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LSHTM_analysis/scripts/plotting/logo_multiple_muts.R

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#!/usr/bin/env Rscript
#########################################################
# TASK: producing logo-type plot showing
# multiple muts per position coloured by aa property
#########################################################
#=======================================================================
# working dir and loading libraries
getwd()
setwd("~/git/LSHTM_analysis/scripts/plotting")
getwd()
source("Header_TT.R")
source("../functions/plotting_globals.R")
source("../functions/plotting_data.R")
source("../functions/combining_dfs_plotting.R")
###########################################################
# command line args
#********************
#drug = 'streptomycin'
#gene = 'gid'
#********************
# !!!FUTURE TODO!!!
# Can pass additional params of output/plot dir by user.
# Not strictly required for my workflow since it is optimised
# to have a streamlined input/output flow without filename worries.
#********************
spec = matrix(c(
"drug" , "d", 1, "character",
"gene" , "g", 1, "character",
"data_file1" , "fa", 2, "character",
"data_file2" , "fb", 2, "character"
), byrow = TRUE, ncol = 4)
opt = getopt(spec)
#FIXME: detect if script running from cmd, then set these
drug = opt$drug
gene = opt$gene
infile_params = opt$data_file1
infile_metadata = opt$data_file2
if(is.null(drug)|is.null(gene)) {
stop("Missing arguments: --drug and --gene must both be specified (case-sensitive)")
}
#===========
# input
#===========
#---------------------
# call: import_dirs()
#---------------------
import_dirs(drug_name = drug, gene_name = gene)
#---------------------------
# call: plotting_data()
#---------------------------
#if (!exists("infile_params") && exists("gene")){
if (!is.character(infile_params) && exists("gene")){ # when running as cmd
#in_filename_params = paste0(tolower(gene), "_all_params.csv")
in_filename_params = paste0(tolower(gene), "_comb_afor.csv") # part combined for gid
infile_params = paste0(outdir, "/", in_filename_params)
cat("\nInput file for mcsm comb data not specified, assuming filename: ", infile_params, "\n")
}
# Input 1: read <gene>_comb_afor.csv
cat("\nReading mcsm combined data file: ", infile_params)
mcsm_df = read.csv(infile_params, header = T)
pd_df = plotting_data(mcsm_df)
my_df_u = pd_df[[1]] # this forms one of the input for combining_dfs_plotting()
#--------------------------------
# call: combining_dfs_plotting()
#--------------------------------
#if (!exists("infile_metadata") && exists("gene")){
if (!is.character(infile_metadata) && exists("gene")){ # when running as cmd
in_filename_metadata = paste0(tolower(gene), "_metadata.csv") # part combined for gid
infile_metadata = paste0(outdir, "/", in_filename_metadata)
cat("\nInput file for gene metadata not specified, assuming filename: ", infile_metadata, "\n")
}
# Input 2: read <gene>_meta data.csv
cat("\nReading meta data file: ", infile_metadata)
gene_metadata <- read.csv(infile_metadata
, stringsAsFactors = F
, header = T)
all_plot_dfs = combining_dfs_plotting(my_df_u
, gene_metadata
, lig_dist_colname = 'ligand_distance'
, lig_dist_cutoff = 10)
merged_df3 = all_plot_dfs[[2]]
#===========
# output
#===========
logo_multiple_muts = "logo_multiple_muts.svg"
plot_logo_multiple_muts = paste0(plotdir,"/", logo_multiple_muts)
##########################################################################
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# REASSIGNMENT
my_df = merged_df3
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%
colnames(my_df)
str(my_df)
#rownames(my_df) = my_df$mutation
c1 = unique(my_df$position)
nrow(my_df)
# get freq count of positions so you can subset freq<1
require(data.table)
setDT(my_df)[, mut_pos_occurrence := .N, by = .(position)] #189, 36
table(my_df$position)
table(my_df$mut_pos_occurrence)
max_mut = max(table(my_df$position))
# extract freq_pos>1
my_data_snp = my_df[my_df$mut_pos_occurrence!=1,]
u = unique(my_data_snp$position)
max_mult_mut = max(table(my_data_snp$position))
if (nrow(my_data_snp) == nrow(my_df) - table(my_df$mut_pos_occurrence)[[1]] ){
cat("PASS: positions with multiple muts extracted"
, "\nNo. of mutations:", nrow(my_data_snp)
, "\nNo. of positions:", length(u)
, "\nMax no. of muts at any position", max_mult_mut)
}else{
cat("FAIL: positions with multiple muts could NOT be extracted"
, "\nExpected:",nrow(my_df) - table(my_df$mut_pos_occurrence)[[1]]
, "\nGot:", nrow(my_data_snp) )
}
cat("\nNo. of sites with only 1 mutations:", table(my_df$mut_pos_occurrence)[[1]])
########################################################################
# end of data extraction and cleaning for_mychisq plots #
########################################################################
#==============
# matrix for_mychisq mutant type
# frequency of mutant type by position
#==============
table(my_data_snp$mutant_type, my_data_snp$position)
tab_mt = table(my_data_snp$mutant_type, my_data_snp$position)
class(tab_mt)
# unclass to convert to matrix
tab_mt = unclass(tab_mt)
tab_mt = as.matrix(tab_mt, rownames = T)
#should be TRUE
is.matrix(tab_mt)
rownames(tab_mt) #aa
colnames(tab_mt) #pos
#**************
# Plot 1: mutant logo
#**************
p0 = ggseqlogo(tab_mt
, method = 'custom'
, seq_type = 'aa') +
#ylab('my custom height') +
theme(axis.text.x = element_blank()) +
theme_logo()+
scale_x_continuous(breaks = 1:ncol(tab_mt)
, labels = colnames(tab_mt))+
scale_y_continuous( breaks = 1:max_mult_mut
, limits = c(0, max_mult_mut))
p0
# further customisation
p1 = p0 + theme(legend.position = "none"
, legend.title = element_blank()
, legend.text = element_text(size = 20)
, axis.text.x = element_text(size = 17, angle = 90)
, axis.text.y = element_blank())
p1
#==============
# matrix for wild type
# frequency of wild type by position
#==============
tab_wt = table(my_data_snp$wild_type, my_data_snp$position); tab_wt
tab_wt = unclass(tab_wt)
#remove wt duplicates
wt = my_data_snp[, c("position", "wild_type")]
wt = wt[!duplicated(wt),]
tab_wt = table(wt$wild_type, wt$position); tab_wt # should all be 1
rownames(tab_wt)
rownames(tab_wt)
#**************
# Plot 2: wild_type logo
#**************
# sanity check: MUST BE TRUE
identical(colnames(tab_mt), colnames(tab_wt))
identical(ncol(tab_mt), ncol(tab_wt))
p2 = ggseqlogo(tab_wt
, method = 'custom'
, seq_type = 'aa'
#, col_scheme = "taylor"
#, col_scheme = chemistry2
) +
#ylab('my custom height') +
theme(axis.text.x = element_blank()
, axis.text.y = element_blank()) +
theme_logo() +
scale_x_continuous(breaks = 1:ncol(tab_wt)
, labels = colnames(tab_wt))
p2
# further customise
p3 = p2 +
theme(legend.position = "bottom"
#, legend.title = element_blank()
, legend.title = element_text("Amino acid properties", size = 20)
, legend.text = element_text(size = 20)
, axis.text.x = element_text(size = 17, angle = 90)
, axis.text.y = element_blank()
, axis.title.x = element_text(size = 22))+
labs(x= "Position")
p3
# Now combine using cowplot, which ensures the plots are aligned
suppressMessages( require(cowplot) )
plot_grid(p1, p3, ncol = 1, align = 'v') #+
#colour scheme
#https://rdrr.io/cran/ggseqlogo/src/R/col_schemes.r
cat("Output plot:", plot_logo_multiple_muts)
svg(plot_logo_multiple_muts, width = 32, height = 10)
OutPlot1 = cowplot::plot_grid(p1, p3
, nrow = 2
, align = "v"
, rel_heights = c(3/4, 1/4))
print(OutPlot1)
dev.off()
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