moved corr_data and corr_PS_LIG.R to redundant
This commit is contained in:
parent
55b5d31c07
commit
237e293ca3
2 changed files with 0 additions and 0 deletions
251
scripts/plotting/redundant/corr_PS_LIG.R
Normal file
251
scripts/plotting/redundant/corr_PS_LIG.R
Normal file
|
|
@ -0,0 +1,251 @@
|
|||
#!/usr/bin/env Rscript
|
||||
#########################################################
|
||||
# TASK: Corr plots for PS and Lig
|
||||
|
||||
# Output: 1 svg
|
||||
|
||||
#=======================================================================
|
||||
# working dir and loading libraries
|
||||
getwd()
|
||||
setwd("~/git/LSHTM_analysis/scripts/plotting")
|
||||
getwd()
|
||||
|
||||
|
||||
source("Header_TT.R")
|
||||
require(cowplot)
|
||||
source("../functions/combining_dfs_plotting.R")
|
||||
|
||||
# should return the following dfs, directories and variables
|
||||
|
||||
# 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(paste0("Directories imported:"
|
||||
, "\ndatadir:", datadir
|
||||
, "\nindir:", indir
|
||||
, "\noutdir:", outdir
|
||||
, "\nplotdir:", plotdir))
|
||||
|
||||
cat(paste0("Variables imported:"
|
||||
, "\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))
|
||||
|
||||
#=======
|
||||
# output
|
||||
#=======
|
||||
|
||||
# PS
|
||||
corr_ps = "corr_PS.svg"
|
||||
plot_corr_ps = paste0(plotdir,"/", corr_ps)
|
||||
|
||||
# LIG
|
||||
corr_lig = "corr_LIG.svg"
|
||||
plot_corr_lig = paste0(plotdir,"/", corr_lig)
|
||||
|
||||
####################################################################
|
||||
# end of loading libraries and functions #
|
||||
########################################################################
|
||||
|
||||
#%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||
df_ps = merged_df3
|
||||
df_lig = merged_df3_lig
|
||||
#%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||
rm( merged_df2, merged_df2_comp, merged_df2_lig, merged_df2_comp_lig, my_df_u, my_df_u_lig)
|
||||
########################################################################
|
||||
|
||||
################################
|
||||
# Data for Correlation plots: PS
|
||||
#################################
|
||||
|
||||
#======================
|
||||
# adding log cols
|
||||
#======================
|
||||
|
||||
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)
|
||||
|
||||
#===============================
|
||||
# Data for Correlation plots:PS
|
||||
#===============================
|
||||
# subset data to generate pairwise correlations
|
||||
cols_to_select = c("duet_scaled"
|
||||
|
||||
, "foldx_scaled"
|
||||
|
||||
, "log10_or_mychisq"
|
||||
, "neglog_pval_fisher"
|
||||
|
||||
, "af"
|
||||
|
||||
, "duet_outcome"
|
||||
, drug)
|
||||
|
||||
corr_data_ps = df_ps[cols_to_select]
|
||||
|
||||
dim(corr_data_ps)
|
||||
|
||||
# assign nice colnames (for display)
|
||||
my_corr_colnames = c("DUET"
|
||||
|
||||
, "Foldx"
|
||||
|
||||
, "Log (OR)"
|
||||
, "-Log (P)"
|
||||
|
||||
, "MAF"
|
||||
|
||||
, "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
|
||||
|
||||
my_corr_ps = corr_data_ps[start:(end-offset)]
|
||||
head(my_corr_ps)
|
||||
|
||||
#my_cols = c("#f8766d", "#00bfc4")
|
||||
# deep blue :#007d85
|
||||
# deep red: #ae301e
|
||||
|
||||
#---------------------------------------
|
||||
# generate corr PS plot: both panels
|
||||
#---------------------------------------
|
||||
cat("Corr plot PS DUET with coloured dots:", plot_corr_ps)
|
||||
svg(plot_corr_ps, width = 15, height = 15)
|
||||
|
||||
pairs.panels(my_corr_ps[1:(length(my_corr_ps)-1)]
|
||||
, method = "spearman" # correlation method
|
||||
, hist.col = "grey" ##00AFBB
|
||||
, density = TRUE # show density plots
|
||||
, ellipses = F # show correlation ellipses
|
||||
, stars = T
|
||||
, rug = F
|
||||
, breaks = "Sturges"
|
||||
, show.points = T
|
||||
, bg = c("#f8766d", "#00bfc4")[unclass(factor(my_corr_ps$duet_outcome))] # foldx colours are reveresed
|
||||
, pch = 21 # for bg
|
||||
, jitter = T
|
||||
, alpha = 1
|
||||
, cex = 1.8
|
||||
, cex.axis = 2
|
||||
, cex.labels = 4
|
||||
, cex.cor = 1
|
||||
, smooth = F
|
||||
|
||||
)
|
||||
|
||||
dev.off()
|
||||
|
||||
corr_ps_rho = corr.test(my_corr_ps[1:5], method = "spearman")$r
|
||||
corr_ps_p = corr.test(my_corr_ps[1:5], method = "spearman")$p
|
||||
|
||||
################################################################################################
|
||||
###################################
|
||||
# Data for Correlation plots: LIG
|
||||
##################################
|
||||
table(df_lig$ligand_outcome)
|
||||
|
||||
df_lig$log10_or_mychisq = log10(df_lig$or_mychisq)
|
||||
df_lig$neglog_pval_fisher = -log10(df_lig$pval_fisher)
|
||||
|
||||
df_lig$log10_or_kin = log10(df_lig$or_kin)
|
||||
df_lig$neglog_pwald_kin = -log10(df_lig$pwald_kin)
|
||||
|
||||
# subset data to generate pairwise correlations
|
||||
cols_to_select = c("affinity_scaled"
|
||||
|
||||
, "log10_or_mychisq"
|
||||
, "neglog_pval_fisher"
|
||||
|
||||
, "af"
|
||||
|
||||
, "ligand_outcome"
|
||||
, drug)
|
||||
|
||||
corr_data_lig = df_lig[, cols_to_select]
|
||||
|
||||
|
||||
dim(corr_data_lig)
|
||||
|
||||
# assign nice colnames (for display)
|
||||
my_corr_colnames = c("Ligand Affinity"
|
||||
|
||||
, "Log (OR)"
|
||||
, "-Log (P)"
|
||||
|
||||
, "MAF"
|
||||
|
||||
, "ligand_outcome"
|
||||
, drug)
|
||||
|
||||
length(my_corr_colnames)
|
||||
|
||||
colnames(corr_data_lig)
|
||||
colnames(corr_data_lig) <- my_corr_colnames
|
||||
colnames(corr_data_lig)
|
||||
|
||||
start = 1
|
||||
end = which(colnames(corr_data_lig) == drug); end # should be the last column
|
||||
offset = 1
|
||||
|
||||
my_corr_lig = corr_data_lig[start:(end-offset)]
|
||||
head(my_corr_lig)
|
||||
|
||||
#---------------------------------------
|
||||
# generate corr LIG plot: both panels
|
||||
#---------------------------------------
|
||||
cat("Corr LIG plot:", plot_corr_lig)
|
||||
svg(plot_corr_lig, width = 15, height = 15)
|
||||
|
||||
# uncomment as necessary
|
||||
pairs.panels(my_corr_lig[1:(length(my_corr_lig)-1)]
|
||||
, method = "spearman" # correlation method
|
||||
, hist.col = "grey" ##00AFBB
|
||||
, density = TRUE # show density plots
|
||||
, ellipses = F # show correlation ellipses
|
||||
, stars = T
|
||||
, rug = F
|
||||
, breaks = "Sturges"
|
||||
, show.points = T
|
||||
, bg = c("#f8766d", "#00bfc4")[unclass(factor(my_corr_lig$ligand_outcome))]
|
||||
, pch = 21 # for bg
|
||||
, jitter = T
|
||||
, cex = 2
|
||||
, cex.axis = 2
|
||||
, cex.labels = 4
|
||||
, cex.cor = 1
|
||||
, smooth = F
|
||||
)
|
||||
|
||||
dev.off()
|
||||
corr_lig_rho = corr.test(my_corr_lig[1:4], method = "spearman")$r
|
||||
corr_lig_p = corr.test(my_corr_lig[1:4], method = "spearman")$p
|
||||
#######################################################
|
||||
263
scripts/plotting/redundant/corr_data.R
Normal file
263
scripts/plotting/redundant/corr_data.R
Normal file
|
|
@ -0,0 +1,263 @@
|
|||
#!/usr/bin/env Rscript
|
||||
#########################################################
|
||||
# TASK: Prepare for correlation data
|
||||
|
||||
#=======================================================================
|
||||
# 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")
|
||||
###########################################################
|
||||
#===========
|
||||
# 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")
|
||||
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)
|
||||
|
||||
cat(paste0("Directories imported:"
|
||||
, "\ndatadir:", datadir
|
||||
, "\nindir:", indir
|
||||
, "\noutdir:", outdir
|
||||
, "\nplotdir:", plotdir))
|
||||
|
||||
cat(paste0("Variables imported:"
|
||||
, "\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))
|
||||
|
||||
#=======
|
||||
# output
|
||||
#=======
|
||||
# corr_ps_df2
|
||||
# corr_lig_df2
|
||||
|
||||
####################################################################
|
||||
# end of loading libraries and functions
|
||||
####################################################################
|
||||
|
||||
#%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||
#df_ps = merged_df3
|
||||
df_ps = merged_df2
|
||||
|
||||
#df_lig = merged_df3_lig
|
||||
df_lig = merged_df2_lig
|
||||
|
||||
#%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||
|
||||
|
||||
########################################################################
|
||||
# end of data extraction and cleaning for plots #
|
||||
########################################################################
|
||||
|
||||
#======================
|
||||
# adding log cols
|
||||
#======================
|
||||
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$mutation_info == dr_muts_col, 1, 0)
|
||||
|
||||
#===============================
|
||||
# Data for Correlation plots:PS
|
||||
#===============================
|
||||
# subset data to generate pairwise correlations
|
||||
cols_to_select = c("mutationinformation"
|
||||
, "duet_scaled"
|
||||
, "foldx_scaled"
|
||||
#, "mutation_info_labels"
|
||||
, "asa"
|
||||
, "rsa"
|
||||
, "rd_values"
|
||||
, "kd_values"
|
||||
, "log10_or_mychisq"
|
||||
, "neglog_pval_fisher"
|
||||
, "or_kin"
|
||||
, "neglog_pwald_kin"
|
||||
, "af"
|
||||
#, "af_kin"
|
||||
, "duet_outcome"
|
||||
, drug)
|
||||
|
||||
corr_data_ps = df_ps[cols_to_select]
|
||||
|
||||
dim(corr_data_ps)
|
||||
|
||||
# assign nice colnames (for display)
|
||||
my_corr_colnames = c("Mutation"
|
||||
, "DUET"
|
||||
, "Foldx"
|
||||
#, "Mutation class"
|
||||
, "ASA"
|
||||
, "RSA"
|
||||
, "RD"
|
||||
, "KD"
|
||||
, "Log (OR)"
|
||||
, "-Log (P)"
|
||||
, "Adjusted (OR)"
|
||||
, "-Log (P wald)"
|
||||
, "AF"
|
||||
, "AF_kin"
|
||||
, "duet_outcome"
|
||||
, drug)
|
||||
|
||||
length(my_corr_colnames)
|
||||
|
||||
colnames(corr_data_ps)
|
||||
colnames(corr_data_ps) <- my_corr_colnames
|
||||
colnames(corr_data_ps)
|
||||
|
||||
start = 1
|
||||
end = which(colnames(corr_data_ps) == drug); end # should be the last column
|
||||
offset = 1
|
||||
|
||||
#corr_ps_df2 = corr_data_ps[start:(end-offset)] # without drug
|
||||
corr_ps_df2 = corr_data_ps[start:end]
|
||||
head(corr_ps_df2)
|
||||
|
||||
#--------------------------
|
||||
# short_df ps: merged_df3
|
||||
#--------------------------
|
||||
corr_ps_df3 = corr_ps_df2[!duplicated(corr_ps_df2$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) ) {
|
||||
# cat( "PASS: No. of rows for corr_ps_df3 match" )
|
||||
#}if ( nrow(merged_df3_comp) == check1 ){
|
||||
# cat( "PASS: No. of OR values checked" )
|
||||
#}
|
||||
|
||||
################################################################################################
|
||||
#=================================
|
||||
# Data for Correlation plots: LIG
|
||||
#=================================
|
||||
table(df_lig$ligand_outcome)
|
||||
|
||||
df_lig$log10_or_mychisq = log10(df_lig$or_mychisq)
|
||||
df_lig$neglog_pval_fisher = -log10(df_lig$pval_fisher)
|
||||
|
||||
df_lig$log10_or_kin = log10(df_lig$or_kin)
|
||||
df_lig$neglog_pwald_kin = -log10(df_lig$pwald_kin)
|
||||
|
||||
# subset data to generate pairwise correlations
|
||||
cols_to_select = c("mutationinformation"
|
||||
, "affinity_scaled"
|
||||
#, "mutation_info_labels"
|
||||
, "asa"
|
||||
, "rsa"
|
||||
, "rd_values"
|
||||
, "kd_values"
|
||||
, "log10_or_mychisq"
|
||||
, "neglog_pval_fisher"
|
||||
, "or_kin"
|
||||
, "neglog_pwald_kin"
|
||||
, "af"
|
||||
, "af_kin"
|
||||
, "ligand_outcome"
|
||||
, drug)
|
||||
|
||||
corr_data_lig = df_lig[, cols_to_select]
|
||||
|
||||
|
||||
dim(corr_data_lig)
|
||||
|
||||
# assign nice colnames (for display)
|
||||
my_corr_colnames = c("Mutation"
|
||||
, "Ligand Affinity"
|
||||
#, "Mutation class"
|
||||
, "ASA"
|
||||
, "RSA"
|
||||
, "RD"
|
||||
, "KD"
|
||||
, "Log (OR)"
|
||||
, "-Log (P)"
|
||||
, "Adjusted (OR)"
|
||||
, "-Log (P wald)"
|
||||
, "AF"
|
||||
, "AF_kin"
|
||||
, "ligand_outcome"
|
||||
, drug)
|
||||
|
||||
length(my_corr_colnames)
|
||||
|
||||
colnames(corr_data_lig)
|
||||
colnames(corr_data_lig) <- my_corr_colnames
|
||||
colnames(corr_data_lig)
|
||||
|
||||
start = 1
|
||||
end = which(colnames(corr_data_lig) == drug); end # should be the last column
|
||||
offset = 1
|
||||
|
||||
#corr_lig_df2 = corr_data_lig[start:(end-offset)] # without drug
|
||||
corr_lig_df2 = corr_data_lig[start:end]
|
||||
head(corr_lig_df2)
|
||||
|
||||
#-----------------
|
||||
# short_df lig: merged_df3_lig
|
||||
#-----------------
|
||||
|
||||
corr_lig_df3 = corr_lig_df2[!duplicated(corr_lig_df2$Mutation),]
|
||||
|
||||
#######################################################
|
||||
rm(merged_df2, merged_df2_lig, merged_df3, merged_df3_lig
|
||||
, merged_df2_comp , merged_df3_comp, merged_df2_comp_lig, merged_df3_comp_lig
|
||||
, corr_data_ps, corr_data_lig)
|
||||
Loading…
Add table
Add a link
Reference in a new issue