)
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table(my_df_u$duet_outcome)
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table(df$duet_outcome)
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table(df$duet_outcome)
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#===========================
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# Data preparation 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, my_df_u)
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# add frequency of positions
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library(data.table)
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# add frequency of positions (from lib data.table)
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setDT(df)[, pos_count := .N, by = .(position)]
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# this is cummulative
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@ -93,8 +125,8 @@ snp_count = sort(unique(snpsBYpos_df$snpsBYpos))
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# sanity checks
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# should be a factor
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df$duet_outcome = as.factor(df$duet_outcome)
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is.factor(df$duet_outcome)
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#TRUE
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table(df$duet_outcome)
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@ -116,13 +148,14 @@ tapply(df$duet_scaled, df$duet_outcome, max)
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# check unique values in normalised data
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u = unique(df$duet_scaled)
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cat("No. of unique values in normalised data:", length(u))
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#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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# Run this section if rounding is to be used
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# specify number for rounding
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n = 3
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df$duet_scaledR = round(df$duet_scaled, n)
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ur = unique(df$duet_scaledR)
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#n = 3
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#df$duet_scaledR = round(df$duet_scaled, n)
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#ur = unique(df$duet_scaledR)
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# create an extra column called group which contains the "gp name and score"
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# so colours can be generated for each unique values in this column
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@ -158,7 +191,8 @@ my_yats = 18
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#******************
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# plot name and location
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# outdir/ (should be imported from reading file)
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print(paste0("plot will be in:", outdir))
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plotdir = paste0(outdir, "/", "plots") #should be imported from reading file
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print(paste0("plot will be in:", plotdir))
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bp_aa_subcols_duet = "barplot_acoloured_PS.svg"
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plot_bp_aa_subcols_duet = paste0(outdir, "/plots/", bp_aa_subcols_duet)
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@ -4,6 +4,7 @@
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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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#=======================================================================
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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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@ -14,18 +15,30 @@ 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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# 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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# dup_muts
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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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, "\nplotdir:", plotdir))
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cat(paste0("Variables imported:"
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, "\ndrug:", drug
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, "\ngene:", gene
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, "\ngene_match:", gene_match
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, "\nLength of upos:", length(upos))
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, "\nAngstrom symbol:", angstroms_symbol))
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# clear excess variable
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rm(my_df, upos, dup_muts, my_df_u_lig)
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#=======================================================================
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#=======
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# output
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#=======
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@ -37,17 +50,16 @@ plot_basic_bp_duet = paste0(plotdir,"/", basic_bp_duet)
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pos_count_duet = "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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#=======================================================================
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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, upos, dup_muts)
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# sanity checks
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str(df)
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#%%=======================================================================
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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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@ -89,7 +101,9 @@ outPlot = g + geom_bar(aes(fill = duet_outcome)
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print(outPlot)
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dev.off()
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#%%=======================================================================
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table(df$duet_outcome)
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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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@ -173,6 +187,6 @@ outPlot_pos = g + geom_bar(aes (alpha = 0.5)
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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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# end of Ligand barplots
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########################################################################
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@ -11,6 +11,10 @@ setwd("~/git/LSHTM_analysis/scripts/plotting")
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getwd()
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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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#========================================================
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# command line args
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@ -1,52 +1,21 @@
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#########################################################
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# TASK: Adding colours to positions labels according to
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# active site residues. This is so these can be seen promptly
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# when visualising the barplot.
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#########################################################
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#=======================================================================
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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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source("plotting_data.R")
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# should return the following dfs and directories
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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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########################################################################
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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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#source("barplot_colour_function.R")
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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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#?????????????
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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:", infile_params) )
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#=======
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# output
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#=======
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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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@ -83,7 +52,7 @@ if ( length(unique(my_df$mutationinformation)) != length(my_df$mutationinformati
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upos = unique(my_df_u$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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#=======================================================================
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# create a new df with unique position numbers and cols
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position = unique(my_df$position) #130
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position_cols = as.data.frame(position)
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@ -235,6 +204,5 @@ rm(aa_cols_ref
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, lab_bg
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, lab_bg2
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, lab_fg
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, position
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, dup_muts)
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, position)
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