all barplots generated for ps and lig
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45
scripts/plotting/autoviz.py
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45
scripts/plotting/autoviz.py
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#!/usr/bin/env python3
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#=======================================================================
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#%% useful links
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#https://towardsdatascience.com/autoviz-automatically-visualize-any-dataset-ba2691a8b55a
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#https://pypi.org/project/autoviz/
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#=======================================================================
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import os, sys
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import pandas as pd
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import numpy as np
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import re
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import argparse
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from autoviz.AutoViz_Class import AutoViz_Class
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homedir = os.path.expanduser('~')
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os.chdir(homedir + '/git/LSHTM_analysis/scripts')
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#%%============================================================================
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# variables
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gene = 'pncA'
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drug = 'pyrazinamide'
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#%%============================================================================
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#==============
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# directories
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#==============
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datadir = homedir + '/' + 'git/Data'
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indir = datadir + '/' + drug + '/input'
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outdir = datadir + '/' + drug + '/output'
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#=======
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# input
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#=======
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in_filename_plotting = 'car_design.csv'
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in_filename_plotting = gene.lower() + '_all_params.csv'
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infile_plotting = outdir + '/' + in_filename_plotting
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print('plotting file: ', infile_plotting
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, '\n============================================================')
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#=======================================================================
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plotting_df = pd.read_csv(infile_plotting, sep = ',')
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#Instantiate the AutoViz class
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AV = AutoViz_Class()
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df = AV.AutoViz(infile_plotting)
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#df2 = AV.AutoViz(plotting_df)
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plotting_df.columns[~plotting_df.columns.isin(df.columns)]
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265
scripts/plotting/barplots_subcolours_aa_LIG.R
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265
scripts/plotting/barplots_subcolours_aa_LIG.R
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#!/usr/bin/env Rscript
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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: 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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#source("subcols_axis.R")
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source("subcols_axis_PS.R")
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# should return the following dfs, directories and variables
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# mut_pos_cols
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# mut_pos_cols_lig
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# my_df_cols
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# my_df_u_cols
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# my_df_u_lig_cols
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# dup_muts_cols
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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(dup_muts_cols, mut_pos_cols, my_df_cols, my_df_u_cols, upos)
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#=======================================================================
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# !!! very important!!!!
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#================
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# Inspecting mut_pos_cols
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# position numbers and colours and assigning axis colours based on lab_fg
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# of the correct df
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# open file from desktop ("sample_axis_cols") for cross checking
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#================
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if ( nrow(mut_pos_cols_lig) == length(unique(my_df_u_cols_lig$position)) ){
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print("PASS: lengths checked, assigning axis colours")
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my_axis_colours = mut_pos_cols_lig$lab_fg
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cat("length of axis colours:", length(my_axis_colours)
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, "\nwhich corresponds to the number of positions on the x-axis of the plot")
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}else{
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print("FAIL:lengths mismatch, could not assign axis colours")
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quit()
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}
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# further sanity checks
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table(mut_pos_cols_lig$lab_bg)
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check_lab_bg = sum( table(mut_pos_cols_lig$lab_bg) ) == nrow(mut_pos_cols_lig) # should be True
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check_lab_bg
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table(mut_pos_cols_lig$lab_bg2)
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check_lab_bg2 = sum( table(mut_pos_cols_lig$lab_bg2) ) == nrow(mut_pos_cols_lig) # should be True
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check_lab_bg2
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table(mut_pos_cols_lig$lab_fg)
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check_lab_fg = sum( table(mut_pos_cols_lig$lab_fg) ) == nrow(mut_pos_cols_lig) # 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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#=======================================================================
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#=======
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# output
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#=======
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# plot name and location
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print(paste0("plot will be in:", plotdir))
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bp_aa_subcols_ligand = "barplot_acoloured_LIG.svg"
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plot_bp_aa_subcols_ligand = paste0(plotdir, "/", bp_aa_subcols_ligand)
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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_cols_lig
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cat("ligand df dim:", dim(df))
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# sanity checks
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str(df)
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# sanity check
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df[df$position == "49",]
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df[df$position == "13",]
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df[df$position == "103",]
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###########################
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# Plot: Ligand affinity
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###########################
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#==========================
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# Barplot with scores (unordered)
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# corresponds to ligand_outcome
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# Stacked Barplot with colours: ligand_outcome @ position coloured by
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# stability scores. This is a barplot where each bar corresponds
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# to a SNP and is coloured by its corresponding ligand stability value.
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# Normalised values (range between -1 and 1 ) to aid visualisation
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# NOTE: since barplot plots discrete values, colour = score, so number of
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# colours will be equal to the no. of unique normalised scores
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# rather than a continuous scale
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# will require generating the colour scale separately.
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#============================
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# sanity checks
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upos = unique(df$position)
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table(df$ligand_outcome)
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table(df$ligand_outcome)
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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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table(df$pos_count)
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# use group by on this
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library(dplyr)
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snpsBYpos_df <- df %>%
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group_by(position) %>%
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summarize(snpsBYpos = mean(pos_count))
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table(snpsBYpos_df$snpsBYpos)
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snp_count = sort(unique(snpsBYpos_df$snpsBYpos))
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# sanity checks
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# should be a factor
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if (is.factor(df$ligand_outcome)){
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print("ligand_outcome is factor")
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}else{
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print("converting ligand_outcome to factor")
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df$ligand_outcome = as.factor(df$ligand_outcome)
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}
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is.factor(df$ligand_outcome)
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table(df$ligand_outcome)
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# may not be -1 and 1 since these are filtered within 10A
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min(df$affinity_scaled)
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max(df$affinity_scaled)
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# sanity checks
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# very important!!!!
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tapply(df$affinity_scaled, df$ligand_outcome, min)
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tapply(df$affinity_scaled, df$ligand_outcome, max)
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# My colour FUNCTION: based on group and subgroup
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# in my case;
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# df = df
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# group = ligand_outcome
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# subgroup = normalised score i.e affinity_scaled
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# check unique values in normalised data
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u = unique(df$affinity_scaled)
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cat("No. of unique values in normalised data:", length(u))
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# Define group
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# Create an extra column called group which contains the "gp name and score"
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# so colours can be generated for each unique values in this column
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my_grp = df$affinity_scaled
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df$group <- paste0(df$ligand_outcome, "_", my_grp, sep = "")
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# Call the function to create the palette based on the group defined above
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colours <- ColourPalleteMulti(df, "ligand_outcome", "my_grp")
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print(paste0("Colour palette generated for: ", length(colours), " colours"))
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my_title = "Ligand affinity"
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cat("No. of axis colours: ", length(my_axis_colours))
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#========================
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# plot with axis colours
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#========================
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class(df$lab_bg)
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# define cartesian coord
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my_xlim = length(unique(df$position)); my_xlim
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# axis label size
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my_xals = 18
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my_yals = 18
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# axes text size
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my_xats = 14
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my_yats = 18
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#******************
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# generate plot: with axis colours
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#******************
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print(paste0("plot name:", plot_bp_aa_subcols_ligand))
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svg(plot_bp_aa_subcols_ligand, width = 26, height = 4)
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g = ggplot(df, aes(factor(position, ordered = T)))
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outPlot = g +
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coord_cartesian(xlim = c(1, my_xlim)
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#, ylim = c(0, 6)
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, ylim = c(0, max(snp_count))
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, clip = "off") +
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geom_bar(aes(fill = group), colour = "grey") +
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scale_fill_manual(values = colours
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, guide = "none") +
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geom_tile(aes(,-0.8, width = 0.95, height = 0.85)
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, fill = df$lab_bg) +
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geom_tile(aes(,-1.2, width = 0.95, height = -0.2)
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, fill = df$lab_bg2) +
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# Here it"s important to specify that your axis goes from 1 to max number of levels
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theme(axis.text.x = element_text(size = my_xats
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, angle = 90
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, hjust = 1
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, vjust = 0.4
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, colour = my_axis_colours)
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, axis.text.y = element_text(size = my_yats
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, angle = 0
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, hjust = 1
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, vjust = 0)
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, axis.title.x = element_text(size = my_xals)
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#, hjust = 1
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#, vjust = 0.4)
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, axis.title.y = element_text(size = my_yals )
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, axis.ticks.x = element_blank()) +
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labs(title = ""
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#title = my_title
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, x = "position"
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, y = "Frequency")
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print(outPlot)
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dev.off()
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#!!!!!!!!!!!!!!!!
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#Warning message:
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# Vectorized input to `element_text()` is not officially supported.
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#Results may be unexpected or may change in future versions of ggplot2.
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#!!!!!!!!!!!!!!!!!
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# for sanity and good practice
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#rm(df)
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193
scripts/plotting/basic_barplots_LIG.R
Executable file
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scripts/plotting/basic_barplots_LIG.R
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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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#=======================================================================
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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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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 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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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)
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#=======================================================================
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#=======
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# output
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#=======
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# plot 1
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basic_bp_ligand = "basic_barplot_LIG.svg"
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plot_basic_bp_ligand = paste0(plotdir,"/", basic_bp_ligand)
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# plot 2
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pos_count_ligand = "position_count_LIG.svg"
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plot_pos_count_ligand = paste0(plotdir, "/", pos_count_ligand)
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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_lig
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rm(my_df_u, 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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# Plot 1:Count of stabilising and destabilsing muts
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#****************
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#svg("basic_barplots_LIG.svg")
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svg(plot_basic_bp_ligand)
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print(paste0("plot1 filename:", basic_bp_ligand))
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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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|
|
||||||
|
#--------------
|
||||||
|
# start plot 1
|
||||||
|
#--------------
|
||||||
|
g = ggplot(df, aes(x = ligand_outcome))
|
||||||
|
outPlot = g + geom_bar(aes(fill = ligand_outcome)
|
||||||
|
, show.legend = TRUE) +
|
||||||
|
geom_label(stat = "count"
|
||||||
|
, aes(label = ..count..)
|
||||||
|
, color = "black"
|
||||||
|
, show.legend = FALSE
|
||||||
|
, size = 10) +
|
||||||
|
theme(axis.text.x = element_blank()
|
||||||
|
, axis.title.x = element_blank()
|
||||||
|
, axis.title.y = element_text(size=my_als)
|
||||||
|
, axis.text.y = element_text(size = my_ats)
|
||||||
|
, legend.position = c(0.73,0.8)
|
||||||
|
, legend.text = element_text(size=my_als-2)
|
||||||
|
, legend.title = element_text(size=my_als)
|
||||||
|
, plot.title = element_blank()) +
|
||||||
|
labs(title = ""
|
||||||
|
, y = "Number of SNPs"
|
||||||
|
#, fill="ligand_outcome"
|
||||||
|
) +
|
||||||
|
scale_fill_discrete(name = "Ligand Outcome"
|
||||||
|
, labels = c("Destabilising", "Stabilising"))
|
||||||
|
|
||||||
|
print(outPlot)
|
||||||
|
dev.off()
|
||||||
|
|
||||||
|
table(df$ligand_outcome)
|
||||||
|
#=======================================================================
|
||||||
|
#****************
|
||||||
|
# Plot 2: frequency of positions
|
||||||
|
#****************
|
||||||
|
df_ncols = ncol(df)
|
||||||
|
df_nrows = nrow(df)
|
||||||
|
|
||||||
|
cat(paste0("original df dimensions:"
|
||||||
|
, "\nNo. of rows:", df_nrows
|
||||||
|
, "\nNo. of cols:", df_ncols
|
||||||
|
, "\nNow adding column: frequency of mutational positions"))
|
||||||
|
|
||||||
|
#setDT(df)[, .(pos_count := .N), by = .(position)]
|
||||||
|
setDT(df)[, pos_count := .N, by = .(position)]
|
||||||
|
|
||||||
|
rm(df_nrows, df_ncols)
|
||||||
|
|
||||||
|
df_nrows = nrow(df)
|
||||||
|
df_ncols = ncol(df)
|
||||||
|
|
||||||
|
cat(paste0("revised df dimensions:"
|
||||||
|
, "\nNo. of rows:", df_nrows
|
||||||
|
, "\nNo. of cols:", df_ncols))
|
||||||
|
|
||||||
|
# this is cummulative
|
||||||
|
table(df$pos_count)
|
||||||
|
|
||||||
|
# use group by on this
|
||||||
|
snpsBYpos_df <- df %>%
|
||||||
|
group_by(position) %>%
|
||||||
|
summarize(snpsBYpos = mean(pos_count))
|
||||||
|
|
||||||
|
table(snpsBYpos_df$snpsBYpos)
|
||||||
|
|
||||||
|
#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
|
||||||
|
# FIXME, get this mutation_info, perhaLIG useful!
|
||||||
|
foo = select(df, mutationinformation
|
||||||
|
, wild_pos
|
||||||
|
, wild_type
|
||||||
|
, mutant_type
|
||||||
|
#, mutation_info # comes from meta data, notused yet
|
||||||
|
, position
|
||||||
|
, pos_count)
|
||||||
|
|
||||||
|
#write.csv(foo, "/pos_count_freq.csv")
|
||||||
|
#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
|
||||||
|
|
||||||
|
#--------------
|
||||||
|
# start plot 2
|
||||||
|
#--------------
|
||||||
|
#svg("position_count_LIG.svg")
|
||||||
|
svg(plot_pos_count_ligand)
|
||||||
|
print(paste0("plot filename:", plot_pos_count_ligand))
|
||||||
|
|
||||||
|
my_ats = 25 # axis text size
|
||||||
|
my_als = 22 # axis label size
|
||||||
|
|
||||||
|
# to make x axis display all positions
|
||||||
|
# not sure if to use with sort or directly
|
||||||
|
my_x = sort(unique(snpsBYpos_df$snpsBYpos))
|
||||||
|
|
||||||
|
g = ggplot(snpsBYpos_df, aes(x = snpsBYpos))
|
||||||
|
outPlot_pos = g + geom_bar(aes (alpha = 0.5)
|
||||||
|
, show.legend = FALSE) +
|
||||||
|
scale_x_continuous(breaks = unique(snpsBYpos_df$snpsBYpos)) +
|
||||||
|
#scale_x_continuous(breaks = my_x) +
|
||||||
|
geom_label(stat = "count", aes(label = ..count..)
|
||||||
|
, color = "black"
|
||||||
|
, size = 10) +
|
||||||
|
theme(axis.text.x = element_text(size = my_ats
|
||||||
|
, angle = 0)
|
||||||
|
, axis.text.y = element_text(size = my_ats
|
||||||
|
, angle = 0
|
||||||
|
, hjust = 1)
|
||||||
|
, axis.title.x = element_text(size = my_als)
|
||||||
|
, axis.title.y = element_text(size = my_als)
|
||||||
|
, plot.title = element_blank()) +
|
||||||
|
|
||||||
|
labs(x = "Number of SNPs"
|
||||||
|
, y = "Number of Sites")
|
||||||
|
|
||||||
|
print(outPlot_pos)
|
||||||
|
dev.off()
|
||||||
|
########################################################################
|
||||||
|
# end of lig barplots
|
||||||
|
########################################################################
|
||||||
|
|
||||||
87
scripts/plotting/columns_all_params.csv
Normal file
87
scripts/plotting/columns_all_params.csv
Normal file
|
|
@ -0,0 +1,87 @@
|
||||||
|
,x,,changes,
|
||||||
|
1,mutationinformation,,Mutationinformation,
|
||||||
|
2,wild_type,,,consider...wild_aa
|
||||||
|
3,position,,Position,
|
||||||
|
4,mutant_type,,,consider...mutant_aa
|
||||||
|
5,chain,,,
|
||||||
|
6,ligand_id,,,
|
||||||
|
7,ligand_distance,,,
|
||||||
|
8,duet_stability_change,,,
|
||||||
|
9,duet_outcome,,DUET_outcome,
|
||||||
|
10,ligand_affinity_change,,,
|
||||||
|
11,ligand_outcome,,Lig_outcome,
|
||||||
|
12,duet_scaled,,ratioDUET,
|
||||||
|
13,affinity_scaled,,ratioPredAff,
|
||||||
|
14,wild_pos,,WildPos,
|
||||||
|
15,wild_chain_pos,,,
|
||||||
|
16,ddg,,,
|
||||||
|
17,contacts,,,
|
||||||
|
18,electro_rr,,,
|
||||||
|
19,electro_mm,,,
|
||||||
|
20,electro_sm,,,
|
||||||
|
21,electro_ss,,,
|
||||||
|
22,disulfide_rr,,,
|
||||||
|
23,disulfide_mm,,,
|
||||||
|
24,disulfide_sm,,,
|
||||||
|
25,disulfide_ss,,,
|
||||||
|
26,hbonds_rr,,,
|
||||||
|
27,hbonds_mm,,,
|
||||||
|
28,hbonds_sm,,,
|
||||||
|
29,hbonds_ss,,,
|
||||||
|
30,partcov_rr,,,
|
||||||
|
31,partcov_mm,,,
|
||||||
|
32,partcov_sm,,,
|
||||||
|
33,partcov_ss,,,
|
||||||
|
34,vdwclashes_rr,,,
|
||||||
|
35,vdwclashes_mm,,,
|
||||||
|
36,vdwclashes_sm,,,
|
||||||
|
37,vdwclashes_ss,,,
|
||||||
|
38,volumetric_rr,,,
|
||||||
|
39,volumetric_mm,,,
|
||||||
|
40,volumetric_sm,,,
|
||||||
|
41,volumetric_ss,,,
|
||||||
|
42,wild_type_dssp,,,
|
||||||
|
43,asa,,,
|
||||||
|
44,rsa,,,
|
||||||
|
45,ss,,,
|
||||||
|
46,ss_class,,,
|
||||||
|
47,chain_id,,,
|
||||||
|
48,wild_type_kd,,,
|
||||||
|
49,kd_values,,,
|
||||||
|
50,rd_values,,,
|
||||||
|
51,wt_3letter_caps,,,
|
||||||
|
52,mutation,,,
|
||||||
|
53,af,,,
|
||||||
|
54,beta_logistic,,,
|
||||||
|
55,or_logistic,,,
|
||||||
|
56,pval_logistic,,,
|
||||||
|
57,se_logistic,,,
|
||||||
|
58,zval_logistic,,,
|
||||||
|
59,ci_low_logistic,,,
|
||||||
|
60,ci_hi_logistic,,,
|
||||||
|
61,or_mychisq,,,
|
||||||
|
62,or_fisher,,,
|
||||||
|
63,pval_fisher,,,
|
||||||
|
64,ci_low_fisher,,,
|
||||||
|
65,ci_hi_fisher,,,
|
||||||
|
66,est_chisq,,,
|
||||||
|
67,pval_chisq,,,
|
||||||
|
68,chromosome_number,,,
|
||||||
|
69,ref_allele,,,
|
||||||
|
70,alt_allele,,,
|
||||||
|
71,mut_type,,,
|
||||||
|
72,gene_id,,,
|
||||||
|
73,gene_number,,,
|
||||||
|
74,mut_region,,,
|
||||||
|
75,mut_info,,,
|
||||||
|
76,chr_num_allele,,,
|
||||||
|
77,wt_3let,,,
|
||||||
|
78,mt_3let,,,
|
||||||
|
79,af_kin,,,
|
||||||
|
80,or_kin,,,
|
||||||
|
81,pwald_kin,,,
|
||||||
|
82,beta_kin,,,
|
||||||
|
83,se_kin,,,
|
||||||
|
84,logl_h1_kin,,,
|
||||||
|
85,l_remle_kin,,,
|
||||||
|
86,n_miss,,,
|
||||||
|
66
scripts/plotting/notes
Normal file
66
scripts/plotting/notes
Normal file
|
|
@ -0,0 +1,66 @@
|
||||||
|
#####################
|
||||||
|
# combining_two_df.R
|
||||||
|
#####################
|
||||||
|
orig_col ==> df_ncols
|
||||||
|
Mutationinformation ==> mutationinformation
|
||||||
|
Position ==> position
|
||||||
|
DUET_outcome ==> duet_outcome
|
||||||
|
Lig_outcome ==> ligand_outcome
|
||||||
|
|
||||||
|
infile ==> infile_params
|
||||||
|
in_filename_comb ==> in_filename_metadata
|
||||||
|
meta_with_afor ==> gene_metadata
|
||||||
|
|
||||||
|
#!!!!!!!!!
|
||||||
|
# FIXME: plotting_data.R
|
||||||
|
#!!!!!!!!!
|
||||||
|
|
||||||
|
# This script will be called by various plotting scripts.
|
||||||
|
# Ensure you can call this using command line args which are currently commented out
|
||||||
|
|
||||||
|
#####################
|
||||||
|
# basic_barplots_PS.R
|
||||||
|
#####################
|
||||||
|
dim(my_df)
|
||||||
|
416, 86
|
||||||
|
|
||||||
|
# unique mutations
|
||||||
|
dim(my_df_u)
|
||||||
|
403, 86
|
||||||
|
|
||||||
|
# all dups identified are destabilising
|
||||||
|
dups_df = 13 rows
|
||||||
|
11 unique muts
|
||||||
|
|
||||||
|
upos = unique(my_df_u$position)
|
||||||
|
145
|
||||||
|
|
||||||
|
df = my_df_u
|
||||||
|
|
||||||
|
df$duet_outcome
|
||||||
|
|
||||||
|
Destabilising Stabilising
|
||||||
|
346 57
|
||||||
|
|
||||||
|
|
||||||
|
# with dups
|
||||||
|
Destabilising Stabilising
|
||||||
|
359 57
|
||||||
|
|
||||||
|
table(df$pos_count)
|
||||||
|
# this is cummulative
|
||||||
|
#1 2 3 4 5 6
|
||||||
|
#39 62 81 100 85 36
|
||||||
|
|
||||||
|
# with dups
|
||||||
|
1 2 3 4 5 6 7
|
||||||
|
39 60 75 92 100 36 14
|
||||||
|
|
||||||
|
# use group by
|
||||||
|
table(snpsBYpos_df$snpsBYpos)
|
||||||
|
#1 2 3 4 5 6
|
||||||
|
#39 31 27 25 17 6
|
||||||
|
|
||||||
|
# with dups
|
||||||
|
1 2 3 4 5 6 7
|
||||||
|
39 30 25 23 20 6 2
|
||||||
Loading…
Add table
Add a link
Reference in a new issue