moved code for structure figure to sep sir
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138
scripts/plotting/structure_figures/AFFINITY_TEST.R
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138
scripts/plotting/structure_figures/AFFINITY_TEST.R
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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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biggest = max(abs(a2[1,]))
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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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# get row max
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a2$row_maximum = apply(abs(a2[,-1]), 1, max)
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# get colname for abs(max_value)
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#https://stackoverflow.com/questions/36960010/get-column-name-that-matches-specific-row-value-in-dataframe
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#names(df)[which(df == 1, arr.ind=T)[, "col"]]
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# yayy
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names(a2)[which(abs(a2) == biggest, arr.ind=T)[, "col"]]
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#another:https://statisticsglobe.com/return-column-name-of-largest-value-for-each-row-in-r
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colnames(a2)[max.col(abs(a2), ties.method = "first")] # Apply colnames & max.col functions
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#################################################
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# use whole df
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#gene_aff_cols = c('affinity_scaled','mmcsm_lig_scaled','mcsm_ppi2_scaled','mcsm_na_scaled')
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biggest = max(abs(a[gene_aff_cols]))
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a$max_es = biggest
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a$effect = names(a[gene_aff_cols])[which(abs(a[gene_aff_cols]) == biggest, arr.ind=T)[, "col"]]
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effect_name = unique(a$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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241
scripts/plotting/structure_figures/mcsm_affinity_data_only.R
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scripts/plotting/structure_figures/mcsm_affinity_data_only.R
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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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# 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_affinity_all.csv")
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print(paste0("Output file:", outfile_mean_aff))
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#OutFile2
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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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# 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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# 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$mutationinformation)))
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all_colnames = as.data.frame(colnames(df3))
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# FIXME: ADD distance to NA when SP replies
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dist_columns = c("ligand_distance", "interface_dist")
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DistCutOff = 10
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common_cols = c("mutationinformation"
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, "X5uhc_position"
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, "X5uhc_offset"
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, "position"
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, "dst_mode"
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, "mutation_info_labels"
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, "sensitivity", dist_columns )
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all_colnames$`colnames(df3)`[grep("scaled", all_colnames$`colnames(df3)`)]
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all_colnames$`colnames(df3)`[grep("outcome", all_colnames$`colnames(df3)`)]
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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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gene_aff_cols = colnames(df3)[colnames(df3)%in%scaled_cols_affinity]
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gene_stab_cols = colnames(df3)[colnames(df3)%in%scaled_cols_stability]
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gene_common_cols = colnames(df3)[colnames(df3)%in%common_cols]
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sel_cols = c(gene_common_cols
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, gene_stab_cols
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, gene_aff_cols)
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#########################################
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#df3_plot = df3[, cols_to_extract]
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df3_plot = df3[, sel_cols]
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######################
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#FILTERING HMMMM!
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#all dist <10
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#for embb this results in 2 muts
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######################
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df3_affinity_filtered = df3_plot[ (df3_plot$ligand_distance<10 | df3_plot$interface_dist <10),]
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df3_affinity_filtered = df3_plot[ (df3_plot$ligand_distance<10 & df3_plot$interface_dist <10),]
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c0u = unique(df3_affinity_filtered$position)
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length(c0u)
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#df = df3_affinity_filtered
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##########################################
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#NO FILTERING: annotate the whole df
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df = df3_plot
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sum(is.na(df))
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df2 = na.omit(df)
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c0u = unique(df2$position)
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length(c0u)
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# reassign orig
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my_df_raw = df3
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# now subset
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df3 = df2
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#######################################################
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#=================
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# affinity effect
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#=================
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give_col=function(x,y,df=df3){
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df[df$position==x,y]
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}
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for (i in unique(df3$position) ){
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#print(i)
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biggest = max(abs(give_col(i,gene_aff_cols)))
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df3[df3$position==i,'abs_max_effect'] = biggest
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df3[df3$position==i,'effect_type']= names(
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give_col(i,gene_aff_cols)[which(
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abs(
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give_col(i,gene_aff_cols)
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) == biggest, arr.ind=T
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)[, "col"]])
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# effect_name = unique(df3[df3$position==i,'effect_type'])
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effect_name = df3[df3$position==i,'effect_type'][1] # pick first one in case we have multiple exact values
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ind = rownames(which(abs(df3[df3$position==i,c('position',effect_name)][effect_name])== biggest, arr.ind=T))
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df3[df3$position==i,'effect_sign'] = sign(df3[effect_name][ind,])
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}
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df3$effect_type = sub("\\.[0-9]+", "", df3$effect_type) # cull duplicate effect types that happen when there are exact duplicate values
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df3U = df3[!duplicated(df3[c('position')]), ]
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table(df3U$effect_type)
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||||||
|
#########################################################
|
||||||
|
#%% 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$effect_type)
|
||||||
|
|
||||||
|
#%%============================================================
|
||||||
|
# output
|
||||||
|
write.csv(combined_df, outfile_mean_ens_st_aff
|
||||||
|
, row.names = F)
|
||||||
|
cat("Finished writing file:\n"
|
||||||
|
, outfile_mean_ens_st_aff
|
||||||
|
, "\nNo. of rows:", nrow(combined_df)
|
||||||
|
, "\nNo. of cols:", ncol(combined_df))
|
||||||
|
|
||||||
|
# end of script
|
||||||
|
#===============================================================
|
||||||
316
scripts/plotting/structure_figures/mcsm_mean_affinity_ensemble.R
Normal file
316
scripts/plotting/structure_figures/mcsm_mean_affinity_ensemble.R
Normal file
|
|
@ -0,0 +1,316 @@
|
||||||
|
#source("~/git/LSHTM_analysis/config/pnca.R")
|
||||||
|
#source("~/git/LSHTM_analysis/config/alr.R")
|
||||||
|
#source("~/git/LSHTM_analysis/config/gid.R")
|
||||||
|
source("~/git/LSHTM_analysis/config/embb.R")
|
||||||
|
#source("~/git/LSHTM_analysis/config/katg.R")
|
||||||
|
#source("~/git/LSHTM_analysis/config/rpob.R")
|
||||||
|
|
||||||
|
source("/home/tanu/git/LSHTM_analysis/my_header.R")
|
||||||
|
#########################################################
|
||||||
|
# TASK: Generate averaged affinity values
|
||||||
|
# across all affinity tools for a given structure
|
||||||
|
# as applicable...
|
||||||
|
#########################################################
|
||||||
|
#=============
|
||||||
|
# Input
|
||||||
|
#=============
|
||||||
|
df3_filename = paste0("/home/tanu/git/Data/", drug, "/output/", tolower(gene), "_merged_df3.csv")
|
||||||
|
df3 = read.csv(df3_filename)
|
||||||
|
length(df3$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/structure_figures/mcsm_mean_stability.R
Executable file
163
scripts/plotting/structure_figures/mcsm_mean_stability.R
Executable file
|
|
@ -0,0 +1,163 @@
|
||||||
|
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
|
||||||
|
#===============================================================
|
||||||
|
|
@ -0,0 +1,212 @@
|
||||||
|
#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
|
||||||
|
#===============================================================
|
||||||
|
|
@ -0,0 +1,176 @@
|
||||||
|
#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/structure_figures/mut_landscape.R
Normal file
155
scripts/plotting/structure_figures/mut_landscape.R
Normal file
|
|
@ -0,0 +1,155 @@
|
||||||
|
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/structure_figures/mut_landscape_5uhc_rpob.R
Normal file
191
scripts/plotting/structure_figures/mut_landscape_5uhc_rpob.R
Normal file
|
|
@ -0,0 +1,191 @@
|
||||||
|
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/structure_figures/replaceBfactor_pdb.R
Executable file
332
scripts/plotting/structure_figures/replaceBfactor_pdb.R
Executable file
|
|
@ -0,0 +1,332 @@
|
||||||
|
#!/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/structure_figures/replaceBfactor_pdb_affinity.R
Normal file
281
scripts/plotting/structure_figures/replaceBfactor_pdb_affinity.R
Normal file
|
|
@ -0,0 +1,281 @@
|
||||||
|
#!/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/structure_figures/replaceBfactor_pdb_ppi2.R
Normal file
277
scripts/plotting/structure_figures/replaceBfactor_pdb_ppi2.R
Normal file
|
|
@ -0,0 +1,277 @@
|
||||||
|
#!/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???
|
||||||
|
#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
|
||||||
|
|
@ -0,0 +1,281 @@
|
||||||
|
#!/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???
|
||||||
|
#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
|
||||||
|
|
@ -0,0 +1,280 @@
|
||||||
|
#!/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))
|
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|
#=============
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||||||
|
# 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")
|
||||||
|
|
||||||
|
#=============
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||||||
|
# Row 2 plots: original mean stability values
|
||||||
|
# duet and affinity
|
||||||
|
#=============
|
||||||
|
|
||||||
|
#hist(my_df$averaged_duet
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||||||
|
hist(my_df$avg_ens_stability_scaled
|
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|
, xlab = ""
|
||||||
|
, main = "mean stability values")
|
||||||
|
|
||||||
|
#plot(density(my_df$averaged_duet)
|
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|
plot(density(my_df$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???
|
||||||
|
#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
|
||||||
Loading…
Add table
Add a link
Reference in a new issue