added scripts
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10 changed files with 147 additions and 1014 deletions
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@ -2,12 +2,18 @@
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# Data: Input
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#==============
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source("~/git/LSHTM_analysis/config/gid.R")
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source("~/git/LSHTM_analysis/scripts/plotting/get_plotting_dfs.R")
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source("~/git/LSHTM_analysis/scripts/plotting/get_plotting_dfs.R")
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#cat("\nSourced plotting cols as well:", length(plotting_cols))
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source("/home/tanu/git/LSHTM_analysis/scripts/plotting/plotting_thesis/gid/basic_barplots_gid.R")
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source("/home/tanu/git/LSHTM_analysis/scripts/plotting/plotting_thesis/gid/pe_sens_site_count_gid.R")
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#=======
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# output
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#=======
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outdir_images = paste0("~/git/Writing/thesis/images/results/", tolower(gene), "/")
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cat("plots will output to:", outdir_images)
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#########################################################
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if ( tolower(gene)%in%c("gid") ){
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cat("\nPlots available for layout are:")
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@ -12,11 +12,12 @@ sensP_df = merged_df3[,c("mutationinformation",
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head(sensP_df)
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table(sensP_df$sensitivity)
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#---------------
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#--------------------------
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# Total unique positions
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#----------------
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#--------------------------
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tot_mut_pos = length(unique(sensP_df[[pos_colname_c]]))
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cat("\nNo of Tot muts sites:", tot_mut_pos)
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cat("\nThese are:", unique(sensP_df[[pos_colname_c]]))
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# resistant mut pos
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sens_site_allR = sensP_df[[pos_colname_c]][sensP_df$sensitivity=="R"]
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@ -34,7 +35,7 @@ length(sens_site_UR)
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common_pos = intersect(sens_site_UR,sens_site_US)
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site_Cc = length(common_pos)
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cat("\nNo of Common sites:", site_Cc
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, "\nThese are:", common_pos)
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, "\nThese are:", sort(unique(common_pos)))
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#---------------
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# Resistant muts
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@ -1,138 +0,0 @@
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foo = df3_affinity_filtered[df3_affinity_filtered$ligand_distance<10,]
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bar = df3_affinity_filtered[df3_affinity_filtered$interface_dist<10,]
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wilcox.test(foo$mmcsm_lig_scaled~foo$sensitivity)
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wilcox.test(foo$mmcsm_lig~foo$sensitivity)
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wilcox.test(foo$affinity_scaled~foo$sensitivity)
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wilcox.test(foo$ligand_affinity_change~foo$sensitivity)
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wilcox.test(bar$mcsm_na_scaled~bar$sensitivity)
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wilcox.test(bar$mcsm_na_affinity~bar$sensitivity)
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wilcox.test(bar$mcsm_ppi2_scaled~bar$sensitivity)
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wilcox.test(bar$mcsm_ppi2_affinity~bar$sensitivity)
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# find the most "impactful" effect value
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biggest=max(abs((a[c('affinity_scaled','mmcsm_lig_scaled','mcsm_ppi2_scaled','mcsm_na_scaled')])))
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abs((a[c('affinity_scaled','mmcsm_lig_scaled','mcsm_ppi2_scaled','mcsm_na_scaled')]))==biggest
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abs((a[c('affinity_scaled','mmcsm_lig_scaled','mcsm_ppi2_scaled','mcsm_na_scaled')]))==c(,biggest)
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max(abs((a[c('affinity_scaled','mmcsm_lig_scaled','mcsm_ppi2_scaled','mcsm_na_scaled')])))
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a2 = (a[c('affinity_scaled','mmcsm_lig_scaled','mcsm_ppi2_scaled','mcsm_na_scaled')])
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a2
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#
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# biggest = max(abs(a2[1,]))
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#
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# #hmm
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# #which(abs(a2) == biggest)
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# #names(a2)[apply(a2, 1:4, function(i) which(i == max()))]
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#
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# # get row max
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# a2$row_maximum = apply(abs(a2[,-1]), 1, max)
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#
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# # get colname for abs(max_value)
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# #https://stackoverflow.com/questions/36960010/get-column-name-that-matches-specific-row-value-in-dataframe
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# #names(df)[which(df == 1, arr.ind=T)[, "col"]]
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# # yayy
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# names(a2)[which(abs(a2) == biggest, arr.ind=T)[, "col"]]
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#
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# #another:https://statisticsglobe.com/return-column-name-of-largest-value-for-each-row-in-r
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# colnames(a2)[max.col(abs(a2), ties.method = "first")] # Apply colnames & max.col functions
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# #################################################
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# # use whole df
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# #gene_aff_cols = c('affinity_scaled','mmcsm_lig_scaled','mcsm_ppi2_scaled','mcsm_na_scaled')
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#
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# biggest = max(abs(a[gene_aff_cols]))
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# a$max_es = biggest
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# a$effect = names(a[gene_aff_cols])[which(abs(a[gene_aff_cols]) == biggest, arr.ind=T)[, "col"]]
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#
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# effect_name = unique(a$effect)
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# #get index of value of max effect
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# ind = (which(abs(a[effect_name]) == biggest, arr.ind=T))
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# a[effect_name][ind]
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# # extract sign
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# a$effect_sign = sign(a[effect_name][ind])
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########################################################
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# maxn <- function(n) function(x) order(x, decreasing = TRUE)[n]
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# second_big = abs(a[gene_aff_cols])[maxn(2)(abs(a[gene_aff_cols])]
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# apply(df, 1, function(x)x[maxn(1)(x)])
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# apply(a[gene_aff_cols], 1, function(x) abs(a[gene_aff_cols])[maxn(2)(abs(a[gene_aff_cols]))])
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#########################################################
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# loop
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a2 = df2[df2$position%in%c(167, 423, 427),]
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test <- a2 %>%
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dplyr::group_by(position) %>%
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biggest = max(abs(a2[gene_aff_cols]))
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a2$max_es = max(abs(a2[gene_aff_cols]))
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a2$effect = names(a2[gene_aff_cols])[which(abs(a2[gene_aff_cols]) == biggest, arr.ind=T)[, "col"]]
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effect_name = unique(a2$effect)
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#get index of value of max effect
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ind = (which(abs(a2[effect_name]) == biggest, arr.ind=T))
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a2[effect_name][ind]
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# extract sign
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a2$effect_dir = sign(a2[effect_name][ind])
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#################################
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df2_short = df2[df2$position%in%c(167, 423, 427),]
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for (i in unique(df2_short$position) ){
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#print(i)
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#print(paste0("\nNo. of unique positions:", length(unique(df2$position))) )
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#cat(length(unique(df2$position)))
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a2 = df2_short[df2_short$position==i,]
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biggest = max(abs(a2[gene_aff_cols]))
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a2$max_es = max(abs(a2[gene_aff_cols]))
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a2$effect = names(a2[gene_aff_cols])[which(abs(a2[gene_aff_cols]) == biggest, arr.ind=T)[, "col"]]
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effect_name = unique(a2$effect)
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#get index of value of max effect
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ind = (which(abs(a2[effect_name]) == biggest, arr.ind=T))
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a2[effect_name][ind]
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# extract sign
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a2$effect_sign = sign(a2[effect_name][ind])
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}
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#========================
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df2_short = df3[df3$position%in%c(167, 423, 427),]
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df2_short = df3[df3$position%in%c(170, 167, 493, 453, 435, 433, 480, 456, 445),]
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df2_short = df3[df3$position%in%c(435, 480),]
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df2_short = df3[df3$position%in%c(435, 480),]
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give_col=function(x,y,df=df2_short){
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df[df$position==x,y]
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}
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for (i in unique(df2_short$position) ){
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#print(i)
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#print(paste0("\nNo. of unique positions:", length(unique(df2$position))) )
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#cat(length(unique(df2$position)))
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#df2_short[df2_short$position==i,gene_aff_cols]
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biggest = max(abs(give_col(i,gene_aff_cols)))
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df2_short[df2_short$position==i,'abs_max_effect'] = biggest
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df2_short[df2_short$position==i,'effect_type']= names(
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give_col(i,gene_aff_cols)[which(
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abs(
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give_col(i,gene_aff_cols)
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) == biggest, arr.ind=T
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)[, "col"]])
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effect_name = df2_short[df2_short$position==i,'effect_type'][1] # pick first one in case we have multiple exact values
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# get index/rowname for value of max effect, and then use it to get the original sign
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# here
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#df2_short[df2_short$position==i,c(effect_name)]
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#which(abs(df2_short[df2_short$position==i,c('position',effect_name)][effect_name])==biggest, arr.ind=T)
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ind = rownames(which(abs(df2_short[df2_short$position==i,c('position',effect_name)][effect_name])== biggest, arr.ind=T))
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df2_short[df2_short$position==i,'effect_sign'] = sign(df2_short[effect_name][ind,])
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}
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df2_short$effect_type = sub("\\.[0-9]+", "", df2_short$effect_type) # cull duplicate effect types that happen when there are exact duplicate values
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@ -1,316 +0,0 @@
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#source("~/git/LSHTM_analysis/config/pnca.R")
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#source("~/git/LSHTM_analysis/config/alr.R")
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#source("~/git/LSHTM_analysis/config/gid.R")
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source("~/git/LSHTM_analysis/config/embb.R")
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#source("~/git/LSHTM_analysis/config/katg.R")
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#source("~/git/LSHTM_analysis/config/rpob.R")
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source("/home/tanu/git/LSHTM_analysis/my_header.R")
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#########################################################
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# TASK: Generate averaged affinity values
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# across all affinity tools for a given structure
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# as applicable...
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#########################################################
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#=============
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# Input
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#=============
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df3_filename = paste0("/home/tanu/git/Data/", drug, "/output/", tolower(gene), "_merged_df3.csv")
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df3 = read.csv(df3_filename)
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length(df3$mutationinformation)
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all_colnames= colnames(df3)
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#%%===============================================================
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# FIXME: ADD distance to NA when SP replies
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dist_columns = c("ligand_distance", "interface_dist")
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DistCutOff = 10
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common_cols = c("mutationinformation"
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, "X5uhc_position"
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, "X5uhc_offset"
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, "position"
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, "dst_mode"
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, "mutation_info_labels"
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, "sensitivity", dist_columns )
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all_colnames[grep("scaled" , all_colnames)]
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all_colnames[grep("outcome" , all_colnames)]
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#===================
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# stability cols
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#===================
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raw_cols_stability = c("duet_stability_change"
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, "deepddg"
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, "ddg_dynamut2"
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, "ddg_foldx")
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scaled_cols_stability = c("duet_scaled"
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, "deepddg_scaled"
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, "ddg_dynamut2_scaled"
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, "foldx_scaled")
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outcome_cols_stability = c("duet_outcome"
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, "deepddg_outcome"
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, "ddg_dynamut2_outcome"
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, "foldx_outcome")
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#===================
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# affinity cols
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#===================
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raw_cols_affinity = c("ligand_affinity_change"
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, "mmcsm_lig"
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, "mcsm_ppi2_affinity"
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, "mcsm_na_affinity")
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scaled_cols_affinity = c("affinity_scaled"
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, "mmcsm_lig_scaled"
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, "mcsm_ppi2_scaled"
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, "mcsm_na_scaled" )
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outcome_cols_affinity = c( "ligand_outcome"
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, "mmcsm_lig_outcome"
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, "mcsm_ppi2_outcome"
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, "mcsm_na_outcome")
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#===================
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# conservation cols
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#===================
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# raw_cols_conservation = c("consurf_score"
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# , "snap2_score"
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# , "provean_score")
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#
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# scaled_cols_conservation = c("consurf_scaled"
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# , "snap2_scaled"
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# , "provean_scaled")
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#
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# # CANNOT strictly be used, as categories are not identical with conssurf missing altogether
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# outcome_cols_conservation = c("provean_outcome"
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# , "snap2_outcome"
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# #consurf outcome doesn't exist
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# )
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all_cols= c(common_cols
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,raw_cols_stability, scaled_cols_stability, outcome_cols_stability
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, raw_cols_affinity, scaled_cols_affinity, outcome_cols_affinity)
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#=======
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# output
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#=======
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outdir_images = paste0("~/git/Writing/thesis/images/results/", tolower(gene))
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#OutFile1
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outfile_mean_aff = paste0(outdir_images, "/", tolower(gene)
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, "_mean_ligand.csv")
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print(paste0("Output file:", outfile_mean_aff))
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#OutFile2
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outfile_ppi2 = paste0(outdir_images, "/", tolower(gene)
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, "_mean_ppi2.csv")
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print(paste0("Output file:", outfile_ppi2))
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#OutFile4
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#outfile_mean_aff_priorty = paste0(outdir_images, "/", tolower(gene)
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# , "_mean_affinity_priority.csv")
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#print(paste0("Output file:", outfile_mean_aff_priorty))
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#################################################################
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#################################################################
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# mut positions
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length(unique(df3$position))
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# mut_info checks
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table(df3$mutation_info)
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table(df3$mutation_info_orig)
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table(df3$mutation_info_labels_orig)
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# used in plots and analyses
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table(df3$mutation_info_labels) # different, and matches dst_mode
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table(df3$dst_mode)
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# create column based on dst mode with different colname
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table(is.na(df3$dst))
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table(is.na(df3$dst_mode))
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#===============
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# Create column: sensitivity mapped to dst_mode
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#===============
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df3$sensitivity = ifelse(df3$dst_mode == 1, "R", "S")
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table(df3$sensitivity)
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length(unique((df3$mutationinformation)))
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all_colnames = as.data.frame(colnames(df3))
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#===============
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# select columns specific to gene
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#===============
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gene_aff_cols = colnames(df3)[colnames(df3)%in%c(outcome_cols_affinity
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, scaled_cols_affinity)]
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gene_common_cols = colnames(df3)[colnames(df3)%in%common_cols]
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cols_to_extract = c(gene_common_cols
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, gene_aff_cols)
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cat("\nExtracting", length(cols_to_extract), "columns")
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df3_plot = df3[, cols_to_extract]
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table(df3_plot$mmcsm_lig_outcome)
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table(df3_plot$ligand_outcome)
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##############################################################
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# mCSM-lig, mCSM-NA, mCSM-ppi2, mmCSM-lig
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#########################################
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cols_to_numeric = c("ligand_outcome"
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, "mcsm_na_outcome"
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, "mcsm_ppi2_outcome"
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, "mmcsm_lig_outcome")
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#=====================================
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# mCSM-lig: Filter ligand distance <10
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#DistCutOff = 10
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#LigDist_colname = "ligand_distance"
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# extract outcome cols and map numeric values to the categories
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# Destabilising == 0, and stabilising == 1 so rescaling can let -1 be destabilising
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#=====================================
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df3_lig = df3[, c("mutationinformation"
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, "position"
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, "ligand_distance"
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, "ligand_affinity_change"
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, "affinity_scaled"
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, "ligand_outcome")]
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df3_lig = df3_lig[df3_lig["ligand_distance"]<DistCutOff,]
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expected_npos = sum(table(df3_lig["ligand_distance"]<DistCutOff))
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expected_npos
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if ( nrow(df3_lig) == expected_npos ){
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cat(paste0("\nPASS:", LigDist_colname, " filtered according to criteria:", LigDist_cutoff, angstroms_symbol ))
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}else{
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stop(paste0("\nAbort:", LigDist_colname, " could not be filtered according to criteria:", LigDist_cutoff, angstroms_symbol))
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}
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# group by position
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mean_lig_by_position <- df3_lig %>%
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dplyr::group_by(position) %>%
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#dplyr::summarize(avg_lig = max(df3_lig_num))
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#dplyr::summarize(avg_lig = mean(ligand_outcome))
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#dplyr::summarize(avg_lig = mean(affinity_scaled, na.rm = T))
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dplyr::summarize(avg_lig = mean(ligand_affinity_change, na.rm = T))
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class(mean_lig_by_position)
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# convert to a df
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mean_lig_by_position = as.data.frame(mean_lig_by_position)
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table(mean_lig_by_position$avg_lig)
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# REscale b/w -1 and 1
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lig_min = min(mean_lig_by_position['avg_lig'])
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lig_max = max(mean_lig_by_position['avg_lig'])
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mean_lig_by_position['avg_lig_scaled'] = lapply(mean_lig_by_position['avg_lig']
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, function(x) {
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scales::rescale_mid(x
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, to = c(-1,1)
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, from = c(lig_min,lig_max)
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, mid = 0)
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#, from = c(0,1))
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})
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cat(paste0('Average (mcsm-lig+mmcsm-lig) scores:\n'
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, head(mean_lig_by_position['avg_lig'])
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, '\n---------------------------------------------------------------'
|
||||
, '\nAverage (mcsm-lig+mmcsm-lig) scaled scores:\n'
|
||||
, head(mean_lig_by_position['avg_lig_scaled'])))
|
||||
|
||||
if ( nrow(mean_lig_by_position) == length(unique(df3_lig$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
|
||||
#===============================================================
|
||||
|
|
@ -1,74 +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")
|
||||
|
||||
#########################################################
|
||||
# TASK: Generate averaged stability values by position
|
||||
# calculated 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: merged_df3
|
||||
#=============
|
||||
source("~/git/LSHTM_analysis/scripts/plotting/get_plotting_dfs.R")
|
||||
#merged_df3= paste0("/home/tanu/git/Data/", drug, "/output/", tolower(gene), "_merged_df3.csv")
|
||||
|
||||
cols_to_extract_ms = c("mutationinformation", "position", "avg_stability_scaled")
|
||||
|
||||
df3 = merged_df3[, cols_to_extract_ms]
|
||||
length(df3$mutationinformation)
|
||||
|
||||
# ensemble average of predictors by position
|
||||
avg_stability_by_position <- df3 %>%
|
||||
dplyr::group_by(position) %>%
|
||||
dplyr::summarize(avg_stability_scaled_pos = mean(avg_stability_scaled))
|
||||
|
||||
min(avg_stability_by_position$avg_stability_scaled_pos)
|
||||
max(avg_stability_by_position$avg_stability_scaled_pos)
|
||||
|
||||
avg_stability_by_position['avg_stability_scaled_pos_scaled'] = lapply(avg_stability_by_position['avg_stability_scaled_pos']
|
||||
, function(x) {
|
||||
scales::rescale_mid(x, to = c(-1,1)
|
||||
#, from = c(en_stab_min,en_stab_max))
|
||||
, mid = 0
|
||||
, from = c(0,1))
|
||||
})
|
||||
cat(paste0('Average stability scores:\n'
|
||||
, head(avg_stability_by_position['avg_stability_scaled_pos'])
|
||||
, '\n---------------------------------------------------------------'
|
||||
, '\nAverage stability scaled scores:\n'
|
||||
, head(avg_stability_by_position['avg_stability_scaled_pos_scaled'])
|
||||
))
|
||||
|
||||
all(avg_stability_by_position['avg_stability_scaled_pos'] == avg_stability_by_position['avg_stability_scaled_pos_scaled'])
|
||||
|
||||
# convert to a data frame
|
||||
avg_stability_by_position = as.data.frame(avg_stability_by_position)
|
||||
|
||||
##################################################################
|
||||
# output
|
||||
#write.csv(combined_df, outfile_mean_ens_st_aff
|
||||
write.csv(avg_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(avg_stability_by_position)
|
||||
, "\nNo. of cols:", ncol(avg_stability_by_position))
|
||||
|
||||
# end of script
|
||||
#===============================================================
|
||||
|
|
@ -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
|
||||
#===============================================================
|
||||
|
|
@ -1,46 +1,23 @@
|
|||
#!/usr/bin/env Rscript
|
||||
source("~/git/LSHTM_analysis/config/gid.R")
|
||||
|
||||
source("~/git/LSHTM_analysis/scripts/plotting/get_plotting_dfs.R")
|
||||
|
||||
#########################################################
|
||||
# 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
|
||||
# normalised affinity values
|
||||
|
||||
#########################################################
|
||||
# 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)
|
||||
gene_match = paste0(gene,"_p."); cat(gene_match)
|
||||
cat(gene_match)
|
||||
|
||||
#=============
|
||||
|
|
@ -49,9 +26,13 @@ cat(gene_match)
|
|||
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))
|
||||
#outdir_plots = paste0("~/git/Writing/thesis/images/results/", tolower(gene))
|
||||
|
||||
#=======
|
||||
# output
|
||||
#=======
|
||||
outdir_images = paste0("~/git/Writing/thesis/images/results/", tolower(gene), "/")
|
||||
cat("plots will output to:", outdir_images)
|
||||
#======
|
||||
# input
|
||||
#======
|
||||
|
|
@ -59,31 +40,31 @@ 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)
|
||||
outfile_lig_mspdb = paste0(outdir_images,out_filename_lig_mspdb)
|
||||
print(paste0("Output file:", outfile_lig_mspdb))
|
||||
|
||||
#%%===============================================================
|
||||
#NOTE: duet here refers to the ensemble stability values
|
||||
#NOTE: duet here refers to the ensemble affinity values
|
||||
|
||||
###########################
|
||||
# Read file: average stability values
|
||||
# Read file: average affinity values
|
||||
# or mcsm_normalised file
|
||||
###########################
|
||||
my_df <- read.csv(infile_mean_stability, header = T)
|
||||
str(my_df)
|
||||
my_df_raw = merged_df3[, c("position", "ligand_distance", "avg_lig_affinity_scaled", "avg_lig_affinity")]
|
||||
my_df_raw = my_df_raw[my_df_raw$ligand_distance<10,]
|
||||
|
||||
# avg by position on the SCALED values
|
||||
my_df <- my_df_raw %>%
|
||||
group_by(position) %>%
|
||||
summarize(avg_ligaff_sc_pos = mean(avg_lig_affinity_scaled))
|
||||
|
||||
max(my_df$avg_ligaff_sc_pos)
|
||||
min(my_df$avg_ligaff_sc_pos)
|
||||
|
||||
#############
|
||||
# Read pdb
|
||||
|
|
@ -98,13 +79,11 @@ my_pdb = read.pdb(infile_pdb
|
|||
, hex = FALSE
|
||||
, verbose = TRUE)
|
||||
|
||||
rm(in_filename_mean_affinity, in_filename_pdb)
|
||||
|
||||
# assign separately for duet and ligand
|
||||
my_pdb_duet = my_pdb
|
||||
|
||||
#=========================================================
|
||||
# Replacing B factor with mean stability scores
|
||||
# Replacing B factor with mean affinity scores
|
||||
# within the respective dfs
|
||||
#==========================================================
|
||||
# extract atom list into a variable
|
||||
|
|
@ -121,8 +100,8 @@ max(df_duet$b); min(df_duet$b)
|
|||
#==================================================
|
||||
# histograms and density plots for inspection
|
||||
# 1: original B-factors
|
||||
# 2: original mean stability values
|
||||
# 3: replaced B-factors with mean stability values
|
||||
# 2: original mean affinity values
|
||||
# 3: replaced B-factors with mean affinity values
|
||||
#==================================================
|
||||
# Set the margin on all sides
|
||||
par(oma = c(3,2,3,0)
|
||||
|
|
@ -131,6 +110,7 @@ par(oma = c(3,2,3,0)
|
|||
#, mfrow = c(3,4))
|
||||
, mfrow = c(3,2))
|
||||
|
||||
|
||||
#=============
|
||||
# Row 1 plots: original B-factors
|
||||
# duet and affinity
|
||||
|
|
@ -144,40 +124,28 @@ plot(density(df_duet$b)
|
|||
, main = "Bfactor affinity")
|
||||
|
||||
#=============
|
||||
# Row 2 plots: original mean stability values
|
||||
# duet and affinity
|
||||
# Row 2 plots: original mean affinity values
|
||||
# affinity
|
||||
#=============
|
||||
|
||||
#hist(my_df$averaged_duet
|
||||
hist(my_df$avg_lig_scaled
|
||||
hist(my_df$avg_ligaff_sc_pos
|
||||
, xlab = ""
|
||||
, main = "mean affinity values")
|
||||
|
||||
#plot(density(my_df$averaged_duet)
|
||||
plot(density(my_df$avg_lig_scaled)
|
||||
plot(density(my_df$avg_ligaff_sc_pos)
|
||||
, 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)]
|
||||
df_duet$b = my_df$avg_ligaff_sc_pos[match(df_duet$resno, my_df$position)]
|
||||
|
||||
#=========
|
||||
# step 2_P1
|
||||
|
|
@ -192,32 +160,6 @@ sum(df_duet$b == 0)
|
|||
na_rep = 2
|
||||
df_duet$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$avg_ens_affinity_scaled)) ){
|
||||
# print("PASS: B-factors replaced correctly in df_lig")
|
||||
# } else {
|
||||
# print ("FAIL: To replace B-factors in df_lig")
|
||||
# quit()
|
||||
# }
|
||||
|
||||
#=========
|
||||
# step 3_P1
|
||||
#=========
|
||||
|
|
@ -241,17 +183,23 @@ table(df_duet$b)
|
|||
sum(is.na(df_duet$b))
|
||||
|
||||
#=========
|
||||
# step 5_P1
|
||||
# step 5_P1: OUTPUT
|
||||
#=========
|
||||
cat(paste0("output file duet mean stability pdb:"
|
||||
, outfile_lig_mspdb))
|
||||
cat(paste0("output file duet mean affinity pdb:", outfile_lig_mspdb))
|
||||
write.pdb(my_pdb_duet, outfile_lig_mspdb)
|
||||
|
||||
# OUTPUT: position file
|
||||
poscsvF = paste0(outdir_images, tolower(gene), "_ligaff_positions.csv")
|
||||
cat(paste0("output file duet mean NA affinity POSITIONS:", poscsvF))
|
||||
|
||||
filtered_pos = toString(my_df$position)
|
||||
write.table(filtered_pos, poscsvF, row.names = F, col.names = F )
|
||||
|
||||
#============================
|
||||
# Add the 3rd histogram and density plots for comparisons
|
||||
#============================
|
||||
# Plots continued...
|
||||
# Row 3 plots: hist and density of replaced B-factors with stability values
|
||||
# Row 3 plots: hist and density of replaced B-factors with affinity values
|
||||
hist(df_duet$b
|
||||
, xlab = ""
|
||||
, main = "repalcedB duet")
|
||||
|
|
@ -266,16 +214,8 @@ mtext(text = "Frequency"
|
|||
, line = 0
|
||||
, outer = TRUE)
|
||||
|
||||
mtext(text = paste0(tolower(gene), ": afinity distribution")
|
||||
mtext(text = paste0(tolower(gene), ": affinity 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???
|
||||
#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
|
||||
|
|
@ -1,46 +1,19 @@
|
|||
#!/usr/bin/env Rscript
|
||||
source("~/git/LSHTM_analysis/config/gid.R")
|
||||
source("~/git/LSHTM_analysis/scripts/plotting/get_plotting_dfs.R")
|
||||
|
||||
#########################################################
|
||||
# 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
|
||||
|
||||
# normalised ppi2 values.
|
||||
#########################################################
|
||||
# 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)
|
||||
gene_match = paste0(gene,"_p."); cat(gene_match)
|
||||
cat(gene_match)
|
||||
|
||||
#=============
|
||||
|
|
@ -49,9 +22,13 @@ cat(gene_match)
|
|||
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))
|
||||
#outdir_plots = paste0("~/git/Writing/thesis/images/results/", tolower(gene))
|
||||
|
||||
#=======
|
||||
# output
|
||||
#=======
|
||||
outdir_images = paste0("~/git/Writing/thesis/images/results/", tolower(gene), "/")
|
||||
cat("plots will output to:", outdir_images)
|
||||
#======
|
||||
# input
|
||||
#======
|
||||
|
|
@ -59,30 +36,33 @@ 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)
|
||||
outfile_ppi2_mspdb = paste0(outdir_images,out_filename_ppi2_mspdb)
|
||||
print(paste0("Output file:", outfile_ppi2_mspdb))
|
||||
|
||||
#%%===============================================================
|
||||
#NOTE: duet here refers to the ensemble stability values
|
||||
#NOTE: duet here refers to the ensemble ppi2 values
|
||||
|
||||
###########################
|
||||
# Read file: average stability values
|
||||
# Read file: average ppi2 values
|
||||
# or mcsm_normalised file
|
||||
###########################
|
||||
my_df <- read.csv(infile_mean_ppi2, header = T)
|
||||
str(my_df)
|
||||
my_df_raw = merged_df3[, c("position", "mcsm_ppi2_scaled", "interface_dist")]
|
||||
head(my_df_raw)
|
||||
my_df_raw = my_df_raw[my_df_raw$interface_dist<10,]
|
||||
my_df_raw$position
|
||||
|
||||
# avg by position on the SCALED values
|
||||
my_df <- my_df_raw %>%
|
||||
group_by(position) %>%
|
||||
summarize(avg_ppi2_sc_pos = mean(mcsm_ppi2_scaled))
|
||||
|
||||
max(my_df$avg_ppi2_sc_pos)
|
||||
min(my_df$avg_ppi2_sc_pos)
|
||||
#============================================================
|
||||
#############
|
||||
# Read pdb
|
||||
#############
|
||||
|
|
@ -100,7 +80,7 @@ my_pdb = read.pdb(infile_pdb
|
|||
my_pdb_duet = my_pdb
|
||||
|
||||
#=========================================================
|
||||
# Replacing B factor with mean stability scores
|
||||
# Replacing B factor with mean ppi2 scores
|
||||
# within the respective dfs
|
||||
#==========================================================
|
||||
# extract atom list into a variable
|
||||
|
|
@ -117,8 +97,8 @@ max(df_duet$b); min(df_duet$b)
|
|||
#==================================================
|
||||
# histograms and density plots for inspection
|
||||
# 1: original B-factors
|
||||
# 2: original mean stability values
|
||||
# 3: replaced B-factors with mean stability values
|
||||
# 2: original mean ppi2 values
|
||||
# 3: replaced B-factors with mean ppi2 values
|
||||
#==================================================
|
||||
# Set the margin on all sides
|
||||
par(oma = c(3,2,3,0)
|
||||
|
|
@ -129,7 +109,7 @@ par(oma = c(3,2,3,0)
|
|||
|
||||
#=============
|
||||
# Row 1 plots: original B-factors
|
||||
# duet and affinity
|
||||
# duet and ppi2
|
||||
#=============
|
||||
hist(df_duet$b
|
||||
, xlab = ""
|
||||
|
|
@ -140,40 +120,24 @@ plot(density(df_duet$b)
|
|||
, main = "Bfactor ppi2")
|
||||
|
||||
#=============
|
||||
# Row 2 plots: original mean stability values
|
||||
# duet and affinity
|
||||
# Row 2 plots: original mean ppi2 values
|
||||
# ppi2
|
||||
#=============
|
||||
|
||||
#hist(my_df$averaged_duet
|
||||
hist(my_df$avg_ppi2_scaled
|
||||
hist(my_df$avg_ppi2_sc_pos
|
||||
, xlab = ""
|
||||
, main = "mean ppi2 values")
|
||||
|
||||
#plot(density(my_df$averaged_duet)
|
||||
plot(density(my_df$avg_ppi2_scaled)
|
||||
plot(density(my_df$avg_ppi2_sc_pos)
|
||||
, xlab = ""
|
||||
, main = "mean ppi2 values")
|
||||
|
||||
#==============
|
||||
# Row 3 plots: replaced B-factors with mean stability values
|
||||
# After actual replacement in the b factor column
|
||||
#===============
|
||||
################################################################
|
||||
#=========
|
||||
# step 0_P1: DONT RUN once you have double checked the matched output
|
||||
#=========
|
||||
# sanity check: match and assign to a separate column to double check
|
||||
# colnames(my_df)
|
||||
# df_duet$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$position)]
|
||||
df_duet$b = my_df$avg_ppi2_sc_pos[match(df_duet$resno, my_df$position)]
|
||||
|
||||
#=========
|
||||
# step 2_P1
|
||||
|
|
@ -188,32 +152,6 @@ sum(df_duet$b == 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
|
||||
#=========
|
||||
|
|
@ -237,17 +175,23 @@ table(df_duet$b)
|
|||
sum(is.na(df_duet$b))
|
||||
|
||||
#=========
|
||||
# step 5_P1
|
||||
# step 5_P1: OUTPUT
|
||||
#=========
|
||||
cat(paste0("output file mean ppi2 pdb:"
|
||||
, outfile_ppi2_mspdb))
|
||||
cat(paste0("output file duet mean ppi2 pdb:", outfile_ppi2_mspdb))
|
||||
write.pdb(my_pdb_duet, outfile_ppi2_mspdb)
|
||||
|
||||
# OUTPUT: position file
|
||||
poscsvF = paste0(outdir_images, tolower(gene), "_ppi2_positions.csv")
|
||||
cat(paste0("output file duet mean ppi2 POSITIONS:", poscsvF))
|
||||
|
||||
filtered_pos = toString(my_df$position)
|
||||
write.table(filtered_pos, poscsvF, row.names = F, col.names = F )
|
||||
|
||||
#============================
|
||||
# Add the 3rd histogram and density plots for comparisons
|
||||
#============================
|
||||
# Plots continued...
|
||||
# Row 3 plots: hist and density of replaced B-factors with stability values
|
||||
# Row 3 plots: hist and density of replaced B-factors with ppi2 values
|
||||
hist(df_duet$b
|
||||
, xlab = ""
|
||||
, main = "repalcedB duet")
|
||||
|
|
@ -266,12 +210,4 @@ 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???
|
||||
#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
|
||||
#============================================
|
||||
|
|
@ -1,11 +1,7 @@
|
|||
#!/usr/bin/env Rscript
|
||||
|
||||
#source("~/git/LSHTM_analysis/config/alr.R")
|
||||
source("~/git/LSHTM_analysis/config/embb.R")
|
||||
#source("~/git/LSHTM_analysis/config/katg.R")
|
||||
#source("~/git/LSHTM_analysis/config/gid.R")
|
||||
#source("~/git/LSHTM_analysis/config/pnca.R")
|
||||
#source("~/git/LSHTM_analysis/config/rpob.R")
|
||||
source("~/git/LSHTM_analysis/config/gid.R")
|
||||
source("~/git/LSHTM_analysis/scripts/plotting/get_plotting_dfs.R")
|
||||
|
||||
#########################################################
|
||||
# TASK: Replace B-factors in the pdb file with the mean
|
||||
# normalised stability values.
|
||||
|
|
@ -20,31 +16,12 @@ source("~/git/LSHTM_analysis/config/embb.R")
|
|||
|
||||
#########################################################
|
||||
# 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 = ""
|
||||
#gene = ""
|
||||
|
||||
# # 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)")
|
||||
# }
|
||||
#========================================================
|
||||
cat(gene)
|
||||
gene_match = paste0(gene,"_p."); cat(gene_match)
|
||||
|
|
@ -56,29 +33,25 @@ cat(gene_match)
|
|||
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))
|
||||
#outdir_plots = paste0("~/git/Writing/thesis/images/results/", tolower(gene))
|
||||
|
||||
#======
|
||||
# input
|
||||
#======
|
||||
in_filename_pdb = paste0(tolower(gene), "_complex.pdb")
|
||||
#in_filename_pdb = "/home/tanu/git/Writing/thesis/images/results/gid/str_figures/gid_complex_copy_arpeg.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
|
||||
#=======
|
||||
outdir_images = paste0("~/git/Writing/thesis/images/results/", tolower(gene), "/")
|
||||
cat("plots will output to:", outdir_images)
|
||||
|
||||
#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)
|
||||
outfile_duet_mspdb = paste0(outdir_images, out_filename_duet_mspdb)
|
||||
print(paste0("Output file:", outfile_duet_mspdb))
|
||||
|
||||
#%%===============================================================
|
||||
|
|
@ -88,8 +61,31 @@ print(paste0("Output file:", outfile_duet_mspdb))
|
|||
# Read file: average stability values
|
||||
# or mcsm_normalised file
|
||||
###########################
|
||||
my_df <- read.csv(infile_mean_stability, header = T)
|
||||
str(my_df)
|
||||
my_df_raw = merged_df3[, c("position", "avg_stability", "avg_stability_scaled")]
|
||||
|
||||
# avg by position on the SCALED values
|
||||
my_df <- my_df_raw %>%
|
||||
group_by(position) %>%
|
||||
summarize(avg_stab_sc_pos = mean(avg_stability_scaled))
|
||||
|
||||
max(my_df$avg_stab_sc_pos)
|
||||
min(my_df$avg_stab_sc_pos)
|
||||
#============================================================
|
||||
# # scale b/w -1 and 1
|
||||
# duet_min = min(my_df_by_position['avg_stab_sc_pos'])
|
||||
# duet_max = max(my_df_by_position['avg_stab_sc_pos'])
|
||||
#
|
||||
# # scale the averaged_duet values
|
||||
# my_df_by_position['avg_stab_sc_pos_scaled'] = lapply(my_df_by_position['avg_stab_sc_pos']
|
||||
# , function(x) ifelse(x < 0, x/abs(duet_min), x/duet_max))
|
||||
#
|
||||
# cat(paste0('Average duet scores:\n', head(my_df_by_position['avg_stab_sc_pos_scaled'])
|
||||
# , '\n---------------------------------------------------------------'
|
||||
# , '\nScaled duet scores:\n', head(my_df_by_position['avg_stab_sc_pos_scaled'])))
|
||||
#
|
||||
# min(my_df_by_position['avg_stab_sc_pos_scaled'])
|
||||
# max(my_df_by_position['avg_stab_sc_pos_scaled'])
|
||||
#============================================================
|
||||
|
||||
#############
|
||||
# Read pdb
|
||||
|
|
@ -104,8 +100,6 @@ my_pdb = read.pdb(infile_pdb
|
|||
, hex = FALSE
|
||||
, verbose = TRUE)
|
||||
|
||||
rm(in_filename_mean_stability, in_filename_pdb)
|
||||
|
||||
# assign separately for duet and ligand
|
||||
my_pdb_duet = my_pdb
|
||||
|
||||
|
|
@ -113,9 +107,6 @@ 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
|
||||
|
|
@ -156,35 +147,22 @@ plot(density(df_duet$b)
|
|||
#=============
|
||||
|
||||
#hist(my_df$averaged_duet
|
||||
hist(my_df$avg_stability_scaled_pos_scaled
|
||||
hist(my_df$avg_stab_sc_pos
|
||||
, xlab = ""
|
||||
, main = "mean stability values")
|
||||
|
||||
#plot(density(my_df$averaged_duet)
|
||||
plot(density(my_df$avg_stability_scaled_pos_scaled)
|
||||
plot(density(my_df$avg_stab_sc_pos)
|
||||
, xlab = ""
|
||||
, main = "mean stability values")
|
||||
|
||||
#==============
|
||||
# Row 3 plots: replaced B-factors with mean stability values
|
||||
# After actual replacement in the b factor column
|
||||
#===============
|
||||
################################################################
|
||||
#=========
|
||||
# step 0_P1: DONT RUN once you have double checked the matched output
|
||||
#=========
|
||||
# sanity check: match and assign to a separate column to double check
|
||||
# colnames(my_df)
|
||||
# df_duet$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$avg_stability_scaled_pos_scaled[match(df_duet$resno, my_df$position)]
|
||||
df_duet$b = my_df$avg_stab_sc_pos[match(df_duet$resno, my_df$position)]
|
||||
|
||||
#=========
|
||||
# step 2_P1
|
||||
|
|
@ -198,26 +176,6 @@ sum(df_duet$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
|
||||
# if ( (sum(df_duet$b == na_rep) == b_na_duet) {
|
||||
# print ("PASS: NA's replaced with 0s successfully in df_duet and df_lig")
|
||||
# } else {
|
||||
# print("FAIL: NA replacement in df_duet NOT successful")
|
||||
# quit()
|
||||
# }
|
||||
#
|
||||
# max(df_duet$b); min(df_duet$b)
|
||||
#
|
||||
# # sanity checks: should be True
|
||||
# if( (max(df_duet$b) == max(my_df$avg_stability_scaled_pos_scaled)) & (min(df_duet$b) == min(my_df$avg_stability_scaled_pos_scaled)) ){
|
||||
# print("PASS: B-factors replaced correctly in df_duet")
|
||||
# } else {
|
||||
# print ("FAIL: To replace B-factors in df_duet")
|
||||
# quit()
|
||||
# }
|
||||
|
||||
#=========
|
||||
# step 3_P1
|
||||
#=========
|
||||
|
|
@ -269,12 +227,4 @@ 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???
|
||||
#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
|
||||
#============================================
|
||||
Loading…
Add table
Add a link
Reference in a new issue