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LSHTM_analysis/scripts/plotting/get_plotting_dfs.R

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#!/usr/bin/env Rscript
#########################################################
# TASK: Get formatted data for plots
#=======================================================================
# working dir and loading libraries
getwd()
setwd("~/git/LSHTM_analysis/scripts/plotting")
getwd()
source("Header_TT.R")
source("../functions/my_pairs_panel.R") # with lower panel turned off
source("../functions/plotting_globals.R")
source("../functions/plotting_data.R")
source("../functions/combining_dfs_plotting.R")
source("../functions/bp_subcolours.R")
#********************
# cmd args passed
# in from other scripts
# to call this
#********************
#drug = 'streptomycin'
#gene = 'gid'
#====================
# variables for lig
#====================
#LigDist_colname = "ligand_distance"
#LigDist_cutoff = 10
#===========
# input
#===========
#---------------------
# call: import_dirs()
#---------------------
import_dirs(drug, 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") #for pncA
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
, lig_dist_colname = LigDist_colname
, lig_dist_cutoff = LigDist_cutoff)
my_df = pd_df[[1]]
my_df_u = pd_df[[2]] # this forms one of the input for combining_dfs_plotting()
max_ang <- round(max(my_df_u[LigDist_colname]))
min_ang <- round(min(my_df_u[LigDist_colname]))
cat("\nLigand distance cut off, colname:", LigDist_colname
, "\nThe max distance", gene, "structure df" , ":", max_ang, "\u212b"
, "\nThe min distance", gene, "structure df" , ":", min_ang, "\u212b")
#--------------------------------
# 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 = LigDist_colname
, lig_dist_cutoff = LigDist_cutoff)
merged_df2 = all_plot_dfs[[1]]
merged_df3 = all_plot_dfs[[2]]
merged_df2_comp = all_plot_dfs[[3]]
merged_df3_comp = all_plot_dfs[[4]]
#======================================================================
# read other files
infilename_dynamut = paste0("~/git/Data/", drug, "/output/dynamut_results/", gene
, "_complex_dynamut_norm.csv")
infilename_dynamut2 = paste0("~/git/Data/", drug, "/output/dynamut_results/dynamut2/", gene
, "_complex_dynamut2_norm.csv")
infilename_mcsm_na = paste0("~/git/Data/", drug, "/output/mcsm_na_results/", gene
, "_complex_mcsm_na_norm.csv")
infilename_mcsm_f_snps <- paste0("~/git/Data/", drug, "/output/", gene
, "_mcsm_formatted_snps.csv")
dynamut_df = read.csv(infilename_dynamut)
dynamut2_df = read.csv(infilename_dynamut2)
mcsm_na_df = read.csv(infilename_mcsm_na)
mcsm_f_snps = read.csv(infilename_mcsm_f_snps, header = F)
names(mcsm_f_snps) = "mutationinformation"
####################################################################
# Data for subcols barplot (~heatmpa)
####################################################################
# can include: mutation, or_kin, pwald, af_kin
cols_to_select = c("mutationinformation", "drtype"
, "wild_type"
, "position"
, "mutant_type"
, "chain", "ligand_id", "ligand_distance"
, "duet_stability_change", "duet_outcome", "duet_scaled"
, "ligand_affinity_change", "ligand_outcome", "affinity_scaled"
, "ddg_foldx", "foldx_scaled", "foldx_outcome"
, "deepddg", "deepddg_outcome" # comment out as not available for pnca
, "asa", "rsa", "rd_values", "kd_values"
, "af", "or_mychisq", "pval_fisher"
, "or_fisher", "or_logistic", "pval_logistic"
, "wt_prop_water", "mut_prop_water", "wt_prop_polarity", "mut_prop_polarity"
, "wt_calcprop", "mut_calcprop")
#=======================
# Data for sub colours
# barplot: PS
#=======================
cat("\nNo. of cols to select:", length(cols_to_select))
subcols_df_ps = merged_df3[, cols_to_select]
cat("\nNo of unique positions for ps:"
, length(unique(subcols_df_ps$position)))
# add count_pos col that counts the no. of nsSNPS at a position
setDT(subcols_df_ps)[, pos_count := .N, by = .(position)]
# should be a factor
if (is.factor(subcols_df_ps$duet_outcome)){
cat("\nDuet_outcome is factor")
table(subcols_df_ps$duet_outcome)
}else{
cat("\nConverting duet_outcome to factor")
subcols_df_ps$duet_outcome = as.factor(subcols_df_ps$duet_outcome)
table(subcols_df_ps$duet_outcome)
}
# should be -1 and 1
min(subcols_df_ps$duet_scaled)
max(subcols_df_ps$duet_scaled)
tapply(subcols_df_ps$duet_scaled, subcols_df_ps$duet_outcome, min)
tapply(subcols_df_ps$duet_scaled, subcols_df_ps$duet_outcome, max)
# check unique values in normalised data
cat("\nNo. of unique values in duet scaled, no rounding:"
, length(unique(subcols_df_ps$duet_scaled)))
# No rounding
my_grp = subcols_df_ps$duet_scaled; length(my_grp)
# Add rounding is to be used
n = 3
subcols_df_ps$duet_scaledR = round(subcols_df_ps$duet_scaled, n)
cat("\nNo. of unique values in duet scaled", n, "places rounding:"
, length(unique(subcols_df_ps$duet_scaledR)))
my_grp_r = subcols_df_ps$duet_scaledR # rounding
# Add grp cols
subcols_df_ps$group <- paste0(subcols_df_ps$duet_outcome, "_", my_grp, sep = "")
subcols_df_ps$groupR <- paste0(subcols_df_ps$duet_outcome, "_", my_grp_r, sep = "")
# Call the function to create the palette based on the group defined above
subcols_ps <- ColourPalleteMulti(subcols_df_ps, "duet_outcome", "my_grp")
subcolsR_ps <- ColourPalleteMulti(subcols_df_ps, "duet_outcome", "my_grp_r")
print(paste0("Colour palette generated for my_grp: ", length(subcols_ps), " colours"))
print(paste0("Colour palette generated for my_grp_r: ", length(subcolsR_ps), " colours"))
####################################################################
# Data for logoplots
####################################################################
#-------------------------
# choose df for logoplot
#-------------------------
logo_data = merged_df3
#logo_data = merged_df3_comp
# quick checks
colnames(logo_data)
str(logo_data)
c1 = unique(logo_data$position)
nrow(logo_data)
cat("No. of rows in my_data:", nrow(logo_data)
, "\nDistinct positions corresponding to snps:", length(c1)
, "\n===========================================================")
#=======================================================================
#==================
# logo data: OR
#==================
foo = logo_data[, c("position"
, "mutant_type","duet_scaled", "or_mychisq"
, "mut_prop_polarity", "mut_prop_water")]
logo_data$log10or = log10(logo_data$or_mychisq)
logo_data_plot = logo_data[, c("position"
, "mutant_type", "or_mychisq", "log10or")]
logo_data_plot_or = logo_data[, c("position", "mutant_type", "or_mychisq")]
wide_df_or <- logo_data_plot_or %>% spread(position, or_mychisq, fill = 0.0)
wide_df_or = as.matrix(wide_df_or)
rownames(wide_df_or) = wide_df_or[,1]
dim(wide_df_or)
wide_df_or = wide_df_or[,-1]
str(wide_df_or)
position_or = as.numeric(colnames(wide_df_or))
#==================
# logo data: logOR
#==================
logo_data_plot_logor = logo_data[, c("position", "mutant_type", "log10or")]
wide_df_logor <- logo_data_plot_logor %>% spread(position, log10or, fill = 0.0)
wide_df_logor = as.matrix(wide_df_logor)
rownames(wide_df_logor) = wide_df_logor[,1]
wide_df_logor = subset(wide_df_logor, select = -c(1) )
colnames(wide_df_logor)
wide_df_logor_m = data.matrix(wide_df_logor)
rownames(wide_df_logor_m)
colnames(wide_df_logor_m)
position_logor = as.numeric(colnames(wide_df_logor_m))
#===============================
# logo data: multiple nsSNPs (>1)
#=================================
#require(data.table)
# get freq count of positions so you can subset freq<1
setDT(logo_data)[, mut_pos_occurrence := .N, by = .(position)]
table(logo_data$position)
table(logo_data$mut_pos_occurrence)
max_mut = max(table(logo_data$position))
# extract freq_pos > 1
my_data_snp = logo_data[logo_data$mut_pos_occurrence!=1,]
u = unique(my_data_snp$position)
max_mult_mut = max(table(my_data_snp$position))
if (nrow(my_data_snp) == nrow(logo_data) - table(logo_data$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(logo_data) - table(logo_data$mut_pos_occurrence)[[1]]
, "\nGot:", nrow(my_data_snp) )
}
cat("\nNo. of sites with only 1 mutations:", table(logo_data$mut_pos_occurrence)[[1]])
#--------------------------------------
# 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
#-------------------------------------
# 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)
identical(colnames(tab_mt), colnames(tab_wt))
identical(ncol(tab_mt), ncol(tab_wt))
#----------------------------------
# logo data OR: multiple nsSNPs (>1)
#----------------------------------
logo_data_or_mult = my_data_snp[, c("position", "mutant_type", "or_mychisq")]
#wide_df_or <- logo_data_or %>% spread(position, or_mychisq, fill = 0.0)
wide_df_or_mult <- logo_data_or_mult %>% spread(position, or_mychisq, fill = NA)
wide_df_or_mult = as.matrix(wide_df_or_mult)
rownames(wide_df_or_mult) = wide_df_or_mult[,1]
wide_df_or_mult = wide_df_or_mult[,-1]
str(wide_df_or_mult)
position_or_mult = as.numeric(colnames(wide_df_or_mult))
####################################################################
# Data for Corrplots
####################################################################
cat("\n=========================================="
, "\nCORR PLOTS data: PS"
, "\n===========================================")
df_ps = merged_df2
#--------------------
# adding log cols : NEW UNCOMMENT
#--------------------
#df_ps$log10_or_mychisq = log10(df_ps$or_mychisq)
#df_ps$neglog_pval_fisher = -log10(df_ps$pval_fisher)
##df_ps$log10_or_kin = log10(df_ps$or_kin)
##df_ps$neglog_pwald_kin = -log10(df_ps$pwald_kin)
#df_ps$mutation_info_labels = ifelse(df_ps$mutation_info == dr_muts_col, 1, 0)
#----------------------------
# columns for corr plots:PS
#----------------------------
# subset data to generate pairwise correlations
cols_to_select = c("mutationinformation"
, "duet_scaled"
, "foldx_scaled"
#, "mutation_info_labels"
, "asa"
, "rsa"
, "rd_values"
, "kd_values"
, "log10_or_mychisq"
, "neglog_pval_fisher"
##, "or_kin"
##, "neglog_pwald_kin"
, "af"
##, "af_kin"
, "duet_outcome"
, drug)
corr_data_ps = df_ps[cols_to_select]
dim(corr_data_ps)
#--------------------------------------
# assign nice colnames (for display)
#--------------------------------------
my_corr_colnames = c("Mutation"
, "DUET"
, "FoldX"
#, "Mutation class"
, "ASA"
, "RSA"
, "RD"
, "KD"
, "Log (OR)"
, "-Log (P)"
##, "Adjusted (OR)"
##, "-Log (P wald)"
, "MAF"
##, "AF_kin"
, "duet_outcome"
, drug)
length(my_corr_colnames)
colnames(corr_data_ps)
colnames(corr_data_ps) <- my_corr_colnames
colnames(corr_data_ps)
start = 1
end = which(colnames(corr_data_ps) == drug); end # should be the last column
offset = 1
#===========================
# Corr data for plots: PS
# big_df ps: ~ merged_df2
#===========================
#corr_ps_df2 = corr_data_ps[start:(end-offset)] # without drug
corr_ps_df2 = corr_data_ps[start:end]
head(corr_ps_df2)
#===========================
# Corr data for plots: PS
# short_df ps: ~merged_df3
#===========================
corr_ps_df3 = corr_ps_df2[!duplicated(corr_ps_df2$Mutation),]
na_or = sum(is.na(corr_ps_df3$`Log (OR)`))
check1 = nrow(corr_ps_df3) - na_or
##na_adj_or = sum(is.na(corr_ps_df3$`adjusted (OR)`))
##check2 = nrow(corr_ps_df3) - na_adj_or
if (nrow(corr_ps_df3) == nrow(merged_df3) && nrow(merged_df3_comp) == check1) {
cat( "\nPASS: No. of rows for corr_ps_df3 match"
, "\nPASS: No. of OR values checked: " , check1)
} else {
cat("\nFAIL: Numbers mismatch:"
, "\nExpected nrows: ", nrow(merged_df3)
, "\nGot: ", nrow(corr_ps_df3)
, "\nExpected OR values: ", nrow(merged_df3_comp)
, "\nGot: ", check1)
}
rm(foo)
####################################################################
# Data for DM OM Plots: Long format dfs
####################################################################
source("other_plots_data.R")
########################################################################
# End of script
########################################################################
cat("\n######################################################\n"
, "\nSuccessful: get_plotting_dfs.R worked!"
, "\n###################################################\n")
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