rearranged corr plot cols and also added example for ggpairs
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fdb3f00503
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7 changed files with 227 additions and 681 deletions
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@ -1,138 +0,0 @@
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foo = df3_affinity_filtered[df3_affinity_filtered$ligand_distance<10,]
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bar = df3_affinity_filtered[df3_affinity_filtered$interface_dist<10,]
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wilcox.test(foo$mmcsm_lig_scaled~foo$sensitivity)
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wilcox.test(foo$mmcsm_lig~foo$sensitivity)
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wilcox.test(foo$affinity_scaled~foo$sensitivity)
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wilcox.test(foo$ligand_affinity_change~foo$sensitivity)
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wilcox.test(bar$mcsm_na_scaled~bar$sensitivity)
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wilcox.test(bar$mcsm_na_affinity~bar$sensitivity)
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wilcox.test(bar$mcsm_ppi2_scaled~bar$sensitivity)
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wilcox.test(bar$mcsm_ppi2_affinity~bar$sensitivity)
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# find the most "impactful" effect value
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biggest=max(abs((a[c('affinity_scaled','mmcsm_lig_scaled','mcsm_ppi2_scaled','mcsm_na_scaled')])))
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abs((a[c('affinity_scaled','mmcsm_lig_scaled','mcsm_ppi2_scaled','mcsm_na_scaled')]))==biggest
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abs((a[c('affinity_scaled','mmcsm_lig_scaled','mcsm_ppi2_scaled','mcsm_na_scaled')]))==c(,biggest)
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max(abs((a[c('affinity_scaled','mmcsm_lig_scaled','mcsm_ppi2_scaled','mcsm_na_scaled')])))
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a2 = (a[c('affinity_scaled','mmcsm_lig_scaled','mcsm_ppi2_scaled','mcsm_na_scaled')])
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a2
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#
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# biggest = max(abs(a2[1,]))
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#
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# #hmm
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# #which(abs(a2) == biggest)
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# #names(a2)[apply(a2, 1:4, function(i) which(i == max()))]
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#
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# # get row max
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# a2$row_maximum = apply(abs(a2[,-1]), 1, max)
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#
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# # get colname for abs(max_value)
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# #https://stackoverflow.com/questions/36960010/get-column-name-that-matches-specific-row-value-in-dataframe
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# #names(df)[which(df == 1, arr.ind=T)[, "col"]]
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# # yayy
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# names(a2)[which(abs(a2) == biggest, arr.ind=T)[, "col"]]
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#
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# #another:https://statisticsglobe.com/return-column-name-of-largest-value-for-each-row-in-r
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# colnames(a2)[max.col(abs(a2), ties.method = "first")] # Apply colnames & max.col functions
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# #################################################
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# # use whole df
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# #gene_aff_cols = c('affinity_scaled','mmcsm_lig_scaled','mcsm_ppi2_scaled','mcsm_na_scaled')
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#
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# biggest = max(abs(a[gene_aff_cols]))
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# a$max_es = biggest
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# a$effect = names(a[gene_aff_cols])[which(abs(a[gene_aff_cols]) == biggest, arr.ind=T)[, "col"]]
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#
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# effect_name = unique(a$effect)
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# #get index of value of max effect
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# ind = (which(abs(a[effect_name]) == biggest, arr.ind=T))
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# a[effect_name][ind]
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# # extract sign
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# a$effect_sign = sign(a[effect_name][ind])
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########################################################
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# maxn <- function(n) function(x) order(x, decreasing = TRUE)[n]
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# second_big = abs(a[gene_aff_cols])[maxn(2)(abs(a[gene_aff_cols])]
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# apply(df, 1, function(x)x[maxn(1)(x)])
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# apply(a[gene_aff_cols], 1, function(x) abs(a[gene_aff_cols])[maxn(2)(abs(a[gene_aff_cols]))])
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#########################################################
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# loop
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a2 = df2[df2$position%in%c(167, 423, 427),]
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test <- a2 %>%
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dplyr::group_by(position) %>%
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biggest = max(abs(a2[gene_aff_cols]))
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a2$max_es = max(abs(a2[gene_aff_cols]))
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a2$effect = names(a2[gene_aff_cols])[which(abs(a2[gene_aff_cols]) == biggest, arr.ind=T)[, "col"]]
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effect_name = unique(a2$effect)
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#get index of value of max effect
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ind = (which(abs(a2[effect_name]) == biggest, arr.ind=T))
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a2[effect_name][ind]
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# extract sign
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a2$effect_dir = sign(a2[effect_name][ind])
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#################################
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df2_short = df2[df2$position%in%c(167, 423, 427),]
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for (i in unique(df2_short$position) ){
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#print(i)
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#print(paste0("\nNo. of unique positions:", length(unique(df2$position))) )
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#cat(length(unique(df2$position)))
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a2 = df2_short[df2_short$position==i,]
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biggest = max(abs(a2[gene_aff_cols]))
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a2$max_es = max(abs(a2[gene_aff_cols]))
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a2$effect = names(a2[gene_aff_cols])[which(abs(a2[gene_aff_cols]) == biggest, arr.ind=T)[, "col"]]
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effect_name = unique(a2$effect)
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#get index of value of max effect
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ind = (which(abs(a2[effect_name]) == biggest, arr.ind=T))
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a2[effect_name][ind]
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# extract sign
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a2$effect_sign = sign(a2[effect_name][ind])
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}
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#========================
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df2_short = df3[df3$position%in%c(167, 423, 427),]
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df2_short = df3[df3$position%in%c(170, 167, 493, 453, 435, 433, 480, 456, 445),]
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df2_short = df3[df3$position%in%c(435, 480),]
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df2_short = df3[df3$position%in%c(435, 480),]
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give_col=function(x,y,df=df2_short){
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df[df$position==x,y]
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}
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for (i in unique(df2_short$position) ){
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#print(i)
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#print(paste0("\nNo. of unique positions:", length(unique(df2$position))) )
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#cat(length(unique(df2$position)))
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#df2_short[df2_short$position==i,gene_aff_cols]
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biggest = max(abs(give_col(i,gene_aff_cols)))
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df2_short[df2_short$position==i,'abs_max_effect'] = biggest
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df2_short[df2_short$position==i,'effect_type']= names(
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give_col(i,gene_aff_cols)[which(
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abs(
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give_col(i,gene_aff_cols)
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) == biggest, arr.ind=T
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)[, "col"]])
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effect_name = df2_short[df2_short$position==i,'effect_type'][1] # pick first one in case we have multiple exact values
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# get index/rowname for value of max effect, and then use it to get the original sign
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# here
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#df2_short[df2_short$position==i,c(effect_name)]
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#which(abs(df2_short[df2_short$position==i,c('position',effect_name)][effect_name])==biggest, arr.ind=T)
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ind = rownames(which(abs(df2_short[df2_short$position==i,c('position',effect_name)][effect_name])== biggest, arr.ind=T))
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df2_short[df2_short$position==i,'effect_sign'] = sign(df2_short[effect_name][ind,])
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}
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df2_short$effect_type = sub("\\.[0-9]+", "", df2_short$effect_type) # cull duplicate effect types that happen when there are exact duplicate values
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@ -1,241 +0,0 @@
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#source("~/git/LSHTM_analysis/config/pnca.R")
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#source("~/git/LSHTM_analysis/config/alr.R")
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#source("~/git/LSHTM_analysis/config/gid.R")
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#source("~/git/LSHTM_analysis/config/embb.R")
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#source("~/git/LSHTM_analysis/config/katg.R")
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#source("~/git/LSHTM_analysis/config/rpob.R")
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source("/home/tanu/git/LSHTM_analysis/my_header.R")
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#########################################################
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# TASK: Generate averaged affinity values
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# across all affinity tools for a given structure
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# as applicable...
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#########################################################
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#=======
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# 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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#########################################################
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#%% consider stability as well
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df4 = df2
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#=================
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# stability + affinity effect
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#=================
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effect_cols = c(gene_aff_cols, gene_stab_cols)
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give_col=function(x,y,df=df4){
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df[df$position==x,y]
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}
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for (i in unique(df4$position) ){
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#print(i)
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biggest = max(abs(give_col(i,effect_cols)))
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df4[df4$position==i,'abs_max_effect'] = biggest
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df4[df4$position==i,'effect_type']= names(
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give_col(i,effect_cols)[which(
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abs(
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give_col(i,effect_cols)
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) == biggest, arr.ind=T
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)[, "col"]])
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# effect_name = unique(df4[df4$position==i,'effect_type'])
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effect_name = df4[df4$position==i,'effect_type'][1] # pick first one in case we have multiple exact values
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ind = rownames(which(abs(df4[df4$position==i,c('position',effect_name)][effect_name])== biggest, arr.ind=T))
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df4[df4$position==i,'effect_sign'] = sign(df4[effect_name][ind,])
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}
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df4$effect_type = sub("\\.[0-9]+", "", df4$effect_type) # cull duplicate effect types that happen when there are exact duplicate values
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df4U = df4[!duplicated(df4[c('position')]), ]
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table(df4U$effect_type)
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#%%============================================================
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# output
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write.csv(combined_df, outfile_mean_ens_st_aff
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, row.names = F)
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cat("Finished writing file:\n"
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, outfile_mean_ens_st_aff
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, "\nNo. of rows:", nrow(combined_df)
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, "\nNo. of cols:", ncol(combined_df))
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# end of script
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#===============================================================
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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/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 stability values
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# across all stability tools
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# TASK: Generate averaged stability values by position
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# calculated across all stability tools
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# for a given structure
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#########################################################
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@ -23,190 +22,53 @@ print(paste0("Output file:", outfile_mean_ens_st_aff))
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#%%===============================================================
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#=============
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# Input
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# Input: merged_df3
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#=============
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df3_filename = paste0("/home/tanu/git/Data/", drug, "/output/", tolower(gene), "_merged_df3.csv")
|
||||
df3 = read.csv(df3_filename)
|
||||
source("~/git/LSHTM_analysis/scripts/plotting/get_plotting_dfs.R")
|
||||
#merged_df3= paste0("/home/tanu/git/Data/", drug, "/output/", tolower(gene), "_merged_df3.csv")
|
||||
|
||||
cols_to_extract_ms = c("mutationinformation", "position", "avg_stability_scaled")
|
||||
|
||||
df3 = merged_df3[, cols_to_extract_ms]
|
||||
length(df3$mutationinformation)
|
||||
|
||||
# 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 %>%
|
||||
avg_stability_by_position <- df3 %>%
|
||||
dplyr::group_by(position) %>%
|
||||
dplyr::summarize(avg_ens_stability = mean(ens_stability))
|
||||
dplyr::summarize(avg_stability_scaled_pos = mean(avg_stability_scaled))
|
||||
|
||||
# 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'])
|
||||
min(avg_stability_by_position$avg_stability_scaled_pos)
|
||||
max(avg_stability_by_position$avg_stability_scaled_pos)
|
||||
|
||||
# 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']
|
||||
avg_stability_by_position['avg_stability_scaled_pos_scaled'] = lapply(avg_stability_by_position['avg_stability_scaled_pos']
|
||||
, function(x) {
|
||||
scales::rescale(x, to = c(-1,1)
|
||||
scales::rescale_mid(x, to = c(-1,1)
|
||||
#, from = c(en_stab_min,en_stab_max))
|
||||
, mid = 0
|
||||
, from = c(0,1))
|
||||
})
|
||||
cat(paste0('Average stability scores:\n'
|
||||
, head(mean_ens_stability_by_position['avg_ens_stability'])
|
||||
, head(avg_stability_by_position['avg_stability_scaled_pos'])
|
||||
, '\n---------------------------------------------------------------'
|
||||
, '\nAverage stability scaled scores:\n'
|
||||
, head(mean_ens_stability_by_position['avg_ens_stability_scaled'])))
|
||||
, head(avg_stability_by_position['avg_stability_scaled_pos_scaled'])
|
||||
))
|
||||
|
||||
all(avg_stability_by_position['avg_stability_scaled_pos'] == avg_stability_by_position['avg_stability_scaled_pos_scaled'])
|
||||
|
||||
# convert to a data frame
|
||||
mean_ens_stability_by_position = as.data.frame(mean_ens_stability_by_position)
|
||||
avg_stability_by_position = as.data.frame(avg_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
|
||||
write.csv(avg_stability_by_position
|
||||
, outfile_mean_ens_st_aff
|
||||
, row.names = F)
|
||||
cat("Finished writing file:\n"
|
||||
, outfile_mean_ens_st_aff
|
||||
, "\nNo. of rows:", nrow(mean_ens_stability_by_position)
|
||||
, "\nNo. of cols:", ncol(mean_ens_stability_by_position))
|
||||
, "\nNo. of rows:", nrow(avg_stability_by_position)
|
||||
, "\nNo. of cols:", ncol(avg_stability_by_position))
|
||||
|
||||
# end of script
|
||||
#===============================================================
|
||||
|
|
|
|||
|
|
@ -1,5 +1,11 @@
|
|||
#!/usr/bin/env Rscript
|
||||
|
||||
|
||||
#source("~/git/LSHTM_analysis/config/alr.R")
|
||||
source("~/git/LSHTM_analysis/config/embb.R")
|
||||
#source("~/git/LSHTM_analysis/config/katg.R")
|
||||
#source("~/git/LSHTM_analysis/config/gid.R")
|
||||
#source("~/git/LSHTM_analysis/config/pnca.R")
|
||||
#source("~/git/LSHTM_analysis/config/rpob.R")
|
||||
#########################################################
|
||||
# TASK: Replace B-factors in the pdb file with the mean
|
||||
# normalised stability values.
|
||||
|
|
@ -22,25 +28,26 @@ cat(c(getwd(),"\n"))
|
|||
library(bio3d)
|
||||
require("getopt", quietly = TRUE) # cmd parse arguments
|
||||
#========================================================
|
||||
#drug = "pyrazinamide"
|
||||
#gene = "pncA"
|
||||
#drug = ""
|
||||
#gene = ""
|
||||
|
||||
# command line args
|
||||
spec = matrix(c(
|
||||
"drug" , "d", 1, "character",
|
||||
"gene" , "g", 1, "character"
|
||||
), byrow = TRUE, ncol = 4)
|
||||
|
||||
opt = getopt(spec)
|
||||
|
||||
drug = opt$drug
|
||||
gene = opt$gene
|
||||
|
||||
if(is.null(drug)|is.null(gene)) {
|
||||
stop("Missing arguments: --drug and --gene must both be specified (case-sensitive)")
|
||||
}
|
||||
# # command line args
|
||||
# spec = matrix(c(
|
||||
# "drug" , "d", 1, "character",
|
||||
# "gene" , "g", 1, "character"
|
||||
# ), byrow = TRUE, ncol = 4)
|
||||
#
|
||||
# opt = getopt(spec)
|
||||
#
|
||||
# drug = opt$drug
|
||||
# gene = opt$gene
|
||||
#
|
||||
# if(is.null(drug)|is.null(gene)) {
|
||||
# stop("Missing arguments: --drug and --gene must both be specified (case-sensitive)")
|
||||
# }
|
||||
#========================================================
|
||||
gene_match = paste0(gene,"_p.")
|
||||
cat(gene)
|
||||
gene_match = paste0(gene,"_p."); cat(gene_match)
|
||||
cat(gene_match)
|
||||
|
||||
#=============
|
||||
|
|
@ -64,7 +71,6 @@ cat(paste0("Input file:", infile_pdb) )
|
|||
|
||||
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) )
|
||||
|
||||
#=======
|
||||
|
|
@ -150,12 +156,12 @@ plot(density(df_duet$b)
|
|||
#=============
|
||||
|
||||
#hist(my_df$averaged_duet
|
||||
hist(my_df$avg_ens_stability_scaled
|
||||
hist(my_df$avg_stability_scaled_pos_scaled
|
||||
, xlab = ""
|
||||
, main = "mean stability values")
|
||||
|
||||
#plot(density(my_df$averaged_duet)
|
||||
plot(density(my_df$avg_ens_stability_scaled)
|
||||
plot(density(my_df$avg_stability_scaled_pos_scaled)
|
||||
, xlab = ""
|
||||
, main = "mean stability values")
|
||||
|
||||
|
|
@ -178,7 +184,7 @@ plot(density(my_df$avg_ens_stability_scaled)
|
|||
# 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)]
|
||||
df_duet$b = my_df$avg_stability_scaled_pos_scaled[match(df_duet$resno, my_df$position)]
|
||||
|
||||
#=========
|
||||
# step 2_P1
|
||||
|
|
@ -194,8 +200,8 @@ 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) ) {
|
||||
# # duet
|
||||
# if ( (sum(df_duet$b == na_rep) == b_na_duet) {
|
||||
# print ("PASS: NA's replaced with 0s successfully in df_duet and df_lig")
|
||||
# } else {
|
||||
# print("FAIL: NA replacement in df_duet NOT successful")
|
||||
|
|
@ -205,20 +211,13 @@ df_duet$b[is.na(df_duet$b)] = na_rep
|
|||
# max(df_duet$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$avg_ens_stability_scaled)) ){
|
||||
# if( (max(df_duet$b) == max(my_df$avg_stability_scaled_pos_scaled)) & (min(df_duet$b) == min(my_df$avg_stability_scaled_pos_scaled)) ){
|
||||
# print("PASS: B-factors replaced correctly in df_duet")
|
||||
# } else {
|
||||
# print ("FAIL: To replace B-factors in df_duet")
|
||||
# quit()
|
||||
# }
|
||||
|
||||
# if( (max(df_lig$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
|
||||
#=========
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue