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added plots for thesis

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This commit is contained in:
Tanushree Tunstall 2022-08-03 21:32:47 +01:00
parent 41c4996426
commit aabe466599
17 changed files with 24 additions and 2913 deletions
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13
scripts/Header_TT.R
View file

@ -222,6 +222,19 @@ consurf_palette2 = c("0" = "yellow2"
, "8" = "orchid4"
, "9" = "darkorchid4")
consurf_colours = c("0" = rgb(1.00,1.00,0.59)
, "1" = rgb(0.63,0.16,0.37)
, "2" = rgb(0.94,0.49,0.67)
, "3" = rgb(0.98,0.78,0.86)
, "4" = rgb(0.98,0.92,0.96)
, "5" = rgb(1.00,1.00,1.00)
, "6" = rgb(0.84,0.94,0.94)
, "7" = rgb(0.65,0.86,0.90)
, "8" = rgb(0.29,0.69,0.75)
, "9" = rgb(0.04,0.49,0.51)
)
##################################################
# Function name clashes with plyr and dplyr

2
scripts/functions/lineage_plot_data.R
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@ -34,6 +34,8 @@ lineage_plot_data <- function(df
################################################################
# Get WF and LF data with lineage count, and snp diversity
################################################################
df[lineage_column_name] =
# Initialise output list
lineage_dataL = list(
lin_wf = data.frame()

5
scripts/functions/position_count_bp.R
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@ -96,7 +96,7 @@ site_snp_count_bp <- function (plotdf
# but atm being using as plot title
#my_leg_title
bp_plot_title = paste0("Total nsSNPs: ", tot_muts
, ", Total no. of nsSNPs sites: ", tot_sites)
, "\nTotal sites: ", tot_sites)
#-------------
# start plot 2
@ -123,7 +123,8 @@ site_snp_count_bp <- function (plotdf
#, legend.text = element_text(size = leg_text_size)
#, legend.title = element_text(size = axis_label_size)
, plot.title = element_text(size = leg_text_size
, colour = title_colour)
, colour = title_colour
, hjust = 0.5)
, plot.subtitle = element_text(size = subtitle_size
, hjust = 0.5
, colour = subtitle_colour)) +

9
scripts/functions/stability_count_bp.R
View file

@ -30,7 +30,8 @@ stability_count_bp <- function(plotdf
, sts = 20
, subtitle_colour = "pink"
#, leg_position = c(0.73,0.8) # within plot area
, leg_position = "top"){
, leg_position = "top"
, bar_fill_values = c("#F8766D", "#00BFC4")){
# OutPlot_count = ggplot(plotdf, aes(x = eval(parse(text = df_colname)))) +
OutPlot_count = ggplot(plotdf, aes_string(x = df_colname)) +
@ -57,8 +58,10 @@ stability_count_bp <- function(plotdf
, subtitle = subtitle_text
, y = yaxis_title) +
scale_fill_discrete(name = leg_title
#, labels = c("Destabilising", "Stabilising")
, labels = label_categories)
, labels = label_categories) +
scale_fill_manual("", values=bar_fill_values)
return(OutPlot_count)
}

3
scripts/plotting/LINEAGE2.R
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@ -128,9 +128,6 @@ plot_df$pvalRF = ifelse(plot_df$pvalR > 0.05, paste0("p=",plot_df$pvalR), plot_d
# plot_df$pvalF = ifelse(plot_df$pval < 0.05, paste0(round(plot_df$pval, 3), "* "), plot_df$pval )
# plot_df$pvalF = ifelse(plot_df$pval == 0.05, paste0(round(plot_df$pval, 3), ". "), plot_df$pval )
round(plot_df$pvalF, 3)
#================================================
# Plot attempt 1 [no stats]: WORKS beeautifully
#================================================

241
scripts/plotting/mcsm_affinity_data_only.R
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@ -1,241 +0,0 @@
#source("~/git/LSHTM_analysis/config/pnca.R")
#source("~/git/LSHTM_analysis/config/alr.R")
#source("~/git/LSHTM_analysis/config/gid.R")
#source("~/git/LSHTM_analysis/config/embb.R")
#source("~/git/LSHTM_analysis/config/katg.R")
#source("~/git/LSHTM_analysis/config/rpob.R")
source("/home/tanu/git/LSHTM_analysis/my_header.R")
#########################################################
# TASK: Generate averaged affinity values
# across all affinity tools for a given structure
# as applicable...
#########################################################
#=======
# output
#=======
outdir_images = paste0("~/git/Writing/thesis/images/results/", tolower(gene))
#OutFile1
outfile_mean_aff = paste0(outdir_images, "/", tolower(gene)
, "_mean_affinity_all.csv")
print(paste0("Output file:", outfile_mean_aff))
#OutFile2
outfile_mean_aff_priorty = paste0(outdir_images, "/", tolower(gene)
, "_mean_affinity_priority.csv")
print(paste0("Output file:", outfile_mean_aff_priorty))
#%%===============================================================
#=============
# Input
#=============
df3_filename = paste0("/home/tanu/git/Data/", drug, "/output/", tolower(gene), "_merged_df3.csv")
df3 = read.csv(df3_filename)
length(df3$mutationinformation)
# mut_info checks
table(df3$mutation_info)
table(df3$mutation_info_orig)
table(df3$mutation_info_labels_orig)
# used in plots and analyses
table(df3$mutation_info_labels) # different, and matches dst_mode
table(df3$dst_mode)
# create column based on dst mode with different colname
table(is.na(df3$dst))
table(is.na(df3$dst_mode))
#===============
# Create column: sensitivity mapped to dst_mode
#===============
df3$sensitivity = ifelse(df3$dst_mode == 1, "R", "S")
table(df3$sensitivity)
length(unique((df3$mutationinformation)))
all_colnames = as.data.frame(colnames(df3))
# FIXME: ADD distance to NA when SP replies
dist_columns = c("ligand_distance", "interface_dist")
DistCutOff = 10
common_cols = c("mutationinformation"
, "X5uhc_position"
, "X5uhc_offset"
, "position"
, "dst_mode"
, "mutation_info_labels"
, "sensitivity", dist_columns )
all_colnames$`colnames(df3)`[grep("scaled", all_colnames$`colnames(df3)`)]
all_colnames$`colnames(df3)`[grep("outcome", all_colnames$`colnames(df3)`)]
#===================
# stability cols
#===================
raw_cols_stability = c("duet_stability_change"
, "deepddg"
, "ddg_dynamut2"
, "ddg_foldx")
scaled_cols_stability = c("duet_scaled"
, "deepddg_scaled"
, "ddg_dynamut2_scaled"
, "foldx_scaled")
outcome_cols_stability = c("duet_outcome"
, "deepddg_outcome"
, "ddg_dynamut2_outcome"
, "foldx_outcome")
#===================
# affinity cols
#===================
raw_cols_affinity = c("ligand_affinity_change"
, "mmcsm_lig"
, "mcsm_ppi2_affinity"
, "mcsm_na_affinity")
scaled_cols_affinity = c("affinity_scaled"
, "mmcsm_lig_scaled"
, "mcsm_ppi2_scaled"
, "mcsm_na_scaled" )
outcome_cols_affinity = c( "ligand_outcome"
, "mmcsm_lig_outcome"
, "mcsm_ppi2_outcome"
, "mcsm_na_outcome")
#===================
# conservation cols
#===================
# raw_cols_conservation = c("consurf_score"
# , "snap2_score"
# , "provean_score")
#
# scaled_cols_conservation = c("consurf_scaled"
# , "snap2_scaled"
# , "provean_scaled")
#
# # CANNOT strictly be used, as categories are not identical with conssurf missing altogether
# outcome_cols_conservation = c("provean_outcome"
# , "snap2_outcome"
# #consurf outcome doesn't exist
# )
gene_aff_cols = colnames(df3)[colnames(df3)%in%scaled_cols_affinity]
gene_stab_cols = colnames(df3)[colnames(df3)%in%scaled_cols_stability]
gene_common_cols = colnames(df3)[colnames(df3)%in%common_cols]
sel_cols = c(gene_common_cols
, gene_stab_cols
, gene_aff_cols)
#########################################
#df3_plot = df3[, cols_to_extract]
df3_plot = df3[, sel_cols]
######################
#FILTERING HMMMM!
#all dist <10
#for embb this results in 2 muts
######################
df3_affinity_filtered = df3_plot[ (df3_plot$ligand_distance<10 | df3_plot$interface_dist <10),]
df3_affinity_filtered = df3_plot[ (df3_plot$ligand_distance<10 & df3_plot$interface_dist <10),]
c0u = unique(df3_affinity_filtered$position)
length(c0u)
#df = df3_affinity_filtered
##########################################
#NO FILTERING: annotate the whole df
df = df3_plot
sum(is.na(df))
df2 = na.omit(df)
c0u = unique(df2$position)
length(c0u)
# reassign orig
my_df_raw = df3
# now subset
df3 = df2
#######################################################
#=================
# affinity effect
#=================
give_col=function(x,y,df=df3){
df[df$position==x,y]
}
for (i in unique(df3$position) ){
#print(i)
biggest = max(abs(give_col(i,gene_aff_cols)))
df3[df3$position==i,'abs_max_effect'] = biggest
df3[df3$position==i,'effect_type']= names(
give_col(i,gene_aff_cols)[which(
abs(
give_col(i,gene_aff_cols)
) == biggest, arr.ind=T
)[, "col"]])
# effect_name = unique(df3[df3$position==i,'effect_type'])
effect_name = df3[df3$position==i,'effect_type'][1] # pick first one in case we have multiple exact values
ind = rownames(which(abs(df3[df3$position==i,c('position',effect_name)][effect_name])== biggest, arr.ind=T))
df3[df3$position==i,'effect_sign'] = sign(df3[effect_name][ind,])
}
df3$effect_type = sub("\\.[0-9]+", "", df3$effect_type) # cull duplicate effect types that happen when there are exact duplicate values
df3U = df3[!duplicated(df3[c('position')]), ]
table(df3U$effect_type)
#########################################################
#%% consider stability as well
df4 = df2
#=================
# stability + affinity effect
#=================
effect_cols = c(gene_aff_cols, gene_stab_cols)
give_col=function(x,y,df=df4){
df[df$position==x,y]
}
for (i in unique(df4$position) ){
#print(i)
biggest = max(abs(give_col(i,effect_cols)))
df4[df4$position==i,'abs_max_effect'] = biggest
df4[df4$position==i,'effect_type']= names(
give_col(i,effect_cols)[which(
abs(
give_col(i,effect_cols)
) == biggest, arr.ind=T
)[, "col"]])
# effect_name = unique(df4[df4$position==i,'effect_type'])
effect_name = df4[df4$position==i,'effect_type'][1] # pick first one in case we have multiple exact values
ind = rownames(which(abs(df4[df4$position==i,c('position',effect_name)][effect_name])== biggest, arr.ind=T))
df4[df4$position==i,'effect_sign'] = sign(df4[effect_name][ind,])
}
df4$effect_type = sub("\\.[0-9]+", "", df4$effect_type) # cull duplicate effect types that happen when there are exact duplicate values
df4U = df4[!duplicated(df4[c('position')]), ]
table(df4U$effect_type)
#%%============================================================
# output
write.csv(combined_df, outfile_mean_ens_st_aff
, row.names = F)
cat("Finished writing file:\n"
, outfile_mean_ens_st_aff
, "\nNo. of rows:", nrow(combined_df)
, "\nNo. of cols:", ncol(combined_df))
# end of script
#===============================================================

316
scripts/plotting/mcsm_mean_affinity_ensemble.R
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@ -1,316 +0,0 @@
#source("~/git/LSHTM_analysis/config/pnca.R")
#source("~/git/LSHTM_analysis/config/alr.R")
#source("~/git/LSHTM_analysis/config/gid.R")
source("~/git/LSHTM_analysis/config/embb.R")
#source("~/git/LSHTM_analysis/config/katg.R")
#source("~/git/LSHTM_analysis/config/rpob.R")
source("/home/tanu/git/LSHTM_analysis/my_header.R")
#########################################################
# TASK: Generate averaged affinity values
# across all affinity tools for a given structure
# as applicable...
#########################################################
#=============
# Input
#=============
df3_filename = paste0("/home/tanu/git/Data/", drug, "/output/", tolower(gene), "_merged_df3.csv")
df3 = read.csv(df3_filename)
length(df3$mutationinformation)
all_colnames= colnames(df3)
#%%===============================================================
# FIXME: ADD distance to NA when SP replies
dist_columns = c("ligand_distance", "interface_dist")
DistCutOff = 10
common_cols = c("mutationinformation"
, "X5uhc_position"
, "X5uhc_offset"
, "position"
, "dst_mode"
, "mutation_info_labels"
, "sensitivity", dist_columns )
all_colnames[grep("scaled" , all_colnames)]
all_colnames[grep("outcome" , all_colnames)]
#===================
# stability cols
#===================
raw_cols_stability = c("duet_stability_change"
, "deepddg"
, "ddg_dynamut2"
, "ddg_foldx")
scaled_cols_stability = c("duet_scaled"
, "deepddg_scaled"
, "ddg_dynamut2_scaled"
, "foldx_scaled")
outcome_cols_stability = c("duet_outcome"
, "deepddg_outcome"
, "ddg_dynamut2_outcome"
, "foldx_outcome")
#===================
# affinity cols
#===================
raw_cols_affinity = c("ligand_affinity_change"
, "mmcsm_lig"
, "mcsm_ppi2_affinity"
, "mcsm_na_affinity")
scaled_cols_affinity = c("affinity_scaled"
, "mmcsm_lig_scaled"
, "mcsm_ppi2_scaled"
, "mcsm_na_scaled" )
outcome_cols_affinity = c( "ligand_outcome"
, "mmcsm_lig_outcome"
, "mcsm_ppi2_outcome"
, "mcsm_na_outcome")
#===================
# conservation cols
#===================
# raw_cols_conservation = c("consurf_score"
# , "snap2_score"
# , "provean_score")
#
# scaled_cols_conservation = c("consurf_scaled"
# , "snap2_scaled"
# , "provean_scaled")
#
# # CANNOT strictly be used, as categories are not identical with conssurf missing altogether
# outcome_cols_conservation = c("provean_outcome"
# , "snap2_outcome"
# #consurf outcome doesn't exist
# )
all_cols= c(common_cols
,raw_cols_stability, scaled_cols_stability, outcome_cols_stability
, raw_cols_affinity, scaled_cols_affinity, outcome_cols_affinity)
#=======
# output
#=======
outdir_images = paste0("~/git/Writing/thesis/images/results/", tolower(gene))
#OutFile1
outfile_mean_aff = paste0(outdir_images, "/", tolower(gene)
, "_mean_ligand.csv")
print(paste0("Output file:", outfile_mean_aff))
#OutFile2
outfile_ppi2 = paste0(outdir_images, "/", tolower(gene)
, "_mean_ppi2.csv")
print(paste0("Output file:", outfile_ppi2))
#OutFile4
#outfile_mean_aff_priorty = paste0(outdir_images, "/", tolower(gene)
# , "_mean_affinity_priority.csv")
#print(paste0("Output file:", outfile_mean_aff_priorty))
#################################################################
#################################################################
# mut positions
length(unique(df3$position))
# mut_info checks
table(df3$mutation_info)
table(df3$mutation_info_orig)
table(df3$mutation_info_labels_orig)
# used in plots and analyses
table(df3$mutation_info_labels) # different, and matches dst_mode
table(df3$dst_mode)
# create column based on dst mode with different colname
table(is.na(df3$dst))
table(is.na(df3$dst_mode))
#===============
# Create column: sensitivity mapped to dst_mode
#===============
df3$sensitivity = ifelse(df3$dst_mode == 1, "R", "S")
table(df3$sensitivity)
length(unique((df3$mutationinformation)))
all_colnames = as.data.frame(colnames(df3))
#===============
# select columns specific to gene
#===============
gene_aff_cols = colnames(df3)[colnames(df3)%in%c(outcome_cols_affinity
, scaled_cols_affinity)]
gene_common_cols = colnames(df3)[colnames(df3)%in%common_cols]
cols_to_extract = c(gene_common_cols
, gene_aff_cols)
cat("\nExtracting", length(cols_to_extract), "columns")
df3_plot = df3[, cols_to_extract]
table(df3_plot$mmcsm_lig_outcome)
table(df3_plot$ligand_outcome)
##############################################################
# mCSM-lig, mCSM-NA, mCSM-ppi2, mmCSM-lig
#########################################
cols_to_numeric = c("ligand_outcome"
, "mcsm_na_outcome"
, "mcsm_ppi2_outcome"
, "mmcsm_lig_outcome")
#=====================================
# mCSM-lig: Filter ligand distance <10
#DistCutOff = 10
#LigDist_colname = "ligand_distance"
# extract outcome cols and map numeric values to the categories
# Destabilising == 0, and stabilising == 1 so rescaling can let -1 be destabilising
#=====================================
df3_lig = df3[, c("mutationinformation"
, "position"
, "ligand_distance"
, "ligand_affinity_change"
, "affinity_scaled"
, "ligand_outcome")]
df3_lig = df3_lig[df3_lig["ligand_distance"]<DistCutOff,]
expected_npos = sum(table(df3_lig["ligand_distance"]<DistCutOff))
expected_npos
if ( nrow(df3_lig) == expected_npos ){
cat(paste0("\nPASS:", LigDist_colname, " filtered according to criteria:", LigDist_cutoff, angstroms_symbol ))
}else{
stop(paste0("\nAbort:", LigDist_colname, " could not be filtered according to criteria:", LigDist_cutoff, angstroms_symbol))
}
# group by position
mean_lig_by_position <- df3_lig %>%
dplyr::group_by(position) %>%
#dplyr::summarize(avg_lig = max(df3_lig_num))
#dplyr::summarize(avg_lig = mean(ligand_outcome))
#dplyr::summarize(avg_lig = mean(affinity_scaled, na.rm = T))
dplyr::summarize(avg_lig = mean(ligand_affinity_change, na.rm = T))
class(mean_lig_by_position)
# convert to a df
mean_lig_by_position = as.data.frame(mean_lig_by_position)
table(mean_lig_by_position$avg_lig)
# REscale b/w -1 and 1
lig_min = min(mean_lig_by_position['avg_lig'])
lig_max = max(mean_lig_by_position['avg_lig'])
mean_lig_by_position['avg_lig_scaled'] = lapply(mean_lig_by_position['avg_lig']
, function(x) {
scales::rescale_mid(x
, to = c(-1,1)
, from = c(lig_min,lig_max)
, mid = 0)
#, from = c(0,1))
})
cat(paste0('Average (mcsm-lig+mmcsm-lig) scores:\n'
, head(mean_lig_by_position['avg_lig'])
, '\n---------------------------------------------------------------'
, '\nAverage (mcsm-lig+mmcsm-lig) scaled scores:\n'
, head(mean_lig_by_position['avg_lig_scaled'])))
if ( nrow(mean_lig_by_position) == length(unique(df3_lig$position)) ){
cat("\nPASS: Generated average values for ligand affinity" )
}else{
stop(paste0("\nAbort: length mismatch for ligand affinity data"))
}
max(mean_lig_by_position$avg_lig); min(mean_lig_by_position$avg_lig)
max(mean_lig_by_position$avg_lig_scaled); min(mean_lig_by_position$avg_lig_scaled)
#################################################################
# output
write.csv(mean_lig_by_position, outfile_mean_aff
, row.names = F)
cat("Finished writing file:\n"
, outfile_mean_aff
, "\nNo. of rows:", nrow(mean_lig_by_position)
, "\nNo. of cols:", ncol(mean_lig_by_position))
##################################################################
##################################################################
#=====================================
# mCSM-ppi2: Filter interface_dist <10
#DistCutOff = 10
#=====================================
df3_ppi2 = df3[, c("mutationinformation"
, "position"
, "interface_dist"
, "mcsm_ppi2_affinity"
, "mcsm_ppi2_scaled"
, "mcsm_ppi2_outcome")]
df3_ppi2 = df3_ppi2[df3_ppi2["interface_dist"]<DistCutOff,]
expected_npos = sum(table(df3_ppi2["interface_dist"]<DistCutOff))
expected_npos
if ( nrow(df3_ppi2) == expected_npos ){
cat(paste0("\nPASS:", "interface_dist", " filtered according to criteria:", LigDist_cutoff, angstroms_symbol ))
}else{
stop(paste0("\nAbort:", "interface_dist", " could not be filtered according to criteria:", LigDist_cutoff, angstroms_symbol))
}
# group by position
mean_ppi2_by_position <- df3_ppi2 %>%
dplyr::group_by(position) %>%
#dplyr::summarize(avg_ppi2 = max(df3_ppi2_num))
#dplyr::summarize(avg_ppi2 = mean(mcsm_ppi2_outcome))
#dplyr::summarize(avg_ppi2 = mean(mcsm_ppi2_scaled, na.rm = T))
dplyr::summarize(avg_ppi2 = mean(mcsm_ppi2_affinity, na.rm = T))
class(mean_ppi2_by_position)
# convert to a df
mean_ppi2_by_position = as.data.frame(mean_ppi2_by_position)
table(mean_ppi2_by_position$avg_ppi2)
# REscale b/w -1 and 1
lig_min = min(mean_ppi2_by_position['avg_ppi2'])
lig_max = max(mean_ppi2_by_position['avg_ppi2'])
mean_ppi2_by_position['avg_ppi2_scaled'] = lapply(mean_ppi2_by_position['avg_ppi2']
, function(x) {
scales::rescale_mid(x
, to = c(-1,1)
, from = c(lig_min,lig_max)
, mid = 0)
#, from = c(0,1))
})
cat(paste0('Average ppi2 scores:\n'
, head(mean_ppi2_by_position['avg_ppi2'])
, '\n---------------------------------------------------------------'
, '\nAverage ppi2 scaled scores:\n'
, head(mean_ppi2_by_position['avg_ppi2_scaled'])))
if ( nrow(mean_ppi2_by_position) == length(unique(df3_ppi2$position)) ){
cat("\nPASS: Generated average values for ppi2" )
}else{
stop(paste0("\nAbort: length mismatch for ppi2 data"))
}
max(mean_ppi2_by_position$avg_ppi2); min(mean_ppi2_by_position$avg_ppi2)
max(mean_ppi2_by_position$avg_ppi2_scaled); min(mean_ppi2_by_position$avg_ppi2_scaled)
write.csv(mean_ppi2_by_position, outfile_ppi2
, row.names = F)
cat("Finished writing file:\n"
, outfile_ppi2
, "\nNo. of rows:", nrow(mean_ppi2_by_position)
, "\nNo. of cols:", ncol(mean_ppi2_by_position))
# end of script
#===============================================================

163
scripts/plotting/mcsm_mean_stability.R
View file

@ -1,163 +0,0 @@
getwd()
setwd("~/git/LSHTM_analysis/scripts/plotting")
getwd()
#########################################################
# TASK:
#########################################################
#source("~/git/LSHTM_analysis/scripts/Header_TT.R")
#require(data.table)
#require(dplyr)
source("plotting_data.R")
# should return
#my_df
#my_df_u
#dup_muts
# cmd parse arguments
#require('getopt', quietly = TRUE)
#========================================================
#========================================================
# Read file: call script for combining df for PS
#source("../combining_two_df.R")
#========================================================
# plotting_data.R imports all the dir names, etc
#=======
# output
#=======
out_filename_mean_stability = paste0(tolower(gene), "_mean_stability.csv")
outfile_mean_stability = paste0(outdir, "/", out_filename_mean_stability)
print(paste0("Output file:", outfile_mean_stability))
#%%===============================================================
#================
# Data for plots
#================
# REASSIGNMENT as necessary
df = my_df_u
rm(my_df)
###########################
# Data for bfactor figure
# PS (duet) average
# Ligand affinity average
###########################
head(df$position); head(df$mutationinformation)
head(df$duet_stability_change)
# order data frame
#df = df[order(df$position),] #already done
#head(df$position); head(df$mutationinformation)
#head(df$duet_stability_change)
#***********
# PS(duet): average by position and then scale b/w -1 and 1
# column to average: duet_stability_change (NOT scaled!)
#***********
mean_duet_by_position <- df %>%
group_by(position) %>%
summarize(averaged_duet = mean(duet_stability_change))
# scale b/w -1 and 1
duet_min = min(mean_duet_by_position['averaged_duet'])
duet_max = max(mean_duet_by_position['averaged_duet'])
# scale the averaged_duet values
mean_duet_by_position['averaged_duet_scaled'] = lapply(mean_duet_by_position['averaged_duet']
, function(x) ifelse(x < 0, x/abs(duet_min), x/duet_max))
cat(paste0('Average duet scores:\n', head(mean_duet_by_position['averaged_duet'])
, '\n---------------------------------------------------------------'
, '\nScaled duet scores:\n', head(mean_duet_by_position['averaged_duet_scaled'])))
# sanity checks
l_bound_duet = min(mean_duet_by_position['averaged_duet_scaled'])
u_bound_duet = max(mean_duet_by_position['averaged_duet_scaled'])
if ( (l_bound_duet == -1) && (u_bound_duet == 1) ){
cat(paste0("PASS: duet scores averaged by position and then scaled"
, "\nmin averaged duet: ", l_bound_duet
, "\nmax averaged duet: ", u_bound_duet))
}else{
cat(paste0("FAIL: avergaed duet scores could not be scaled b/w -1 and 1"
, "\nmin averaged duet: ", l_bound_duet
, "\nmax averaged duet: ", u_bound_duet))
quit()
}
#***********
# Lig: average by position and then scale b/w -1 and 1
# column: ligand_affinity_change (NOT scaled!)
#***********
mean_affinity_by_position <- df %>%
group_by(position) %>%
summarize(averaged_affinity = mean(ligand_affinity_change))
# scale b/w -1 and 1
affinity_min = min(mean_affinity_by_position['averaged_affinity'])
affinity_max = max(mean_affinity_by_position['averaged_affinity'])
# scale the averaged_affinity values
mean_affinity_by_position['averaged_affinity_scaled'] = lapply(mean_affinity_by_position['averaged_affinity']
, function(x) ifelse(x < 0, x/abs(affinity_min), x/affinity_max))
cat(paste0('Average affinity scores:\n', head(mean_affinity_by_position['averaged_affinity'])
, '\n---------------------------------------------------------------'
, '\nScaled affinity scores:\n', head(mean_affinity_by_position['averaged_affinity_scaled'])))
# sanity checks
l_bound_affinity = min(mean_affinity_by_position['averaged_affinity_scaled'])
u_bound_affinity = max(mean_affinity_by_position['averaged_affinity_scaled'])
if ( (l_bound_affinity == -1) && (u_bound_affinity == 1) ){
cat(paste0("PASS: affinity scores averaged by position and then scaled"
, "\nmin averaged affintiy: ", l_bound_affinity
, "\nmax averaged affintiy: ", u_bound_affinity))
}else{
cat(paste0("FAIL: avergaed affinity scores could not be scaled b/w -1 and 1"
, "\nmin averaged affintiy: ", l_bound_affinity
, "\nmax averaged affintiy: ", u_bound_affinity))
quit()
}
#***********
# merge: mean_duet_by_position and mean_affinity_by_position
#***********
common_cols = intersect(colnames(mean_duet_by_position), colnames(mean_affinity_by_position))
if (dim(mean_duet_by_position) && dim(mean_affinity_by_position)){
print(paste0("PASS: dim's match, mering dfs by column :", common_cols))
#combined = as.data.frame(cbind(mean_duet_by_position, mean_affinity_by_position ))
combined_df = as.data.frame(merge(mean_duet_by_position
, mean_affinity_by_position
, by = common_cols
, all = T))
cat(paste0("\nnrows combined_df:", nrow(combined_df)
, "\nnrows combined_df:", ncol(combined_df)))
}else{
cat(paste0("FAIL: dim's mismatch, aborting cbind!"
, "\nnrows df1:", nrow(mean_duet_by_position)
, "\nnrows df2:", nrow(mean_affinity_by_position)))
quit()
}
#%%============================================================
# output
write.csv(combined_df, outfile_mean_stability
, row.names = F)
cat("Finished writing file:\n"
, outfile_mean_stability
, "\nNo. of rows:", nrow(combined_df)
, "\nNo. of cols:", ncol(combined_df))
# end of script
#===============================================================

212
scripts/plotting/mcsm_mean_stability_ensemble.R
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@ -1,212 +0,0 @@
#source("~/git/LSHTM_analysis/config/pnca.R")
#source("~/git/LSHTM_analysis/config/alr.R")
#source("~/git/LSHTM_analysis/config/gid.R")
#source("~/git/LSHTM_analysis/config/embb.R")
#source("~/git/LSHTM_analysis/config/katg.R")
#source("~/git/LSHTM_analysis/config/rpob.R")
source("/home/tanu/git/LSHTM_analysis/my_header.R")
#########################################################
# TASK: Generate averaged stability values
# across all stability tools
# for a given structure
#########################################################
#=======
# output
#=======
outdir_images = paste0("~/git/Writing/thesis/images/results/", tolower(gene))
outfile_mean_ens_st_aff = paste0(outdir_images, "/", tolower(gene)
, "_mean_ens_stability.csv")
print(paste0("Output file:", outfile_mean_ens_st_aff))
#%%===============================================================
#=============
# Input
#=============
df3_filename = paste0("/home/tanu/git/Data/", drug, "/output/", tolower(gene), "_merged_df3.csv")
df3 = read.csv(df3_filename)
length(df3$mutationinformation)
# mut_info checks
table(df3$mutation_info)
table(df3$mutation_info_orig)
table(df3$mutation_info_labels_orig)
# used in plots and analyses
table(df3$mutation_info_labels) # different, and matches dst_mode
table(df3$dst_mode)
# create column based on dst mode with different colname
table(is.na(df3$dst))
table(is.na(df3$dst_mode))
#===============
# Create column: sensitivity mapped to dst_mode
#===============
df3$sensitivity = ifelse(df3$dst_mode == 1, "R", "S")
table(df3$sensitivity)
length(unique((df3$mutationinformation)))
all_colnames = as.data.frame(colnames(df3))
common_cols = c("mutationinformation"
, "position"
, "dst_mode"
, "mutation_info_labels"
, "sensitivity"
, "ligand_distance"
, "interface_dist")
all_colnames$`colnames(df3)`[grep("scaled", all_colnames$`colnames(df3)`)]
all_colnames$`colnames(df3)`[grep("outcome", all_colnames$`colnames(df3)`)]
#===================
# stability cols
#===================
raw_cols_stability = c("duet_stability_change"
, "deepddg"
, "ddg_dynamut2"
, "ddg_foldx")
scaled_cols_stability = c("duet_scaled"
, "deepddg_scaled"
, "ddg_dynamut2_scaled"
, "foldx_scaled")
outcome_cols_stability = c("duet_outcome"
, "deepddg_outcome"
, "ddg_dynamut2_outcome"
, "foldx_outcome")
#===================
# affinity cols
#===================
raw_cols_affinity = c("ligand_affinity_change"
, "mmcsm_lig"
, "mcsm_ppi2_affinity"
, "mcsm_na_affinity")
scaled_cols_affinity = c("affinity_scaled"
, "mmcsm_lig_scaled"
, "mcsm_ppi2_scaled"
, "mcsm_na_scaled" )
outcome_cols_affinity = c( "ligand_outcome"
, "mmcsm_lig_outcome"
, "mcsm_ppi2_outcome"
, "mcsm_na_outcome")
#===================
# conservation cols
#===================
# raw_cols_conservation = c("consurf_score"
# , "snap2_score"
# , "provean_score")
#
# scaled_cols_conservation = c("consurf_scaled"
# , "snap2_scaled"
# , "provean_scaled")
#
# # CANNOT strictly be used, as categories are not identical with conssurf missing altogether
# outcome_cols_conservation = c("provean_outcome"
# , "snap2_outcome"
# #consurf outcome doesn't exist
# )
###########################################################
cols_to_consider = colnames(df3)[colnames(df3)%in%c(common_cols
, raw_cols_stability
, scaled_cols_stability
, outcome_cols_stability
, raw_cols_affinity
, scaled_cols_affinity
, outcome_cols_affinity)]
cols_to_extract = cols_to_consider[cols_to_consider%in%c(common_cols
, outcome_cols_stability)]
##############################################################
#####################
# Ensemble stability: outcome_cols_stability
#####################
# extract outcome cols and map numeric values to the categories
# Destabilising == 0, and stabilising == 1, so rescaling can let -1 be destabilising
df3_plot = df3[, cols_to_extract]
# assign numeric values to outcome
df3_plot[, outcome_cols_stability] <- sapply(df3_plot[, outcome_cols_stability]
, function(x){ifelse(x == "Destabilising", 0, 1)})
table(df3$duet_outcome)
table(df3_plot$duet_outcome)
#=====================================
# Stability (4 cols): average the scores
# across predictors ==> average by
# position ==> scale b/w -1 and 1
# column to average: ens_stability
#=====================================
cols_to_average = which(colnames(df3_plot)%in%outcome_cols_stability)
# ensemble average across predictors
df3_plot$ens_stability = rowMeans(df3_plot[,cols_to_average])
head(df3_plot$position); head(df3_plot$mutationinformation)
head(df3_plot$ens_stability)
table(df3_plot$ens_stability)
# ensemble average of predictors by position
mean_ens_stability_by_position <- df3_plot %>%
dplyr::group_by(position) %>%
dplyr::summarize(avg_ens_stability = mean(ens_stability))
# REscale b/w -1 and 1
#en_stab_min = min(mean_ens_stability_by_position['avg_ens_stability'])
#en_stab_max = max(mean_ens_stability_by_position['avg_ens_stability'])
# scale the average stability value between -1 and 1
# mean_ens_by_position['averaged_stability3_scaled'] = lapply(mean_ens_by_position['averaged_stability3']
# , function(x) ifelse(x < 0, x/abs(en3_min), x/en3_max))
mean_ens_stability_by_position['avg_ens_stability_scaled'] = lapply(mean_ens_stability_by_position['avg_ens_stability']
, function(x) {
scales::rescale(x, to = c(-1,1)
#, from = c(en_stab_min,en_stab_max))
, from = c(0,1))
})
cat(paste0('Average stability scores:\n'
, head(mean_ens_stability_by_position['avg_ens_stability'])
, '\n---------------------------------------------------------------'
, '\nAverage stability scaled scores:\n'
, head(mean_ens_stability_by_position['avg_ens_stability_scaled'])))
# convert to a data frame
mean_ens_stability_by_position = as.data.frame(mean_ens_stability_by_position)
#FIXME: sanity checks
# TODO: predetermine the bounds
# l_bound_ens = min(mean_ens_stability_by_position['avg_ens_stability_scaled'])
# u_bound_ens = max(mean_ens_stability_by_position['avg_ens_stability_scaled'])
#
# if ( (l_bound_ens == -1) && (u_bound_ens == 1) ){
# cat(paste0("PASS: ensemble stability scores averaged by position and then scaled"
# , "\nmin ensemble averaged stability: ", l_bound_ens
# , "\nmax ensemble averaged stability: ", u_bound_ens))
# }else{
# cat(paste0("FAIL: avergaed duet scores could not be scaled b/w -1 and 1"
# , "\nmin ensemble averaged stability: ", l_bound_ens
# , "\nmax ensemble averaged stability: ", u_bound_ens))
# quit()
# }
##################################################################
# output
#write.csv(combined_df, outfile_mean_ens_st_aff
write.csv(mean_ens_stability_by_position
, outfile_mean_ens_st_aff
, row.names = F)
cat("Finished writing file:\n"
, outfile_mean_ens_st_aff
, "\nNo. of rows:", nrow(mean_ens_stability_by_position)
, "\nNo. of cols:", ncol(mean_ens_stability_by_position))
# end of script
#===============================================================

176
scripts/plotting/mcsm_mean_stability_ensemble_5uhc_rpob.R
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@ -1,176 +0,0 @@
#source("~/git/LSHTM_analysis/config/pnca.R")
#source("~/git/LSHTM_analysis/config/alr.R")
#source("~/git/LSHTM_analysis/config/gid.R")
#source("~/git/LSHTM_analysis/config/embb.R")
#source("~/git/LSHTM_analysis/config/katg.R")
#source("~/git/LSHTM_analysis/config/rpob.R")
source("/home/tanu/git/LSHTM_analysis/my_header.R")
#########################################################
# TASK: Generate averaged stability values
# across all stability tools
# for a given structure
#########################################################
#=======
# output
#=======
outdir_images = paste0("~/git/Writing/thesis/images/results/", tolower(gene))
outfile_mean_ens_st_aff = paste0(outdir_images, "/5uhc_", tolower(gene)
, "_mean_ens_stability.csv")
print(paste0("Output file:", outfile_mean_ens_st_aff))
#%%===============================================================
#=============
# Input
#=============
df3_filename = paste0("/home/tanu/git/Data/", drug, "/output/", tolower(gene), "_merged_df3.csv")
df3 = read.csv(df3_filename)
length(df3$mutationinformation)
# mut_info checks
table(df3$mutation_info)
table(df3$mutation_info_orig)
table(df3$mutation_info_labels_orig)
# used in plots and analyses
table(df3$mutation_info_labels) # different, and matches dst_mode
table(df3$dst_mode)
# create column based on dst mode with different colname
table(is.na(df3$dst))
table(is.na(df3$dst_mode))
#===============
# Create column: sensitivity mapped to dst_mode
#===============
df3$sensitivity = ifelse(df3$dst_mode == 1, "R", "S")
table(df3$sensitivity)
length(unique((df3$mutationinformation)))
all_colnames = as.data.frame(colnames(df3))
common_cols = c("mutationinformation"
, "X5uhc_position"
, "dst_mode"
, "mutation_info_labels"
, "sensitivity"
, "X5uhc_position"
, "X5uhc_offset"
, "ligand_distance"
, "interface_dist")
all_colnames$`colnames(df3)`[grep("scaled", all_colnames$`colnames(df3)`)]
all_colnames$`colnames(df3)`[grep("outcome", all_colnames$`colnames(df3)`)]
#===================
# stability cols
#===================
raw_cols_stability = c("duet_stability_change"
, "deepddg"
, "ddg_dynamut2"
, "ddg_foldx")
scaled_cols_stability = c("duet_scaled"
, "deepddg_scaled"
, "ddg_dynamut2_scaled"
, "foldx_scaled")
outcome_cols_stability = c("duet_outcome"
, "deepddg_outcome"
, "ddg_dynamut2_outcome"
, "foldx_outcome")
###########################################################
cols_to_consider = colnames(df3)[colnames(df3)%in%c(common_cols
, raw_cols_stability
, scaled_cols_stability
, outcome_cols_stability)]
cols_to_extract = cols_to_consider[cols_to_consider%in%c(common_cols
, outcome_cols_stability)]
##############################################################
#####################
# Ensemble stability: outcome_cols_stability
#####################
# extract outcome cols and map numeric values to the categories
# Destabilising == 0, and stabilising == 1, so rescaling can let -1 be destabilising
df3_plot = df3[, cols_to_extract]
# assign numeric values to outcome
df3_plot[, outcome_cols_stability] <- sapply(df3_plot[, outcome_cols_stability]
, function(x){ifelse(x == "Destabilising", 0, 1)})
table(df3$duet_outcome)
table(df3_plot$duet_outcome)
#=====================================
# Stability (4 cols): average the scores
# across predictors ==> average by
# X5uhc_position ==> scale b/w -1 and 1
# column to average: ens_stability
#=====================================
cols_to_average = which(colnames(df3_plot)%in%outcome_cols_stability)
# ensemble average across predictors
df3_plot$ens_stability = rowMeans(df3_plot[,cols_to_average])
head(df3_plot$X5uhc_position); head(df3_plot$mutationinformation)
head(df3_plot$ens_stability)
table(df3_plot$ens_stability)
# ensemble average of predictors by X5uhc_position
mean_ens_stability_by_position <- df3_plot %>%
dplyr::group_by(X5uhc_position) %>%
dplyr::summarize(avg_ens_stability = mean(ens_stability))
# REscale b/w -1 and 1
#en_stab_min = min(mean_ens_stability_by_position['avg_ens_stability'])
#en_stab_max = max(mean_ens_stability_by_position['avg_ens_stability'])
# scale the average stability value between -1 and 1
# mean_ens_by_position['averaged_stability3_scaled'] = lapply(mean_ens_by_position['averaged_stability3']
# , function(x) ifelse(x < 0, x/abs(en3_min), x/en3_max))
mean_ens_stability_by_position['avg_ens_stability_scaled'] = lapply(mean_ens_stability_by_position['avg_ens_stability']
, function(x) {
scales::rescale(x, to = c(-1,1)
#, from = c(en_stab_min,en_stab_max))
, from = c(0,1))
})
cat(paste0('Average stability scores:\n'
, head(mean_ens_stability_by_position['avg_ens_stability'])
, '\n---------------------------------------------------------------'
, '\nAverage stability scaled scores:\n'
, head(mean_ens_stability_by_position['avg_ens_stability_scaled'])))
# convert to a data frame
mean_ens_stability_by_position = as.data.frame(mean_ens_stability_by_position)
#FIXME: sanity checks
# TODO: predetermine the bounds
# l_bound_ens = min(mean_ens_stability_by_position['avg_ens_stability_scaled'])
# u_bound_ens = max(mean_ens_stability_by_position['avg_ens_stability_scaled'])
#
# if ( (l_bound_ens == -1) && (u_bound_ens == 1) ){
# cat(paste0("PASS: ensemble stability scores averaged by X5uhc_position and then scaled"
# , "\nmin ensemble averaged stability: ", l_bound_ens
# , "\nmax ensemble averaged stability: ", u_bound_ens))
# }else{
# cat(paste0("FAIL: avergaed duet scores could not be scaled b/w -1 and 1"
# , "\nmin ensemble averaged stability: ", l_bound_ens
# , "\nmax ensemble averaged stability: ", u_bound_ens))
# quit()
# }
##################################################################
# output
#write.csv(combined_df, outfile_mean_ens_st_aff
write.csv(mean_ens_stability_by_position
, outfile_mean_ens_st_aff
, row.names = F)
cat("Finished writing file:\n"
, outfile_mean_ens_st_aff
, "\nNo. of rows:", nrow(mean_ens_stability_by_position)
, "\nNo. of cols:", ncol(mean_ens_stability_by_position))
# end of script
#===============================================================

155
scripts/plotting/mut_landscape.R
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@ -1,155 +0,0 @@
source("~/git/LSHTM_analysis/config/alr.R")
source("~/git/LSHTM_analysis/config/embb.R")
source("~/git/LSHTM_analysis/config/gid.R")
source("~/git/LSHTM_analysis/config/katg.R")
source("~/git/LSHTM_analysis/config/pnca.R")
source("~/git/LSHTM_analysis/config/rpob.R")
#================================
# output files
# In total: 6 files are written
#================================
outdir_images = paste0("~/git/Writing/thesis/images/results/", tolower(gene))
# mutational positions: all
outfile_mutpos = paste0(outdir_images, "/", tolower(gene), "_mutpos_all.txt")
outfile_meta1 = paste0(outdir_images, "/", tolower(gene), "_mutpos_cu.txt")
# mutational positions with sensitivity: S, R and common
outfile_mutpos_S = paste0(outdir_images, "/", tolower(gene), "_mutpos_S.txt")
outfile_mutpos_R = paste0(outdir_images, "/", tolower(gene), "_mutpos_R.txt")
outfile_mutpos_common = paste0(outdir_images, "/", tolower(gene), "_mutpos_common.txt")
outfile_meta2 = paste0(outdir_images, "/", tolower(gene), "_mutpos_annot_cu.txt")
#=============
# Input
#=============
df3_filename = paste0("/home/tanu/git/Data/", drug, "/output/", tolower(gene), "_merged_df3.csv")
df3 = read.csv(df3_filename)
# Determine for each gene
if (tolower(gene) == "embb"){
chain_suffix = ".B"
} else{
chain_suffix = ".A"
}
# mut_info checks
table(df3$mutation_info)
table(df3$mutation_info_orig)
table(df3$mutation_info_labels_orig)
# used in plots and analyses
table(df3$mutation_info_labels) # different, and matches dst_mode
table(df3$dst_mode)
# create column based on dst mode with different colname
table(is.na(df3$dst))
table(is.na(df3$dst_mode))
#===============
# Create column: sensitivity mapped to dst_mode
#===============
df3$sensitivity = ifelse(df3$dst_mode == 1, "R", "S")
table(df3$sensitivity)
length(unique((df3$mutationinformation)))
all_colnames = as.data.frame(colnames(df3))
############################################################
cols_to_extract = c("mutationinformation"
, "wild_type"
, "chain"
, "mutant_type"
, "position"
, "dst_mode"
, "mutation_info_labels_orig"
, "mutation_info_labels"
, "sensitivity")
df3_plot = df3[, cols_to_extract]
# create pos_chain column: allows easier colouring in chimera
df3_plot$pos_chain = paste(df3_plot$position, df3_plot$chain, sep = ".")
pos_cu = length(unique(df3_plot$position))
#===========================
# positions with mutations
#===========================
#df3_all_mut_pos = df3_plot[, c("mutationinformation", "position", "chain")]
#df3_all_mut_pos$pos_chain = paste(df3_all_mut_pos$position, df3_all_mut_pos$chain, sep = ".")
df3_all_mut_pos = df3_plot[, c("position", "pos_chain")]
gene_mut_pos_u = unique(df3_all_mut_pos$pos_chain)
class(gene_mut_pos_u)
paste(gene_mut_pos_u, collapse=',')
if (length(gene_mut_pos_u) == pos_cu){
cat("\nPASS: all mutation positions extracted"
, "\nWriting file:", outfile_mutpos)
} else{
stop("\nAbort: mutation position count mismatch")
}
write.table(paste(gene_mut_pos_u, collapse=',')
, outfile_mutpos
, row.names = F
, col.names = F)
write.table(paste("Count of positions with mutations in gene"
, tolower(gene), ":", pos_cu)
, outfile_meta1
, row.names = F
, col.names = F)
#========================================
# positions with sensitivity annotations
#========================================
df3_muts_annot = df3_plot[, c("mutationinformation", "position", "pos_chain", "sensitivity")]
# aggregrate position count by sensitivity
result <- aggregate(sensitivity ~ position, data = df3_muts_annot, paste, collapse = "")
sensitive_pos = result$position[grep("(^S+$)", result$sensitivity)]
sensitive_pos = paste0(sensitive_pos, chain_suffix)
resistant_pos = result$position[grep("(^R+$)", result$sensitivity)]
resistant_pos = paste0(resistant_pos, chain_suffix)
common_pos = result$position[grep("SR|RS" , result$sensitivity)]
common_pos = paste0(common_pos, chain_suffix)
if (tolower(gene)!= "alr"){
length_check = length(sensitive_pos) + length(resistant_pos) + length(common_pos)
cpl = length(common_pos)
}else{
length_check = length(sensitive_pos) + length(resistant_pos)
cpl = 0
}
if (length_check == pos_cu){
cat("\nPASS: position with mutational sensitivity extracted"
, "\nWriting files: sensitive, resistant and common position counts" )
} else{
stop("\nAbort: position with mutational sensitivity count mismatch")
}
write.table(paste(sensitive_pos, collapse = ',')
, outfile_mutpos_S
, row.names = F, col.names = F)
write.table(paste(resistant_pos, collapse = ',')
, outfile_mutpos_R
, row.names = F, col.names = F)
write.table(paste(common_pos, collapse = ',')
, outfile_mutpos_common
, row.names = F, col.names = F)
write.table(paste("Count of positions with mutations in gene:"
, tolower(gene)
, "\nTotal mutational positions:", pos_cu
, "\nsensitive:", length(sensitive_pos)
, "\nresistant:", length(resistant_pos)
, "\ncommon:" , cpl)
, outfile_meta2
, row.names = F
, col.names = F)

191
scripts/plotting/mut_landscape_5uhc_rpob.R
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@ -1,191 +0,0 @@
source("~/git/LSHTM_analysis/config/rpob.R")
#================================
# output files
# In total: 6 files are written
#================================
outdir_images = paste0("~/git/Writing/thesis/images/results/", tolower(gene))
# mutational positions: all
outfile_mutpos = paste0(outdir_images, "/5uhc_", tolower(gene), "_mutpos_all.txt")
outfile_meta1 = paste0(outdir_images, "/5uhc_", tolower(gene), "_mutpos_cu.txt")
# mutational positions with sensitivity: S, R and common
outfile_mutpos_S = paste0(outdir_images, "/5uhc_", tolower(gene), "_mutpos_S.txt")
outfile_mutpos_R = paste0(outdir_images, "/5uhc_", tolower(gene), "_mutpos_R.txt")
outfile_mutpos_common = paste0(outdir_images, "/5uhc_", tolower(gene), "_mutpos_common.txt")
outfile_meta2 = paste0(outdir_images, "/5uhc_", tolower(gene), "_mutpos_annot_cu.txt")
#=============
# Input
#=============
df3_filename = paste0("/home/tanu/git/Data/", drug, "/output/", tolower(gene), "_merged_df3.csv")
df3 = read.csv(df3_filename)
chain_suffix = ".C"
# mut_info checks
table(df3$mutation_info)
table(df3$mutation_info_orig)
table(df3$mutation_info_labels_orig)
# used in plots and analyses
table(df3$mutation_info_labels) # different, and matches dst_mode
table(df3$dst_mode)
# create column based on dst mode with different colname
table(is.na(df3$dst))
table(is.na(df3$dst_mode))
#===============
# Create column: sensitivity mapped to dst_mode
#===============
df3$sensitivity = ifelse(df3$dst_mode == 1, "R", "S")
table(df3$sensitivity)
length(unique((df3$mutationinformation)))
all_colnames = as.data.frame(colnames(df3))
############################################################
cols_to_extract = c("mutationinformation"
, "wild_type"
, "chain"
, "mutant_type"
, "position"
, "X5uhc_position"
, "X5uhc_offset"
, "dst_mode"
, "mutation_info_labels_orig"
, "mutation_info_labels"
, "sensitivity")
df3_plot = df3[, cols_to_extract]
# use the x5uhc_position column
# create pos_chain column: allows easier colouring in chimera
df3_plot$pos_chain = paste(df3_plot$X5uhc_position, chain_suffix, sep = ".")
pos_cu = length(unique(df3_plot$X5uhc_position))
X5uhc_pos = unique(df3_plot$X5uhc_position)
X5uhc_pos = paste0(X5uhc_pos, chain_suffix)
#===========================
# positions with mutations
#===========================
df3_all_mut_pos = df3_plot[, c("X5uhc_position", "pos_chain")]
gene_mut_pos_u = unique(df3_all_mut_pos$pos_chain)
class(gene_mut_pos_u)
paste(gene_mut_pos_u, collapse=',')
if (length(gene_mut_pos_u) == pos_cu){
cat("\nPASS: all mutation positions extracted"
, "\nWriting file:", outfile_mutpos)
} else{
stop("\nAbort: mutation position count mismatch")
}
write.table(paste(gene_mut_pos_u, collapse=',')
, outfile_mutpos
, row.names = F
, col.names = F)
write.table(paste("Count of positions with mutations in gene"
, tolower(gene), ":", pos_cu)
, outfile_meta1
, row.names = F
, col.names = F)
#========================================
# positions with sensitivity annotations
#========================================
df3_muts_annot = df3_plot[, c("mutationinformation", "X5uhc_position", "pos_chain", "sensitivity")]
# aggregrate position count by sensitivity
result <- aggregate(sensitivity ~ X5uhc_position, data = df3_muts_annot, paste, collapse = "")
sensitive_pos = result$X5uhc_position[grep("(^S+$)", result$sensitivity)]
sensitive_pos = paste0(sensitive_pos, chain_suffix)
resistant_pos = result$X5uhc_position[grep("(^R+$)", result$sensitivity)]
resistant_pos = paste0(resistant_pos, chain_suffix)
common_pos = result$X5uhc_position[grep("SR|RS" , result$sensitivity)]
common_pos = paste0(common_pos, chain_suffix)
if (tolower(gene)!= "alr"){
length_check = length(sensitive_pos) + length(resistant_pos) + length(common_pos)
cpl = length(common_pos)
}else{
length_check = length(sensitive_pos) + length(resistant_pos)
cpl = 0
}
if (length_check == pos_cu){
cat("\nPASS: position with mutational sensitivity extracted"
, "\nWriting files: sensitive, resistant and common position counts" )
} else{
stop("\nAbort: position with mutational sensitivity count mismatch")
}
# spl handling for rpob 5uhc
revised_gene_mut_pos_u = c(sensitive_pos, resistant_pos, common_pos)
revised_pos_cu = length(unique(revised_gene_mut_pos_u))
if (length(revised_gene_mut_pos_u) == revised_pos_cu){
cat("\nPASS: all mutation positions extracted"
, "\nWriting file:", outfile_mutpos)
} else{
stop("\nAbort: mutation position count mismatch")
}
write.table(paste(revised_gene_mut_pos_u, collapse=',')
, outfile_mutpos
, row.names = F
, col.names = F)
write.table(paste("Count of positions with mutations in gene"
, tolower(gene), ":", revised_pos_cu)
, outfile_meta1
, row.names = F
, col.names = F)
# mut_annot
write.table(paste(sensitive_pos, collapse = ',')
, outfile_mutpos_S
, row.names = F, col.names = F)
write.table(paste(resistant_pos, collapse = ',')
, outfile_mutpos_R
, row.names = F, col.names = F)
write.table(paste(common_pos, collapse = ',')
, outfile_mutpos_common
, row.names = F, col.names = F)
write.table(paste("Count of positions with mutations in gene:"
, tolower(gene)
, "\nTotal mutational positions:", revised_pos_cu
, "\nsensitive:", length(sensitive_pos)
, "\nresistant:", length(resistant_pos)
, "\ncommon:" , cpl)
, outfile_meta2
, row.names = F
, col.names = F)
#Quick check to find out the discrepancy
revised_gene_mut_pos_u
gene_mut_pos_u
library("qpcR")
foo <- data.frame(qpcR:::cbind.na(gene_mut_pos_u, revised_gene_mut_pos_u))
table(!gene_mut_pos_u%in%revised_gene_mut_pos_u)
table(!revised_gene_mut_pos_u%in%gene_mut_pos_u)
X5uhc_pos
#table(!gene_mut_pos_u%in%X5uhc_pos)
table(X5uhc_pos%in%gene_mut_pos_u)
X5uhc_pos[!X5uhc_pos%in%gene_mut_pos_u]
X5uhc_pos[!gene_mut_pos_u%in%X5uhc_pos]
#TODO: NOTE
#D1148G (i.e D1154) is NOT Present in 5UHC

332
scripts/plotting/replaceBfactor_pdb.R
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@ -1,332 +0,0 @@
#!/usr/bin/env Rscript
#########################################################
# TASK: Replace B-factors in the pdb file with the mean
# normalised stability values.
# read pdb file
# make two copies so you can replace B factors for 1)duet
# 2)affinity values and output 2 separate pdbs for
# rendering on chimera
# read mcsm mean stability value files
# extract the respective mean values and assign to the
# b-factor column within their respective pdbs
# generate some distribution plots for inspection
#########################################################
# working dir and loading libraries
getwd()
setwd("~/git/LSHTM_analysis/scripts/plotting")
cat(c(getwd(),"\n"))
#source("~/git/LSHTM_analysis/scripts/Header_TT.R")
library(bio3d)
require("getopt", quietly = TRUE) # cmd parse arguments
#========================================================
#drug = "pyrazinamide"
#gene = "pncA"
# command line args
spec = matrix(c(
"drug" , "d", 1, "character",
"gene" , "g", 1, "character"
), byrow = TRUE, ncol = 4)
opt = getopt(spec)
drug = opt$drug
gene = opt$gene
if(is.null(drug)|is.null(gene)) {
stop("Missing arguments: --drug and --gene must both be specified (case-sensitive)")
}
#========================================================
gene_match = paste0(gene,"_p.")
cat(gene_match)
#=============
# directories
#=============
datadir = paste0("~/git/Data")
indir = paste0(datadir, "/", drug, "/input")
outdir = paste0("~/git/Data", "/", drug, "/output")
#outdir_plots = paste0("~/git/Data", "/", drug, "/output/plots")
outdir_plots = paste0("~/git/Writing/thesis/images/results/", tolower(gene))
#======
# input
#======
in_filename_pdb = paste0(tolower(gene), "_complex.pdb")
infile_pdb = paste0(indir, "/", in_filename_pdb)
cat(paste0("Input file:", infile_pdb) )
#in_filename_mean_stability = paste0(tolower(gene), "_mean_stability.csv")
#infile_mean_stability = paste0(outdir, "/", in_filename_mean_stability)
in_filename_mean_stability = paste0(tolower(gene), "_mean_ens_stab_aff.csv")
infile_mean_stability = paste0(outdir_plots, "/", in_filename_mean_stability)
cat(paste0("Input file:", infile_mean_stability) )
#=======
# output
#=======
#out_filename_duet_mspdb = paste0(tolower(gene), "_complex_bduet_ms.pdb")
out_filename_duet_mspdb = paste0(tolower(gene), "_complex_b_stab_ms.pdb")
outfile_duet_mspdb = paste0(outdir_plots, "/", out_filename_duet_mspdb)
print(paste0("Output file:", outfile_duet_mspdb))
out_filename_lig_mspdb = paste0(tolower(gene), "_complex_blig_ms.pdb")
outfile_lig_mspdb = paste0(outdir_plots, "/", out_filename_lig_mspdb)
print(paste0("Output file:", outfile_lig_mspdb))
#%%===============================================================
#NOTE: duet here refers to the ensemble stability values
###########################
# Read file: average stability values
# or mcsm_normalised file
###########################
my_df <- read.csv(infile_mean_stability, header = T)
str(my_df)
#############
# Read pdb
#############
# list of 8
my_pdb = read.pdb(infile_pdb
, maxlines = -1
, multi = FALSE
, rm.insert = FALSE
, rm.alt = TRUE
, ATOM.only = FALSE
, hex = FALSE
, verbose = TRUE)
rm(in_filename_mean_stability, in_filename_pdb)
# assign separately for duet and ligand
my_pdb_duet = my_pdb
my_pdb_lig = my_pdb
#=========================================================
# Replacing B factor with mean stability scores
# within the respective dfs
#==========================================================
# extract atom list into a variable
# since in the list this corresponds to data frame, variable will be a df
#df_duet = my_pdb_duet[[1]]
df_duet= my_pdb_duet[['atom']]
df_lig = my_pdb_lig[['atom']]
# make a copy: required for downstream sanity checks
d2_duet = df_duet
d2_lig = df_lig
# sanity checks: B factor
max(df_duet$b); min(df_duet$b)
max(df_lig$b); min(df_lig$b)
#*******************************************
# histograms and density plots for inspection
# 1: original B-factors
# 2: original mean stability values
# 3: replaced B-factors with mean stability values
#*********************************************
# Set the margin on all sides
par(oma = c(3,2,3,0)
, mar = c(1,3,5,2)
#, mfrow = c(3,2)
, mfrow = c(3,4))
#=============
# Row 1 plots: original B-factors
# duet and affinity
#=============
hist(df_duet$b
, xlab = ""
, main = "Bfactor stability")
plot(density(df_duet$b)
, xlab = ""
, main = "Bfactor stability")
hist(df_lig$b
, xlab = ""
, main = "Bfactor affinity")
plot(density(df_lig$b)
, xlab = ""
, main = "Bfactor affinity")
#=============
# Row 2 plots: original mean stability values
# duet and affinity
#=============
#hist(my_df$averaged_duet
hist(my_df$avg_ens_stability_scaled
, xlab = ""
, main = "mean stability values")
#plot(density(my_df$averaged_duet)
plot(density(my_df$avg_ens_stability_scaled)
, xlab = ""
, main = "mean stability values")
#hist(my_df$averaged_affinity
hist(my_df$avg_ens_affinity_scaled
, xlab = ""
, main = "mean affinity values")
#plot(density(my_df$averaged_affinity)
plot(density(my_df$avg_ens_affinity_scaled)
, xlab = ""
, main = "mean affinity values")
#==============
# Row 3 plots: replaced B-factors with mean stability values
# After actual replacement in the b factor column
#===============
################################################################
#=========
# step 0_P1: DONT RUN once you have double checked the matched output
#=========
# sanity check: match and assign to a separate column to double check
# colnames(my_df)
# df_duet$duet_scaled = my_df$averge_duet_scaled[match(df_duet$resno, my_df$position)]
#=========
# step 1_P1
#=========
# Be brave and replace in place now (don"t run sanity check)
# this makes all the B-factor values in the non-matched positions as NA
#df_duet$b = my_df$averaged_duet_scaled[match(df_duet$resno, my_df$position)]
#df_lig$b = my_df$averaged_affinity_scaled[match(df_lig$resno, my_df$position)]
df_duet$b = my_df$avg_ens_stability_scaled[match(df_duet$resno, my_df$position)]
df_lig$b = my_df$avg_ens_affinity_scaled[match(df_lig$resno, my_df$position)]
#=========
# step 2_P1
#=========
# count NA in Bfactor
b_na_duet = sum(is.na(df_duet$b)) ; b_na_duet
b_na_lig = sum(is.na(df_lig$b)) ; b_na_lig
# count number of 0"s in Bactor
sum(df_duet$b == 0)
sum(df_lig$b == 0)
# replace all NA in b factor with 0
na_rep = 2
df_duet$b[is.na(df_duet$b)] = na_rep
df_lig$b[is.na(df_lig$b)] = na_rep
# # sanity check: should be 0 and True
# # duet and lig
# if ( (sum(df_duet$b == na_rep) == b_na_duet) && (sum(df_lig$b == na_rep) == b_na_lig) ) {
# print ("PASS: NA's replaced with 0s successfully in df_duet and df_lig")
# } else {
# print("FAIL: NA replacement in df_duet NOT successful")
# quit()
# }
#
# max(df_duet$b); min(df_duet$b)
#
# # sanity checks: should be True
# if( (max(df_duet$b) == max(my_df$avg_ens_stability_scaled)) & (min(df_duet$b) == min(my_df$avg_ens_stability_scaled)) ){
# print("PASS: B-factors replaced correctly in df_duet")
# } else {
# print ("FAIL: To replace B-factors in df_duet")
# quit()
# }
# if( (max(df_lig$b) == max(my_df$avg_ens_affinity_scaled)) & (min(df_lig$b) == min(my_df$avg_ens_affinity_scaled)) ){
# print("PASS: B-factors replaced correctly in df_lig")
# } else {
# print ("FAIL: To replace B-factors in df_lig")
# quit()
# }
#=========
# step 3_P1
#=========
# sanity check: dim should be same before reassignment
if ( (dim(df_duet)[1] == dim(d2_duet)[1]) & (dim(df_lig)[1] == dim(d2_lig)[1]) &
(dim(df_duet)[2] == dim(d2_duet)[2]) & (dim(df_lig)[2] == dim(d2_lig)[2])
){
print("PASS: Dims of both dfs as expected")
} else {
print ("FAIL: Dims mismatch")
quit()}
#=========
# step 4_P1:
# VERY important
#=========
# assign it back to the pdb file
my_pdb_duet[['atom']] = df_duet
max(df_duet$b); min(df_duet$b)
table(df_duet$b)
sum(is.na(df_duet$b))
my_pdb_lig[['atom']] = df_lig
max(df_lig$b); min(df_lig$b)
#=========
# step 5_P1
#=========
cat(paste0("output file duet mean stability pdb:", outfile_duet_mspdb))
write.pdb(my_pdb_duet, outfile_duet_mspdb)
cat(paste0("output file ligand mean stability pdb:", outfile_lig_mspdb))
write.pdb(my_pdb_lig, outfile_lig_mspdb)
#============================
# Add the 3rd histogram and density plots for comparisons
#============================
# Plots continued...
# Row 3 plots: hist and density of replaced B-factors with stability values
hist(df_duet$b
, xlab = ""
, main = "repalcedB duet")
plot(density(df_duet$b)
, xlab = ""
, main = "replacedB duet")
hist(df_lig$b
, xlab = ""
, main = "repalcedB affinity")
plot(density(df_lig$b)
, xlab = ""
, main = "replacedB affinity")
# graph titles
mtext(text = "Frequency"
, side = 2
, line = 0
, outer = TRUE)
mtext(text = paste0(tolower(gene), ": Stability Distribution")
, side = 3
, line = 0
, outer = TRUE)
#============================================
#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
# NOTE: This replaced B-factor distribution has the same
# x-axis as the PredAff normalised values, but the distribution
# is affected since 0 is overinflated/or hs an additional blip because
# of the positions not associated with resistance. This is because all the positions
# where there are no SNPs have been assigned 0???
#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!

281
scripts/plotting/replaceBfactor_pdb_affinity.R
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@ -1,281 +0,0 @@
#!/usr/bin/env Rscript
#########################################################
# TASK: Replace B-factors in the pdb file with the mean
# normalised stability values.
# read pdb file
# read mcsm mean stability value files
# extract the respective mean values and assign to the
# b-factor column within their respective pdbs
# generate some distribution plots for inspection
#########################################################
# working dir and loading libraries
getwd()
setwd("~/git/LSHTM_analysis/scripts/plotting")
cat(c(getwd(),"\n"))
#source("~/git/LSHTM_analysis/scripts/Header_TT.R")
library(bio3d)
require("getopt", quietly = TRUE) # cmd parse arguments
#========================================================
#drug = "pyrazinamide"
#gene = "pncA"
# command line args
spec = matrix(c(
"drug" , "d", 1, "character",
"gene" , "g", 1, "character"
), byrow = TRUE, ncol = 4)
opt = getopt(spec)
drug = opt$drug
gene = opt$gene
if(is.null(drug)|is.null(gene)) {
stop("Missing arguments: --drug and --gene must both be specified (case-sensitive)")
}
#========================================================
gene_match = paste0(gene,"_p.")
cat(gene_match)
#=============
# directories
#=============
datadir = paste0("~/git/Data")
indir = paste0(datadir, "/", drug, "/input")
outdir = paste0("~/git/Data", "/", drug, "/output")
#outdir_plots = paste0("~/git/Data", "/", drug, "/output/plots")
outdir_plots = paste0("~/git/Writing/thesis/images/results/", tolower(gene))
#======
# input
#======
in_filename_pdb = paste0(tolower(gene), "_complex.pdb")
infile_pdb = paste0(indir, "/", in_filename_pdb)
cat(paste0("Input file:", infile_pdb) )
#in_filename_mean_stability = paste0(tolower(gene), "_mean_stability.csv")
#infile_mean_stability = paste0(outdir, "/", in_filename_mean_stability)
in_filename_mean_affinity = paste0(tolower(gene), "_mean_ligand.csv")
infile_mean_affinity = paste0(outdir_plots, "/", in_filename_mean_affinity)
cat(paste0("Input file:", infile_mean_affinity) )
#=======
# output
#=======
#out_filename_duet_mspdb = paste0(tolower(gene), "_complex_bduet_ms.pdb")
out_filename_lig_mspdb = paste0(tolower(gene), "_complex_b_lig_ms.pdb")
outfile_lig_mspdb = paste0(outdir_plots, "/", out_filename_lig_mspdb)
print(paste0("Output file:", outfile_lig_mspdb))
#%%===============================================================
#NOTE: duet here refers to the ensemble stability values
###########################
# Read file: average stability values
# or mcsm_normalised file
###########################
my_df <- read.csv(infile_mean_stability, header = T)
str(my_df)
#############
# Read pdb
#############
# list of 8
my_pdb = read.pdb(infile_pdb
, maxlines = -1
, multi = FALSE
, rm.insert = FALSE
, rm.alt = TRUE
, ATOM.only = FALSE
, hex = FALSE
, verbose = TRUE)
rm(in_filename_mean_affinity, in_filename_pdb)
# assign separately for duet and ligand
my_pdb_duet = my_pdb
#=========================================================
# Replacing B factor with mean stability scores
# within the respective dfs
#==========================================================
# extract atom list into a variable
# since in the list this corresponds to data frame, variable will be a df
#df_duet = my_pdb_duet[[1]]
df_duet= my_pdb_duet[['atom']]
# make a copy: required for downstream sanity checks
d2_duet = df_duet
# sanity checks: B factor
max(df_duet$b); min(df_duet$b)
#==================================================
# histograms and density plots for inspection
# 1: original B-factors
# 2: original mean stability values
# 3: replaced B-factors with mean stability values
#==================================================
# Set the margin on all sides
par(oma = c(3,2,3,0)
, mar = c(1,3,5,2)
#, mfrow = c(3,2)
#, mfrow = c(3,4))
, mfrow = c(3,2))
#=============
# Row 1 plots: original B-factors
# duet and affinity
#=============
hist(df_duet$b
, xlab = ""
, main = "Bfactor affinity")
plot(density(df_duet$b)
, xlab = ""
, main = "Bfactor affinity")
#=============
# Row 2 plots: original mean stability values
# duet and affinity
#=============
#hist(my_df$averaged_duet
hist(my_df$avg_lig_scaled
, xlab = ""
, main = "mean affinity values")
#plot(density(my_df$averaged_duet)
plot(density(my_df$avg_lig_scaled)
, xlab = ""
, main = "mean affinity values")
#==============
# Row 3 plots: replaced B-factors with mean stability values
# After actual replacement in the b factor column
#===============
################################################################
#=========
# step 0_P1: DONT RUN once you have double checked the matched output
#=========
# sanity check: match and assign to a separate column to double check
# colnames(my_df)
# df_duet$duet_scaled = my_df$averge_duet_scaled[match(df_duet$resno, my_df$position)]
#=========
# step 1_P1
#=========
# Be brave and replace in place now (don"t run sanity check)
# this makes all the B-factor values in the non-matched positions as NA
#df_duet$b = my_df$averaged_duet_scaled[match(df_duet$resno, my_df$position)]
df_duet$b = my_df$avg_lig_scaled[match(df_duet$resno, my_df$position)]
#=========
# step 2_P1
#=========
# count NA in Bfactor
b_na_duet = sum(is.na(df_duet$b)) ; b_na_duet
# count number of 0"s in Bactor
sum(df_duet$b == 0)
# replace all NA in b factor with 0
na_rep = 2
df_duet$b[is.na(df_duet$b)] = na_rep
# # sanity check: should be 0 and True
# # duet and lig
# if ( (sum(df_duet$b == na_rep) == b_na_duet) && (sum(df_lig$b == na_rep) == b_na_lig) ) {
# print ("PASS: NA's replaced with 0s successfully in df_duet and df_lig")
# } else {
# print("FAIL: NA replacement in df_duet NOT successful")
# quit()
# }
#
# max(df_duet$b); min(df_duet$b)
#
# # sanity checks: should be True
# if( (max(df_duet$b) == max(my_df$avg_ens_stability_scaled)) & (min(df_duet$b) == min(my_df$avg_ens_stability_scaled)) ){
# print("PASS: B-factors replaced correctly in df_duet")
# } else {
# print ("FAIL: To replace B-factors in df_duet")
# quit()
# }
# if( (max(df_lig$b) == max(my_df$avg_ens_affinity_scaled)) & (min(df_lig$b) == min(my_df$avg_ens_affinity_scaled)) ){
# print("PASS: B-factors replaced correctly in df_lig")
# } else {
# print ("FAIL: To replace B-factors in df_lig")
# quit()
# }
#=========
# step 3_P1
#=========
# sanity check: dim should be same before reassignment
if ( (dim(df_duet)[1] == dim(d2_duet)[1]) &
(dim(df_duet)[2] == dim(d2_duet)[2])
){
print("PASS: Dims of both dfs as expected")
} else {
print ("FAIL: Dims mismatch")
quit()}
#=========
# step 4_P1:
# VERY important
#=========
# assign it back to the pdb file
my_pdb_duet[['atom']] = df_duet
max(df_duet$b); min(df_duet$b)
table(df_duet$b)
sum(is.na(df_duet$b))
#=========
# step 5_P1
#=========
cat(paste0("output file duet mean stability pdb:"
, outfile_lig_mspdb))
write.pdb(my_pdb_duet, outfile_lig_mspdb)
#============================
# Add the 3rd histogram and density plots for comparisons
#============================
# Plots continued...
# Row 3 plots: hist and density of replaced B-factors with stability values
hist(df_duet$b
, xlab = ""
, main = "repalcedB duet")
plot(density(df_duet$b)
, xlab = ""
, main = "replacedB duet")
# graph titles
mtext(text = "Frequency"
, side = 2
, line = 0
, outer = TRUE)
mtext(text = paste0(tolower(gene), ": afinity distribution")
, side = 3
, line = 0
, outer = TRUE)
#============================================
#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
# NOTE: This replaced B-factor distribution has the same
# x-axis as the PredAff normalised values, but the distribution
# is affected since 0 is overinflated/or hs an additional blip because
# of the positions not associated with resistance. This is because all the positions
# where there are no SNPs have been assigned 0???
#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!

277
scripts/plotting/replaceBfactor_pdb_ppi2.R
View file

@ -1,277 +0,0 @@
#!/usr/bin/env Rscript
#########################################################
# TASK: Replace B-factors in the pdb file with the mean
# normalised stability values.
# read pdb file
# read mcsm mean stability value files
# extract the respective mean values and assign to the
# b-factor column within their respective pdbs
# generate some distribution plots for inspection
#########################################################
# working dir and loading libraries
getwd()
setwd("~/git/LSHTM_analysis/scripts/plotting")
cat(c(getwd(),"\n"))
#source("~/git/LSHTM_analysis/scripts/Header_TT.R")
library(bio3d)
require("getopt", quietly = TRUE) # cmd parse arguments
#========================================================
#drug = "pyrazinamide"
#gene = "pncA"
# command line args
spec = matrix(c(
"drug" , "d", 1, "character",
"gene" , "g", 1, "character"
), byrow = TRUE, ncol = 4)
opt = getopt(spec)
drug = opt$drug
gene = opt$gene
if(is.null(drug)|is.null(gene)) {
stop("Missing arguments: --drug and --gene must both be specified (case-sensitive)")
}
#========================================================
gene_match = paste0(gene,"_p.")
cat(gene_match)
#=============
# directories
#=============
datadir = paste0("~/git/Data")
indir = paste0(datadir, "/", drug, "/input")
outdir = paste0("~/git/Data", "/", drug, "/output")
#outdir_plots = paste0("~/git/Data", "/", drug, "/output/plots")
outdir_plots = paste0("~/git/Writing/thesis/images/results/", tolower(gene))
#======
# input
#======
in_filename_pdb = paste0(tolower(gene), "_complex.pdb")
infile_pdb = paste0(indir, "/", in_filename_pdb)
cat(paste0("Input file:", infile_pdb) )
# mean ppi2
in_filename_mean_ppi2 = paste0(tolower(gene), "_mean_ppi2.csv")
infile_mean_ppi2 = paste0(outdir_plots, "/", in_filename_mean_ppi2)
cat(paste0("Input file:", infile_mean_ppi2) )
#=======
# output
#=======
#out_filename_duet_mspdb = paste0(tolower(gene), "_complex_bduet_ms.pdb")
out_filename_ppi2_mspdb = paste0(tolower(gene), "_complex_b_ppi2_ms.pdb")
outfile_ppi2_mspdb = paste0(outdir_plots, "/", out_filename_ppi2_mspdb)
print(paste0("Output file:", outfile_ppi2_mspdb))
#%%===============================================================
#NOTE: duet here refers to the ensemble stability values
###########################
# Read file: average stability values
# or mcsm_normalised file
###########################
my_df <- read.csv(infile_mean_ppi2, header = T)
str(my_df)
#############
# Read pdb
#############
# list of 8
my_pdb = read.pdb(infile_pdb
, maxlines = -1
, multi = FALSE
, rm.insert = FALSE
, rm.alt = TRUE
, ATOM.only = FALSE
, hex = FALSE
, verbose = TRUE)
# assign separately for duet and ligand
my_pdb_duet = my_pdb
#=========================================================
# Replacing B factor with mean stability scores
# within the respective dfs
#==========================================================
# extract atom list into a variable
# since in the list this corresponds to data frame, variable will be a df
#df_duet = my_pdb_duet[[1]]
df_duet= my_pdb_duet[['atom']]
# make a copy: required for downstream sanity checks
d2_duet = df_duet
# sanity checks: B factor
max(df_duet$b); min(df_duet$b)
#==================================================
# histograms and density plots for inspection
# 1: original B-factors
# 2: original mean stability values
# 3: replaced B-factors with mean stability values
#==================================================
# Set the margin on all sides
par(oma = c(3,2,3,0)
, mar = c(1,3,5,2)
#, mfrow = c(3,2)
#, mfrow = c(3,4))
, mfrow = c(3,2))
#=============
# Row 1 plots: original B-factors
# duet and affinity
#=============
hist(df_duet$b
, xlab = ""
, main = "Bfactor ppi2")
plot(density(df_duet$b)
, xlab = ""
, main = "Bfactor ppi2")
#=============
# Row 2 plots: original mean stability values
# duet and affinity
#=============
#hist(my_df$averaged_duet
hist(my_df$avg_ppi2_scaled
, xlab = ""
, main = "mean ppi2 values")
#plot(density(my_df$averaged_duet)
plot(density(my_df$avg_ppi2_scaled)
, xlab = ""
, main = "mean ppi2 values")
#==============
# Row 3 plots: replaced B-factors with mean stability values
# After actual replacement in the b factor column
#===============
################################################################
#=========
# step 0_P1: DONT RUN once you have double checked the matched output
#=========
# sanity check: match and assign to a separate column to double check
# colnames(my_df)
# df_duet$duet_scaled = my_df$averge_duet_scaled[match(df_duet$resno, my_df$position)]
#=========
# step 1_P1
#=========
# Be brave and replace in place now (don"t run sanity check)
# this makes all the B-factor values in the non-matched positions as NA
#df_duet$b = my_df$averaged_duet_scaled[match(df_duet$resno, my_df$position)]
df_duet$b = my_df$avg_ppi2_scaled[match(df_duet$resno, my_df$position)]
#=========
# step 2_P1
#=========
# count NA in Bfactor
b_na_duet = sum(is.na(df_duet$b)) ; b_na_duet
# count number of 0"s in Bactor
sum(df_duet$b == 0)
# replace all NA in b factor with 0
na_rep = 2
df_duet$b[is.na(df_duet$b)] = na_rep
# # sanity check: should be 0 and True
# # duet and lig
# if ( (sum(df_duet$b == na_rep) == b_na_duet) && (sum(df_lig$b == na_rep) == b_na_lig) ) {
# print ("PASS: NA's replaced with 0s successfully in df_duet and df_lig")
# } else {
# print("FAIL: NA replacement in df_duet NOT successful")
# quit()
# }
#
# max(df_duet$b); min(df_duet$b)
#
# # sanity checks: should be True
# if( (max(df_duet$b) == max(my_df$avg_ens_stability_scaled)) & (min(df_duet$b) == min(my_df$avg_ens_stability_scaled)) ){
# print("PASS: B-factors replaced correctly in df_duet")
# } else {
# print ("FAIL: To replace B-factors in df_duet")
# quit()
# }
# if( (max(df_lig$b) == max(my_df$avg_ens_affinity_scaled)) & (min(df_lig$b) == min(my_df$avg_ens_affinity_scaled)) ){
# print("PASS: B-factors replaced correctly in df_lig")
# } else {
# print ("FAIL: To replace B-factors in df_lig")
# quit()
# }
#=========
# step 3_P1
#=========
# sanity check: dim should be same before reassignment
if ( (dim(df_duet)[1] == dim(d2_duet)[1]) &
(dim(df_duet)[2] == dim(d2_duet)[2])
){
print("PASS: Dims of both dfs as expected")
} else {
print ("FAIL: Dims mismatch")
quit()}
#=========
# step 4_P1:
# VERY important
#=========
# assign it back to the pdb file
my_pdb_duet[['atom']] = df_duet
max(df_duet$b); min(df_duet$b)
table(df_duet$b)
sum(is.na(df_duet$b))
#=========
# step 5_P1
#=========
cat(paste0("output file mean ppi2 pdb:"
, outfile_ppi2_mspdb))
write.pdb(my_pdb_duet, outfile_ppi2_mspdb)
#============================
# Add the 3rd histogram and density plots for comparisons
#============================
# Plots continued...
# Row 3 plots: hist and density of replaced B-factors with stability values
hist(df_duet$b
, xlab = ""
, main = "repalcedB duet")
plot(density(df_duet$b)
, xlab = ""
, main = "replacedB duet")
# graph titles
mtext(text = "Frequency"
, side = 2
, line = 0
, outer = TRUE)
mtext(text = paste0(tolower(gene), ": ppi2 distribution")
, side = 3
, line = 0
, outer = TRUE)
#============================================
#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
# NOTE: This replaced B-factor distribution has the same
# x-axis as the PredAff normalised values, but the distribution
# is affected since 0 is overinflated/or hs an additional blip because
# of the positions not associated with resistance. This is because all the positions
# where there are no SNPs have been assigned 0???
#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!

281
scripts/plotting/replaceBfactor_pdb_stability.R
View file

@ -1,281 +0,0 @@
#!/usr/bin/env Rscript
#########################################################
# TASK: Replace B-factors in the pdb file with the mean
# normalised stability values.
# read pdb file
# read mcsm mean stability value files
# extract the respective mean values and assign to the
# b-factor column within their respective pdbs
# generate some distribution plots for inspection
#########################################################
# working dir and loading libraries
getwd()
setwd("~/git/LSHTM_analysis/scripts/plotting")
cat(c(getwd(),"\n"))
#source("~/git/LSHTM_analysis/scripts/Header_TT.R")
library(bio3d)
require("getopt", quietly = TRUE) # cmd parse arguments
#========================================================
#drug = "pyrazinamide"
#gene = "pncA"
# command line args
spec = matrix(c(
"drug" , "d", 1, "character",
"gene" , "g", 1, "character"
), byrow = TRUE, ncol = 4)
opt = getopt(spec)
drug = opt$drug
gene = opt$gene
if(is.null(drug)|is.null(gene)) {
stop("Missing arguments: --drug and --gene must both be specified (case-sensitive)")
}
#========================================================
gene_match = paste0(gene,"_p.")
cat(gene_match)
#=============
# directories
#=============
datadir = paste0("~/git/Data")
indir = paste0(datadir, "/", drug, "/input")
outdir = paste0("~/git/Data", "/", drug, "/output")
#outdir_plots = paste0("~/git/Data", "/", drug, "/output/plots")
outdir_plots = paste0("~/git/Writing/thesis/images/results/", tolower(gene))
#======
# input
#======
in_filename_pdb = paste0(tolower(gene), "_complex.pdb")
infile_pdb = paste0(indir, "/", in_filename_pdb)
cat(paste0("Input file:", infile_pdb) )
#in_filename_mean_stability = paste0(tolower(gene), "_mean_stability.csv")
#infile_mean_stability = paste0(outdir, "/", in_filename_mean_stability)
in_filename_mean_stability = paste0(tolower(gene), "_mean_ens_stability.csv")
infile_mean_stability = paste0(outdir_plots, "/", in_filename_mean_stability)
cat(paste0("Input file:", infile_mean_stability) )
#=======
# output
#=======
#out_filename_duet_mspdb = paste0(tolower(gene), "_complex_bduet_ms.pdb")
out_filename_duet_mspdb = paste0(tolower(gene), "_complex_b_stab_ms.pdb")
outfile_duet_mspdb = paste0(outdir_plots, "/", out_filename_duet_mspdb)
print(paste0("Output file:", outfile_duet_mspdb))
#%%===============================================================
#NOTE: duet here refers to the ensemble stability values
###########################
# Read file: average stability values
# or mcsm_normalised file
###########################
my_df <- read.csv(infile_mean_stability, header = T)
str(my_df)
#############
# Read pdb
#############
# list of 8
my_pdb = read.pdb(infile_pdb
, maxlines = -1
, multi = FALSE
, rm.insert = FALSE
, rm.alt = TRUE
, ATOM.only = FALSE
, hex = FALSE
, verbose = TRUE)
rm(in_filename_mean_stability, in_filename_pdb)
# assign separately for duet and ligand
my_pdb_duet = my_pdb
#=========================================================
# Replacing B factor with mean stability scores
# within the respective dfs
#==========================================================
# extract atom list into a variable
# since in the list this corresponds to data frame, variable will be a df
#df_duet = my_pdb_duet[[1]]
df_duet= my_pdb_duet[['atom']]
# make a copy: required for downstream sanity checks
d2_duet = df_duet
# sanity checks: B factor
max(df_duet$b); min(df_duet$b)
#==================================================
# histograms and density plots for inspection
# 1: original B-factors
# 2: original mean stability values
# 3: replaced B-factors with mean stability values
#==================================================
# Set the margin on all sides
par(oma = c(3,2,3,0)
, mar = c(1,3,5,2)
#, mfrow = c(3,2)
#, mfrow = c(3,4))
, mfrow = c(3,2))
#=============
# Row 1 plots: original B-factors
# duet and affinity
#=============
hist(df_duet$b
, xlab = ""
, main = "Bfactor stability")
plot(density(df_duet$b)
, xlab = ""
, main = "Bfactor stability")
#=============
# Row 2 plots: original mean stability values
# duet and affinity
#=============
#hist(my_df$averaged_duet
hist(my_df$avg_ens_stability_scaled
, xlab = ""
, main = "mean stability values")
#plot(density(my_df$averaged_duet)
plot(density(my_df$avg_ens_stability_scaled)
, xlab = ""
, main = "mean stability values")
#==============
# Row 3 plots: replaced B-factors with mean stability values
# After actual replacement in the b factor column
#===============
################################################################
#=========
# step 0_P1: DONT RUN once you have double checked the matched output
#=========
# sanity check: match and assign to a separate column to double check
# colnames(my_df)
# df_duet$duet_scaled = my_df$averge_duet_scaled[match(df_duet$resno, my_df$position)]
#=========
# step 1_P1
#=========
# Be brave and replace in place now (don"t run sanity check)
# this makes all the B-factor values in the non-matched positions as NA
#df_duet$b = my_df$averaged_duet_scaled[match(df_duet$resno, my_df$position)]
df_duet$b = my_df$avg_ens_stability_scaled[match(df_duet$resno, my_df$position)]
#=========
# step 2_P1
#=========
# count NA in Bfactor
b_na_duet = sum(is.na(df_duet$b)) ; b_na_duet
# count number of 0"s in Bactor
sum(df_duet$b == 0)
# replace all NA in b factor with 0
na_rep = 2
df_duet$b[is.na(df_duet$b)] = na_rep
# # sanity check: should be 0 and True
# # duet and lig
# if ( (sum(df_duet$b == na_rep) == b_na_duet) && (sum(df_lig$b == na_rep) == b_na_lig) ) {
# print ("PASS: NA's replaced with 0s successfully in df_duet and df_lig")
# } else {
# print("FAIL: NA replacement in df_duet NOT successful")
# quit()
# }
#
# max(df_duet$b); min(df_duet$b)
#
# # sanity checks: should be True
# if( (max(df_duet$b) == max(my_df$avg_ens_stability_scaled)) & (min(df_duet$b) == min(my_df$avg_ens_stability_scaled)) ){
# print("PASS: B-factors replaced correctly in df_duet")
# } else {
# print ("FAIL: To replace B-factors in df_duet")
# quit()
# }
# if( (max(df_lig$b) == max(my_df$avg_ens_affinity_scaled)) & (min(df_lig$b) == min(my_df$avg_ens_affinity_scaled)) ){
# print("PASS: B-factors replaced correctly in df_lig")
# } else {
# print ("FAIL: To replace B-factors in df_lig")
# quit()
# }
#=========
# step 3_P1
#=========
# sanity check: dim should be same before reassignment
if ( (dim(df_duet)[1] == dim(d2_duet)[1]) &
(dim(df_duet)[2] == dim(d2_duet)[2])
){
print("PASS: Dims of both dfs as expected")
} else {
print ("FAIL: Dims mismatch")
quit()}
#=========
# step 4_P1:
# VERY important
#=========
# assign it back to the pdb file
my_pdb_duet[['atom']] = df_duet
max(df_duet$b); min(df_duet$b)
table(df_duet$b)
sum(is.na(df_duet$b))
#=========
# step 5_P1
#=========
cat(paste0("output file duet mean stability pdb:", outfile_duet_mspdb))
write.pdb(my_pdb_duet, outfile_duet_mspdb)
#============================
# Add the 3rd histogram and density plots for comparisons
#============================
# Plots continued...
# Row 3 plots: hist and density of replaced B-factors with stability values
hist(df_duet$b
, xlab = ""
, main = "repalcedB duet")
plot(density(df_duet$b)
, xlab = ""
, main = "replacedB duet")
# graph titles
mtext(text = "Frequency"
, side = 2
, line = 0
, outer = TRUE)
mtext(text = paste0(tolower(gene), ": stability distribution")
, side = 3
, line = 0
, outer = TRUE)
#============================================
#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
# NOTE: This replaced B-factor distribution has the same
# x-axis as the PredAff normalised values, but the distribution
# is affected since 0 is overinflated/or hs an additional blip because
# of the positions not associated with resistance. This is because all the positions
# where there are no SNPs have been assigned 0???
#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!

280
scripts/plotting/replaceBfactor_pdb_stability_5uhc_rpob.R
View file

@ -1,280 +0,0 @@
#!/usr/bin/env Rscript
#########################################################
# TASK: Replace B-factors in the pdb file with the mean
# normalised stability values.
# read pdb file
# read mcsm mean stability value files
# extract the respective mean values and assign to the
# b-factor column within their respective pdbs
# generate some distribution plots for inspection
#########################################################
# working dir and loading libraries
getwd()
setwd("~/git/LSHTM_analysis/scripts/plotting")
cat(c(getwd(),"\n"))
#source("~/git/LSHTM_analysis/scripts/Header_TT.R")
library(bio3d)
require("getopt", quietly = TRUE) # cmd parse arguments
#========================================================
#drug = "pyrazinamide"
#gene = "pncA"
# command line args
spec = matrix(c(
"drug" , "d", 1, "character",
"gene" , "g", 1, "character"
), byrow = TRUE, ncol = 4)
opt = getopt(spec)
drug = opt$drug
gene = opt$gene
if(is.null(drug)|is.null(gene)) {
stop("Missing arguments: --drug and --gene must both be specified (case-sensitive)")
}
#========================================================
gene_match = paste0(gene,"_p.")
cat(gene_match)
#=============
# directories
#=============
datadir = paste0("~/git/Data")
indir = paste0(datadir, "/", drug, "/input")
outdir = paste0("~/git/Data", "/", drug, "/output")
#outdir_plots = paste0("~/git/Data", "/", drug, "/output/plots")
outdir_plots = paste0("~/git/Writing/thesis/images/results/", tolower(gene))
#======
# input
#======
#in_filename_pdb = paste0(tolower(gene), "_complex.pdb")
in_filename_pdb = "5uhc.pdb"
infile_pdb = paste0(indir, "/", in_filename_pdb)
cat(paste0("Input file:", infile_pdb) )
#in_filename_mean_stability = paste0(tolower(gene), "_mean_stability.csv")
#infile_mean_stability = paste0(outdir, "/", in_filename_mean_stability)
in_filename_mean_stability = paste0("/5uhc_", tolower(gene), "_mean_ens_stability.csv")
infile_mean_stability = paste0(outdir_plots, in_filename_mean_stability)
cat(paste0("Input file:", infile_mean_stability) )
#=======
# output
#=======
#out_filename_duet_mspdb = paste0(tolower(gene), "_complex_bduet_ms.pdb")
out_filename_duet_mspdb = paste0("/5uhc_", tolower(gene), "_complex_b_stab_ms.pdb")
outfile_duet_mspdb = paste0(outdir_plots, out_filename_duet_mspdb)
print(paste0("Output file:", outfile_duet_mspdb))
#%%===============================================================
#NOTE: duet here refers to the ensemble stability values
###########################
# Read file: average stability values
# or mcsm_normalised file
###########################
my_df <- read.csv(infile_mean_stability, header = T)
str(my_df)
my_df = na.omit(my_df)
#############
# Read pdb
#############
# list of 8
my_pdb = read.pdb(infile_pdb
, maxlines = -1
, multi = FALSE
, rm.insert = FALSE
, rm.alt = TRUE
, ATOM.only = FALSE
, hex = FALSE
, verbose = TRUE)
rm(in_filename_mean_stability, in_filename_pdb)
# assign separately for duet and ligand
my_pdb_duet = my_pdb
#=========================================================
# Replacing B factor with mean stability scores
# within the respective dfs
#==========================================================
# extract atom list into a variable
# since in the list this corresponds to data frame, variable will be a df
#df_duet = my_pdb_duet[[1]]
df_duet= my_pdb_duet[['atom']]
# make a copy: required for downstream sanity checks
d2_duet = df_duet
# sanity checks: B factor
max(df_duet$b); min(df_duet$b)
#==================================================
# histograms and density plots for inspection
# 1: original B-factors
# 2: original mean stability values
# 3: replaced B-factors with mean stability values
#==================================================
# Set the margin on all sides
par(oma = c(3,2,3,0)
, mar = c(1,3,5,2)
#, mfrow = c(3,2)
#, mfrow = c(3,4))
, mfrow = c(3,2))
#=============
# Row 1 plots: original B-factors
# duet and affinity
#=============
hist(df_duet$b
, xlab = ""
, main = "Bfactor stability")
plot(density(df_duet$b)
, xlab = ""
, main = "Bfactor stability")
#=============
# Row 2 plots: original mean stability values
# duet and affinity
#=============
#hist(my_df$averaged_duet
hist(my_df$avg_ens_stability_scaled
, xlab = ""
, main = "mean stability values")
#plot(density(my_df$averaged_duet)
plot(density(my_df$avg_ens_stability_scaled)
, xlab = ""
, main = "mean stability values")
#==============
# Row 3 plots: replaced B-factors with mean stability values
# After actual replacement in the b factor column
#===============
################################################################
#=========
# step 0_P1: DONT RUN once you have double checked the matched output
#=========
# sanity check: match and assign to a separate column to double check
# colnames(my_df)
# df_duet$duet_scaled = my_df$averge_duet_scaled[match(df_duet$resno, my_df$position)]
#=========
# step 1_P1
#=========
# Be brave and replace in place now (don"t run sanity check)
# this makes all the B-factor values in the non-matched positions as NA
#df_duet$b = my_df$averaged_duet_scaled[match(df_duet$resno, my_df$position)]
df_duet$b = my_df$avg_ens_stability_scaled[match(df_duet$resno, my_df$X5uhc_position)]
#=========
# step 2_P1
#=========
# count NA in Bfactor
b_na_duet = sum(is.na(df_duet$b)) ; b_na_duet
# count number of 0"s in Bactor
sum(df_duet$b == 0)
# replace all NA in b factor with 0
na_rep = 2
df_duet$b[is.na(df_duet$b)] = na_rep
# # sanity check: should be 0 and True
# # duet and lig
# if ( (sum(df_duet$b == na_rep) == b_na_duet) && (sum(df_lig$b == na_rep) == b_na_lig) ) {
# print ("PASS: NA's replaced with 0s successfully in df_duet and df_lig")
# } else {
# print("FAIL: NA replacement in df_duet NOT successful")
# quit()
# }
#
# max(df_duet$b); min(df_duet$b)
#
# # sanity checks: should be True
# if( (max(df_duet$b) == max(my_df$avg_ens_stability_scaled)) & (min(df_duet$b) == min(my_df$avg_ens_stability_scaled)) ){
# print("PASS: B-factors replaced correctly in df_duet")
# } else {
# print ("FAIL: To replace B-factors in df_duet")
# quit()
# }
# if( (max(df_lig$b) == max(my_df$avg_ens_affinity_scaled)) & (min(df_lig$b) == min(my_df$avg_ens_affinity_scaled)) ){
# print("PASS: B-factors replaced correctly in df_lig")
# } else {
# print ("FAIL: To replace B-factors in df_lig")
# quit()
# }
#=========
# step 3_P1
#=========
# sanity check: dim should be same before reassignment
if ( (dim(df_duet)[1] == dim(d2_duet)[1]) &
(dim(df_duet)[2] == dim(d2_duet)[2])
){
print("PASS: Dims of both dfs as expected")
} else {
print ("FAIL: Dims mismatch")
quit()}
#=========
# step 4_P1:
# VERY important
#=========
# assign it back to the pdb file
my_pdb_duet[['atom']] = df_duet
max(df_duet$b); min(df_duet$b)
table(df_duet$b)
sum(is.na(df_duet$b))
#=========
# step 5_P1
#=========
cat(paste0("output file duet mean stability pdb:", outfile_duet_mspdb))
write.pdb(my_pdb_duet, outfile_duet_mspdb)
#============================
# Add the 3rd histogram and density plots for comparisons
#============================
# Plots continued...
# Row 3 plots: hist and density of replaced B-factors with stability values
hist(df_duet$b
, xlab = ""
, main = "repalcedB duet")
plot(density(df_duet$b)
, xlab = ""
, main = "replacedB duet")
# graph titles
mtext(text = "Frequency"
, side = 2
, line = 0
, outer = TRUE)
mtext(text = paste0(tolower(gene), ": stability distribution")
, side = 3
, line = 0
, outer = TRUE)
#============================================
#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
# NOTE: This replaced B-factor distribution has the same
# x-axis as the PredAff normalised values, but the distribution
# is affected since 0 is overinflated/or hs an additional blip because
# of the positions not associated with resistance. This is because all the positions
# where there are no SNPs have been assigned 0???
#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!