moved all test scripts for functions to tests/
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15 changed files with 162 additions and 776 deletions
32
scripts/functions/tests/test_lineage_dist.R
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32
scripts/functions/tests/test_lineage_dist.R
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@ -0,0 +1,32 @@
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###############################
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# TEST function lineage_dist.R
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# to plot lineage
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# dist plots with or without facet
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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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source("Header_TT.R")
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source("get_plotting_dfs.R")
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cat("cols imported:"
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, mcsm_red2, mcsm_red1, mcsm_mid, mcsm_blue1, mcsm_blue2)
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#############################################################
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lineage_distP(lin_dist_plot
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, with_facet = F
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, leg_label = "Mutation Class"
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)
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lineage_distP(lin_dist_plot
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, with_facet = T
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, facet_wrap_var = "mutation_info_labels"
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, leg_label = "Mutation Class"
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, leg_pos_wf = "none"
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, leg_dir_wf = "horizontal"
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)
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@ -1,8 +1,12 @@
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#########################################################
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### A) Installing and loading required packages
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# A) Installing and loading required packages
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# B) My functions
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#########################################################
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#########################################################
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#lib_loc = "/usr/local/lib/R/site-library")
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require("getopt", quietly = TRUE) # cmd parse arguments
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if (!require("tidyverse")) {
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@ -10,6 +14,21 @@ if (!require("tidyverse")) {
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library(tidyverse)
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}
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if (!require("shiny")) {
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install.packages("shiny", dependencies = TRUE)
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library(shiny)
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}
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if (!require("gridExtra")) {
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install.packages("gridExtra", dependencies = TRUE)
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library(gridExtra)
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}
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if (!require("ggridges")) {
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install.packages("ggridges", dependencies = TRUE)
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library(ggridges)
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}
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# if (!require("ggplot2")) {
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# install.packages("ggplot2", dependencies = TRUE)
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# library(ggplot2)
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@ -20,6 +39,11 @@ if (!require("tidyverse")) {
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# library(dplyr)
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# }
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if (!require ("plyr")){
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install.packages("plyr")
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library(plyr)
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}
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# Install
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#if(!require(devtools)) install.packages("devtools")
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#devtools::install_github("kassambara/ggcorrplot")
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@ -141,3 +165,15 @@ if(!require(protr)){
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#BiocManager::install("Logolas")
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library("Logolas")
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####################################
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# Load all my functions:
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# only works if tidyverse is loaded
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# hence included it here!
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####################################
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func_path = "~/git/LSHTM_analysis/scripts/functions/"
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source_files <- list.files(func_path, "\\.R$") # locate all .R files
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map(paste0(func_path, source_files), source) # source all your R scripts!
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@ -39,8 +39,8 @@ import_dirs(drug, gene)
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#---------------------------
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# call: plotting_data()
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#---------------------------
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#if (!exists("infile_params") && exists("gene")){
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if (!is.character(infile_params) && exists("gene")){ # when running as cmd
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if (!exists("infile_params") && exists("gene")){
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#if (!is.character(infile_params) && exists("gene")){ # when running as cmd
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#in_filename_params = paste0(tolower(gene), "_all_params.csv") #for pncA
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in_filename_params = paste0(tolower(gene), "_comb_afor.csv") # part combined for gid
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infile_params = paste0(outdir, "/", in_filename_params)
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@ -67,8 +67,8 @@ cat("\nLigand distance cut off, colname:", LigDist_colname
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#--------------------------------
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# call: combining_dfs_plotting()
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#--------------------------------
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#if (!exists("infile_metadata") && exists("gene")){
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if (!is.character(infile_metadata) && exists("gene")){ # when running as cmd
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if (!exists("infile_metadata") && exists("gene")){
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#if (!is.character(infile_metadata) && exists("gene")){ # when running as cmd
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in_filename_metadata = paste0(tolower(gene), "_metadata.csv") # part combined for gid
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infile_metadata = paste0(outdir, "/", in_filename_metadata)
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cat("\nInput file for gene metadata not specified, assuming filename: ", infile_metadata, "\n")
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@ -12,7 +12,6 @@ getwd()
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# Installing and loading required packages
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##########################################################
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source("Header_TT.R")
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source("../functions/bp_lineage.R")
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#===========
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# input
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@ -40,24 +39,6 @@ if(is.null(drug)|is.null(gene)) {
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source ('get_plotting_dfs.R')
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cat("Directories imported:"
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, "\n===================="
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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("Variables imported:"
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, "\n====================="
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, "\ndrug:", drug
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, "\ngene:", gene
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, "\ngene_match:", gene_match
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, "\nAngstrom symbol:", angstroms_symbol
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#, "\nNo. of duplicated muts:", dup_muts_nu
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, "\ndr_muts_col:", dr_muts_col
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, "\nother_muts_col:", other_muts_col
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, "\ndrtype_col:", resistance_col)
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#=======
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# output
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#=======
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@ -74,21 +55,21 @@ plot_basic_bp_lineage_cl
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# Data: All lineages or
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# selected few
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#------------------------
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sel_lineages = levels(lin_lf$sel_lineages_f)[1:4]
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sel_lineages = levels(lin_lf$sel_lineages)[1:4]
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sel_lineages
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lin_lf_plot = lin_lf[lin_lf$sel_lineages_f%in%sel_lineages,]
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lin_lf_plot = lin_lf[lin_lf$sel_lineages%in%sel_lineages,]
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str(lin_lf_plot)
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# drop unused factor levels
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lin_lf_plot$sel_lineages_f = factor(lin_lf_plot$sel_lineages_f)
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levels(lin_lf_plot$sel_lineages_f)
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lin_lf_plot$sel_lineages = factor(lin_lf_plot$sel_lineages)
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levels(lin_lf_plot$sel_lineages)
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str(lin_lf_plot)
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#------------------------
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# plot from my function:
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#------------------------
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lin_countP = lin_count_bp(lin_lf_plot
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, x_categ = "sel_lineages_f"
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, x_categ = "sel_lineages"
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, y_count = "p_count"
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, bar_fill_categ = "count_categ"
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, display_label_col = "p_count"
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@ -109,21 +90,21 @@ lin_countP
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# Data: All lineages or
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# selected few
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#------------------------
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sel_lineages = levels(lin_wf$sel_lineages_f)[1:4]
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sel_lineages = levels(lin_wf$sel_lineages)[1:4]
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sel_lineages
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lin_wf_plot = lin_wf[lin_wf$sel_lineages_f%in%sel_lineages,]
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lin_wf_plot = lin_wf[lin_wf$sel_lineages%in%sel_lineages,]
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str(lin_wf_plot)
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# drop unused factor levels
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lin_wf_plot$sel_lineages_f = factor(lin_wf_plot$sel_lineages_f)
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levels(lin_wf_plot$sel_lineages_f)
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lin_wf_plot$sel_lineages = factor(lin_wf_plot$sel_lineages)
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levels(lin_wf_plot$sel_lineages)
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str(lin_wf_plot)
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#------------------------
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# plot from my function:
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#------------------------
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lin_diversityP = lin_count_bp(lin_wf_plot
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, x_categ = "sel_lineages_f"
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, x_categ = "sel_lineages"
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, y_count = "snp_diversity"
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, display_label_col = "snp_diversity_f"
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, bar_stat_stype = "identity"
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@ -27,13 +27,44 @@ cat("\nMissing samples with lineage classification:", table(merged_df2$lineage =
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}
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# Add pretty lineage labels and mut_info_labels
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class(merged_df2$lineage); table(merged_df2$lineage)
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merged_df2$lineage_labels = gsub("lineage", "L", merged_df2$lineage)
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table(merged_df2$lineage_labels)
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class(merged_df2$lineage_labels)
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merged_df2$lineage_labels = factor(merged_df2$lineage_labels, c("L1"
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, "L2"
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, "L3"
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, "L4"
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, "L5"
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, "L6"
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, "L7"
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, "LBOV"
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, "L1;L2"
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, "L1;L3"
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, "L1;L4"
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, "L2;L3"
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, "L2;L3;L4"
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, "L2;L4"
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, "L2;L6"
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, "L2;LBOV"
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, "L3;L4"
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, "L4;L6"
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, "L4;L7"
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, ""))
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class(merged_df2$lineage_labels); nlevels(merged_df2$lineage_labels)
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##################################
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# WF data: lineages with
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# snp count
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# total_samples
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# snp diversity (perc)
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##################################
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sel_lineages = levels(as.factor(merged_df2$lineage))
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sel_lineages = levels(merged_df2$lineage_labels)
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lin_wf = data.frame(sel_lineages) #4, 1
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total_snps_u = NULL
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@ -41,12 +72,12 @@ total_samples = NULL
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for (i in sel_lineages){
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#print(i)
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curr_total = length(unique(merged_df2$id)[merged_df2$lineage==i])
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curr_total = length(unique(merged_df2$id)[merged_df2$lineage_labels==i])
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#print(curr_total)
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total_samples = c(total_samples, curr_total)
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print(total_samples)
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foo = merged_df2[merged_df2$lineage==i,]
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foo = merged_df2[merged_df2$lineage_labels==i,]
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print(paste0(i, "=======\n"))
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print(length(unique(foo$mutationinformation)))
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curr_count = length(unique(foo$mutationinformation))
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@ -70,33 +101,29 @@ lin_wf
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lin_wf$snp_diversity_f = round( (lin_wf$snp_diversity * 100), digits = 0)
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lin_wf$snp_diversity_f = paste0(lin_wf$snp_diversity_f, "%")
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# Lineage names
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lin_wf$sel_lineages_f = gsub("lineage", "L", lin_wf$sel_lineages)
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lin_wf
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# Important: Check factors so that x-axis categ appear as you want
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lin_wf$sel_lineages = factor(lin_wf$sel_lineages, c("L1"
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, "L2"
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, "L3"
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, "L4"
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, "L5"
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, "L6"
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, "L7"
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, "LBOV"
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, "L1;L2"
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, "L1;L3"
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, "L1;L4"
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, "L2;L3"
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, "L2;L3;L4"
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, "L2;L4"
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, "L2;L6"
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, "L2;LBOV"
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, "L3;L4"
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, "L4;L6"
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, "L4;L7"
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, ""))
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# Important: Relevel factors so that x-axis categ appear as you want
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lin_wf$sel_lineages_f = factor(lin_wf$sel_lineages_f, c("L1"
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, "L2"
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, "L3"
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, "L4"
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, "L5"
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, "L6"
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, "L7"
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, "LBOV"
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, "L1;L2"
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, "L1;L3"
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, "L1;L4"
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, "L2;L3"
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, "L2;L3;L4"
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, "L2;L4"
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, "L2;L6"
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, "L2;LBOV"
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, "L3;L4"
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, "L4;L6"
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, "L4;L7"
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, ""))
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levels(lin_wf$sel_lineages_f)
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levels(lin_wf$sel_lineages)
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##################################
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# LF data: lineages with
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@ -106,7 +133,7 @@ levels(lin_wf$sel_lineages_f)
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##################################
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names(lin_wf)
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tot_cols = ncol(lin_wf)
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pivot_cols = c("sel_lineages", "sel_lineages_f", "snp_diversity", "snp_diversity_f")
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pivot_cols = c("sel_lineages", "snp_diversity", "snp_diversity_f")
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pivot_cols_n = length(pivot_cols)
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expected_rows = nrow(lin_wf) * ( length(lin_wf) - pivot_cols_n )
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@ -129,25 +156,25 @@ if ( nrow(lin_lf) == expected_rows ){
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}
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# Important: Relevel factors so that x-axis categ appear as you want
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lin_lf$sel_lineages_f = factor(lin_lf$sel_lineages_f, c("L1"
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, "L2"
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, "L3"
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, "L4"
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, "L5"
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, "L6"
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, "L7"
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, "LBOV"
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, "L1;L2"
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, "L1;L3"
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, "L1;L4"
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, "L2;L3"
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, "L2;L3;L4"
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, "L2;L4"
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, "L2;L6"
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, "L2;LBOV"
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, "L3;L4"
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, "L4;L6"
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, "L4;L7"
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, ""))
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lin_lf$sel_lineages = factor(lin_lf$sel_lineages, c("L1"
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, "L2"
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, "L3"
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, "L4"
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, "L5"
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, "L6"
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, "L7"
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, "LBOV"
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, "L1;L2"
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, "L1;L3"
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, "L1;L4"
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, "L2;L3"
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, "L2;L3;L4"
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, "L2;L4"
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, "L2;L6"
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, "L2;LBOV"
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, "L3;L4"
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, "L4;L6"
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, "L4;L7"
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, ""))
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levels(lin_lf$sel_lineages_f)
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levels(lin_lf$sel_lineages)
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@ -1,303 +0,0 @@
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#!/usr/bin/env Rscript
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#########################################################
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# TASK: Lineage dist plots: ggridges
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# Output: 2 SVGs for PS stability
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# 1) all muts
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# 2) dr_muts
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##########################################################
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# Installing and loading required packages
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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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source("Header_TT.R")
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library(ggridges)
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source("combining_dfs_plotting.R")
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# PS combined:
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# 1) merged_df2
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# 2) merged_df2_comp
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# 3) merged_df3
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# 4) merged_df3_comp
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# LIG combined:
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# 5) merged_df2_lig
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# 6) merged_df2_comp_lig
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# 7) merged_df3_lig
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# 8) merged_df3_comp_lig
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# 9) my_df_u
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# 10) my_df_u_lig
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cat("Directories imported:"
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, "\n===================="
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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("Variables imported:"
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, "\n====================="
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, "\ndrug:", drug
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, "\ngene:", gene
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, "\ngene_match:", gene_match
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, "\nAngstrom symbol:", angstroms_symbol
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, "\nNo. of duplicated muts:", dup_muts_nu
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, "\nNA count for ORs:", na_count
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, "\nNA count in df2:", na_count_df2
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, "\nNA count in df3:", na_count_df3
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, "\ndr_muts_col:", dr_muts_col
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, "\nother_muts_col:", other_muts_col
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, "\ndrtype_col:", resistance_col)
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#=======
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# output
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#=======
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lineage_dist_combined = "lineage_dist_combined_PS.svg"
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plot_lineage_dist_combined = paste0(plotdir,"/", lineage_dist_combined)
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#========================================================================
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###########################
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# Data for plots
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# you need merged_df2 or merged_df2_comp
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# since this is one-many relationship
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# i.e the same SNP can belong to multiple lineages
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# using the _comp dataset means
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# we lose some muts and at this level, we should use
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||||
# as much info as available, hence use df with NA
|
||||
###########################
|
||||
# REASSIGNMENT
|
||||
my_df = merged_df2
|
||||
|
||||
# delete variables not required
|
||||
rm(my_df_u, merged_df2, merged_df2_comp, merged_df3, merged_df3_comp)
|
||||
|
||||
# quick checks
|
||||
colnames(my_df)
|
||||
str(my_df)
|
||||
|
||||
# Ensure correct data type in columns to plot: need to be factor
|
||||
is.factor(my_df$lineage)
|
||||
my_df$lineage = as.factor(my_df$lineage)
|
||||
is.factor(my_df$lineage)
|
||||
|
||||
table(my_df$mutation_info)
|
||||
|
||||
# subset df with dr muts only
|
||||
my_df_dr = subset(my_df, mutation_info == "dr_mutations_pyrazinamide")
|
||||
table(my_df_dr$mutation_info)
|
||||
|
||||
########################################################################
|
||||
# end of data extraction and cleaning for plots #
|
||||
########################################################################
|
||||
|
||||
#==========================
|
||||
# Plot 1: ALL Muts
|
||||
# x = mcsm_values, y = dist
|
||||
# fill = stability
|
||||
#============================
|
||||
|
||||
my_plot_name = 'lineage_dist_PS.svg'
|
||||
|
||||
plot_lineage_duet = paste0(plotdir,"/", my_plot_name)
|
||||
|
||||
#===================
|
||||
# Data for plots
|
||||
#===================
|
||||
table(my_df$lineage); str(my_df$lineage)
|
||||
|
||||
# subset only lineages1-4
|
||||
sel_lineages = c("lineage1"
|
||||
, "lineage2"
|
||||
, "lineage3"
|
||||
, "lineage4"
|
||||
#, "lineage5"
|
||||
#, "lineage6"
|
||||
#, "lineage7"
|
||||
)
|
||||
|
||||
# uncomment as necessary
|
||||
df_lin = subset(my_df, subset = lineage %in% sel_lineages )
|
||||
table(df_lin$lineage)
|
||||
|
||||
# refactor
|
||||
df_lin$lineage = factor(df_lin$lineage)
|
||||
|
||||
sum(table(df_lin$lineage)) #{RESULT: Total number of samples for lineage}
|
||||
|
||||
table(df_lin$lineage)#{RESULT: No of samples within lineage}
|
||||
|
||||
length(unique(df_lin$mutationinformation))#{Result: No. of unique mutations the 4 lineages contribute to}
|
||||
|
||||
length(df_lin$mutationinformation)
|
||||
|
||||
u2 = unique(my_df$mutationinformation)
|
||||
u = unique(df_lin$mutationinformation)
|
||||
check = u2[!u2%in%u]; print(check) #{Muts not present within selected lineages}
|
||||
|
||||
#%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||
# REASSIGNMENT
|
||||
df <- df_lin
|
||||
#%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||
|
||||
rm(df_lin)
|
||||
|
||||
#******************
|
||||
# generate distribution plot of lineages
|
||||
#******************
|
||||
# 2 : ggridges (good!)
|
||||
my_ats = 15 # axis text size
|
||||
my_als = 20 # axis label size
|
||||
|
||||
my_labels = c('Lineage 1', 'Lineage 2', 'Lineage 3', 'Lineage 4'
|
||||
#, 'Lineage 5', 'Lineage 6', 'Lineage 7'
|
||||
)
|
||||
names(my_labels) = c('lineage1', 'lineage2', 'lineage3', 'lineage4'
|
||||
# , 'lineage5', 'lineage6', 'lineage7'
|
||||
)
|
||||
# check plot name
|
||||
plot_lineage_duet
|
||||
|
||||
# output svg
|
||||
#svg(plot_lineage_duet)
|
||||
p1 = ggplot(df, aes(x = duet_scaled
|
||||
, y = duet_outcome))+
|
||||
|
||||
#printFile=geom_density_ridges_gradient(
|
||||
geom_density_ridges_gradient(aes(fill = ..x..)
|
||||
#, jittered_points = TRUE
|
||||
, scale = 3
|
||||
, size = 0.3 ) +
|
||||
facet_wrap( ~lineage
|
||||
, scales = "free"
|
||||
#, switch = 'x'
|
||||
, labeller = labeller(lineage = my_labels) ) +
|
||||
coord_cartesian( xlim = c(-1, 1)) +
|
||||
scale_fill_gradientn(colours = c("#f8766d", "white", "#00bfc4")
|
||||
, name = "DUET" ) +
|
||||
theme(axis.text.x = element_text(size = my_ats
|
||||
, angle = 90
|
||||
, hjust = 1
|
||||
, vjust = 0.4)
|
||||
|
||||
, axis.text.y = element_blank()
|
||||
, axis.title.x = element_blank()
|
||||
, axis.title.y = element_blank()
|
||||
, axis.ticks.y = element_blank()
|
||||
, plot.title = element_blank()
|
||||
, strip.text = element_text(size = my_als)
|
||||
, legend.text = element_text(size = my_als-5)
|
||||
, legend.title = element_text(size = my_als)
|
||||
)
|
||||
|
||||
print(p1)
|
||||
#dev.off()
|
||||
|
||||
#######################################################################
|
||||
# lineage distribution plot for dr_muts
|
||||
#######################################################################
|
||||
|
||||
#==========================
|
||||
# Plot 2: dr muts ONLY
|
||||
# x = mcsm_values, y = dist
|
||||
# fill = stability
|
||||
#============================
|
||||
|
||||
my_plot_name_dr = 'lineage_dist_dr_muts_PS.svg'
|
||||
|
||||
plot_lineage_dr_duet = paste0(plotdir,"/", my_plot_name_dr)
|
||||
|
||||
#===================
|
||||
# Data for plots
|
||||
#===================
|
||||
table(my_df_dr$lineage); str(my_df_dr$lineage)
|
||||
|
||||
# uncomment as necessary
|
||||
df_lin_dr = subset(my_df_dr, subset = lineage %in% sel_lineages)
|
||||
table(df_lin_dr$lineage)
|
||||
|
||||
# refactor
|
||||
df_lin_dr$lineage = factor(df_lin_dr$lineage)
|
||||
|
||||
sum(table(df_lin_dr$lineage)) #{RESULT: Total number of samples for lineage}
|
||||
|
||||
table(df_lin_dr$lineage)#{RESULT: No of samples within lineage}
|
||||
|
||||
length(unique(df_lin_dr$mutationinformation))#{Result: No. of unique mutations the 4 lineages contribute to}
|
||||
|
||||
length(df_lin_dr$mutationinformation)
|
||||
|
||||
u2 = unique(my_df_dr$mutationinformation)
|
||||
u = unique(df_lin_dr$mutationinformation)
|
||||
check = u2[!u2%in%u]; print(check) #{Muts not present within selected lineages}
|
||||
|
||||
#%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||
# REASSIGNMENT
|
||||
df_dr <- df_lin_dr
|
||||
#%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||
|
||||
rm(df_lin_dr)
|
||||
|
||||
#******************
|
||||
# generate distribution plot of lineages
|
||||
#******************
|
||||
# 2 : ggridges (good!)
|
||||
my_ats = 15 # axis text size
|
||||
my_als = 20 # axis label size
|
||||
|
||||
|
||||
# check plot name
|
||||
plot_lineage_dr_duet
|
||||
|
||||
# output svg
|
||||
#svg(plot_lineage_dr_duet)
|
||||
p2 = ggplot(df_dr, aes(x = duet_scaled
|
||||
, y = duet_outcome))+
|
||||
|
||||
geom_density_ridges_gradient(aes(fill = ..x..)
|
||||
#, jittered_points = TRUE
|
||||
, scale = 3
|
||||
, size = 0.3) +
|
||||
#geom_point(aes(size = or_mychisq))+
|
||||
facet_wrap( ~lineage
|
||||
, scales = "free"
|
||||
#, switch = 'x'
|
||||
, labeller = labeller(lineage = my_labels) ) +
|
||||
coord_cartesian( xlim = c(-1, 1)
|
||||
#, ylim = c(0, 6)
|
||||
#, clip = "off"
|
||||
) +
|
||||
scale_fill_gradientn(colours = c("#f8766d", "white", "#00bfc4")
|
||||
, name = "DUET" ) +
|
||||
theme(axis.text.x = element_text(size = my_ats
|
||||
, angle = 90
|
||||
, hjust = 1
|
||||
, vjust = 0.4)
|
||||
, axis.text.y = element_blank()
|
||||
, axis.title.x = element_blank()
|
||||
, axis.title.y = element_blank()
|
||||
, axis.ticks.y = element_blank()
|
||||
, plot.title = element_blank()
|
||||
, strip.text = element_text(size = my_als)
|
||||
, legend.text = element_text(size = 10)
|
||||
, legend.title = element_text(size = my_als)
|
||||
#, legend.position = "none"
|
||||
)
|
||||
|
||||
print(p2)
|
||||
#dev.off()
|
||||
########################################################################
|
||||
#==============
|
||||
# combine plot
|
||||
#===============
|
||||
|
||||
svg(plot_lineage_dist_combined, width = 12, height = 6)
|
||||
|
||||
printFile = cowplot::plot_grid(p1, p2
|
||||
, label_size = my_als+10)
|
||||
|
||||
print(printFile)
|
||||
dev.off()
|
||||
|
|
@ -1,387 +0,0 @@
|
|||
#!/usr/bin/env Rscript
|
||||
#########################################################
|
||||
# TASK: Lineage dist plots: ggridges
|
||||
|
||||
# Output: 2 SVGs for PS stability
|
||||
|
||||
# 1) all muts
|
||||
# 2) dr_muts
|
||||
|
||||
##########################################################
|
||||
# Installing and loading required packages
|
||||
##########################################################
|
||||
getwd()
|
||||
setwd("~/git/LSHTM_analysis/scripts/plotting/")
|
||||
getwd()
|
||||
|
||||
source("Header_TT.R")
|
||||
library(ggridges)
|
||||
library(plyr)
|
||||
source("combining_dfs_plotting.R")
|
||||
# PS combined:
|
||||
# 1) merged_df2
|
||||
# 2) merged_df2_comp
|
||||
# 3) merged_df3
|
||||
# 4) merged_df3_comp
|
||||
|
||||
# LIG combined:
|
||||
# 5) merged_df2_lig
|
||||
# 6) merged_df2_comp_lig
|
||||
# 7) merged_df3_lig
|
||||
# 8) merged_df3_comp_lig
|
||||
|
||||
# 9) my_df_u
|
||||
# 10) my_df_u_lig
|
||||
|
||||
cat("Directories imported:"
|
||||
, "\n===================="
|
||||
, "\ndatadir:", datadir
|
||||
, "\nindir:", indir
|
||||
, "\noutdir:", outdir
|
||||
, "\nplotdir:", plotdir)
|
||||
|
||||
cat("Variables imported:"
|
||||
, "\n====================="
|
||||
, "\ndrug:", drug
|
||||
, "\ngene:", gene
|
||||
, "\ngene_match:", gene_match
|
||||
, "\nAngstrom symbol:", angstroms_symbol
|
||||
, "\nNo. of duplicated muts:", dup_muts_nu
|
||||
, "\nNA count for ORs:", na_count
|
||||
, "\nNA count in df2:", na_count_df2
|
||||
, "\nNA count in df3:", na_count_df3
|
||||
, "\ndr_muts_col:", dr_muts_col
|
||||
, "\nother_muts_col:", other_muts_col
|
||||
, "\ndrtype_col:", resistance_col)
|
||||
|
||||
cat("cols imported:"
|
||||
, mcsm_red2, mcsm_red1, mcsm_mid, mcsm_blue1, mcsm_blue2)
|
||||
|
||||
#=======
|
||||
# output
|
||||
#=======
|
||||
lineage_dist_combined_dm_om = "lineage_dist_combined_dm_om_PS.svg"
|
||||
plot_lineage_dist_combined_dm_om = paste0(plotdir,"/", lineage_dist_combined_dm_om)
|
||||
|
||||
lineage_dist_combined_dm_om_L = "lineage_dist_combined_dm_om_PS_labelled.svg"
|
||||
plot_lineage_dist_combined_dm_om_L = paste0(plotdir,"/", lineage_dist_combined_dm_om_L)
|
||||
|
||||
#========================================================================
|
||||
|
||||
###########################
|
||||
# Data for plots
|
||||
# you need merged_df2 or merged_df2_comp
|
||||
# since this is one-many relationship
|
||||
# i.e the same SNP can belong to multiple lineages
|
||||
# using the _comp dataset means
|
||||
# we lose some muts and at this level, we should use
|
||||
# as much info as available, hence use df with NA
|
||||
###########################
|
||||
# REASSIGNMENT
|
||||
my_df = merged_df2
|
||||
|
||||
# delete variables not required
|
||||
rm(my_df_u, merged_df2, merged_df2_comp, merged_df3, merged_df3_comp
|
||||
, merged_df2_lig, merged_df2_comp_lig, merged_df3_lig, merged_df3_comp_lig)
|
||||
|
||||
# quick checks
|
||||
colnames(my_df)
|
||||
str(my_df)
|
||||
|
||||
table(my_df$mutation_info)
|
||||
|
||||
#===================
|
||||
# Data for plots
|
||||
#===================
|
||||
table(my_df$lineage); str(my_df$lineage)
|
||||
|
||||
# select lineages 1-4
|
||||
sel_lineages = c("lineage1"
|
||||
, "lineage2"
|
||||
, "lineage3"
|
||||
, "lineage4")
|
||||
#, "lineage5"
|
||||
#, "lineage6"
|
||||
#, "lineage7")
|
||||
|
||||
# works nicely with facet wrap using labeller, but not otherwise
|
||||
#my_labels = c('Lineage 1'
|
||||
# , 'Lineage 2'
|
||||
# , 'Lineage 3'
|
||||
# , 'Lineage 4')
|
||||
# #, 'Lineage 5'
|
||||
# #, 'Lineage 6'
|
||||
# #, 'Lineage 7')
|
||||
|
||||
#names(my_labels) = c('lineage1'
|
||||
# , 'lineage2'
|
||||
# , 'lineage3'
|
||||
# , 'lineage4')
|
||||
# #, 'lineage5'
|
||||
# #, 'lineage6'
|
||||
# #, 'lineage7')
|
||||
|
||||
#==========================
|
||||
# subset selected lineages
|
||||
#==========================
|
||||
df_lin = subset(my_df, subset = lineage %in% sel_lineages)
|
||||
table(df_lin$lineage)
|
||||
|
||||
#{RESULT: Total number of samples for lineage}
|
||||
sum(table(df_lin$lineage))
|
||||
|
||||
#{RESULT: No of samples within lineage}
|
||||
table(df_lin$lineage)
|
||||
|
||||
#{Result: No. of unique mutations the 4 lineages contribute to}
|
||||
length(unique(df_lin$mutationinformation))
|
||||
|
||||
u2 = unique(my_df$mutationinformation)
|
||||
u = unique(df_lin$mutationinformation)
|
||||
|
||||
#{Result:Muts not present within selected lineages}
|
||||
check = u2[!u2%in%u]; print(check)
|
||||
|
||||
# workaround to make labels appear nicely for in otherwise cases
|
||||
#==================
|
||||
# lineage: labels
|
||||
# from "plyr"
|
||||
#==================
|
||||
#{Result:No of samples in selected lineages}
|
||||
table(df_lin$lineage)
|
||||
|
||||
df_lin$lineage_labels = mapvalues(df_lin$lineage
|
||||
, from = c("lineage1","lineage2", "lineage3", "lineage4")
|
||||
, to = c("Lineage 1", "Lineage 2", "Lineage 3", "Lineage 4"))
|
||||
table(df_lin$lineage_labels)
|
||||
|
||||
table(df_lin$lineage_labels) == table(df_lin$lineage)
|
||||
|
||||
#========================
|
||||
# mutation_info: labels
|
||||
#========================
|
||||
#{Result:No of DM and OM muts in selected lineages}
|
||||
table(df_lin$mutation_info)
|
||||
|
||||
df_lin$mutation_info_labels = ifelse(df_lin$mutation_info == dr_muts_col, "DM", "OM")
|
||||
table(df_lin$mutation_info_labels)
|
||||
|
||||
table(df_lin$mutation_info) == table(df_lin$mutation_info_labels)
|
||||
|
||||
|
||||
#========================
|
||||
# duet_outcome: labels
|
||||
#========================
|
||||
#{Result: No. of D and S mutations in selected lineages}
|
||||
table(df_lin$duet_outcome)
|
||||
|
||||
df_lin$duet_outcome_labels = ifelse(df_lin$duet_outcome == "Destabilising", "D", "S")
|
||||
table(df_lin$duet_outcome_labels)
|
||||
|
||||
table(df_lin$duet_outcome) == table(df_lin$duet_outcome_labels)
|
||||
|
||||
|
||||
#=======================
|
||||
# subset dr muts only
|
||||
#=======================
|
||||
#my_df_dr = subset(df_lin, mutation_info == dr_muts_col)
|
||||
#table(my_df_dr$mutation_info)
|
||||
#table(my_df_dr$lineage)
|
||||
|
||||
#=========================
|
||||
# subset other muts only
|
||||
#=========================
|
||||
#my_df_other = subset(df_lin, mutation_info == other_muts_col)
|
||||
#table(my_df_other$mutation_info)
|
||||
#table(my_df_other$lineage)
|
||||
|
||||
########################################################################
|
||||
# end of data extraction and cleaning for plots #
|
||||
########################################################################
|
||||
|
||||
#==========================
|
||||
# Distribution plots
|
||||
#============================
|
||||
|
||||
#%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||
# REASSIGNMENT
|
||||
df <- df_lin
|
||||
#%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||
|
||||
rm(df_lin)
|
||||
|
||||
#******************
|
||||
# generate distribution plot of lineages
|
||||
#******************
|
||||
# 2 : ggridges (good!)
|
||||
my_ats = 15 # axis text size
|
||||
my_als = 20 # axis label size
|
||||
n_colours = length(unique(df$duet_scaled))
|
||||
my_palette <- colorRampPalette(c(mcsm_red2, mcsm_red1, mcsm_mid, mcsm_blue1, mcsm_blue2))(n = n_colours+1)
|
||||
|
||||
#=======================================
|
||||
# Plot 1: lineage dist: geom_density_ridges_gradient (allows aesthetics to vary along ridgeline, no alpha setting!)
|
||||
# else same as geom_density_ridges)
|
||||
# x = duet_scaled
|
||||
# y = duet_outcome
|
||||
# fill = duet_scaled
|
||||
# Facet: Lineage
|
||||
#=======================================
|
||||
# output individual svg
|
||||
#plot_lineage_dist_duet_f paste0(plotdir,"/", "lineage_dist_duet_f.svg")
|
||||
#plot_lineage_dist_duet_f
|
||||
#svg(plot_lineage_dist_duet_f)
|
||||
|
||||
p1 = ggplot(df, aes(x = duet_scaled
|
||||
, y = duet_outcome))+
|
||||
geom_density_ridges_gradient(aes(fill = ..x..)
|
||||
#, jittered_points = TRUE
|
||||
, scale = 3
|
||||
, size = 0.3 ) +
|
||||
facet_wrap( ~lineage_labels
|
||||
# , scales = "free"
|
||||
# , labeller = labeller(lineage = my_labels)
|
||||
) +
|
||||
coord_cartesian( xlim = c(-1, 1)) +
|
||||
scale_fill_gradientn(colours = my_palette
|
||||
, name = "DUET"
|
||||
#, breaks = c(-1, 0, 1)
|
||||
#, labels = c(-1,0,1)
|
||||
#, limits = c(-1,1)
|
||||
) +
|
||||
theme(axis.text.x = element_text(size = my_ats
|
||||
, angle = 90
|
||||
, hjust = 1
|
||||
, vjust = 0.4)
|
||||
#, axis.text.y = element_blank()
|
||||
, axis.text.y = element_text(size = my_ats)
|
||||
, axis.title.x = element_text(size = my_ats)
|
||||
, axis.title.y = element_blank()
|
||||
, axis.ticks.y = element_blank()
|
||||
, plot.title = element_blank()
|
||||
, strip.text = element_text(size = my_als)
|
||||
, legend.text = element_text(size = my_als-10)
|
||||
#, legend.title = element_text(size = my_als-6)
|
||||
, legend.title = element_blank()
|
||||
, legend.position = c(-0.08, 0.41)
|
||||
#, legend.direction = "horizontal"
|
||||
#, legend.position = "left"
|
||||
)+
|
||||
labs(x = "DUET")
|
||||
|
||||
p1
|
||||
|
||||
|
||||
#p1_with_legend = p1 + guides(fill = guide_colourbar(label = FALSE))
|
||||
|
||||
#=======================================
|
||||
# Plot 2: lineage dist: geom_density_ridges, allows alpha to be set
|
||||
# x = duet_scaled
|
||||
# y = lineage_labels
|
||||
# fill = mutation_info
|
||||
# NO FACET
|
||||
#=======================================
|
||||
# output svg
|
||||
#plot_lineage_dist_duet_dm_om = paste0(plotdir,"/", "lineage_dist_duet_dm_om.svg")
|
||||
#plot_lineage_dist_duet_dm_om
|
||||
#svg(plot_lineage_dist_duet_dm_om)
|
||||
|
||||
p2 = ggplot(df, aes(x = duet_scaled
|
||||
, y = lineage_labels))+
|
||||
geom_density_ridges(aes(fill = factor(mutation_info_labels))
|
||||
, scale = 3
|
||||
, size = 0.3
|
||||
, alpha = 0.8) +
|
||||
coord_cartesian( xlim = c(-1, 1)) +
|
||||
scale_fill_manual(values = c("#E69F00", "#999999")) +
|
||||
theme(axis.text.x = element_text(size = my_ats
|
||||
, angle = 90
|
||||
, hjust = 1
|
||||
, vjust = 0.4)
|
||||
, axis.text.y = element_text(size = my_ats)
|
||||
, axis.title.x = element_text(size = my_ats)
|
||||
, axis.title.y = element_blank()
|
||||
, axis.ticks.y = element_blank()
|
||||
, plot.title = element_blank()
|
||||
, strip.text = element_text(size = my_als)
|
||||
, legend.text = element_text(size = my_als-4)
|
||||
, legend.title = element_text(size = my_als-4)
|
||||
, legend.position = c(0.8, 0.9)) +
|
||||
labs(x = "DUET"
|
||||
, fill = "Mutation class") # legend title
|
||||
|
||||
p2
|
||||
|
||||
#=======================================
|
||||
# Plot 3: lineage dist: geom_density_ridges_gradient (allows aesthetics to vary along ridgeline, no alpha setting!)
|
||||
# else same as geom_density_ridges)
|
||||
# x = duet_scaled
|
||||
# y = lineage_labels
|
||||
# fill = duet_scaled
|
||||
# NO FACET (nf)
|
||||
#=======================================
|
||||
# output individual svg
|
||||
#plot_lineage_dist_duet_nf = paste0(plotdir,"/", "lineage_dist_duet_nf.svg")
|
||||
#plot_lineage_dist_duet_nf
|
||||
#svg(plot_lineage_dist_duet_nf)
|
||||
|
||||
p3 = ggplot(df, aes(x = duet_scaled
|
||||
, y = lineage_labels))+
|
||||
geom_density_ridges_gradient(aes(fill = ..x..)
|
||||
#, jittered_points = TRUE
|
||||
, scale = 3
|
||||
, size = 0.3 ) +
|
||||
coord_cartesian( xlim = c(-1, 1)) +
|
||||
scale_fill_gradientn(colours = my_palette, name = "DUET") +
|
||||
theme(axis.text.x = element_text(size = my_ats
|
||||
, angle = 90
|
||||
, hjust = 1
|
||||
, vjust = 0.4)
|
||||
|
||||
, axis.text.y = element_text(size = my_ats)
|
||||
, axis.title.x = element_text(size = my_ats)
|
||||
, axis.title.y = element_blank()
|
||||
, axis.ticks.y = element_blank()
|
||||
, plot.title = element_blank()
|
||||
, strip.text = element_text(size = my_als)
|
||||
, legend.text = element_text(size = my_als-10)
|
||||
, legend.title = element_text(size = my_als-3)
|
||||
, legend.position = c(0.8, 0.8)) +
|
||||
#, legend.direction = "horizontal")+
|
||||
#, legend.position = "top")+
|
||||
labs(x = "DUET")
|
||||
|
||||
p3
|
||||
|
||||
########################################################################
|
||||
#==============
|
||||
# combine plots
|
||||
#===============
|
||||
# 1) without labels
|
||||
plot_lineage_dist_combined_dm_om
|
||||
svg(plot_lineage_dist_combined_dm_om, width = 12, height = 6)
|
||||
|
||||
OutPlot1 = cowplot::plot_grid(p1, p2
|
||||
, rel_widths = c(0.5/2, 0.5/2))
|
||||
|
||||
print(OutPlot1)
|
||||
dev.off()
|
||||
|
||||
|
||||
# 2) with labels
|
||||
plot_lineage_dist_combined_dm_om_L
|
||||
svg(plot_lineage_dist_combined_dm_om_L, width = 12, height = 6)
|
||||
|
||||
OutPlot2 = cowplot::plot_grid(p1, p2
|
||||
#, labels = c("(a)", "(b)")
|
||||
, labels = "AUTO"
|
||||
#, label_x = -0.045, label_y = 0.92
|
||||
#, hjust = -0.7, vjust = -0.5
|
||||
#, align = "h"
|
||||
, rel_widths = c(0.5/2, 0.5/2)
|
||||
, label_size = my_als)
|
||||
|
||||
print(OutPlot2)
|
||||
dev.off()
|
||||
|
||||
##############################################################################
|
||||
Loading…
Add table
Add a link
Reference in a new issue