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120
scripts/plotting/AFFINITY_TEST.R
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120
scripts/plotting/AFFINITY_TEST.R
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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$effect_sign = sign(a2[effect_name][ind])
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}
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#========================
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df2_short = df2[df2$position%in%c(167, 423, 427),]
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df2_short = df2[df2$position%in%c(170, 167, 493, 453, 435, 433, 480, 456, 445),]
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df2_short = df2[df2$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$position==i,'effect_type']= names(
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give_col(i,gene_aff_cols)[which(
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abs(
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give_col(i,gene_aff_cols)
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) == biggest, arr.ind=T
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)[, "col"]])
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effect_name = unique(df2_short[df2_short$position==i,'effect_type'])
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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$position==i
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,c('position',effect_name)][effect_name])== biggest
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, arr.ind=T))
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df2_short[df2_short$position==i,'effect_sign'] = sign(df2_short[effect_name][ind,])
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}
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223
scripts/plotting/mcsm_affinity_data_only.R
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scripts/plotting/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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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"
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, "ligand_distance"
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, "interface_dist")
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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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######################################################################
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cols_to_consider = colnames(df3)[colnames(df3)%in%c(common_cols
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, raw_cols_affinity
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, scaled_cols_affinity
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, outcome_cols_affinity
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, raw_cols_stability
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, scaled_cols_stability
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, outcome_cols_stability
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)]
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cols_to_extract = cols_to_consider[cols_to_consider%in%c(common_cols
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#, raw_cols_affinity
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, scaled_cols_stability
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, scaled_cols_affinity)]
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df3_plot = df3[, cols_to_extract]
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# FIXME: ADD distance to NA when SP replies
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DistCutOff_colnames = c("ligand_distance", "interface_dist")
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DistCutOff = 10
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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$position)
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length(c0u)
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foo = df3_affinity_filtered[df3_affinity_filtered$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$sensitivity)
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##############################################################
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df = df3_affinity_filtered
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sum(is.na(df))
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df2 = na.omit(df) # Apply na.omit function
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gene_dist_cols = colnames(df2)[colnames(df2)%in%DistCutOff_colnames]
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gene_aff_cols = colnames(df2)[colnames(df2)%in%scaled_cols_affinity]
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gene_stab_cols = colnames(df2)[colnames(df2)%in%scaled_cols_stability]
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sel_cols = c("mutationinformation", "position", gene_stab_cols, gene_dist_cols, gene_aff_cols)
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df3 = df2[, sel_cols]
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give_col=function(x,y,df=df3){
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df[df$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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# 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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#df3[df3$position==i,c(effect_name)]
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#which(abs(df3[df3$position==i,c('position',effect_name)][effect_name])==biggest, arr.ind=T)
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ind = rownames(which(abs(df3[df3$position==i
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,c('position', effect_name)][effect_name])== biggest
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, arr.ind=T))
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df3[df3$position==i,'effect_sign'] = sign(df3[effect_name][ind,])
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}
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#%%============================================================
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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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364
scripts/plotting/plotting_thesis/alr/basic_barplots_alr.R
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364
scripts/plotting/plotting_thesis/alr/basic_barplots_alr.R
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#!/usr/bin/env Rscript
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#########################################################
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# TASK: Barplots
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# basic barplots with outcome
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# basic barplots with frequency of count of mutations
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#########################################################
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#=============
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# Data: Input
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#==============
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#source("~/git/LSHTM_analysis/config/katg.R")
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#source("~/git/LSHTM_analysis/scripts/plotting/get_plotting_dfs.R")
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#cat("\nSourced plotting cols as well:", length(plotting_cols))
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####################################################
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class(merged_df3)
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df3 = subset(merged_df3, select = -c(pos_count))
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#=======
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# output
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#=======
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outdir_images = paste0("~/git/Writing/thesis/images/results/", tolower(gene), "/")
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cat("plots will output to:", outdir_images)
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##########################################################
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# blue, red bp
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sts = 8
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lts = 8
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ats = 8
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als = 8
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ltis = 8
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geom_ls = 2.2
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#pos_count
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subtitle_size = 8
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geom_ls_pc = 2.2
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leg_text_size = 8
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axis_text_size = 8
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axis_label_size = 8
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###########################################################
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#------------------------------
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# plot default sizes
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#------------------------------
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#=========================
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# Affinity outcome
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# check this var: outcome_cols_affinity
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# get from preformatting or put in globals
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#==========================
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DistCutOff
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LigDist_colname # = "ligand_distance" # from globals
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ppi2Dist_colname
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naDist_colname
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###########################################################
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# get plotting data within the distance
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df3_lig = df3[df3[[LigDist_colname]]<DistCutOff,]
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df3_ppi2 = df3[df3[[ppi2Dist_colname]]<DistCutOff,]
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df3_na = df3[df3[[naDist_colname]]<DistCutOff,]
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common_bp_title = paste0("Sites <", DistCutOff, angstroms_symbol)
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#------------------------------
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# barplot for ligand affinity:
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# <10 Ang of ligand
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#------------------------------
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mLigP = stability_count_bp(plotdf = df3_lig
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, df_colname = "ligand_outcome"
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#, leg_title = "mCSM-lig"
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#, bp_plot_title = paste(common_bp_title, "ligand")
|
||||
, yaxis_title = "Number of nsSNPs"
|
||||
, leg_position = "none"
|
||||
, subtitle_text = "mCSM\nLig"
|
||||
, bar_fill_values = c("#F8766D", "#00BFC4")
|
||||
, subtitle_colour= "black"
|
||||
, sts = sts
|
||||
, lts = lts
|
||||
, ats = ats
|
||||
, als = als
|
||||
, ltis = ltis
|
||||
, geom_ls = geom_ls
|
||||
)
|
||||
mLigP
|
||||
#------------------------------
|
||||
# barplot for ligand affinity:
|
||||
# <10 Ang of ligand
|
||||
# mmCSM-lig: will be the same no. of sites but the effect will be different
|
||||
#------------------------------
|
||||
mmLigP = stability_count_bp(plotdf = df3_lig
|
||||
, df_colname = "mmcsm_lig_outcome"
|
||||
#, leg_title = "mmCSM-lig"
|
||||
#, label_categories = labels_mmlig
|
||||
#, bp_plot_title = paste(common_bp_title, "ligand")
|
||||
|
||||
, yaxis_title = ""
|
||||
, leg_position = "none"
|
||||
, subtitle_text = "mmCSM\nLig"
|
||||
, bar_fill_values = c("#F8766D", "#00BFC4")
|
||||
, subtitle_colour= "black"
|
||||
, sts = sts
|
||||
, lts = lts
|
||||
, ats = ats
|
||||
, als = als
|
||||
, ltis = ltis
|
||||
, geom_ls = geom_ls
|
||||
)
|
||||
mmLigP
|
||||
#------------------------------
|
||||
# barplot for ppi2 affinity
|
||||
# <10 Ang of interface
|
||||
#------------------------------
|
||||
if (tolower(gene)%in%geneL_ppi2){
|
||||
ppi2P = stability_count_bp(plotdf = df3_ppi2
|
||||
, df_colname = "mcsm_ppi2_outcome"
|
||||
#, leg_title = "mCSM-ppi2"
|
||||
#, label_categories = labels_ppi2
|
||||
#, bp_plot_title = paste(common_bp_title, "PP-interface")
|
||||
|
||||
, yaxis_title = "Number of nsSNPs"
|
||||
, leg_position = "none"
|
||||
, subtitle_text = "mCSM\nPPI2"
|
||||
, bar_fill_values = c("#F8766D", "#00BFC4")
|
||||
, subtitle_colour= "black"
|
||||
, sts = sts
|
||||
, lts = lts
|
||||
, ats = ats
|
||||
, als = als
|
||||
, ltis = ltis
|
||||
, geom_ls = geom_ls
|
||||
)
|
||||
ppi2P
|
||||
}
|
||||
#----------------------------
|
||||
# barplot for ppi2 affinity
|
||||
# <10 Ang of interface
|
||||
#------------------------------
|
||||
if (tolower(gene)%in%geneL_na){
|
||||
nca_distP = stability_count_bp(plotdf = df3_na
|
||||
, df_colname = "mcsm_na_outcome"
|
||||
#, leg_title = "mCSM-NA"
|
||||
#, label_categories =
|
||||
#, bp_plot_title = paste(common_bp_title, "Dist to NA")
|
||||
|
||||
, yaxis_title = "Number of nsSNPs"
|
||||
, leg_position = "none"
|
||||
, subtitle_text = "mCSM\nNA"
|
||||
, bar_fill_values = c("#F8766D", "#00BFC4")
|
||||
, subtitle_colour= "black"
|
||||
, sts = sts
|
||||
, lts = lts
|
||||
, ats = ats
|
||||
, als = als
|
||||
, ltis = ltis
|
||||
, geom_ls = geom_ls
|
||||
)
|
||||
nca_distP
|
||||
}
|
||||
|
||||
#####################################################################
|
||||
# ------------------------------
|
||||
# bp site site count: mCSM-lig
|
||||
# < 10 Ang ligand
|
||||
# ------------------------------
|
||||
common_bp_title = paste0("Sites <", DistCutOff, angstroms_symbol)
|
||||
|
||||
posC_lig = site_snp_count_bp(plotdf = df3_lig
|
||||
, df_colname = "position"
|
||||
, xaxis_title = "Number of nsSNPs"
|
||||
, yaxis_title = "Number of Sites"
|
||||
, subtitle_colour = "chocolate4"
|
||||
, subtitle_text = ""
|
||||
, subtitle_size = subtitle_size
|
||||
, geom_ls = geom_ls_pc
|
||||
, leg_text_size = leg_text_size
|
||||
, axis_text_size = axis_text_size
|
||||
, axis_label_size = axis_label_size)
|
||||
|
||||
posC_lig
|
||||
#------------------------------
|
||||
# bp site site count: ppi2
|
||||
# < 10 Ang interface
|
||||
#------------------------------
|
||||
if (tolower(gene)%in%geneL_ppi2){
|
||||
posC_ppi2 = site_snp_count_bp(plotdf = df3_ppi2
|
||||
, df_colname = "position"
|
||||
, xaxis_title = "Number of nsSNPs"
|
||||
, yaxis_title = "Number of Sites"
|
||||
, subtitle_colour = "chocolate4"
|
||||
, subtitle_text = ""
|
||||
, subtitle_size = subtitle_size
|
||||
, geom_ls = geom_ls_pc
|
||||
, leg_text_size = leg_text_size
|
||||
, axis_text_size = axis_text_size
|
||||
, axis_label_size = axis_label_size)
|
||||
posC_ppi2
|
||||
}
|
||||
|
||||
#------------------------------
|
||||
# bp site site count: NCA dist
|
||||
# < 10 Ang nca
|
||||
#------------------------------
|
||||
if (tolower(gene)%in%geneL_na){
|
||||
posC_nca = site_snp_count_bp(plotdf = df3_na
|
||||
, df_colname = "position"
|
||||
, xaxis_title = "Number of nsSNPs"
|
||||
, yaxis_title = "Number of Sites"
|
||||
, subtitle_colour = "chocolate4"
|
||||
, subtitle_text = ""
|
||||
, subtitle_size = subtitle_size
|
||||
, geom_ls = geom_ls_pc
|
||||
, leg_text_size = leg_text_size
|
||||
, axis_text_size = axis_text_size
|
||||
, axis_label_size = axis_label_size)
|
||||
posC_nca
|
||||
}
|
||||
#===============================================================
|
||||
#------------------------------
|
||||
# bp site site count: ALL
|
||||
# <10 Ang ligand
|
||||
#------------------------------
|
||||
posC_all = site_snp_count_bp(plotdf = df3
|
||||
, df_colname = "position"
|
||||
, xaxis_title = "Number of nsSNPs"
|
||||
, yaxis_title = "Number of Sites"
|
||||
, subtitle_colour = "chocolate4"
|
||||
, subtitle_text = "All mutations sites"
|
||||
, subtitle_size = subtitle_size
|
||||
, geom_ls = geom_ls_pc
|
||||
, leg_text_size = leg_text_size
|
||||
, axis_text_size = axis_text_size
|
||||
, axis_label_size = axis_label_size)
|
||||
posC_all
|
||||
##################################################################
|
||||
consurfP = stability_count_bp(plotdf = df3
|
||||
, df_colname = "consurf_outcome"
|
||||
#, leg_title = "ConSurf"
|
||||
#, label_categories = labels_consurf
|
||||
, yaxis_title = "Number of nsSNPs"
|
||||
, leg_position = "top"
|
||||
, subtitle_text = "ConSurf"
|
||||
, bar_fill_values = consurf_colours # from globals
|
||||
, subtitle_colour= "black"
|
||||
, sts = sts
|
||||
, lts = lts
|
||||
, ats = ats
|
||||
, als = als
|
||||
, ltis = ltis
|
||||
, geom_ls = geom_ls)
|
||||
|
||||
consurfP
|
||||
|
||||
##############################################################
|
||||
sts_so = 10
|
||||
lts_so = 10
|
||||
ats_so = 10
|
||||
als_so = 10
|
||||
ltis_so = 10
|
||||
geom_ls_so = 2.5
|
||||
#===================
|
||||
# Stability
|
||||
#===================
|
||||
# duetP
|
||||
duetP = stability_count_bp(plotdf = df3
|
||||
, df_colname = "duet_outcome"
|
||||
, leg_title = "mCSM-DUET"
|
||||
#, label_categories = labels_duet
|
||||
, yaxis_title = "Number of nsSNPs"
|
||||
, leg_position = "none"
|
||||
, subtitle_text = "mCSM-DUET"
|
||||
, bar_fill_values = c("#F8766D", "#00BFC4")
|
||||
, subtitle_colour= "black"
|
||||
, sts = sts_so
|
||||
, lts = lts_so
|
||||
, ats = ats_so
|
||||
, als = als_so
|
||||
, ltis = ltis_so
|
||||
, geom_ls = geom_ls_so)
|
||||
duetP
|
||||
|
||||
# foldx
|
||||
foldxP = stability_count_bp(plotdf = df3
|
||||
, df_colname = "foldx_outcome"
|
||||
#, leg_title = "FoldX"
|
||||
#, label_categories = labels_foldx
|
||||
, yaxis_title = ""
|
||||
, leg_position = "none"
|
||||
, subtitle_text = "FoldX"
|
||||
, bar_fill_values = c("#F8766D", "#00BFC4")
|
||||
, sts = sts_so
|
||||
, lts = lts_so
|
||||
, ats = ats_so
|
||||
, als = als_so
|
||||
, ltis = ltis_so
|
||||
, geom_ls = geom_ls_so)
|
||||
foldxP
|
||||
|
||||
# deepddg
|
||||
deepddgP = stability_count_bp(plotdf = df3
|
||||
, df_colname = "deepddg_outcome"
|
||||
#, leg_title = "DeepDDG"
|
||||
#, label_categories = labels_deepddg
|
||||
, yaxis_title = ""
|
||||
, leg_position = "none"
|
||||
, subtitle_text = "DeepDDG"
|
||||
, bar_fill_values = c("#F8766D", "#00BFC4")
|
||||
, sts = sts_so
|
||||
, lts = lts_so
|
||||
, ats = ats_so
|
||||
, als = als_so
|
||||
, ltis = ltis_so
|
||||
, geom_ls = geom_ls_so)
|
||||
deepddgP
|
||||
|
||||
# deepddg
|
||||
dynamut2P = stability_count_bp(plotdf = df3
|
||||
, df_colname = "ddg_dynamut2_outcome"
|
||||
#, leg_title = "Dynamut2"
|
||||
#, label_categories = labels_ddg_dynamut2_outcome
|
||||
, yaxis_title = ""
|
||||
, leg_position = "none"
|
||||
, subtitle_text = "Dynamut2"
|
||||
, bar_fill_values = c("#F8766D", "#00BFC4")
|
||||
, sts = sts_so
|
||||
, lts = lts_so
|
||||
, ats = ats_so
|
||||
, als = als_so
|
||||
, ltis = ltis_so
|
||||
, geom_ls = geom_ls_so)
|
||||
dynamut2P
|
||||
|
||||
# provean
|
||||
proveanP = stability_count_bp(plotdf = df3
|
||||
, df_colname = "provean_outcome"
|
||||
#, leg_title = "PROVEAN"
|
||||
#, label_categories = labels_provean
|
||||
, yaxis_title = "Number of nsSNPs"
|
||||
, leg_position = "none" # top
|
||||
, subtitle_text = "PROVEAN"
|
||||
, bar_fill_values = c("#D01C8B", "#F1B6DA") # light pink and deep
|
||||
, sts = sts_so
|
||||
, lts = lts_so
|
||||
, ats = ats_so
|
||||
, als = als_so
|
||||
, ltis = ltis_so
|
||||
, geom_ls = geom_ls_so)
|
||||
proveanP
|
||||
|
||||
# snap2
|
||||
snap2P = stability_count_bp(plotdf = df3
|
||||
, df_colname = "snap2_outcome"
|
||||
#, leg_title = "SNAP2"
|
||||
#, label_categories = labels_snap2
|
||||
, yaxis_title = ""
|
||||
, leg_position = "none" # top
|
||||
, subtitle_text = "SNAP2"
|
||||
, bar_fill_values = c("#D01C8B", "#F1B6DA") # light pink and deep
|
||||
, sts = sts_so
|
||||
, lts = lts_so
|
||||
, ats = ats_so
|
||||
, als = als_so
|
||||
, ltis = ltis_so
|
||||
, geom_ls = geom_ls_so)
|
||||
snap2P
|
||||
#####################################################################################
|
||||
310
scripts/plotting/plotting_thesis/alr/basic_barplots_alr_layout.R
Normal file
310
scripts/plotting/plotting_thesis/alr/basic_barplots_alr_layout.R
Normal file
|
|
@ -0,0 +1,310 @@
|
|||
#=============
|
||||
# Data: Input
|
||||
#==============
|
||||
source("~/git/LSHTM_analysis/config/alr.R")
|
||||
source("~/git/LSHTM_analysis/scripts/plotting/get_plotting_dfs.R")
|
||||
|
||||
source("/home/tanu/git/LSHTM_analysis/scripts/plotting/plotting_thesis/alr/basic_barplots_alr.R")
|
||||
source("/home/tanu/git/LSHTM_analysis/scripts/plotting/plotting_thesis/alr/pe_sens_site_count_alr.R")
|
||||
|
||||
if ( tolower(gene)%in%c("alr") ){
|
||||
cat("\nPlots available for layout are:")
|
||||
|
||||
duetP
|
||||
foldxP
|
||||
deepddgP
|
||||
dynamut2P
|
||||
proveanP
|
||||
snap2P
|
||||
|
||||
mLigP
|
||||
mmLigP
|
||||
posC_lig
|
||||
|
||||
ppi2P
|
||||
posC_ppi2
|
||||
|
||||
peP2
|
||||
sens_siteP
|
||||
peP # not used
|
||||
sensP # not used
|
||||
}
|
||||
|
||||
|
||||
#========================
|
||||
# Common title settings
|
||||
#=========================
|
||||
theme_georgia <- function(...) {
|
||||
theme_gray(base_family = "sans", ...) +
|
||||
theme(plot.title = element_text(face = "bold"))
|
||||
}
|
||||
title_theme <- calc_element("plot.title", theme_georgia())
|
||||
|
||||
###############################################################
|
||||
common_bp_title = paste0("Sites <", DistCutOff, angstroms_symbol)
|
||||
|
||||
# extract common legends
|
||||
# lig affinity
|
||||
common_legend_outcome = get_legend(mLigP +
|
||||
guides(color = guide_legend(nrow = 1)) +
|
||||
theme(legend.position = "top"))
|
||||
|
||||
# stability
|
||||
common_legend_outcome = get_legend(duetP +
|
||||
guides(color = guide_legend(nrow = 1)) +
|
||||
theme(legend.position = "top"))
|
||||
# conservation
|
||||
cons_common_legend_outcome = get_legend(snap2P +
|
||||
guides(color = guide_legend(nrow = 1)) +
|
||||
theme(legend.position = "top"))
|
||||
###################################################################
|
||||
#==================================
|
||||
# Stability+Conservation: COMBINE
|
||||
#==================================
|
||||
tt_size = 10
|
||||
#----------------------------
|
||||
# stability and consv title
|
||||
#----------------------------
|
||||
tt_stab = ggdraw() +
|
||||
draw_label(
|
||||
paste0("Stability outcome"),
|
||||
fontfamily = title_theme$family,
|
||||
fontface = title_theme$face,
|
||||
#size = title_theme$size
|
||||
size = tt_size
|
||||
)
|
||||
|
||||
tt_cons = ggdraw() +
|
||||
draw_label(
|
||||
paste0("Conservation outcome"),
|
||||
fontfamily = title_theme$family,
|
||||
fontface = title_theme$face,
|
||||
size = tt_size
|
||||
)
|
||||
|
||||
#----------------------
|
||||
# Output plot
|
||||
#-----------------------
|
||||
stab_cons_CLP = paste0(outdir_images
|
||||
,tolower(gene)
|
||||
,"_stab_cons_BP_CLP.png")
|
||||
|
||||
print(paste0("plot filename:", stab_cons_CLP))
|
||||
png(stab_cons_CLP, units = "in", width = 10, height = 5, res = 300 )
|
||||
|
||||
cowplot::plot_grid(
|
||||
cowplot::plot_grid(
|
||||
cowplot::plot_grid(
|
||||
tt_stab,
|
||||
common_legend_outcome,
|
||||
nrow = 2
|
||||
),
|
||||
cowplot::plot_grid(
|
||||
duetP,
|
||||
foldxP,
|
||||
deepddgP,
|
||||
dynamut2P,
|
||||
nrow = 1,
|
||||
labels = c("A", "B", "C", "D"),
|
||||
label_size = 12),
|
||||
nrow = 2,
|
||||
rel_heights=c(1,10)
|
||||
),
|
||||
NULL,
|
||||
cowplot::plot_grid(
|
||||
cowplot::plot_grid(
|
||||
cowplot::plot_grid(
|
||||
tt_cons,
|
||||
cons_common_legend_outcome,
|
||||
nrow = 2
|
||||
),
|
||||
cowplot::plot_grid(
|
||||
proveanP,
|
||||
snap2P,
|
||||
nrow=1,
|
||||
labels = c("E", "F"),
|
||||
align = "hv"),
|
||||
nrow = 2,
|
||||
rel_heights = c(1, 10),
|
||||
label_size = 12),
|
||||
nrow=1
|
||||
),
|
||||
rel_widths = c(2,0.15,1),
|
||||
nrow=1
|
||||
)
|
||||
|
||||
dev.off()
|
||||
|
||||
#################################################################
|
||||
#=======================================
|
||||
# Affinity barplots: COMBINE ALL four
|
||||
#========================================
|
||||
ligT = paste0(common_bp_title, " ligand")
|
||||
lig_affT = ggdraw() +
|
||||
draw_label(
|
||||
ligT,
|
||||
fontfamily = title_theme$family,
|
||||
fontface = title_theme$face,
|
||||
#size = title_theme$size
|
||||
size = 8
|
||||
)
|
||||
|
||||
p1 = cowplot::plot_grid(cowplot::plot_grid(lig_affT
|
||||
, common_legend_outcome
|
||||
, nrow=2),
|
||||
cowplot::plot_grid(mLigP, mmLigP, posC_lig
|
||||
, nrow = 1
|
||||
, rel_widths = c(1,0.65,1.8)
|
||||
, align = "h"),
|
||||
nrow = 2,
|
||||
rel_heights = c(1,8)
|
||||
|
||||
)
|
||||
p1
|
||||
|
||||
###########################################################
|
||||
ppi2T = paste0(common_bp_title, " PP-interface")
|
||||
ppi2_affT = ggdraw() +
|
||||
draw_label(
|
||||
ppi2T,
|
||||
fontfamily = title_theme$family,
|
||||
fontface = title_theme$face,
|
||||
size = 8
|
||||
)
|
||||
|
||||
p3 = cowplot::plot_grid(cowplot::plot_grid(ppi2_affT, common_legend_outcome, nrow=2),
|
||||
cowplot::plot_grid(ppi2P, posC_ppi2
|
||||
, nrow = 1
|
||||
, rel_widths = c(1,1.9)
|
||||
, align = "h"),
|
||||
nrow = 2,
|
||||
rel_heights = c(1,8)
|
||||
)
|
||||
p3
|
||||
|
||||
# PE + All position count
|
||||
peT_allT = ggdraw() +
|
||||
draw_label(
|
||||
paste0("All mutation sites"),
|
||||
fontfamily = title_theme$family,
|
||||
fontface = title_theme$face,
|
||||
#size = title_theme$size
|
||||
size = 8
|
||||
)
|
||||
|
||||
p4 = cowplot::plot_grid(cowplot::plot_grid(peT_allT, nrow = 2
|
||||
, rel_widths = c(1,3),axis = "lr"),
|
||||
cowplot::plot_grid(
|
||||
peP2, posC_all,
|
||||
nrow = 2,
|
||||
rel_widths = c(1,1),
|
||||
align = "v",
|
||||
axis = "lr",
|
||||
rel_heights = c(1,8)
|
||||
),
|
||||
rel_heights = c(1,18),
|
||||
nrow = 2,axis = "lr")
|
||||
p4
|
||||
|
||||
|
||||
#### Combine p1+p3+p4 ####
|
||||
w = 11.79
|
||||
h = 3.5
|
||||
mut_impact_CLP = paste0(outdir_images
|
||||
,tolower(gene)
|
||||
,"_mut_impactCLP.png")
|
||||
|
||||
#svg(affP, width = 20, height = 5.5)
|
||||
print(paste0("plot filename:", mut_impact_CLP))
|
||||
png(mut_impact_CLP, units = "in", width = w, height = h, res = 300 )
|
||||
|
||||
cowplot::plot_grid(p1,
|
||||
p3,
|
||||
p4,
|
||||
nrow = 1,
|
||||
labels = "AUTO",
|
||||
label_size = 12,
|
||||
rel_widths = c(2.5,2,2)
|
||||
#, rel_heights = c(1)
|
||||
)
|
||||
|
||||
dev.off()
|
||||
w = 11.79
|
||||
h = 3.5
|
||||
mut_impact_CLP = paste0(outdir_images
|
||||
,tolower(gene)
|
||||
,"_mut_impactCLP.png")
|
||||
|
||||
#svg(affP, width = 20, height = 5.5)
|
||||
print(paste0("plot filename:", mut_impact_CLP))
|
||||
png(mut_impact_CLP, units = "in", width = w, height = h, res = 300 )
|
||||
|
||||
cowplot::plot_grid(p1,
|
||||
p3,
|
||||
p4,
|
||||
nrow = 1,
|
||||
labels = "AUTO",
|
||||
label_size = 12,
|
||||
rel_widths = c(2.5,2,2)
|
||||
#, rel_heights = c(1)
|
||||
)
|
||||
|
||||
dev.off()
|
||||
|
||||
##################################################
|
||||
sensP
|
||||
consurfP
|
||||
#=================
|
||||
#### Combine sensitivity + ConSurf ####
|
||||
# or ConSurf
|
||||
#=================
|
||||
w = 3
|
||||
h = 3
|
||||
# sens_conP = paste0(outdir_images
|
||||
# ,tolower(gene)
|
||||
# ,"_sens_cons_CLP.png")
|
||||
#
|
||||
# print(paste0("plot filename:", sens_conP))
|
||||
# png(sens_conP, units = "in", width = w, height = h, res = 300 )
|
||||
#
|
||||
# cowplot::plot_grid(sensP, consurfP,
|
||||
# nrow = 2,
|
||||
# rel_heights = c(1, 1.5)
|
||||
# )
|
||||
#
|
||||
# dev.off()
|
||||
|
||||
conCLP = paste0(outdir_images
|
||||
,tolower(gene)
|
||||
,"_consurf_BP.png")
|
||||
|
||||
print(paste0("plot filename:", conCLP))
|
||||
png(conCLP, units = "in", width = w, height = h, res = 300 )
|
||||
consurfP
|
||||
|
||||
dev.off()
|
||||
#================================
|
||||
# Sensitivity mutation numbers: geom_tile
|
||||
#================================
|
||||
sensCLP = paste0(outdir_images
|
||||
,tolower(gene)
|
||||
,"_sensN_tile.png")
|
||||
|
||||
print(paste0("plot filename:", sensCLP))
|
||||
png(sensCLP, units = "in", width = 1, height = 1, res = 300 )
|
||||
sensP
|
||||
dev.off()
|
||||
#================================
|
||||
# Sensitivity SITE numbers: geom_tile
|
||||
#================================
|
||||
sens_siteCLP = paste0(outdir_images
|
||||
,tolower(gene)
|
||||
,"_sens_siteC_tile.png")
|
||||
|
||||
print(paste0("plot filename:", sens_siteCLP))
|
||||
png(sens_siteCLP, units = "in", width = 1.2, height = 1, res = 300 )
|
||||
sens_siteP
|
||||
dev.off()
|
||||
|
||||
###########################################################
|
||||
|
||||
|
|
@ -1,164 +1,177 @@
|
|||
# source dm_om_plots.R
|
||||
#============
|
||||
# Plot labels
|
||||
#============
|
||||
tit1 = "Stability changes"
|
||||
tit2 = "Genomic measure"
|
||||
tit3 = "Distance to partners"
|
||||
tit4 = "Evolutionary Conservation"
|
||||
tit5 = "Affinity changes"
|
||||
pt_size = 30
|
||||
|
||||
theme_georgia <- function(...) {
|
||||
theme_gray(base_family = "sans", ...) +
|
||||
theme(plot.title = element_text(face = "bold"))
|
||||
}
|
||||
|
||||
|
||||
title_theme <- calc_element("plot.title", theme_georgia())
|
||||
|
||||
pt1 = ggdraw() +
|
||||
draw_label(
|
||||
tit1,
|
||||
fontfamily = title_theme$family,
|
||||
fontface = title_theme$face,
|
||||
#size = title_theme$size
|
||||
size = pt_size
|
||||
)
|
||||
|
||||
pt2 = ggdraw() +
|
||||
draw_label(
|
||||
tit2,
|
||||
fontfamily = title_theme$family,
|
||||
fontface = title_theme$face,
|
||||
size = pt_size
|
||||
)
|
||||
|
||||
pt3 = ggdraw() +
|
||||
draw_label(
|
||||
tit3,
|
||||
fontfamily = title_theme$family,
|
||||
fontface = title_theme$face,
|
||||
size = pt_size
|
||||
)
|
||||
|
||||
pt4 = ggdraw() +
|
||||
draw_label(
|
||||
tit4,
|
||||
fontfamily = title_theme$family,
|
||||
fontface = title_theme$face,
|
||||
size = pt_size
|
||||
)
|
||||
|
||||
|
||||
pt5 = ggdraw() +
|
||||
draw_label(
|
||||
tit5,
|
||||
fontfamily = title_theme$family,
|
||||
fontface = title_theme$face,
|
||||
size = pt_size
|
||||
)
|
||||
|
||||
#======================
|
||||
# Output plot function
|
||||
#======================
|
||||
OutPlot_dm_om = function(x){
|
||||
|
||||
# dist b/w plot title and plot
|
||||
relH_tp = c(0.08, 0.92)
|
||||
|
||||
my_label_size = 25
|
||||
#----------------
|
||||
# Top panel
|
||||
#----------------
|
||||
top_panel = cowplot::plot_grid(
|
||||
cowplot::plot_grid(pt1,
|
||||
cowplot::plot_grid(duetP, foldxP, deepddgP, dynamut2P
|
||||
, nrow = 1
|
||||
, labels = c("A", "B", "C", "D")
|
||||
, label_size = my_label_size)
|
||||
, ncol = 1
|
||||
, rel_heights = relH_tp
|
||||
),
|
||||
NULL,
|
||||
cowplot::plot_grid(pt2,
|
||||
cowplot::plot_grid(genomicsP
|
||||
, nrow = 1
|
||||
, labels = c("E")
|
||||
, label_size = my_label_size)
|
||||
, ncol = 1
|
||||
, rel_heights = relH_tp
|
||||
),
|
||||
NULL,
|
||||
cowplot::plot_grid(pt3,
|
||||
cowplot::plot_grid( #distanceP
|
||||
distanceP_lig
|
||||
, distanceP_ppi2
|
||||
#, distanceP_na
|
||||
, nrow = 1
|
||||
, labels = c("F", "G")
|
||||
, label_size = my_label_size)
|
||||
, ncol = 1
|
||||
, rel_heights = relH_tp
|
||||
),
|
||||
nrow = 1,
|
||||
rel_widths = c(2/7, 0.1/7, 0.5/7, 0.1/7, 1/7)
|
||||
)
|
||||
|
||||
#----------------
|
||||
# Bottom panel
|
||||
#----------------
|
||||
bottom_panel = cowplot::plot_grid(
|
||||
cowplot::plot_grid(pt4,
|
||||
cowplot::plot_grid(consurfP, proveanP, snap2P
|
||||
, nrow = 1
|
||||
, labels = c("H", "I", "J")
|
||||
, label_size = my_label_size)
|
||||
, ncol = 1
|
||||
, rel_heights =relH_tp
|
||||
),NULL,
|
||||
cowplot::plot_grid(pt5,
|
||||
cowplot::plot_grid(mcsmligP, mcsmlig2P
|
||||
, mcsmppi2P
|
||||
#, mcsmnaP
|
||||
, nrow = 1
|
||||
, labels = c("K", "L", "M")
|
||||
, label_size = my_label_size)
|
||||
, ncol = 1
|
||||
, rel_heights = relH_tp
|
||||
),NULL,
|
||||
nrow = 1,
|
||||
rel_widths = c(3/6,0.1/6,3/6, 0.1/6 )
|
||||
)
|
||||
|
||||
#-------------------------------
|
||||
# combine: Top and Bottom panel
|
||||
#-------------------------------
|
||||
cowplot::plot_grid (top_panel, bottom_panel
|
||||
, nrow =2
|
||||
, rel_widths = c(1, 1)
|
||||
, align = "hv")
|
||||
}
|
||||
|
||||
#=====================
|
||||
# OutPlot: svg and png
|
||||
#======================
|
||||
dm_om_combinedP = paste0(outdir_images
|
||||
,tolower(gene)
|
||||
,"_dm_om_all.svg")
|
||||
|
||||
cat("DM OM plots with stats:", dm_om_combinedP)
|
||||
svg(dm_om_combinedP, width = 32, height = 18)
|
||||
|
||||
OutPlot_dm_om()
|
||||
dev.off()
|
||||
|
||||
|
||||
dm_om_combinedP_png = paste0(outdir_images
|
||||
,tolower(gene)
|
||||
,"_dm_om_all.png")
|
||||
cat("DM OM plots with stats:", dm_om_combinedP_png)
|
||||
png(dm_om_combinedP_png, width = 32, height = 18, units = "in", res = 300)
|
||||
|
||||
OutPlot_dm_om()
|
||||
dev.off()
|
||||
# # source dm_om_plots.R
|
||||
# source("/home/tanu/git/LSHTM_analysis/scripts/plotting/plotting_thesis/dm_om_plots.R")
|
||||
#
|
||||
# ##### plots to combine ####
|
||||
# duetP
|
||||
# foldxP
|
||||
# deepddgP
|
||||
# dynamut2P
|
||||
# genomicsP
|
||||
# consurfP
|
||||
# proveanP
|
||||
# snap2P
|
||||
# mcsmligP
|
||||
# mcsmlig2P
|
||||
# mcsmppi2P
|
||||
#
|
||||
#
|
||||
# # Plot labels
|
||||
# tit1 = "Stability changes"
|
||||
# tit2 = "Genomic measure"
|
||||
# tit3 = "Distance to partners"
|
||||
# tit4 = "Evolutionary Conservation"
|
||||
# tit5 = "Affinity changes"
|
||||
# pt_size = 30
|
||||
#
|
||||
# theme_georgia <- function(...) {
|
||||
# theme_gray(base_family = "sans", ...) +
|
||||
# theme(plot.title = element_text(face = "bold"))
|
||||
# }
|
||||
#
|
||||
#
|
||||
# title_theme <- calc_element("plot.title", theme_georgia())
|
||||
#
|
||||
# pt1 = ggdraw() +
|
||||
# draw_label(
|
||||
# tit1,
|
||||
# fontfamily = title_theme$family,
|
||||
# fontface = title_theme$face,
|
||||
# #size = title_theme$size
|
||||
# size = pt_size
|
||||
# )
|
||||
#
|
||||
# pt2 = ggdraw() +
|
||||
# draw_label(
|
||||
# tit2,
|
||||
# fontfamily = title_theme$family,
|
||||
# fontface = title_theme$face,
|
||||
# size = pt_size
|
||||
# )
|
||||
#
|
||||
# pt3 = ggdraw() +
|
||||
# draw_label(
|
||||
# tit3,
|
||||
# fontfamily = title_theme$family,
|
||||
# fontface = title_theme$face,
|
||||
# size = pt_size
|
||||
# )
|
||||
#
|
||||
# pt4 = ggdraw() +
|
||||
# draw_label(
|
||||
# tit4,
|
||||
# fontfamily = title_theme$family,
|
||||
# fontface = title_theme$face,
|
||||
# size = pt_size
|
||||
# )
|
||||
#
|
||||
#
|
||||
# pt5 = ggdraw() +
|
||||
# draw_label(
|
||||
# tit5,
|
||||
# fontfamily = title_theme$family,
|
||||
# fontface = title_theme$face,
|
||||
# size = pt_size
|
||||
# )
|
||||
#
|
||||
# #======================
|
||||
# # Output plot function
|
||||
# #======================
|
||||
# OutPlot_dm_om = function(x){
|
||||
#
|
||||
# # dist b/w plot title and plot
|
||||
# relH_tp = c(0.08, 0.92)
|
||||
#
|
||||
# my_label_size = 25
|
||||
# #----------------
|
||||
# # Top panel
|
||||
# #----------------
|
||||
# top_panel = cowplot::plot_grid(
|
||||
# cowplot::plot_grid(pt1,
|
||||
# cowplot::plot_grid(duetP, foldxP, deepddgP, dynamut2P
|
||||
# , nrow = 1
|
||||
# , labels = c("A", "B", "C", "D")
|
||||
# , label_size = my_label_size)
|
||||
# , ncol = 1
|
||||
# , rel_heights = relH_tp
|
||||
# ),
|
||||
# NULL,
|
||||
# cowplot::plot_grid(pt2,
|
||||
# cowplot::plot_grid(genomicsP
|
||||
# , nrow = 1
|
||||
# , labels = c("E")
|
||||
# , label_size = my_label_size)
|
||||
# , ncol = 1
|
||||
# , rel_heights = relH_tp
|
||||
# ),
|
||||
# NULL,
|
||||
# cowplot::plot_grid(pt3,
|
||||
# cowplot::plot_grid( #distanceP
|
||||
# distanceP_lig
|
||||
# , distanceP_ppi2
|
||||
# , nrow = 1
|
||||
# , labels = c("F", "G")
|
||||
# , label_size = my_label_size)
|
||||
# , ncol = 1
|
||||
# , rel_heights = relH_tp
|
||||
# ),
|
||||
# nrow = 1,
|
||||
# rel_widths = c(2/7, 0.1/7, 0.5/7, 0.1/7, 1/7)
|
||||
# )
|
||||
#
|
||||
# #----------------
|
||||
# # Bottom panel
|
||||
# #----------------
|
||||
# bottom_panel = cowplot::plot_grid(
|
||||
# cowplot::plot_grid(pt4,
|
||||
# cowplot::plot_grid(consurfP, proveanP, snap2P
|
||||
# , nrow = 1
|
||||
# , labels = c("H", "I", "J")
|
||||
# , label_size = my_label_size)
|
||||
# , ncol = 1
|
||||
# , rel_heights =relH_tp
|
||||
# ),NULL,
|
||||
# cowplot::plot_grid(pt5,
|
||||
# cowplot::plot_grid(mcsmligP
|
||||
# , mcsmlig2P
|
||||
# , mcsmppi2P
|
||||
# , nrow = 1
|
||||
# , labels = c("K", "L", "M")
|
||||
# , label_size = my_label_size)
|
||||
# , ncol = 1
|
||||
# , rel_heights = relH_tp
|
||||
# ),NULL,
|
||||
# nrow = 1,
|
||||
# rel_widths = c(3/6,0.1/6,3/6, 0.1/6 )
|
||||
# )
|
||||
#
|
||||
# #-------------------------------
|
||||
# # combine: Top and Bottom panel
|
||||
# #-------------------------------
|
||||
# cowplot::plot_grid (top_panel, bottom_panel
|
||||
# , nrow =2
|
||||
# , rel_widths = c(1, 1)
|
||||
# , align = "hv")
|
||||
# }
|
||||
#
|
||||
# #=====================
|
||||
# # OutPlot: svg and png
|
||||
# #======================
|
||||
# dm_om_combinedP = paste0(outdir_images
|
||||
# ,tolower(gene)
|
||||
# ,"_dm_om_all.svg")
|
||||
#
|
||||
# cat("DM OM plots with stats:", dm_om_combinedP)
|
||||
# svg(dm_om_combinedP, width = 32, height = 18)
|
||||
#
|
||||
# OutPlot_dm_om()
|
||||
# dev.off()
|
||||
#
|
||||
#
|
||||
# dm_om_combinedP_png = paste0(outdir_images
|
||||
# ,tolower(gene)
|
||||
# ,"_dm_om_all.png")
|
||||
# cat("DM OM plots with stats:", dm_om_combinedP_png)
|
||||
# png(dm_om_combinedP_png, width = 32, height = 18, units = "in", res = 300)
|
||||
#
|
||||
# OutPlot_dm_om()
|
||||
# dev.off()
|
||||
|
|
|
|||
209
scripts/plotting/plotting_thesis/alr/gg_pairs_all_alr.R
Normal file
209
scripts/plotting/plotting_thesis/alr/gg_pairs_all_alr.R
Normal file
|
|
@ -0,0 +1,209 @@
|
|||
#source("~/git/LSHTM_analysis/config/embb.R")
|
||||
#source("~/git/LSHTM_analysis/scripts/plotting/plotting_colnames.R")
|
||||
#source("~/git/LSHTM_analysis/scripts/plotting/get_plotting_dfs.R")
|
||||
|
||||
#=======
|
||||
# output
|
||||
#=======
|
||||
#outdir_images = paste0("~/git/Writing/thesis/images/results/", tolower(gene), "/")
|
||||
#cat("plots will output to:", outdir_images)
|
||||
|
||||
my_gg_pairs=function(plot_df, plot_title
|
||||
, tt_args_size = 2.5
|
||||
, gp_args_size = 2.5){
|
||||
ggpairs(plot_df,
|
||||
columns = 1:(ncol(plot_df)-1),
|
||||
upper = list(
|
||||
continuous = wrap('cor', # ggally_cor()
|
||||
method = "spearman",
|
||||
use = "pairwise.complete.obs",
|
||||
title="ρ",
|
||||
digits=2,
|
||||
justify_labels = "centre",
|
||||
title_args=list(size=tt_args_size, colour="black"),#2.5
|
||||
group_args=list(size=gp_args_size)#2.5
|
||||
)
|
||||
),
|
||||
lower = list(
|
||||
continuous = wrap("points",
|
||||
alpha = 0.7,
|
||||
size=0.125),
|
||||
combo = wrap("dot",
|
||||
alpha = 0.7,
|
||||
size=0.125)
|
||||
),
|
||||
aes(colour = factor(ifelse(dst_mode==0,
|
||||
"S",
|
||||
"R") ),
|
||||
alpha = 0.5),
|
||||
title=plot_title) +
|
||||
|
||||
scale_colour_manual(values = c("red", "blue")) +
|
||||
scale_fill_manual(values = c("red", "blue")) #+
|
||||
# theme(text = element_text(size=7,
|
||||
# face="bold"))
|
||||
}
|
||||
|
||||
DistCutOff = 10
|
||||
###########################################################################
|
||||
geneL_normal = c("pnca")
|
||||
geneL_na = c("gid", "rpob")
|
||||
geneL_ppi2 = c("alr", "embb", "katg", "rpob")
|
||||
|
||||
merged_df3 = as.data.frame(merged_df3)
|
||||
|
||||
corr_plotdf = corr_data_extract(merged_df3
|
||||
, gene = gene
|
||||
, drug = drug
|
||||
, extract_scaled_cols = F)
|
||||
|
||||
aff_dist_cols = colnames(corr_plotdf)[grep("Dist", colnames(corr_plotdf))]
|
||||
static_cols = c("Log10(MAF)"
|
||||
, "Log10(OR)"
|
||||
)
|
||||
############################################################
|
||||
#=============================================
|
||||
# Creating masked df for affinity data
|
||||
#=============================================
|
||||
corr_affinity_df = corr_plotdf
|
||||
|
||||
#----------------------
|
||||
# Mask affinity columns
|
||||
#-----------------------
|
||||
corr_affinity_df[corr_affinity_df["Lig-Dist"]>DistCutOff,"mCSM-lig"]=0
|
||||
corr_affinity_df[corr_affinity_df["Lig-Dist"]>DistCutOff,"mmCSM-lig"]=0
|
||||
|
||||
if (tolower(gene)%in%geneL_ppi2){
|
||||
corr_affinity_df[corr_affinity_df["PPI-Dist"]>DistCutOff,"mCSM-PPI2"]=0
|
||||
}
|
||||
|
||||
if (tolower(gene)%in%geneL_na){
|
||||
corr_affinity_df[corr_affinity_df["NA-Dist"]>DistCutOff,"mCSM-NA"]=0
|
||||
}
|
||||
|
||||
# count 0
|
||||
#res <- colSums(corr_affinity_df==0)/nrow(corr_affinity_df)*100
|
||||
unmasked_vals <- nrow(corr_affinity_df) - colSums(corr_affinity_df==0)
|
||||
unmasked_vals
|
||||
|
||||
##########################################################
|
||||
#================
|
||||
# Stability
|
||||
#================
|
||||
corr_ps_colnames = c(static_cols
|
||||
, "DUET"
|
||||
, "FoldX"
|
||||
, "DeepDDG"
|
||||
, "Dynamut2"
|
||||
, aff_dist_cols
|
||||
, "dst_mode")
|
||||
|
||||
corr_df_ps = corr_plotdf[, corr_ps_colnames]
|
||||
|
||||
# Plot #1
|
||||
plot_corr_df_ps = my_gg_pairs(corr_df_ps, plot_title="Stability estimates")
|
||||
|
||||
##########################################################
|
||||
#================
|
||||
# Conservation
|
||||
#================
|
||||
corr_conservation_cols = c( static_cols
|
||||
, "ConSurf"
|
||||
, "SNAP2"
|
||||
, "PROVEAN"
|
||||
#, aff_dist_cols
|
||||
, "dst_mode"
|
||||
)
|
||||
|
||||
corr_df_cons = corr_plotdf[, corr_conservation_cols]
|
||||
|
||||
# Plot #2
|
||||
plot_corr_df_cons = my_gg_pairs(corr_df_cons, plot_title="Conservation estimates")
|
||||
|
||||
##########################################################
|
||||
#================
|
||||
# Affinity: lig, ppi and na as applicable
|
||||
#================
|
||||
#corr_df_lig = corr_plotdf[corr_plotdf["Lig-Dist"]<DistCutOff,]
|
||||
common_aff_colnames = c("mCSM-lig"
|
||||
, "mmCSM-lig")
|
||||
|
||||
if (tolower(gene)%in%geneL_normal){
|
||||
aff_colnames = common_aff_colnames
|
||||
}
|
||||
if (tolower(gene)%in%geneL_ppi2){
|
||||
aff_colnames = c(common_aff_colnames, "mCSM-PPI2")
|
||||
}
|
||||
|
||||
if (tolower(gene)%in%geneL_na){
|
||||
aff_colnames = c(common_aff_colnames, "mCSM-NA")
|
||||
}
|
||||
|
||||
# building ffinal affinity colnames for correlation
|
||||
corr_aff_colnames = c(static_cols
|
||||
, aff_colnames
|
||||
, "dst_mode") # imp
|
||||
|
||||
corr_df_aff = corr_affinity_df[, corr_aff_colnames]
|
||||
colnames(corr_df_aff)
|
||||
|
||||
# Plot #3
|
||||
plot_corr_df_aff = my_gg_pairs(corr_df_aff
|
||||
, plot_title="Affinity estimates"
|
||||
#, tt_args_size = 4
|
||||
#, gp_args_size = 4
|
||||
)
|
||||
|
||||
#### Combine plots #####
|
||||
# #png("/home/tanu/tmp/gg_pairs_all.png", height = 6, width=11.75, unit="in",res=300)
|
||||
# png(paste0(outdir_images
|
||||
# ,tolower(gene)
|
||||
# ,"_CorrAB.png"), height = 6, width=11.75, unit="in",res=300)
|
||||
#
|
||||
# cowplot::plot_grid(ggmatrix_gtable(plot_corr_df_ps),
|
||||
# ggmatrix_gtable(plot_corr_df_cons),
|
||||
# # ggmatrix_gtable(plot_corr_df_aff),
|
||||
# # nrow=1, ncol=3, rel_heights = 7,7,3
|
||||
# nrow=1,
|
||||
# #rel_heights = 1,1
|
||||
# labels = "AUTO",
|
||||
# label_size = 12)
|
||||
# dev.off()
|
||||
#
|
||||
# # affinity corr
|
||||
# #png("/home/tanu/tmp/gg_pairs_affinity.png", height =7, width=7, unit="in",res=300)
|
||||
# png(paste0(outdir_images
|
||||
# ,tolower(gene)
|
||||
# ,"_CorrC.png"), height =7, width=7, unit="in",res=300)
|
||||
#
|
||||
# cowplot::plot_grid(ggmatrix_gtable(plot_corr_df_aff),
|
||||
# labels = "C",
|
||||
# label_size = 12)
|
||||
# dev.off()
|
||||
|
||||
#### Combine A ####
|
||||
png(paste0(outdir_images
|
||||
,tolower(gene)
|
||||
,"_CorrA.png"), height =8, width=8, unit="in",res=300)
|
||||
|
||||
cowplot::plot_grid(ggmatrix_gtable(plot_corr_df_ps),
|
||||
labels = "A",
|
||||
label_size = 12)
|
||||
dev.off()
|
||||
|
||||
#### Combine B+C ####
|
||||
# B + C
|
||||
png(paste0(outdir_images
|
||||
,tolower(gene)
|
||||
,"_CorrBC.png"), height = 6, width=11.75, unit="in",res=300)
|
||||
|
||||
cowplot::plot_grid(ggmatrix_gtable(plot_corr_df_cons),
|
||||
ggmatrix_gtable(plot_corr_df_aff),
|
||||
# ggmatrix_gtable(plot_corr_df_aff),
|
||||
# nrow=1, ncol=3, rel_heights = 7,7,3
|
||||
nrow=1,
|
||||
#rel_heights = 1,1
|
||||
labels = c("B", "C"),
|
||||
label_size = 12)
|
||||
dev.off()
|
||||
|
||||
173
scripts/plotting/plotting_thesis/alr/pe_sens_site_count_alr.R
Normal file
173
scripts/plotting/plotting_thesis/alr/pe_sens_site_count_alr.R
Normal file
|
|
@ -0,0 +1,173 @@
|
|||
source("/home/tanu/git/LSHTM_analysis/scripts/plotting/plotting_thesis/alr/prominent_effects_alr.R")
|
||||
source("/home/tanu/git/LSHTM_analysis/scripts/plotting/plotting_thesis/alr/sensitivity_count_alr.R")
|
||||
|
||||
##############################################################
|
||||
# PE count
|
||||
#lig-- na--ppi2--stab
|
||||
# pe_colour_map = c("DD_lig" = "#ffd700" # gold
|
||||
# , "SS_lig" = "#f0e68c" # khaki
|
||||
#
|
||||
# , "DD_nucleic_acid"= "#a0522d" # sienna
|
||||
# , "SS_nucleic_acid"= "#d2b48c" # tan
|
||||
#
|
||||
# , "DD_ppi2" = "#da70d6" # orchid
|
||||
# , "SS_ppi2" = "#ff1493" # deeppink
|
||||
#
|
||||
# , "DD_stability" = "#f8766d" # red
|
||||
# , "SS_stability" = "#00BFC4") # blue
|
||||
table(str_df_plot_cols$pe_effect_outcome)
|
||||
##############################################################
|
||||
#===========
|
||||
#PE count
|
||||
#===========
|
||||
rects <- data.frame(x=1:6,
|
||||
colors = c("#ffd700" ,
|
||||
"#f0e68c" ,
|
||||
|
||||
"#da70d6" ,
|
||||
"#ff1493" ,
|
||||
|
||||
"#f8766d" ,
|
||||
"#00BFC4")
|
||||
)
|
||||
|
||||
rects$text = c("-ve Lig"
|
||||
, "+ve Lig"
|
||||
|
||||
, "-ve PPI2"
|
||||
, "+ve PPI2"
|
||||
|
||||
, "-ve stability"
|
||||
, "+ve stability"
|
||||
)
|
||||
|
||||
cell1 = table(str_df_plot_cols$pe_effect_outcome)[["DD_lig"]]
|
||||
cell2 = 0
|
||||
|
||||
#cell3 = table(str_df_plot_cols$pe_effect_outcome)[["DD_nucleic_acid"]]
|
||||
#cell4 = table(str_df_plot_cols$pe_effect_outcome)[["SS_nucleic_acid"]]
|
||||
|
||||
cell5 = table(str_df_plot_cols$pe_effect_outcome)[["DD_ppi2"]]
|
||||
cell6 = table(str_df_plot_cols$pe_effect_outcome)[["SS_ppi2"]]
|
||||
|
||||
cell7 = table(str_df_plot_cols$pe_effect_outcome)[["DD_stability"]]
|
||||
cell8 = table(str_df_plot_cols$pe_effect_outcome)[["SS_stability"]]
|
||||
|
||||
|
||||
#rects$numbers = c(38, 0, 22, 9, 108, 681) #for embb
|
||||
rects$numbers = c(cell1, cell2,
|
||||
#cell3, cell4,
|
||||
cell5, cell6,
|
||||
cell7, cell8)
|
||||
|
||||
rects$num_labels = paste0("n=", rects$numbers)
|
||||
|
||||
rects
|
||||
#------
|
||||
# Plot
|
||||
#------
|
||||
#https://stackoverflow.com/questions/47986055/create-a-rectangle-filled-with-text
|
||||
peP = ggplot(rects, aes(x, y = 0, fill = colors, label = paste0(text,"\n", num_labels))) +
|
||||
geom_tile(width = 1, height = 1) + # make square tiles
|
||||
geom_text(color = "black", size = 1.7) + # add white text in the middle
|
||||
scale_fill_identity(guide = "none") + # color the tiles with the colors in the data frame
|
||||
coord_fixed() + # make sure tiles are square
|
||||
coord_flip()+ scale_x_reverse() +
|
||||
# theme_void() # remove any axis markings
|
||||
theme_nothing() # remove any axis markings
|
||||
peP
|
||||
|
||||
#------
|
||||
# Plot: this one is better
|
||||
#------
|
||||
peP2 = ggplot(rects, aes(x, y = 0, fill = colors, label = paste0(text,"\n", num_labels))) +
|
||||
geom_tile() + # make square tiles
|
||||
geom_text(color = "black", size = 1.6) + # add white text in the middle
|
||||
scale_fill_identity(guide = "none") + # color the tiles with the colors in the data frame
|
||||
coord_fixed() + # make sure tiles are square
|
||||
theme_nothing() # remove any axis markings
|
||||
peP2
|
||||
|
||||
########################################################
|
||||
# From: script sensitivity_count per gene
|
||||
#===============================
|
||||
# Sensitivity count: SITE
|
||||
#===============================
|
||||
#--------
|
||||
# embb
|
||||
#--------
|
||||
#rsc = 54
|
||||
#ccc = 46
|
||||
#ssc = 470
|
||||
|
||||
rsc = site_Rc; rsc
|
||||
ccc = site_Cc; ccc
|
||||
ssc = site_Sc; ssc
|
||||
|
||||
rect_rs_siteC <- data.frame(x=1:3,
|
||||
colors = c("red",
|
||||
"purple",
|
||||
"blue")
|
||||
)
|
||||
|
||||
rect_rs_siteC
|
||||
rect_rs_siteC$text = c("Resistant",
|
||||
"Common",
|
||||
"Sensitive")
|
||||
|
||||
rect_rs_siteC$numbers = c(rsc,ccc,ssc)
|
||||
rect_rs_siteC$num_labels = paste0("n=", rect_rs_siteC$numbers)
|
||||
rect_rs_siteC
|
||||
|
||||
#------
|
||||
# Plot
|
||||
#------
|
||||
sens_siteP = ggplot(rect_rs_siteC, aes(x, y = 0,
|
||||
fill = colors,
|
||||
label = num_labels
|
||||
#,label = paste0(text,"\n", num_labels)
|
||||
)) +
|
||||
geom_tile(width = 1, height = 1) +
|
||||
#geom_text(color = "black", size = 1.7) +
|
||||
geom_label(color = "black", size = 1.7,fill = "white", alpha=0.7) +
|
||||
scale_fill_identity(guide = "none") +
|
||||
coord_fixed()+
|
||||
theme_nothing() # remove any axis markings
|
||||
sens_siteP
|
||||
|
||||
################################################################
|
||||
#===============================
|
||||
# Sensitivity count: Mutations
|
||||
#===============================
|
||||
table(sensP_df$sensitivity)
|
||||
muts_Rc = table(sensP_df$sensitivity)[["R"]]
|
||||
muts_Sc = table(sensP_df$sensitivity)[["S"]]
|
||||
rect_sens <- data.frame(x=1:2,
|
||||
colors = c("red",
|
||||
"blue")
|
||||
)
|
||||
|
||||
rect_sens$text = c("Resistant",
|
||||
"Sensitive")
|
||||
rect_sens$numbers = c(muts_Rc,muts_Sc)
|
||||
rect_sens$num_labels = paste0("n=", rect_sens$numbers)
|
||||
rect_sens
|
||||
#------
|
||||
# Plot
|
||||
#------
|
||||
sensP = ggplot(rect_sens, aes(x, y = 0,
|
||||
fill = colors,
|
||||
label = paste0(text,"\n", num_labels))) +
|
||||
geom_tile(width = 1, height = 1) +
|
||||
#geom_text(color = "black", size = 1.7) +
|
||||
geom_label(color = "black", size = 1.7,fill = "white", alpha=0.7) +
|
||||
scale_fill_identity(guide = "none") +
|
||||
coord_fixed()+
|
||||
theme_nothing() # remove any axis markings
|
||||
sensP
|
||||
|
||||
sensP2 = sensP +
|
||||
coord_flip() + scale_x_reverse()
|
||||
sensP2
|
||||
|
||||
|
||||
331
scripts/plotting/plotting_thesis/alr/prominent_effects_alr.R
Normal file
331
scripts/plotting/plotting_thesis/alr/prominent_effects_alr.R
Normal file
|
|
@ -0,0 +1,331 @@
|
|||
########################################################
|
||||
pos_colname = "position"
|
||||
|
||||
#-------------
|
||||
# from ~/git/LSHTM_analysis/scripts/plotting/plotting_colnames.R
|
||||
#-------------
|
||||
length(all_stability_cols); length(raw_stability_cols)
|
||||
length(scaled_stability_cols); length(outcome_stability_cols)
|
||||
length(affinity_dist_colnames)
|
||||
|
||||
|
||||
static_cols = c("mutationinformation",
|
||||
#"position",
|
||||
pos_colname,
|
||||
"sensitivity")
|
||||
|
||||
other_cols_all = c(scaled_stability_cols, scaled_affinity_cols, affinity_dist_colnames)
|
||||
|
||||
#omit avg cols and foldx_scaled_signC cols
|
||||
other_cols = other_cols_all[grep("avg", other_cols_all, invert = T)]
|
||||
other_cols = other_cols[grep("foldx_scaled_signC",other_cols, invert = T )]
|
||||
other_cols
|
||||
|
||||
cols_to_extract = c(static_cols, other_cols)
|
||||
cat("\nExtracting cols:", cols_to_extract)
|
||||
expected_ncols = length(static_cols) + length(other_cols)
|
||||
expected_ncols
|
||||
|
||||
str_df = merged_df3[, cols_to_extract]
|
||||
|
||||
if (ncol(str_df) == expected_ncols){
|
||||
cat("\nPASS: successfully extracted cols for calculating prominent effects")
|
||||
}else{
|
||||
stop("\nAbort: Could not extract cols for calculating prominent effects")
|
||||
}
|
||||
|
||||
#=========================
|
||||
# Masking affinity columns
|
||||
#=========================
|
||||
# First make values for affinity cols 0 when their corresponding dist >10
|
||||
head(str_df)
|
||||
|
||||
# replace in place affinity values >10
|
||||
str_df[str_df["ligand_distance"]>10,"affinity_scaled"]=0
|
||||
str_df[str_df["ligand_distance"]>10,"mmcsm_lig_scaled"]=0
|
||||
|
||||
#ppi2 gene: replace in place ppi2 affinity values where ppi2 dist >10
|
||||
if (tolower(gene)%in%geneL_ppi2){
|
||||
str_df[str_df["interface_dist"]>10,"mcsm_ppi2_scaled"]=0
|
||||
}
|
||||
|
||||
# na gene: replace in place na affinity values where na dist >10
|
||||
if (tolower(gene)%in%geneL_na){
|
||||
str_df[str_df["nca_distance"]>10,"mcsm_na_scaled"]=0
|
||||
}
|
||||
|
||||
colnames(str_df)
|
||||
head(str_df)
|
||||
|
||||
scaled_cols_tc = other_cols[grep("scaled", other_cols)]
|
||||
|
||||
|
||||
################################################
|
||||
#===============
|
||||
# whole df
|
||||
#===============
|
||||
give_col=function(x,y,df=str_df){
|
||||
df[df[[pos_colname]]==x,y]
|
||||
}
|
||||
|
||||
for (i in unique(str_df[[pos_colname]]) ){
|
||||
print(i)
|
||||
#cat(length(unique(str_df[[pos_colname]])))
|
||||
|
||||
biggest = max(abs(give_col(i,scaled_cols_tc)))
|
||||
|
||||
str_df[str_df[[pos_colname]]==i,'abs_max_effect'] = biggest
|
||||
str_df[str_df[[pos_colname]]==i,'effect_type']= names(
|
||||
give_col(i,scaled_cols_tc)[which(
|
||||
abs(
|
||||
give_col(i,scaled_cols_tc)
|
||||
) == biggest, arr.ind=T
|
||||
)[, "col"]])[1]
|
||||
|
||||
effect_name = unique(str_df[str_df[[pos_colname]]==i,'effect_type'])#[1] # pick first one in case we have multiple exact values
|
||||
|
||||
# get index/rowname for value of max effect, and then use it to get the original sign
|
||||
# here
|
||||
#ind = rownames(which(abs(str_df[str_df[[pos_colname]]==i,c('position',effect_name)][effect_name])== biggest, arr.ind=T))
|
||||
ind = rownames(which(abs(str_df[str_df[[pos_colname]]==i,c(pos_colname,effect_name)][effect_name])== biggest, arr.ind=T))
|
||||
|
||||
str_df[str_df[[pos_colname]]==i,'effect_sign'] = sign(str_df[effect_name][ind,])[1]
|
||||
}
|
||||
|
||||
# ends with suffix 2 if dups
|
||||
str_df$effect_type = sub("\\.[0-9]+", "", str_df$effect_type) # cull duplicate effect types that happen when there are exact duplicate values
|
||||
colnames(str_df)
|
||||
table(str_df$effect_type)
|
||||
|
||||
# check
|
||||
str_df_check = str_df[str_df[[pos_colname]]%in%c(24, 32, 160, 303, 334),]
|
||||
|
||||
#================
|
||||
# for Plots
|
||||
#================
|
||||
str_df_short = str_df[, c("mutationinformation",
|
||||
#"position",
|
||||
pos_colname,
|
||||
"sensitivity"
|
||||
, "effect_type"
|
||||
, "effect_sign")]
|
||||
|
||||
table(str_df_short$effect_type)
|
||||
table(str_df_short$effect_sign)
|
||||
str(str_df_short)
|
||||
|
||||
# assign pe outcome
|
||||
str_df_short$pe_outcome = ifelse(str_df_short$effect_sign<0, "DD", "SS")
|
||||
table(str_df_short$pe_outcome )
|
||||
table(str_df_short$effect_sign)
|
||||
|
||||
#==============
|
||||
# group effect type:
|
||||
# lig, ppi2, nuc. acid, stability
|
||||
#==============
|
||||
affcols = c("affinity_scaled", "mmcsm_lig_scaled")
|
||||
ppi2_cols = c("mcsm_ppi2_scaled")
|
||||
|
||||
#lig
|
||||
table(str_df_short$effect_type)
|
||||
str_df_short$effect_grouped = ifelse(str_df_short$effect_type%in%affcols
|
||||
, "lig"
|
||||
, str_df_short$effect_type)
|
||||
table(str_df_short$effect_grouped)
|
||||
|
||||
#ppi2
|
||||
str_df_short$effect_grouped = ifelse(str_df_short$effect_grouped%in%ppi2_cols
|
||||
, "ppi2"
|
||||
, str_df_short$effect_grouped)
|
||||
table(str_df_short$effect_grouped)
|
||||
|
||||
|
||||
#stability
|
||||
str_df_short$effect_grouped = ifelse(!str_df_short$effect_grouped%in%c("lig",
|
||||
"ppi2"
|
||||
)
|
||||
, "stability"
|
||||
, str_df_short$effect_grouped)
|
||||
|
||||
table(str_df_short$effect_grouped)
|
||||
|
||||
# create a sign as well
|
||||
str_df_short$pe_effect_outcome = paste0(str_df_short$pe_outcome, "_"
|
||||
, str_df_short$effect_grouped)
|
||||
|
||||
table(str_df_short$pe_effect_outcome)
|
||||
|
||||
#####################################################################
|
||||
# Chimera: for colouring
|
||||
####################################################################
|
||||
|
||||
#-------------------------------------
|
||||
# get df with unique position
|
||||
#--------------------------------------
|
||||
#data[!duplicated(data$x), ]
|
||||
str_df_plot = str_df_short[!duplicated(str_df[[pos_colname]]),]
|
||||
|
||||
if (nrow(str_df_plot) == length(unique(str_df[[pos_colname]]))){
|
||||
cat("\nPASS: successfully extracted df with unique positions")
|
||||
}else{
|
||||
stop("\nAbort: Could not extract df with unique positions")
|
||||
}
|
||||
|
||||
#-------------------------------------
|
||||
# generate colours for effect types
|
||||
#--------------------------------------
|
||||
str_df_plot_cols = str_df_plot[, c(pos_colname,
|
||||
"sensitivity",
|
||||
"pe_outcome",
|
||||
"effect_grouped",
|
||||
"pe_effect_outcome")]
|
||||
head(str_df_plot_cols)
|
||||
|
||||
# colour intensity based on sign
|
||||
#str_df_plot_cols$colour_hue = ifelse(str_df_plot_cols$effect_sign<0, "bright", "light")
|
||||
str_df_plot_cols$colour_hue = ifelse(str_df_plot_cols$pe_outcome=="DD", "bright", "light")
|
||||
|
||||
table(str_df_plot_cols$colour_hue); table(str_df_plot$pe_outcome)
|
||||
head(str_df_plot_cols)
|
||||
|
||||
# colour based on effect
|
||||
table(str_df_plot_cols$pe_effect_outcome)
|
||||
|
||||
# colors = c("#ffd700" #gold
|
||||
# , "#f0e68c" #khaki
|
||||
# , "#da70d6"# orchid
|
||||
# , "#ff1493"# deeppink
|
||||
# , "#a0522d" #sienna
|
||||
# , "#d2b48c" # tan
|
||||
# , "#00BFC4" #, "#007d85" #blue
|
||||
# , "#F8766D" )# red
|
||||
|
||||
pe_colour_map = c("DD_lig" = "#ffd700" # gold
|
||||
, "SS_lig" = "#f0e68c" # khaki
|
||||
|
||||
, "DD_nucleic_acid"= "#a0522d" # sienna
|
||||
, "SS_nucleic_acid"= "#d2b48c" # tan
|
||||
|
||||
, "DD_ppi2" = "#da70d6" # orchid
|
||||
, "SS_ppi2" = "#ff1493" # deeppink
|
||||
|
||||
, "DD_stability" = "#f8766d" # red
|
||||
, "SS_stability" = "#00BFC4") # blue
|
||||
|
||||
#unlist(d[c('a', 'a', 'c', 'b')], use.names=FALSE)
|
||||
|
||||
#map the colours
|
||||
str_df_plot_cols$colour_map= unlist(map(str_df_plot_cols$pe_effect_outcome
|
||||
,function(x){pe_colour_map[[x]]}
|
||||
))
|
||||
head(str_df_plot_cols$colour_map)
|
||||
table(str_df_plot_cols$colour_map)
|
||||
table(str_df_plot_cols$pe_effect_outcome)
|
||||
|
||||
# str_df_plot_cols$colours = paste0(str_df_plot_cols$colour_hue
|
||||
# , "_"
|
||||
# , str_df_plot_cols$colour_map)
|
||||
# head(str_df_plot_cols$colours)
|
||||
# table(str_df_plot_cols$colours)
|
||||
#
|
||||
#
|
||||
# class(str_df_plot_cols$colour_map)
|
||||
# str(str_df_plot_cols)
|
||||
|
||||
# sort by colour
|
||||
head(str_df_plot_cols)
|
||||
str_df_plot_cols = str_df_plot_cols[order(str_df_plot_cols$colour_map), ]
|
||||
head(str_df_plot_cols)
|
||||
|
||||
#======================================
|
||||
# write file with prominent effects
|
||||
#======================================
|
||||
outdir_images = paste0("~/git/Writing/thesis/images/results/", tolower(gene), "/")
|
||||
write.csv(str_df_plot_cols, paste0(outdir_images, tolower(gene), "_prominent_effects.csv"))
|
||||
|
||||
################################
|
||||
# printing for chimera
|
||||
###############################
|
||||
chain_suffix = ".A"
|
||||
str_df_plot_cols$pos_chain = paste0(str_df_plot_cols[[pos_colname]], chain_suffix)
|
||||
table(str_df_plot_cols$colour_map)
|
||||
table(str_df_plot_cols$pe_effect_outcome)
|
||||
|
||||
#===================================================
|
||||
#-------------------
|
||||
# Ligand Affinity
|
||||
#-------------------
|
||||
# -ve Lig Aff
|
||||
dd_lig = str_df_plot_cols[str_df_plot_cols$pe_effect_outcome=="DD_lig",]
|
||||
if (nrow(dd_lig) == table(str_df_plot_cols$pe_effect_outcome)[['DD_lig']]){
|
||||
dd_lig_pos = dd_lig[[pos_colname]]
|
||||
}else{
|
||||
stop("Abort: DD affinity colour numbers mismtatch")
|
||||
}
|
||||
|
||||
# +ve Lig Aff
|
||||
ss_lig = str_df_plot_cols[str_df_plot_cols$pe_effect_outcome=="SS_lig",]
|
||||
if (!empty(ss_lig)){
|
||||
if (nrow(ss_lig) == table(str_df_plot_cols$pe_effect_outcome)[['SS_lig']]){
|
||||
ss_lig_pos = ss_lig[[pos_colname]]
|
||||
}else{
|
||||
stop("Abort: SS affinity colour numbers mismtatch")
|
||||
}
|
||||
#put in chimera cmd
|
||||
paste0(dd_lig_pos, chain_suffix)
|
||||
paste0(ss_lig_pos, chain_suffix)
|
||||
}
|
||||
|
||||
|
||||
|
||||
|
||||
#===================================================
|
||||
#-------------------
|
||||
# PPI2 Affinity
|
||||
#-------------------
|
||||
# -ve PPI2
|
||||
dd_ppi2 = str_df_plot_cols[str_df_plot_cols$pe_effect_outcome=="DD_ppi2",]
|
||||
if (nrow(dd_ppi2) == table(str_df_plot_cols$pe_effect_outcome)[['DD_ppi2']]){
|
||||
dd_ppi2_pos = dd_ppi2[[pos_colname]]
|
||||
}else{
|
||||
stop("Abort: DD PPI2 colour numbers mismtatch")
|
||||
}
|
||||
|
||||
# +ve PPI2
|
||||
ss_ppi2 = str_df_plot_cols[str_df_plot_cols$pe_effect_outcome=="SS_ppi2",]
|
||||
if (nrow(ss_ppi2) == table(str_df_plot_cols$pe_effect_outcome)[['SS_ppi2']]){
|
||||
ss_ppi2_pos = ss_ppi2[[pos_colname]]
|
||||
}else{
|
||||
stop("Abort: SS PPI2 colour numbers mismtatch")
|
||||
}
|
||||
|
||||
#put in chimera cmd
|
||||
paste0(dd_ppi2_pos,chain_suffix)
|
||||
paste0(ss_ppi2_pos,chain_suffix)
|
||||
|
||||
#=========================================================
|
||||
#------------------------
|
||||
# Stability
|
||||
#------------------------
|
||||
# -ve Stability
|
||||
dd_stability = str_df_plot_cols[str_df_plot_cols$pe_effect_outcome=="DD_stability",]
|
||||
if (nrow(dd_stability) == table(str_df_plot_cols$pe_effect_outcome)[['DD_stability']]){
|
||||
dd_stability_pos = dd_stability[[pos_colname]]
|
||||
}else{
|
||||
stop("Abort: DD Stability colour numbers mismtatch")
|
||||
}
|
||||
|
||||
# +ve Stability
|
||||
ss_stability = str_df_plot_cols[str_df_plot_cols$pe_effect_outcome=="SS_stability",]
|
||||
if (nrow(ss_stability) == table(str_df_plot_cols$pe_effect_outcome)[['SS_stability']]){
|
||||
ss_stability_pos = ss_stability[[pos_colname]]
|
||||
}else{
|
||||
stop("Abort: SS Stability colour numbers mismtatch")
|
||||
}
|
||||
|
||||
#put in chimera cmd
|
||||
# stabiliting first as it has less numbers
|
||||
paste0(ss_stability_pos, chain_suffix)
|
||||
paste0(dd_stability_pos, chain_suffix)
|
||||
####################################################################
|
||||
|
||||
65
scripts/plotting/plotting_thesis/alr/sensitivity_count_alr.R
Normal file
65
scripts/plotting/plotting_thesis/alr/sensitivity_count_alr.R
Normal file
|
|
@ -0,0 +1,65 @@
|
|||
#=========================
|
||||
# Count Sensitivity
|
||||
# Mutations and positions
|
||||
#=========================
|
||||
pos_colname_c ="position"
|
||||
|
||||
sensP_df = merged_df3[,c("mutationinformation",
|
||||
#"position",
|
||||
pos_colname_c,
|
||||
"sensitivity")]
|
||||
|
||||
head(sensP_df)
|
||||
table(sensP_df$sensitivity)
|
||||
|
||||
#---------------
|
||||
# Total unique positions
|
||||
#----------------
|
||||
tot_mut_pos = length(unique(sensP_df[[pos_colname_c]]))
|
||||
cat("\nNo of Tot muts sites:", tot_mut_pos)
|
||||
|
||||
# resistant mut pos
|
||||
sens_site_allR = sensP_df[[pos_colname_c]][sensP_df$sensitivity=="R"]
|
||||
sens_site_UR = unique(sens_site_allR)
|
||||
length(sens_site_UR)
|
||||
|
||||
# Sensitive mut pos
|
||||
sens_site_allS = sensP_df[[pos_colname_c]][sensP_df$sensitivity=="S"]
|
||||
sens_site_US = unique(sens_site_allS)
|
||||
length(sens_site_UR)
|
||||
|
||||
#---------------
|
||||
# Common Sites
|
||||
#----------------
|
||||
common_pos = intersect(sens_site_UR,sens_site_US)
|
||||
site_Cc = length(common_pos)
|
||||
cat("\nNo of Common sites:", site_Cc
|
||||
, "\nThese are:", common_pos)
|
||||
|
||||
#---------------
|
||||
# Resistant muts
|
||||
#----------------
|
||||
site_R = sens_site_UR[!sens_site_UR%in%common_pos]
|
||||
site_Rc = length(site_R)
|
||||
|
||||
if ( length(sens_site_allR) == table(sensP_df$sensitivity)[['R']] ){
|
||||
cat("\nNo of R muts:", length(sens_site_allR)
|
||||
, "\nNo. of R sites:",site_Rc
|
||||
, "\nThese are:", site_R
|
||||
)
|
||||
}
|
||||
|
||||
#---------------
|
||||
# Sensitive muts
|
||||
#----------------
|
||||
site_S = sens_site_US[!sens_site_US%in%common_pos]
|
||||
site_Sc = length(site_S)
|
||||
|
||||
if ( length(sens_site_allS) == table(sensP_df$sensitivity)[['S']] ){
|
||||
cat("\nNo of S muts:", length(sens_site_allS)
|
||||
, "\nNo. of S sites:", site_Sc
|
||||
, "\nThese are:", site_S)
|
||||
}
|
||||
|
||||
#########################
|
||||
|
||||
363
scripts/plotting/plotting_thesis/gid/basic_barplots_gid.R
Normal file
363
scripts/plotting/plotting_thesis/gid/basic_barplots_gid.R
Normal file
|
|
@ -0,0 +1,363 @@
|
|||
#!/usr/bin/env Rscript
|
||||
#########################################################
|
||||
# TASK: Barplots
|
||||
# basic barplots with outcome
|
||||
# basic barplots with frequency of count of mutations
|
||||
#########################################################
|
||||
#=============
|
||||
# Data: Input
|
||||
#==============
|
||||
#source("~/git/LSHTM_analysis/config/gid.R")
|
||||
#source("~/git/LSHTM_analysis/scripts/plotting/get_plotting_dfs.R")
|
||||
#cat("\nSourced plotting cols as well:", length(plotting_cols))
|
||||
|
||||
####################################################
|
||||
class(merged_df3)
|
||||
|
||||
df3 = subset(merged_df3, select = -c(pos_count))
|
||||
|
||||
#=======
|
||||
# output
|
||||
#=======
|
||||
outdir_images = paste0("~/git/Writing/thesis/images/results/", tolower(gene), "/")
|
||||
cat("plots will output to:", outdir_images)
|
||||
|
||||
##########################################################
|
||||
# blue, red bp
|
||||
sts = 8
|
||||
lts = 8
|
||||
ats = 8
|
||||
als = 8
|
||||
ltis = 8
|
||||
geom_ls = 2.2
|
||||
|
||||
#pos_count
|
||||
subtitle_size = 8
|
||||
geom_ls_pc = 2.2
|
||||
leg_text_size = 8
|
||||
axis_text_size = 8
|
||||
axis_label_size = 8
|
||||
|
||||
###########################################################
|
||||
#------------------------------
|
||||
# plot default sizes
|
||||
#------------------------------
|
||||
#=========================
|
||||
# Affinity outcome
|
||||
# check this var: outcome_cols_affinity
|
||||
# get from preformatting or put in globals
|
||||
#==========================
|
||||
DistCutOff
|
||||
LigDist_colname # = "ligand_distance" # from globals
|
||||
ppi2Dist_colname
|
||||
naDist_colname
|
||||
|
||||
###########################################################
|
||||
# get plotting data within the distance
|
||||
df3_lig = df3[df3[[LigDist_colname]]<DistCutOff,]
|
||||
df3_ppi2 = df3[df3[[ppi2Dist_colname]]<DistCutOff,]
|
||||
df3_na = df3[df3[[naDist_colname]]<DistCutOff,]
|
||||
common_bp_title = paste0("Sites <", DistCutOff, angstroms_symbol)
|
||||
|
||||
#------------------------------
|
||||
# barplot for ligand affinity:
|
||||
# <10 Ang of ligand
|
||||
#------------------------------
|
||||
mLigP = stability_count_bp(plotdf = df3_lig
|
||||
, df_colname = "ligand_outcome"
|
||||
#, leg_title = "mCSM-lig"
|
||||
#, bp_plot_title = paste(common_bp_title, "ligand")
|
||||
, yaxis_title = "Number of nsSNPs"
|
||||
, leg_position = "none"
|
||||
, subtitle_text = "mCSM\nLig"
|
||||
, bar_fill_values = c("#F8766D", "#00BFC4")
|
||||
, subtitle_colour= "black"
|
||||
, sts = sts
|
||||
, lts = lts
|
||||
, ats = ats
|
||||
, als = als
|
||||
, ltis = ltis
|
||||
, geom_ls = geom_ls
|
||||
)
|
||||
mLigP
|
||||
#------------------------------
|
||||
# barplot for ligand affinity:
|
||||
# <10 Ang of ligand
|
||||
# mmCSM-lig: will be the same no. of sites but the effect will be different
|
||||
#------------------------------
|
||||
mmLigP = stability_count_bp(plotdf = df3_lig
|
||||
, df_colname = "mmcsm_lig_outcome"
|
||||
#, leg_title = "mmCSM-lig"
|
||||
#, label_categories = labels_mmlig
|
||||
#, bp_plot_title = paste(common_bp_title, "ligand")
|
||||
|
||||
, yaxis_title = ""
|
||||
, leg_position = "none"
|
||||
, subtitle_text = "mmCSM\nLig"
|
||||
, bar_fill_values = c("#F8766D", "#00BFC4")
|
||||
, subtitle_colour= "black"
|
||||
, sts = sts
|
||||
, lts = lts
|
||||
, ats = ats
|
||||
, als = als
|
||||
, ltis = ltis
|
||||
, geom_ls = geom_ls
|
||||
)
|
||||
mmLigP
|
||||
#------------------------------
|
||||
# barplot for ppi2 affinity
|
||||
# <10 Ang of interface
|
||||
#------------------------------
|
||||
if (tolower(gene)%in%geneL_ppi2){
|
||||
ppi2P = stability_count_bp(plotdf = df3_ppi2
|
||||
, df_colname = "mcsm_ppi2_outcome"
|
||||
#, leg_title = "mCSM-ppi2"
|
||||
#, label_categories = labels_ppi2
|
||||
#, bp_plot_title = paste(common_bp_title, "PP-interface")
|
||||
|
||||
, yaxis_title = "Number of nsSNPs"
|
||||
, leg_position = "none"
|
||||
, subtitle_text = "mCSM\nPPI2"
|
||||
, bar_fill_values = c("#F8766D", "#00BFC4")
|
||||
, subtitle_colour= "black"
|
||||
, sts = sts
|
||||
, lts = lts
|
||||
, ats = ats
|
||||
, als = als
|
||||
, ltis = ltis
|
||||
, geom_ls = geom_ls
|
||||
)
|
||||
ppi2P
|
||||
}
|
||||
#----------------------------
|
||||
# barplot for ppi2 affinity
|
||||
# <10 Ang of interface
|
||||
#------------------------------
|
||||
if (tolower(gene)%in%geneL_na){
|
||||
nca_distP = stability_count_bp(plotdf = df3_na
|
||||
, df_colname = "mcsm_na_outcome"
|
||||
#, leg_title = "mCSM-NA"
|
||||
#, label_categories =
|
||||
#, bp_plot_title = paste(common_bp_title, "Dist to NA")
|
||||
|
||||
, yaxis_title = "Number of nsSNPs"
|
||||
, leg_position = "none"
|
||||
, subtitle_text = "mCSM\nNA"
|
||||
, bar_fill_values = c("#F8766D", "#00BFC4")
|
||||
, subtitle_colour= "black"
|
||||
, sts = sts
|
||||
, lts = lts
|
||||
, ats = ats
|
||||
, als = als
|
||||
, ltis = ltis
|
||||
, geom_ls = geom_ls
|
||||
)
|
||||
nca_distP
|
||||
}
|
||||
|
||||
#####################################################################
|
||||
# ------------------------------
|
||||
# bp site site count: mCSM-lig
|
||||
# < 10 Ang ligand
|
||||
# ------------------------------
|
||||
common_bp_title = paste0("Sites <", DistCutOff, angstroms_symbol)
|
||||
|
||||
posC_lig = site_snp_count_bp(plotdf = df3_lig
|
||||
, df_colname = "position"
|
||||
, xaxis_title = "Number of nsSNPs"
|
||||
, yaxis_title = "Number of Sites"
|
||||
, subtitle_colour = "chocolate4"
|
||||
, subtitle_text = ""
|
||||
, subtitle_size = subtitle_size
|
||||
, geom_ls = geom_ls_pc
|
||||
, leg_text_size = leg_text_size
|
||||
, axis_text_size = axis_text_size
|
||||
, axis_label_size = axis_label_size)
|
||||
|
||||
posC_lig
|
||||
#------------------------------
|
||||
# bp site site count: ppi2
|
||||
# < 10 Ang interface
|
||||
#------------------------------
|
||||
if (tolower(gene)%in%geneL_ppi2){
|
||||
posC_ppi2 = site_snp_count_bp(plotdf = df3_ppi2
|
||||
, df_colname = "position"
|
||||
, xaxis_title = "Number of nsSNPs"
|
||||
, yaxis_title = "Number of Sites"
|
||||
, subtitle_colour = "chocolate4"
|
||||
, subtitle_text = ""
|
||||
, subtitle_size = subtitle_size
|
||||
, geom_ls = geom_ls_pc
|
||||
, leg_text_size = leg_text_size
|
||||
, axis_text_size = axis_text_size
|
||||
, axis_label_size = axis_label_size)
|
||||
posC_ppi2
|
||||
}
|
||||
|
||||
#------------------------------
|
||||
# bp site site count: NCA dist
|
||||
# < 10 Ang nca
|
||||
#------------------------------
|
||||
if (tolower(gene)%in%geneL_na){
|
||||
posC_nca = site_snp_count_bp(plotdf = df3_na
|
||||
, df_colname = "position"
|
||||
, xaxis_title = "Number of nsSNPs"
|
||||
, yaxis_title = "Number of Sites"
|
||||
, subtitle_colour = "chocolate4"
|
||||
, subtitle_text = ""
|
||||
, subtitle_size = subtitle_size
|
||||
, geom_ls = geom_ls_pc
|
||||
, leg_text_size = leg_text_size
|
||||
, axis_text_size = axis_text_size
|
||||
, axis_label_size = axis_label_size)
|
||||
posC_nca
|
||||
}
|
||||
#===============================================================
|
||||
#------------------------------
|
||||
# bp site site count: ALL
|
||||
# <10 Ang ligand
|
||||
#------------------------------
|
||||
posC_all = site_snp_count_bp(plotdf = df3
|
||||
, df_colname = "position"
|
||||
, xaxis_title = "Number of nsSNPs"
|
||||
, yaxis_title = "Number of Sites"
|
||||
, subtitle_colour = "chocolate4"
|
||||
, subtitle_text = "All mutations sites"
|
||||
, subtitle_size = subtitle_size
|
||||
, geom_ls = geom_ls_pc
|
||||
, leg_text_size = leg_text_size
|
||||
, axis_text_size = axis_text_size
|
||||
, axis_label_size = axis_label_size)
|
||||
posC_all
|
||||
##################################################################
|
||||
consurfP = stability_count_bp(plotdf = df3
|
||||
, df_colname = "consurf_outcome"
|
||||
#, leg_title = "ConSurf"
|
||||
#, label_categories = labels_consurf
|
||||
, yaxis_title = "Number of nsSNPs"
|
||||
, leg_position = "top"
|
||||
, subtitle_text = "ConSurf"
|
||||
, bar_fill_values = consurf_colours # from globals
|
||||
, subtitle_colour= "black"
|
||||
, sts = sts
|
||||
, lts = lts
|
||||
, ats = ats
|
||||
, als = als
|
||||
, ltis = ltis
|
||||
, geom_ls = geom_ls)
|
||||
|
||||
consurfP
|
||||
|
||||
##############################################################
|
||||
sts_so = 10
|
||||
lts_so = 10
|
||||
ats_so = 10
|
||||
als_so = 10
|
||||
ltis_so = 10
|
||||
geom_ls_so = 2.5
|
||||
#===================
|
||||
# Stability
|
||||
#===================
|
||||
# duetP
|
||||
duetP = stability_count_bp(plotdf = df3
|
||||
, df_colname = "duet_outcome"
|
||||
, leg_title = "mCSM-DUET"
|
||||
#, label_categories = labels_duet
|
||||
, yaxis_title = "Number of nsSNPs"
|
||||
, leg_position = "none"
|
||||
, subtitle_text = "mCSM-DUET"
|
||||
, bar_fill_values = c("#F8766D", "#00BFC4")
|
||||
, subtitle_colour= "black"
|
||||
, sts = sts_so
|
||||
, lts = lts_so
|
||||
, ats = ats_so
|
||||
, als = als_so
|
||||
, ltis = ltis_so
|
||||
, geom_ls = geom_ls_so)
|
||||
duetP
|
||||
|
||||
# foldx
|
||||
foldxP = stability_count_bp(plotdf = df3
|
||||
, df_colname = "foldx_outcome"
|
||||
#, leg_title = "FoldX"
|
||||
#, label_categories = labels_foldx
|
||||
, yaxis_title = ""
|
||||
, leg_position = "none"
|
||||
, subtitle_text = "FoldX"
|
||||
, bar_fill_values = c("#F8766D", "#00BFC4")
|
||||
, sts = sts_so
|
||||
, lts = lts_so
|
||||
, ats = ats_so
|
||||
, als = als_so
|
||||
, ltis = ltis_so
|
||||
, geom_ls = geom_ls_so)
|
||||
foldxP
|
||||
|
||||
# deepddg
|
||||
deepddgP = stability_count_bp(plotdf = df3
|
||||
, df_colname = "deepddg_outcome"
|
||||
#, leg_title = "DeepDDG"
|
||||
#, label_categories = labels_deepddg
|
||||
, yaxis_title = ""
|
||||
, leg_position = "none"
|
||||
, subtitle_text = "DeepDDG"
|
||||
, bar_fill_values = c("#F8766D", "#00BFC4")
|
||||
, sts = sts_so
|
||||
, lts = lts_so
|
||||
, ats = ats_so
|
||||
, als = als_so
|
||||
, ltis = ltis_so
|
||||
, geom_ls = geom_ls_so)
|
||||
deepddgP
|
||||
|
||||
# deepddg
|
||||
dynamut2P = stability_count_bp(plotdf = df3
|
||||
, df_colname = "ddg_dynamut2_outcome"
|
||||
#, leg_title = "Dynamut2"
|
||||
#, label_categories = labels_ddg_dynamut2_outcome
|
||||
, yaxis_title = ""
|
||||
, leg_position = "none"
|
||||
, subtitle_text = "Dynamut2"
|
||||
, bar_fill_values = c("#F8766D", "#00BFC4")
|
||||
, sts = sts_so
|
||||
, lts = lts_so
|
||||
, ats = ats_so
|
||||
, als = als_so
|
||||
, ltis = ltis_so
|
||||
, geom_ls = geom_ls_so)
|
||||
dynamut2P
|
||||
|
||||
# provean
|
||||
proveanP = stability_count_bp(plotdf = df3
|
||||
, df_colname = "provean_outcome"
|
||||
#, leg_title = "PROVEAN"
|
||||
#, label_categories = labels_provean
|
||||
, yaxis_title = "Number of nsSNPs"
|
||||
, leg_position = "none" # top
|
||||
, subtitle_text = "PROVEAN"
|
||||
, bar_fill_values = c("#D01C8B", "#F1B6DA") # light pink and deep
|
||||
, sts = sts_so
|
||||
, lts = lts_so
|
||||
, ats = ats_so
|
||||
, als = als_so
|
||||
, ltis = ltis_so
|
||||
, geom_ls = geom_ls_so)
|
||||
proveanP
|
||||
|
||||
# snap2
|
||||
snap2P = stability_count_bp(plotdf = df3
|
||||
, df_colname = "snap2_outcome"
|
||||
#, leg_title = "SNAP2"
|
||||
#, label_categories = labels_snap2
|
||||
, yaxis_title = ""
|
||||
, leg_position = "none" # top
|
||||
, subtitle_text = "SNAP2"
|
||||
, bar_fill_values = c("#D01C8B", "#F1B6DA") # light pink and deep
|
||||
, sts = sts_so
|
||||
, lts = lts_so
|
||||
, ats = ats_so
|
||||
, als = als_so
|
||||
, ltis = ltis_so
|
||||
, geom_ls = geom_ls_so)
|
||||
snap2P
|
||||
#####################################################################################
|
||||
|
|
@ -1,17 +1,38 @@
|
|||
duetP
|
||||
foldxP
|
||||
deepddgP
|
||||
dynamut2P
|
||||
proveanP
|
||||
snap2P
|
||||
#=============
|
||||
# Data: Input
|
||||
#==============
|
||||
source("~/git/LSHTM_analysis/config/gid.R")
|
||||
source("~/git/LSHTM_analysis/scripts/plotting/get_plotting_dfs.R")
|
||||
#cat("\nSourced plotting cols as well:", length(plotting_cols))
|
||||
|
||||
source("/home/tanu/git/LSHTM_analysis/scripts/plotting/plotting_thesis/gid/basic_barplots_gid.R")
|
||||
source("/home/tanu/git/LSHTM_analysis/scripts/plotting/plotting_thesis/gid/pe_sens_site_count_gid.R")
|
||||
|
||||
|
||||
if ( tolower(gene)%in%c("gid") ){
|
||||
cat("\nPlots available for layout are:")
|
||||
|
||||
duetP
|
||||
foldxP
|
||||
deepddgP
|
||||
dynamut2P
|
||||
proveanP
|
||||
snap2P
|
||||
|
||||
mLigP
|
||||
mmLigP
|
||||
posC_lig
|
||||
|
||||
nca_distP
|
||||
posC_nca
|
||||
|
||||
peP2
|
||||
sens_siteP
|
||||
peP # not used
|
||||
sensP # not used
|
||||
}
|
||||
|
||||
|
||||
mLigP
|
||||
mmLigP
|
||||
posC_lig
|
||||
ppi2P
|
||||
posC_nca
|
||||
peP
|
||||
sensP
|
||||
#========================
|
||||
# Common title settings
|
||||
#=========================
|
||||
|
|
@ -118,9 +139,8 @@ dev.off()
|
|||
|
||||
#################################################################
|
||||
#=======================================
|
||||
# Affinity barplots: COMBINE ALL three
|
||||
# Affinity barplots: COMBINE ALL four
|
||||
#========================================
|
||||
|
||||
ligT = paste0(common_bp_title, " ligand")
|
||||
lig_affT = ggdraw() +
|
||||
draw_label(
|
||||
|
|
@ -136,7 +156,7 @@ p1 = cowplot::plot_grid(cowplot::plot_grid(lig_affT
|
|||
, nrow=2),
|
||||
cowplot::plot_grid(mLigP, mmLigP, posC_lig
|
||||
, nrow = 1
|
||||
, rel_widths = c(1,1,1.8)
|
||||
, rel_widths = c(1,0.65,1.8)
|
||||
, align = "h"),
|
||||
nrow = 2,
|
||||
rel_heights = c(1,8)
|
||||
|
|
@ -144,7 +164,7 @@ p1 = cowplot::plot_grid(cowplot::plot_grid(lig_affT
|
|||
)
|
||||
p1
|
||||
###########################################################
|
||||
ncaT = paste0(common_bp_title, " Dist-NA")
|
||||
ncaT = paste0(common_bp_title, " Nucleic Acid")
|
||||
nca_affT = ggdraw() +
|
||||
draw_label(
|
||||
ncaT,
|
||||
|
|
@ -159,7 +179,7 @@ p2 = cowplot::plot_grid(cowplot::plot_grid(nca_affT
|
|||
, nrow=2),
|
||||
cowplot::plot_grid(nca_distP, posC_nca
|
||||
, nrow = 1
|
||||
, rel_widths = c(1.2,1.8)
|
||||
, rel_widths = c(1,1.8)
|
||||
, align = "h"),
|
||||
nrow = 2,
|
||||
rel_heights = c(1,8)
|
||||
|
|
@ -176,7 +196,7 @@ peT_allT = ggdraw() +
|
|||
size = 8
|
||||
)
|
||||
|
||||
p3 = cowplot::plot_grid(cowplot::plot_grid(peT_allT, nrow = 2
|
||||
p4 = cowplot::plot_grid(cowplot::plot_grid(peT_allT, nrow = 2
|
||||
, rel_widths = c(1,3),axis = "lr"),
|
||||
cowplot::plot_grid(
|
||||
peP2, posC_all,
|
||||
|
|
@ -185,13 +205,12 @@ p3 = cowplot::plot_grid(cowplot::plot_grid(peT_allT, nrow = 2
|
|||
align = "v",
|
||||
axis = "lr",
|
||||
rel_heights = c(1,8)
|
||||
),
|
||||
),
|
||||
rel_heights = c(1,18),
|
||||
nrow = 2,axis = "lr")
|
||||
p3
|
||||
#===============
|
||||
# Final combine
|
||||
#===============
|
||||
p4
|
||||
|
||||
#### Combine p1+p2+p4 ####
|
||||
w = 11.79
|
||||
h = 3.5
|
||||
mut_impact_CLP = paste0(outdir_images
|
||||
|
|
@ -202,21 +221,46 @@ mut_impact_CLP = paste0(outdir_images
|
|||
print(paste0("plot filename:", mut_impact_CLP))
|
||||
png(mut_impact_CLP, units = "in", width = w, height = h, res = 300 )
|
||||
|
||||
cowplot::plot_grid(p1, p2
|
||||
#, p3
|
||||
, nrow = 1
|
||||
, labels = "AUTO"
|
||||
, label_size = 12
|
||||
, rel_widths = c(3,2,2)
|
||||
cowplot::plot_grid(p1,
|
||||
p2,
|
||||
p4,
|
||||
nrow = 1,
|
||||
labels = "AUTO",
|
||||
label_size = 12,
|
||||
rel_widths = c(2.5,2,2)
|
||||
#, rel_heights = c(1)
|
||||
)
|
||||
)
|
||||
|
||||
dev.off()
|
||||
w = 11.79
|
||||
h = 3.5
|
||||
mut_impact_CLP = paste0(outdir_images
|
||||
,tolower(gene)
|
||||
,"_mut_impactCLP.png")
|
||||
|
||||
#svg(affP, width = 20, height = 5.5)
|
||||
print(paste0("plot filename:", mut_impact_CLP))
|
||||
png(mut_impact_CLP, units = "in", width = w, height = h, res = 300 )
|
||||
|
||||
cowplot::plot_grid(p1,
|
||||
p2,
|
||||
p4,
|
||||
nrow = 1,
|
||||
labels = "AUTO",
|
||||
label_size = 12,
|
||||
rel_widths = c(2.5,2,2)
|
||||
#, rel_heights = c(1)
|
||||
)
|
||||
|
||||
dev.off()
|
||||
|
||||
|
||||
|
||||
##################################################
|
||||
sensP
|
||||
consurfP
|
||||
#=================
|
||||
# Combine sensitivity + ConSurf
|
||||
#### Combine sensitivity + ConSurf ####
|
||||
# or ConSurf
|
||||
#=================
|
||||
w = 3
|
||||
|
|
@ -263,7 +307,7 @@ sens_siteCLP = paste0(outdir_images
|
|||
,"_sens_siteC_tile.png")
|
||||
|
||||
print(paste0("plot filename:", sens_siteCLP))
|
||||
png(sens_siteCLP, units = "in", width = 1, height = 1, res = 300 )
|
||||
png(sens_siteCLP, units = "in", width = 1.2, height = 1, res = 300 )
|
||||
sens_siteP
|
||||
dev.off()
|
||||
|
||||
|
|
@ -1,320 +0,0 @@
|
|||
#################
|
||||
# Numbers
|
||||
##################
|
||||
#all_dm_om_df = dm_om_wf_lf_data(df = merged_df3, gene = gene)
|
||||
all_dm_om_df = dm_om_wf_lf_data(df = df3, gene = gene)
|
||||
|
||||
# lf_duet = all_dm_om_df[['lf_duet']]
|
||||
# table(lf_duet$param_type)
|
||||
################################################################
|
||||
|
||||
#======================
|
||||
# Data: Dist+Genomics
|
||||
#======================
|
||||
lf_dist_genP = all_dm_om_df[['lf_dist_gen']]
|
||||
wf_dist_genP = all_dm_om_df[['wf_dist_gen']]
|
||||
|
||||
levels(lf_dist_genP$param_type)
|
||||
#lf_dist_genP$param_type <- factor(lf_dist_genP$param_type, levels=c("Log10(MAF)", "Lig Dist(Å)", "PPI Dist(Å)"))
|
||||
table(lf_dist_genP$param_type)
|
||||
|
||||
genomics_param = c("Log10(MAF)")
|
||||
|
||||
dist_genP = lf_bp2(lf_dist_genP
|
||||
#, p_title
|
||||
, violin_quantiles = c(0.5), monochrome = F)
|
||||
dist_genP
|
||||
#-------------------
|
||||
# Genomics data plot
|
||||
#-------------------
|
||||
genomics_dataP = lf_dist_genP[lf_dist_genP$param_type%in%genomics_param,]
|
||||
genomics_dataP$param_type = factor(genomics_dataP$param_type)
|
||||
table(genomics_dataP$param_type)
|
||||
|
||||
genomicsP = lf_bp2(genomics_dataP
|
||||
#, p_title = ""
|
||||
, violin_quantiles = c(0.5), monochrome = F)
|
||||
|
||||
genomicsP
|
||||
|
||||
#check
|
||||
wilcox.test(wf_dist_genP$`Log10(MAF)`[wf_dist_genP$mutation_info_labels=="R"]
|
||||
, wf_dist_genP$`Log10(MAF)`[wf_dist_genP$mutation_info_labels=="S"], paired = FALSE)
|
||||
|
||||
tapply(wf_dist_genP$`Log10(MAF)`, wf_dist_genP$mutation_info_labels, summary)
|
||||
|
||||
#-------------------
|
||||
# Distance data plot:
|
||||
#--------------------
|
||||
# not genomics
|
||||
dist_dataP = lf_dist_genP[!lf_dist_genP$param_type%in%genomics_param,]
|
||||
dist_dataP$param_type = factor(dist_dataP$param_type)
|
||||
table(dist_dataP$param_type)
|
||||
levels(dist_dataP$param_type)
|
||||
# relevel factor to control ordering of appearance of plot
|
||||
dist_dataP$param_type <-relevel(dist_dataP$param_type, "Lig Dist(Å)" )
|
||||
table(dist_dataP$param_type)
|
||||
levels(dist_dataP$param_type)
|
||||
|
||||
distanceP = lf_bp2(dist_dataP
|
||||
#, p_title = ""
|
||||
, violin_quantiles = c(0.5), monochrome = F)
|
||||
|
||||
distanceP
|
||||
|
||||
# check
|
||||
wilcox.test(wf_dist_genP$`PPI Dist(Å)`[wf_dist_genP$mutation_info_labels=="R"]
|
||||
, wf_dist_genP$`PPI Dist(Å)`[wf_dist_genP$mutation_info_labels=="S"], paired = FALSE)
|
||||
|
||||
wilcox.test(wf_dist_genP$`Lig Dist(Å)`[wf_dist_genP$mutation_info_labels=="R"]
|
||||
, wf_dist_genP$`Lig Dist(Å)`[wf_dist_genP$mutation_info_labels=="S"], paired = FALSE)
|
||||
|
||||
|
||||
wilcox.test(wf_dist_genP$`NA Dist(Å)`[wf_dist_genP$mutation_info_labels=="R"]
|
||||
, wf_dist_genP$`NA Dist(Å)`[wf_dist_genP$mutation_info_labels=="S"], paired = FALSE)
|
||||
|
||||
tapply(wf_dist_genP$`PPI Dist(Å)`, wf_dist_genP$mutation_info_labels, summary)
|
||||
|
||||
tapply(wf_dist_genP$`Lig Dist(Å)`, wf_dist_genP$mutation_info_labels, summary)
|
||||
|
||||
|
||||
#-------------------
|
||||
# Distance data plot: LigDist
|
||||
#--------------------
|
||||
levels(dist_dataP$param_type)[[1]]
|
||||
#Lig Dist(Å), PPI Dist(Å)
|
||||
dist_data_lig = dist_dataP[dist_dataP$param_type%in%c(levels(dist_dataP$param_type)[[1]]),]
|
||||
dist_data_lig$param_type = factor(dist_data_lig$param_type)
|
||||
table(dist_data_lig$param_type)
|
||||
levels(dist_data_lig$param_type)
|
||||
distanceP_lig = lf_bp2(dist_data_lig
|
||||
#, p_title = ""
|
||||
, violin_quantiles = c(0.5), monochrome = F)
|
||||
|
||||
distanceP_lig
|
||||
|
||||
if (tolower(gene)%in%geneL_ppi2){
|
||||
#-------------------
|
||||
# Distance data plot: LigDist
|
||||
#--------------------
|
||||
levels(dist_dataP$param_type)[[2]]
|
||||
#Lig Dist(Å), PPI Dist(Å)
|
||||
dist_data_ppi2 = dist_dataP[dist_dataP$param_type%in%c(levels(dist_dataP$param_type)[[2]]),]
|
||||
dist_data_ppi2$param_type = factor(dist_data_ppi2$param_type)
|
||||
table(dist_data_ppi2$param_type)
|
||||
levels(dist_data_ppi2$param_type)
|
||||
distanceP_ppi2 = lf_bp2(dist_data_ppi2
|
||||
#, p_title = ""
|
||||
, violin_quantiles = c(0.5), monochrome = F)
|
||||
|
||||
distanceP_ppi2
|
||||
}
|
||||
|
||||
if (tolower(gene)%in%geneL_na){
|
||||
#-------------------
|
||||
# Distance data plot: NADist
|
||||
#--------------------
|
||||
levels(dist_dataP$param_type)[[2]]
|
||||
#Lig Dist(Å), PPI Dist(Å)
|
||||
dist_data_na = dist_dataP[dist_dataP$param_type%in%c(levels(dist_dataP$param_type)[[2]]),]
|
||||
dist_data_na$param_type = factor(dist_data_na$param_type)
|
||||
table(dist_data_na$param_type)
|
||||
levels(dist_data_na$param_type)
|
||||
distanceP_na = lf_bp2(dist_data_na
|
||||
#, p_title = ""
|
||||
, violin_quantiles = c(0.5), monochrome = F)
|
||||
|
||||
distanceP_na
|
||||
}
|
||||
#==============
|
||||
# Plot:DUET
|
||||
#==============
|
||||
lf_duetP = all_dm_om_df[['lf_duet']]
|
||||
#lf_duetP = lf_duet[!lf_duet$param_type%in%c(static_colsP),]
|
||||
table(lf_duetP$param_type)
|
||||
lf_duetP$param_type = factor(lf_duetP$param_type)
|
||||
table(lf_duetP$param_type)
|
||||
|
||||
duetP = lf_bp2(lf_duetP
|
||||
#, p_title = ""
|
||||
, violin_quantiles = c(0.5), monochrome = F
|
||||
, dot_transparency = 0.2)
|
||||
|
||||
#==============
|
||||
# Plot:FoldX
|
||||
#==============
|
||||
lf_foldxP = all_dm_om_df[['lf_foldx']]
|
||||
#lf_foldxP = lf_foldx[!lf_foldx$param_type%in%c(static_colsP),]
|
||||
table(lf_foldxP$param_type)
|
||||
lf_foldxP$param_type = factor(lf_foldxP$param_type)
|
||||
table(lf_foldxP$param_type)
|
||||
|
||||
foldxP = lf_bp2(lf_foldxP
|
||||
#, p_title = ""
|
||||
, violin_quantiles = c(0.5), monochrome = F
|
||||
, dot_transparency = 0.1)
|
||||
|
||||
#==============
|
||||
# Plot:DeepDDG
|
||||
#==============
|
||||
lf_deepddgP = all_dm_om_df[['lf_deepddg']]
|
||||
#lf_deepddgP = lf_deepddg[!lf_deepddg$param_type%in%c(static_colsP),]
|
||||
table(lf_deepddgP$param_type)
|
||||
lf_deepddgP$param_type = factor(lf_deepddgP$param_type)
|
||||
table(lf_deepddgP$param_type)
|
||||
|
||||
deepddgP = lf_bp2(lf_deepddgP
|
||||
#, p_title = ""
|
||||
, violin_quantiles = c(0.5), monochrome = F
|
||||
, dot_transparency = 0.2)
|
||||
|
||||
deepddgP
|
||||
|
||||
#==============
|
||||
# Plot: Dynamut2
|
||||
#==============
|
||||
lf_dynamut2P = all_dm_om_df[['lf_dynamut2']]
|
||||
#lf_dynamut2P = lf_dynamut2[!lf_dynamut2$param_type%in%c(static_colsP),]
|
||||
table(lf_dynamut2P$param_type)
|
||||
lf_dynamut2P$param_type = factor(lf_dynamut2P$param_type)
|
||||
table(lf_dynamut2P$param_type)
|
||||
|
||||
dynamut2P = lf_bp2(lf_dynamut2P
|
||||
#, p_title = ""
|
||||
, violin_quantiles = c(0.5), monochrome = F
|
||||
, dot_transparency = 0.2)
|
||||
|
||||
|
||||
#==============
|
||||
# Plot:ConSurf
|
||||
#==============
|
||||
lf_consurfP = all_dm_om_df[['lf_consurf']]
|
||||
#lf_consurfP = lf_consurf[!lf_consurf$param_type%in%c(static_colsP),]
|
||||
table(lf_consurfP$param_type)
|
||||
lf_consurfP$param_type = factor(lf_consurfP$param_type)
|
||||
table(lf_consurfP$param_type)
|
||||
|
||||
consurfP = lf_bp2(lf_consurfP
|
||||
#, p_title = ""
|
||||
, violin_quantiles = c(0.5), monochrome = F)
|
||||
|
||||
#==============
|
||||
# Plot:PROVEAN
|
||||
#==============
|
||||
lf_proveanP = all_dm_om_df[['lf_provean']]
|
||||
#lf_proveanP = lf_provean[!lf_provean$param_type%in%c(static_colsP),]
|
||||
table(lf_proveanP$param_type)
|
||||
lf_proveanP$param_type = factor(lf_proveanP$param_type)
|
||||
table(lf_proveanP$param_type)
|
||||
|
||||
proveanP = lf_bp2(lf_proveanP
|
||||
#, p_title = ""
|
||||
, violin_quantiles = c(0.5), monochrome = F)
|
||||
|
||||
#==============
|
||||
# Plot:SNAP2
|
||||
#==============
|
||||
lf_snap2P = all_dm_om_df[['lf_snap2']]
|
||||
#lf_snap2P = lf_snap2[!lf_snap2$param_type%in%c(static_colsP),]
|
||||
table(lf_snap2P$param_type)
|
||||
lf_snap2P$param_type = factor(lf_snap2P$param_type)
|
||||
table(lf_snap2P$param_type)
|
||||
|
||||
snap2P = lf_bp2(lf_snap2P
|
||||
#, p_title = ""
|
||||
, violin_quantiles = c(0.5), monochrome = F)
|
||||
|
||||
|
||||
############################################################################
|
||||
#================
|
||||
# Plot: mCSM-lig
|
||||
#================
|
||||
lf_mcsm_ligP = all_dm_om_df[['lf_mcsm_lig']]
|
||||
#lf_mcsm_ligP = lf_mcsm_lig[!lf_mcsm_lig$param_type%in%c(static_colsP),]
|
||||
table(lf_mcsm_ligP$param_type)
|
||||
lf_mcsm_ligP$param_type = factor(lf_mcsm_ligP$param_type)
|
||||
table(lf_mcsm_ligP$param_type)
|
||||
|
||||
mcsmligP = lf_bp2(lf_mcsm_ligP
|
||||
#, p_title = ""
|
||||
, violin_quantiles = c(0.5), monochrome = F
|
||||
, dot_transparency = 1)
|
||||
|
||||
|
||||
#=================
|
||||
# Plot: mmCSM-lig2
|
||||
#=================
|
||||
lf_mmcsm_lig2P = all_dm_om_df[['lf_mmcsm_lig2']]
|
||||
#lf_mmcsm_lig2P = lf_mmcsm_lig2P[!lf_mmcsm_lig2P$param_type%in%c(static_colsP),]
|
||||
table(lf_mmcsm_lig2P$param_type)
|
||||
lf_mmcsm_lig2P$param_type = factor(lf_mmcsm_lig2P$param_type)
|
||||
table(lf_mmcsm_lig2P$param_type)
|
||||
|
||||
mcsmlig2P = lf_bp2(lf_mmcsm_lig2P
|
||||
#, p_title = ""
|
||||
, violin_quantiles = c(0.5), monochrome = F
|
||||
, dot_transparency = 1)
|
||||
|
||||
mcsmlig2P
|
||||
|
||||
#================
|
||||
# Plot: mCSM-ppi2
|
||||
#================
|
||||
if (tolower(gene)%in%geneL_ppi2){
|
||||
lf_mcsm_ppi2P = all_dm_om_df[['lf_mcsm_ppi2']]
|
||||
#lf_mcsm_ppi2P = lf_mcsm_ppi2[!lf_mcsm_ppi2$param_type%in%c(static_colsP),]
|
||||
table(lf_mcsm_ppi2P$param_type)
|
||||
lf_mcsm_ppi2P$param_type = factor(lf_mcsm_ppi2P$param_type)
|
||||
table(lf_mcsm_ppi2P$param_type)
|
||||
|
||||
mcsmppi2P = lf_bp2(lf_mcsm_ppi2P
|
||||
#, p_title = ""
|
||||
, violin_quantiles = c(0.5), monochrome = F
|
||||
, dot_transparency = 1)
|
||||
|
||||
}
|
||||
|
||||
#==============
|
||||
# Plot: mCSM-NA
|
||||
#==============
|
||||
if (tolower(gene)%in%geneL_na){
|
||||
lf_mcsm_naP = all_dm_om_df[['lf_mcsm_na']]
|
||||
#lf_mcsm_naP = lf_mcsm_na[!lf_mcsm_na$param_type%in%c(static_colsP),]
|
||||
table(lf_mcsm_naP$param_type)
|
||||
lf_mcsm_naP$param_type = factor(lf_mcsm_naP$param_type)
|
||||
table(lf_mcsm_naP$param_type)
|
||||
|
||||
mcsmnaP = lf_bp2(lf_mcsm_naP
|
||||
#, p_title = ""
|
||||
, violin_quantiles = c(0.5), monochrome = F
|
||||
, dot_transparency = 1)
|
||||
|
||||
}
|
||||
|
||||
######################################
|
||||
# Outplot with stats
|
||||
######################################
|
||||
# outdir_images = paste0("~/git/Writing/thesis/images/results/", tolower(gene), "/")
|
||||
#
|
||||
# dm_om_combinedP = paste0(outdir_images
|
||||
# ,tolower(gene)
|
||||
# ,"_dm_om_all.svg" )
|
||||
#
|
||||
# cat("DM OM plots with stats:", dm_om_combinedP)
|
||||
# svg(dm_om_combinedP, width = 32, height = 18)
|
||||
# cowplot::plot_grid(
|
||||
# cowplot::plot_grid(duetP, foldxP, deepddgP, dynamut2P, genomicsP, distanceP
|
||||
# , nrow=1
|
||||
# , rel_widths = c(1/7, 1/7,1/7,1/7, 1/7, 1.75/7)),
|
||||
# #, rel_widths = c(1/8, 1/8,1/8,1/8, 1/8, 2.75/8)), # for 3 distances
|
||||
# cowplot::plot_grid(consurfP, proveanP, snap2P
|
||||
# , mcsmligP
|
||||
# , mcsmlig2P
|
||||
# , mcsmppi2P
|
||||
# #, mcsmnaP
|
||||
# , nrow=1),
|
||||
# nrow=2)
|
||||
#
|
||||
# dev.off()
|
||||
|
||||
|
||||
176
scripts/plotting/plotting_thesis/gid/dm_om_plots_gid_ayout.R
Normal file
176
scripts/plotting/plotting_thesis/gid/dm_om_plots_gid_ayout.R
Normal file
|
|
@ -0,0 +1,176 @@
|
|||
# source dm_om_plots.R
|
||||
source("/home/tanu/git/LSHTM_analysis/scripts/plotting/plotting_thesis/dm_om_plots.R")
|
||||
|
||||
##### plots to combine ####
|
||||
duetP
|
||||
foldxP
|
||||
deepddgP
|
||||
dynamut2P
|
||||
genomicsP
|
||||
consurfP
|
||||
proveanP
|
||||
snap2P
|
||||
mcsmligP
|
||||
mcsmlig2P
|
||||
mcsmnaP
|
||||
|
||||
# Plot labels
|
||||
tit1 = "Stability changes"
|
||||
tit2 = "Genomic measure"
|
||||
tit3 = "Distance to partners"
|
||||
tit4 = "Evolutionary Conservation"
|
||||
tit5 = "Affinity changes"
|
||||
pt_size = 30
|
||||
|
||||
theme_georgia <- function(...) {
|
||||
theme_gray(base_family = "sans", ...) +
|
||||
theme(plot.title = element_text(face = "bold"))
|
||||
}
|
||||
|
||||
|
||||
title_theme <- calc_element("plot.title", theme_georgia())
|
||||
|
||||
pt1 = ggdraw() +
|
||||
draw_label(
|
||||
tit1,
|
||||
fontfamily = title_theme$family,
|
||||
fontface = title_theme$face,
|
||||
#size = title_theme$size
|
||||
size = pt_size
|
||||
)
|
||||
|
||||
pt2 = ggdraw() +
|
||||
draw_label(
|
||||
tit2,
|
||||
fontfamily = title_theme$family,
|
||||
fontface = title_theme$face,
|
||||
size = pt_size
|
||||
)
|
||||
|
||||
pt3 = ggdraw() +
|
||||
draw_label(
|
||||
tit3,
|
||||
fontfamily = title_theme$family,
|
||||
fontface = title_theme$face,
|
||||
size = pt_size
|
||||
)
|
||||
|
||||
pt4 = ggdraw() +
|
||||
draw_label(
|
||||
tit4,
|
||||
fontfamily = title_theme$family,
|
||||
fontface = title_theme$face,
|
||||
size = pt_size
|
||||
)
|
||||
|
||||
|
||||
pt5 = ggdraw() +
|
||||
draw_label(
|
||||
tit5,
|
||||
fontfamily = title_theme$family,
|
||||
fontface = title_theme$face,
|
||||
size = pt_size
|
||||
)
|
||||
|
||||
#======================
|
||||
# Output plot function
|
||||
#======================
|
||||
OutPlot_dm_om = function(x){
|
||||
|
||||
# dist b/w plot title and plot
|
||||
relH_tp = c(0.08, 0.92)
|
||||
|
||||
my_label_size = 25
|
||||
#----------------
|
||||
# Top panel
|
||||
#----------------
|
||||
top_panel = cowplot::plot_grid(
|
||||
cowplot::plot_grid(pt1,
|
||||
cowplot::plot_grid(duetP, foldxP, deepddgP, dynamut2P
|
||||
, nrow = 1
|
||||
, labels = c("A", "B", "C", "D")
|
||||
, label_size = my_label_size)
|
||||
, ncol = 1
|
||||
, rel_heights = relH_tp
|
||||
),
|
||||
NULL,
|
||||
cowplot::plot_grid(pt2,
|
||||
cowplot::plot_grid(genomicsP
|
||||
, nrow = 1
|
||||
, labels = c("E")
|
||||
, label_size = my_label_size)
|
||||
, ncol = 1
|
||||
, rel_heights = relH_tp
|
||||
),
|
||||
NULL,
|
||||
cowplot::plot_grid(pt3,
|
||||
cowplot::plot_grid( #distanceP
|
||||
distanceP_lig
|
||||
, distanceP_na
|
||||
, nrow = 1
|
||||
, labels = c("F", "G")
|
||||
, label_size = my_label_size)
|
||||
, ncol = 1
|
||||
, rel_heights = relH_tp
|
||||
),
|
||||
nrow = 1,
|
||||
rel_widths = c(2/7, 0.1/7, 0.5/7, 0.1/7, 1/7)
|
||||
)
|
||||
|
||||
#----------------
|
||||
# Bottom panel
|
||||
#----------------
|
||||
bottom_panel = cowplot::plot_grid(
|
||||
cowplot::plot_grid(pt4,
|
||||
cowplot::plot_grid(consurfP, proveanP, snap2P
|
||||
, nrow = 1
|
||||
, labels = c("H", "I", "J")
|
||||
, label_size = my_label_size)
|
||||
, ncol = 1
|
||||
, rel_heights =relH_tp
|
||||
),NULL,
|
||||
cowplot::plot_grid(pt5,
|
||||
cowplot::plot_grid(mcsmligP
|
||||
, mcsmlig2P
|
||||
, mcsmnaP
|
||||
, nrow = 1
|
||||
, labels = c("K", "L", "M")
|
||||
, label_size = my_label_size)
|
||||
, ncol = 1
|
||||
, rel_heights = relH_tp
|
||||
),NULL,
|
||||
nrow = 1,
|
||||
rel_widths = c(3/6,0.1/6,3/6, 0.1/6 )
|
||||
)
|
||||
|
||||
#-------------------------------
|
||||
# combine: Top and Bottom panel
|
||||
#-------------------------------
|
||||
cowplot::plot_grid (top_panel, bottom_panel
|
||||
, nrow =2
|
||||
, rel_widths = c(1, 1)
|
||||
, align = "hv")
|
||||
}
|
||||
|
||||
#=====================
|
||||
# OutPlot: svg and png
|
||||
#======================
|
||||
dm_om_combinedP = paste0(outdir_images
|
||||
,tolower(gene)
|
||||
,"_dm_om_all.svg")
|
||||
|
||||
cat("DM OM plots with stats:", dm_om_combinedP)
|
||||
svg(dm_om_combinedP, width = 32, height = 18)
|
||||
|
||||
OutPlot_dm_om()
|
||||
dev.off()
|
||||
|
||||
|
||||
dm_om_combinedP_png = paste0(outdir_images
|
||||
,tolower(gene)
|
||||
,"_dm_om_all.png")
|
||||
cat("DM OM plots with stats:", dm_om_combinedP_png)
|
||||
png(dm_om_combinedP_png, width = 32, height = 18, units = "in", res = 300)
|
||||
|
||||
OutPlot_dm_om()
|
||||
dev.off()
|
||||
|
|
@ -14,9 +14,8 @@ my_gg_pairs=function(plot_df, plot_title
|
|||
title="ρ",
|
||||
digits=2,
|
||||
justify_labels = "centre",
|
||||
#title_args=c(colour="black"),
|
||||
title_args=c(size=tt_args_size),#2.5
|
||||
group_args=c(size=gp_args_size)#2.5
|
||||
title_args=list(size=tt_args_size, colour="black"),#2.5
|
||||
group_args=list(size=gp_args_size)#2.5
|
||||
)
|
||||
),
|
||||
lower = list(
|
||||
|
|
@ -73,7 +72,7 @@ if (tolower(gene)%in%geneL_ppi2){
|
|||
}
|
||||
|
||||
if (tolower(gene)%in%geneL_na){
|
||||
corr_affinity_df[corr_affinity_df["NCA-Dist"]>DistCutOff,"mCSM-NA"]=0
|
||||
corr_affinity_df[corr_affinity_df["NA-Dist"]>DistCutOff,"mCSM-NA"]=0
|
||||
}
|
||||
|
||||
# count 0
|
||||
|
|
@ -97,6 +96,7 @@ corr_df_ps = corr_plotdf[, corr_ps_colnames]
|
|||
|
||||
# Plot #1
|
||||
plot_corr_df_ps = my_gg_pairs(corr_df_ps, plot_title="Stability estimates")
|
||||
|
||||
##########################################################
|
||||
#================
|
||||
# Conservation
|
||||
|
|
@ -142,37 +142,62 @@ corr_df_aff = corr_affinity_df[, corr_aff_colnames]
|
|||
colnames(corr_df_aff)
|
||||
|
||||
# Plot #3
|
||||
plot_corr_df_aff = my_gg_pairs(corr_df_aff,
|
||||
plot_title="Affinity estimates",
|
||||
tt_args_size = 4,
|
||||
gp_args_size = 4)
|
||||
plot_corr_df_aff = my_gg_pairs(corr_df_aff
|
||||
, plot_title="Affinity estimates"
|
||||
#, tt_args_size = 4
|
||||
#, gp_args_size = 4
|
||||
)
|
||||
|
||||
#=============
|
||||
# combine
|
||||
#=============
|
||||
#### Combine plots #####
|
||||
# #png("/home/tanu/tmp/gg_pairs_all.png", height = 6, width=11.75, unit="in",res=300)
|
||||
# png(paste0(outdir_images
|
||||
# ,tolower(gene)
|
||||
# ,"_CorrAB.png"), height = 6, width=11.75, unit="in",res=300)
|
||||
#
|
||||
# cowplot::plot_grid(ggmatrix_gtable(plot_corr_df_ps),
|
||||
# ggmatrix_gtable(plot_corr_df_cons),
|
||||
# # ggmatrix_gtable(plot_corr_df_aff),
|
||||
# # nrow=1, ncol=3, rel_heights = 7,7,3
|
||||
# nrow=1,
|
||||
# #rel_heights = 1,1
|
||||
# labels = "AUTO",
|
||||
# label_size = 12)
|
||||
# dev.off()
|
||||
#
|
||||
# # affinity corr
|
||||
# #png("/home/tanu/tmp/gg_pairs_affinity.png", height =7, width=7, unit="in",res=300)
|
||||
# png(paste0(outdir_images
|
||||
# ,tolower(gene)
|
||||
# ,"_CorrC.png"), height =7, width=7, unit="in",res=300)
|
||||
#
|
||||
# cowplot::plot_grid(ggmatrix_gtable(plot_corr_df_aff),
|
||||
# labels = "C",
|
||||
# label_size = 12)
|
||||
# dev.off()
|
||||
|
||||
#png("/home/tanu/tmp/gg_pairs_all.png", height = 6, width=11.75, unit="in",res=300)
|
||||
#### Combine A ####
|
||||
png(paste0(outdir_images
|
||||
,tolower(gene)
|
||||
,"_CorrAB.png"), height = 7, width=11.75, unit="in",res=300)
|
||||
,"_CorrA.png"), height =8, width=8, unit="in",res=300)
|
||||
|
||||
cowplot::plot_grid(ggmatrix_gtable(plot_corr_df_ps),
|
||||
ggmatrix_gtable(plot_corr_df_cons),
|
||||
labels = "A",
|
||||
label_size = 12)
|
||||
dev.off()
|
||||
|
||||
#### Combine B+C ####
|
||||
# B + C
|
||||
png(paste0(outdir_images
|
||||
,tolower(gene)
|
||||
,"_CorrBC.png"), height = 6, width=11.75, unit="in",res=300)
|
||||
|
||||
cowplot::plot_grid(ggmatrix_gtable(plot_corr_df_cons),
|
||||
ggmatrix_gtable(plot_corr_df_aff),
|
||||
# ggmatrix_gtable(plot_corr_df_aff),
|
||||
# nrow=1, ncol=3, rel_heights = 7,7,3
|
||||
nrow=1,
|
||||
rel_widths = c(1.5,1),
|
||||
labels = "AUTO",
|
||||
#rel_heights = 1,1
|
||||
labels = c("B", "C"),
|
||||
label_size = 12)
|
||||
dev.off()
|
||||
|
||||
# affinity corr
|
||||
#png("/home/tanu/tmp/gg_pairs_affinity.png", height =7, width=7, unit="in",res=300)
|
||||
png(paste0(outdir_images
|
||||
,tolower(gene)
|
||||
,"_CorrC.png"), height =7, width=7, unit="in",res=300)
|
||||
|
||||
cowplot::plot_grid(ggmatrix_gtable(plot_corr_df_aff),
|
||||
labels = "C",
|
||||
label_size = 12)
|
||||
dev.off()
|
||||
|
|
|
|||
2
scripts/plotting/plotting_thesis/gid/gid_other_plots.R
Normal file
2
scripts/plotting/plotting_thesis/gid/gid_other_plots.R
Normal file
|
|
@ -0,0 +1,2 @@
|
|||
|
||||
source("/home/tanu/git/LSHTM_analysis/scripts/plotting/plotting_thesis/linage_bp_dist_layout.R")
|
||||
173
scripts/plotting/plotting_thesis/gid/pe_sens_site_count_gid.R
Normal file
173
scripts/plotting/plotting_thesis/gid/pe_sens_site_count_gid.R
Normal file
|
|
@ -0,0 +1,173 @@
|
|||
source("/home/tanu/git/LSHTM_analysis/scripts/plotting/plotting_thesis/gid/prominent_effects_gid.R")
|
||||
source("/home/tanu/git/LSHTM_analysis/scripts/plotting/plotting_thesis/gid/sensitivity_count_gid.R")
|
||||
|
||||
##############################################################
|
||||
# PE count
|
||||
#lig-- na--ppi2--stab
|
||||
# pe_colour_map = c("DD_lig" = "#ffd700" # gold
|
||||
# , "SS_lig" = "#f0e68c" # khaki
|
||||
#
|
||||
# , "DD_nucleic_acid"= "#a0522d" # sienna
|
||||
# , "SS_nucleic_acid"= "#d2b48c" # tan
|
||||
#
|
||||
# , "DD_ppi2" = "#da70d6" # orchid
|
||||
# , "SS_ppi2" = "#ff1493" # deeppink
|
||||
#
|
||||
# , "DD_stability" = "#f8766d" # red
|
||||
# , "SS_stability" = "#00BFC4") # blue
|
||||
table(str_df_plot_cols$pe_effect_outcome)
|
||||
##############################################################
|
||||
#===========
|
||||
#PE count
|
||||
#===========
|
||||
rects <- data.frame(x=1:6,
|
||||
colors = c("#ffd700" ,
|
||||
"#f0e68c" ,
|
||||
|
||||
"#a0522d" ,
|
||||
"#d2b48c" ,
|
||||
|
||||
"#f8766d" ,
|
||||
"#00BFC4")
|
||||
)
|
||||
|
||||
rects$text = c("-ve Lig"
|
||||
, "+ve Lig"
|
||||
|
||||
, "-ve\nNuc.Acid"
|
||||
, "+ve\nNuc.Acid"
|
||||
|
||||
, "-ve stability"
|
||||
, "+ve stability"
|
||||
)
|
||||
|
||||
cell1 = table(str_df_plot_cols$pe_effect_outcome)[["DD_lig"]]
|
||||
cell2 = 0
|
||||
|
||||
cell3 = table(str_df_plot_cols$pe_effect_outcome)[["DD_nucleic_acid"]]
|
||||
cell4 = table(str_df_plot_cols$pe_effect_outcome)[["SS_nucleic_acid"]]
|
||||
|
||||
#cell5 = table(str_df_plot_cols$pe_effect_outcome)[["DD_ppi2"]]
|
||||
#cell6 = table(str_df_plot_cols$pe_effect_outcome)[["SS_ppi2"]]
|
||||
|
||||
cell7 = table(str_df_plot_cols$pe_effect_outcome)[["DD_stability"]]
|
||||
cell8 = table(str_df_plot_cols$pe_effect_outcome)[["SS_stability"]]
|
||||
|
||||
|
||||
#rects$numbers = c(38, 0, 22, 9, 108, 681) #for embb
|
||||
rects$numbers = c(cell1, cell2,
|
||||
cell3, cell4,
|
||||
# cell5, cell6,
|
||||
cell7, cell8)
|
||||
|
||||
rects$num_labels = paste0("n=", rects$numbers)
|
||||
|
||||
rects
|
||||
#------
|
||||
# Plot
|
||||
#------
|
||||
#https://stackoverflow.com/questions/47986055/create-a-rectangle-filled-with-text
|
||||
peP = ggplot(rects, aes(x, y = 0, fill = colors, label = paste0(text,"\n", num_labels))) +
|
||||
geom_tile(width = 1, height = 1) + # make square tiles
|
||||
geom_text(color = "black", size = 1.7) + # add white text in the middle
|
||||
scale_fill_identity(guide = "none") + # color the tiles with the colors in the data frame
|
||||
coord_fixed() + # make sure tiles are square
|
||||
coord_flip()+ scale_x_reverse() +
|
||||
# theme_void() # remove any axis markings
|
||||
theme_nothing() # remove any axis markings
|
||||
peP
|
||||
|
||||
#------
|
||||
# Plot: this one is better
|
||||
#------
|
||||
peP2 = ggplot(rects, aes(x, y = 0, fill = colors, label = paste0(text,"\n", num_labels))) +
|
||||
geom_tile() + # make square tiles
|
||||
geom_text(color = "black", size = 1.6) + # add white text in the middle
|
||||
scale_fill_identity(guide = "none") + # color the tiles with the colors in the data frame
|
||||
coord_fixed() + # make sure tiles are square
|
||||
theme_nothing() # remove any axis markings
|
||||
peP2
|
||||
|
||||
########################################################
|
||||
# From: script sensitivity_count per gene
|
||||
#===============================
|
||||
# Sensitivity count: SITE
|
||||
#===============================
|
||||
#--------
|
||||
# embb
|
||||
#--------
|
||||
#rsc = 54
|
||||
#ccc = 46
|
||||
#ssc = 470
|
||||
|
||||
rsc = site_Rc; rsc
|
||||
ccc = site_Cc; ccc
|
||||
ssc = site_Sc; ssc
|
||||
|
||||
rect_rs_siteC <- data.frame(x=1:3,
|
||||
colors = c("red",
|
||||
"purple",
|
||||
"blue")
|
||||
)
|
||||
|
||||
rect_rs_siteC
|
||||
rect_rs_siteC$text = c("Resistant",
|
||||
"Common",
|
||||
"Sensitive")
|
||||
|
||||
rect_rs_siteC$numbers = c(rsc,ccc,ssc)
|
||||
rect_rs_siteC$num_labels = paste0("n=", rect_rs_siteC$numbers)
|
||||
rect_rs_siteC
|
||||
|
||||
#------
|
||||
# Plot
|
||||
#------
|
||||
sens_siteP = ggplot(rect_rs_siteC, aes(x, y = 0,
|
||||
fill = colors,
|
||||
label = num_labels
|
||||
#,label = paste0(text,"\n", num_labels)
|
||||
)) +
|
||||
geom_tile(width = 1, height = 1) +
|
||||
#geom_text(color = "black", size = 1.7) +
|
||||
geom_label(color = "black", size = 1.7,fill = "white", alpha=0.7) +
|
||||
scale_fill_identity(guide = "none") +
|
||||
coord_fixed()+
|
||||
theme_nothing() # remove any axis markings
|
||||
sens_siteP
|
||||
|
||||
################################################################
|
||||
#===============================
|
||||
# Sensitivity count: Mutations
|
||||
#===============================
|
||||
table(sensP_df$sensitivity)
|
||||
muts_Rc = table(sensP_df$sensitivity)[["R"]]
|
||||
muts_Sc = table(sensP_df$sensitivity)[["S"]]
|
||||
rect_sens <- data.frame(x=1:2,
|
||||
colors = c("red",
|
||||
"blue")
|
||||
)
|
||||
|
||||
rect_sens$text = c("Resistant",
|
||||
"Sensitive")
|
||||
rect_sens$numbers = c(muts_Rc,muts_Sc)
|
||||
rect_sens$num_labels = paste0("n=", rect_sens$numbers)
|
||||
rect_sens
|
||||
#------
|
||||
# Plot
|
||||
#------
|
||||
sensP = ggplot(rect_sens, aes(x, y = 0,
|
||||
fill = colors,
|
||||
label = paste0(text,"\n", num_labels))) +
|
||||
geom_tile(width = 1, height = 1) +
|
||||
#geom_text(color = "black", size = 1.7) +
|
||||
geom_label(color = "black", size = 1.7,fill = "white", alpha=0.7) +
|
||||
scale_fill_identity(guide = "none") +
|
||||
coord_fixed()+
|
||||
theme_nothing() # remove any axis markings
|
||||
sensP
|
||||
|
||||
sensP2 = sensP +
|
||||
coord_flip() + scale_x_reverse()
|
||||
sensP2
|
||||
|
||||
|
||||
341
scripts/plotting/plotting_thesis/gid/prominent_effects_gid.R
Normal file
341
scripts/plotting/plotting_thesis/gid/prominent_effects_gid.R
Normal file
|
|
@ -0,0 +1,341 @@
|
|||
#!/usr/bin/env Rscript
|
||||
#source("~/git/LSHTM_analysis/config/gid.R")
|
||||
# get plotting dfs
|
||||
#source("~/git/LSHTM_analysis/scripts/plotting/get_plotting_dfs.R")
|
||||
########################################################
|
||||
pos_colname = "position"
|
||||
|
||||
#-------------
|
||||
# from ~/git/LSHTM_analysis/scripts/plotting/plotting_colnames.R
|
||||
#-------------
|
||||
length(all_stability_cols); length(raw_stability_cols)
|
||||
length(scaled_stability_cols); length(outcome_stability_cols)
|
||||
length(affinity_dist_colnames)
|
||||
|
||||
|
||||
static_cols = c("mutationinformation",
|
||||
#"position",
|
||||
pos_colname,
|
||||
"sensitivity")
|
||||
|
||||
other_cols_all = c(scaled_stability_cols, scaled_affinity_cols, affinity_dist_colnames)
|
||||
|
||||
#omit avg cols and foldx_scaled_signC cols
|
||||
other_cols = other_cols_all[grep("avg", other_cols_all, invert = T)]
|
||||
other_cols = other_cols[grep("foldx_scaled_signC",other_cols, invert = T )]
|
||||
other_cols
|
||||
|
||||
cols_to_extract = c(static_cols, other_cols)
|
||||
cat("\nExtracting cols:", cols_to_extract)
|
||||
expected_ncols = length(static_cols) + length(other_cols)
|
||||
expected_ncols
|
||||
|
||||
str_df = merged_df3[, cols_to_extract]
|
||||
|
||||
if (ncol(str_df) == expected_ncols){
|
||||
cat("\nPASS: successfully extracted cols for calculating prominent effects")
|
||||
}else{
|
||||
stop("\nAbort: Could not extract cols for calculating prominent effects")
|
||||
}
|
||||
|
||||
#=========================
|
||||
# Masking affinity columns
|
||||
#=========================
|
||||
# First make values for affinity cols 0 when their corresponding dist >10
|
||||
head(str_df)
|
||||
|
||||
# replace in place affinity values >10
|
||||
str_df[str_df["ligand_distance"]>10,"affinity_scaled"]=0
|
||||
str_df[str_df["ligand_distance"]>10,"mmcsm_lig_scaled"]=0
|
||||
|
||||
#ppi2 gene: replace in place ppi2 affinity values where ppi2 dist >10
|
||||
if (tolower(gene)%in%geneL_ppi2){
|
||||
str_df[str_df["interface_dist"]>10,"mcsm_ppi2_scaled"]=0
|
||||
}
|
||||
|
||||
# na gene: replace in place na affinity values where na dist >10
|
||||
if (tolower(gene)%in%geneL_na){
|
||||
str_df[str_df["nca_distance"]>10,"mcsm_na_scaled"]=0
|
||||
}
|
||||
|
||||
colnames(str_df)
|
||||
head(str_df)
|
||||
|
||||
# get names of cols to calculate the prominent effects from
|
||||
# scaled_cols_tc = c("duet_scaled",
|
||||
# "deepddg_scaled",
|
||||
# "ddg_dynamut2_scaled",
|
||||
# "foldx_scaled",
|
||||
# "affinity_scaled",
|
||||
# "mmcsm_lig_scaled",
|
||||
# "mcsm_ppi2_scaled", "mcsm_na_scaled")
|
||||
|
||||
scaled_cols_tc = other_cols[grep("scaled", other_cols)]
|
||||
###############################################
|
||||
|
||||
|
||||
#===============
|
||||
# whole df
|
||||
#===============
|
||||
give_col=function(x,y,df=str_df){
|
||||
df[df[[pos_colname]]==x,y]
|
||||
}
|
||||
|
||||
for (i in unique(str_df[[pos_colname]]) ){
|
||||
print(i)
|
||||
#cat(length(unique(str_df[[pos_colname]])))
|
||||
|
||||
biggest = max(abs(give_col(i,scaled_cols_tc)))
|
||||
|
||||
str_df[str_df[[pos_colname]]==i,'abs_max_effect'] = biggest
|
||||
str_df[str_df[[pos_colname]]==i,'effect_type']= names(
|
||||
give_col(i,scaled_cols_tc)[which(
|
||||
abs(
|
||||
give_col(i,scaled_cols_tc)
|
||||
) == biggest, arr.ind=T
|
||||
)[, "col"]])[1]
|
||||
|
||||
effect_name = unique(str_df[str_df[[pos_colname]]==i,'effect_type'])#[1] # pick first one in case we have multiple exact values
|
||||
|
||||
# get index/rowname for value of max effect, and then use it to get the original sign
|
||||
# here
|
||||
#ind = rownames(which(abs(str_df[str_df[[pos_colname]]==i,c('position',effect_name)][effect_name])== biggest, arr.ind=T))
|
||||
ind = rownames(which(abs(str_df[str_df[[pos_colname]]==i,c(pos_colname,effect_name)][effect_name])== biggest, arr.ind=T))
|
||||
|
||||
str_df[str_df[[pos_colname]]==i,'effect_sign'] = sign(str_df[effect_name][ind,])[1]
|
||||
}
|
||||
|
||||
# ends with suffix 2 if dups
|
||||
str_df$effect_type = sub("\\.[0-9]+", "", str_df$effect_type) # cull duplicate effect types that happen when there are exact duplicate values
|
||||
colnames(str_df)
|
||||
table(str_df$effect_type)
|
||||
|
||||
# check
|
||||
str_df_check = str_df[str_df[[pos_colname]]%in%c(24, 32, 160, 303, 334),]
|
||||
|
||||
#================
|
||||
# for Plots
|
||||
#================
|
||||
str_df_short = str_df[, c("mutationinformation",
|
||||
#"position",
|
||||
pos_colname,
|
||||
"sensitivity"
|
||||
, "effect_type"
|
||||
, "effect_sign")]
|
||||
|
||||
table(str_df_short$effect_type)
|
||||
table(str_df_short$effect_sign)
|
||||
str(str_df_short)
|
||||
|
||||
# assign pe outcome
|
||||
str_df_short$pe_outcome = ifelse(str_df_short$effect_sign<0, "DD", "SS")
|
||||
table(str_df_short$pe_outcome )
|
||||
table(str_df_short$effect_sign)
|
||||
|
||||
#==============
|
||||
# group effect type:
|
||||
# lig, ppi2, nuc. acid, stability
|
||||
#==============
|
||||
affcols = c("affinity_scaled", "mmcsm_lig_scaled")
|
||||
nuc_na_cols = c("mcsm_na_scaled")
|
||||
|
||||
#lig
|
||||
table(str_df_short$effect_type)
|
||||
str_df_short$effect_grouped = ifelse(str_df_short$effect_type%in%affcols
|
||||
, "lig"
|
||||
, str_df_short$effect_type)
|
||||
table(str_df_short$effect_grouped)
|
||||
|
||||
|
||||
#na
|
||||
str_df_short$effect_grouped = ifelse(str_df_short$effect_grouped%in%nuc_na_cols
|
||||
, "nucleic_acid"
|
||||
, str_df_short$effect_grouped)
|
||||
table(str_df_short$effect_grouped)
|
||||
|
||||
#stability
|
||||
str_df_short$effect_grouped = ifelse(!str_df_short$effect_grouped%in%c("lig",
|
||||
"nucleic_acid")
|
||||
, "stability"
|
||||
, str_df_short$effect_grouped)
|
||||
|
||||
table(str_df_short$effect_grouped)
|
||||
|
||||
# create a sign as well
|
||||
str_df_short$pe_effect_outcome = paste0(str_df_short$pe_outcome, "_"
|
||||
, str_df_short$effect_grouped)
|
||||
|
||||
table(str_df_short$pe_effect_outcome)
|
||||
|
||||
#####################################################################
|
||||
# Chimera: for colouring
|
||||
####################################################################
|
||||
|
||||
#-------------------------------------
|
||||
# get df with unique position
|
||||
#--------------------------------------
|
||||
#data[!duplicated(data$x), ]
|
||||
str_df_plot = str_df_short[!duplicated(str_df[[pos_colname]]),]
|
||||
|
||||
if (nrow(str_df_plot) == length(unique(str_df[[pos_colname]]))){
|
||||
cat("\nPASS: successfully extracted df with unique positions")
|
||||
}else{
|
||||
stop("\nAbort: Could not extract df with unique positions")
|
||||
}
|
||||
|
||||
#-------------------------------------
|
||||
# generate colours for effect types
|
||||
#--------------------------------------
|
||||
str_df_plot_cols = str_df_plot[, c(pos_colname,
|
||||
"sensitivity",
|
||||
"pe_outcome",
|
||||
"effect_grouped",
|
||||
"pe_effect_outcome")]
|
||||
head(str_df_plot_cols)
|
||||
|
||||
# colour intensity based on sign
|
||||
#str_df_plot_cols$colour_hue = ifelse(str_df_plot_cols$effect_sign<0, "bright", "light")
|
||||
str_df_plot_cols$colour_hue = ifelse(str_df_plot_cols$pe_outcome=="DD", "bright", "light")
|
||||
|
||||
table(str_df_plot_cols$colour_hue); table(str_df_plot$pe_outcome)
|
||||
head(str_df_plot_cols)
|
||||
|
||||
# colour based on effect
|
||||
table(str_df_plot_cols$pe_effect_outcome)
|
||||
|
||||
# colors = c("#ffd700" #gold
|
||||
# , "#f0e68c" #khaki
|
||||
# , "#da70d6"# orchid
|
||||
# , "#ff1493"# deeppink
|
||||
# , "#a0522d" #sienna
|
||||
# , "#d2b48c" # tan
|
||||
# , "#00BFC4" #, "#007d85" #blue
|
||||
# , "#F8766D" )# red
|
||||
|
||||
pe_colour_map = c("DD_lig" = "#ffd700" # gold
|
||||
, "SS_lig" = "#f0e68c" # khaki
|
||||
|
||||
, "DD_nucleic_acid"= "#a0522d" # sienna
|
||||
, "SS_nucleic_acid"= "#d2b48c" # tan
|
||||
|
||||
, "DD_ppi2" = "#da70d6" # orchid
|
||||
, "SS_ppi2" = "#ff1493" # deeppink
|
||||
|
||||
, "DD_stability" = "#f8766d" # red
|
||||
, "SS_stability" = "#00BFC4") # blue
|
||||
|
||||
#unlist(d[c('a', 'a', 'c', 'b')], use.names=FALSE)
|
||||
|
||||
#map the colours
|
||||
str_df_plot_cols$colour_map= unlist(map(str_df_plot_cols$pe_effect_outcome
|
||||
,function(x){pe_colour_map[[x]]}
|
||||
))
|
||||
head(str_df_plot_cols$colour_map)
|
||||
table(str_df_plot_cols$colour_map)
|
||||
table(str_df_plot_cols$pe_effect_outcome)
|
||||
|
||||
# str_df_plot_cols$colours = paste0(str_df_plot_cols$colour_hue
|
||||
# , "_"
|
||||
# , str_df_plot_cols$colour_map)
|
||||
# head(str_df_plot_cols$colours)
|
||||
# table(str_df_plot_cols$colours)
|
||||
#
|
||||
#
|
||||
# class(str_df_plot_cols$colour_map)
|
||||
# str(str_df_plot_cols)
|
||||
|
||||
# sort by colour
|
||||
head(str_df_plot_cols)
|
||||
str_df_plot_cols = str_df_plot_cols[order(str_df_plot_cols$colour_map), ]
|
||||
head(str_df_plot_cols)
|
||||
|
||||
#======================================
|
||||
# write file with prominent effects
|
||||
#======================================
|
||||
outdir_images = paste0("~/git/Writing/thesis/images/results/", tolower(gene), "/")
|
||||
write.csv(str_df_plot_cols, paste0(outdir_images, tolower(gene), "_prominent_effects.csv"))
|
||||
|
||||
################################
|
||||
# printing for chimera
|
||||
###############################
|
||||
chain_suffix = ".A"
|
||||
str_df_plot_cols$pos_chain = paste0(str_df_plot_cols[[pos_colname]], chain_suffix)
|
||||
table(str_df_plot_cols$colour_map)
|
||||
table(str_df_plot_cols$pe_effect_outcome)
|
||||
|
||||
#===================================================
|
||||
#-------------------
|
||||
# Ligand Affinity
|
||||
#-------------------
|
||||
# -ve Lig Aff
|
||||
dd_lig = str_df_plot_cols[str_df_plot_cols$pe_effect_outcome=="DD_lig",]
|
||||
if (nrow(dd_lig) == table(str_df_plot_cols$pe_effect_outcome)[['DD_lig']]){
|
||||
dd_lig_pos = dd_lig[[pos_colname]]
|
||||
}else{
|
||||
stop("Abort: DD affinity colour numbers mismtatch")
|
||||
}
|
||||
|
||||
# +ve Lig Aff
|
||||
ss_lig = str_df_plot_cols[str_df_plot_cols$pe_effect_outcome=="SS_lig",]
|
||||
if (!empty(ss_lig)){
|
||||
if (nrow(ss_lig) == table(str_df_plot_cols$pe_effect_outcome)[['SS_lig']]){
|
||||
ss_lig_pos = ss_lig[[pos_colname]]
|
||||
}else{
|
||||
stop("Abort: SS affinity colour numbers mismtatch")
|
||||
}
|
||||
#put in chimera cmd
|
||||
paste0(dd_lig_pos,chain_suffix)
|
||||
paste0(ss_lig_pos,chain_suffix)
|
||||
|
||||
}
|
||||
|
||||
#===================================================
|
||||
#------------------------
|
||||
# Nucleic Acid Affinity
|
||||
#------------------------
|
||||
# -ve NA aff
|
||||
dd_nca = str_df_plot_cols[str_df_plot_cols$pe_effect_outcome=="DD_nucleic_acid",]
|
||||
if (nrow(dd_nca) == table(str_df_plot_cols$pe_effect_outcome)[['DD_nucleic_acid']]){
|
||||
dd_nca_pos = dd_nca[[pos_colname]]
|
||||
}else{
|
||||
stop("Abort: DD nucleic_acid colour numbers mismtatch")
|
||||
}
|
||||
|
||||
# +ve NA aff
|
||||
ss_nca = str_df_plot_cols[str_df_plot_cols$pe_effect_outcome=="SS_nucleic_acid",]
|
||||
if (nrow(ss_nca) == table(str_df_plot_cols$pe_effect_outcome)[['SS_nucleic_acid']]){
|
||||
ss_nca_pos = ss_nca[[pos_colname]]
|
||||
}else{
|
||||
stop("Abort: SS nucleic_acid colour numbers mismtatch")
|
||||
}
|
||||
|
||||
#put in chimera cmd
|
||||
paste0(dd_nca_pos, chain_suffix)
|
||||
paste0(ss_nca_pos, chain_suffix)
|
||||
|
||||
#=========================================================
|
||||
#------------------------
|
||||
# Stability
|
||||
#------------------------
|
||||
# -ve Stability
|
||||
dd_stability = str_df_plot_cols[str_df_plot_cols$pe_effect_outcome=="DD_stability",]
|
||||
if (nrow(dd_stability) == table(str_df_plot_cols$pe_effect_outcome)[['DD_stability']]){
|
||||
dd_stability_pos = dd_stability[[pos_colname]]
|
||||
}else{
|
||||
stop("Abort: DD Stability colour numbers mismtatch")
|
||||
}
|
||||
|
||||
# +ve Stability
|
||||
ss_stability = str_df_plot_cols[str_df_plot_cols$pe_effect_outcome=="SS_stability",]
|
||||
if (nrow(ss_stability) == table(str_df_plot_cols$pe_effect_outcome)[['SS_stability']]){
|
||||
ss_stability_pos = ss_stability[[pos_colname]]
|
||||
}else{
|
||||
stop("Abort: SS Stability colour numbers mismtatch")
|
||||
}
|
||||
|
||||
#put in chimera cmd
|
||||
# stabiliting first as it has less numbers
|
||||
paste0(ss_stability_pos, chain_suffix)
|
||||
paste0(dd_stability_pos, chain_suffix)
|
||||
####################################################################
|
||||
|
||||
65
scripts/plotting/plotting_thesis/gid/sensitivity_count_gid.R
Normal file
65
scripts/plotting/plotting_thesis/gid/sensitivity_count_gid.R
Normal file
|
|
@ -0,0 +1,65 @@
|
|||
#=========================
|
||||
# Count Sensitivity
|
||||
# Mutations and positions
|
||||
#=========================
|
||||
pos_colname_c ="position"
|
||||
|
||||
sensP_df = merged_df3[,c("mutationinformation",
|
||||
#"position",
|
||||
pos_colname_c,
|
||||
"sensitivity")]
|
||||
|
||||
head(sensP_df)
|
||||
table(sensP_df$sensitivity)
|
||||
|
||||
#---------------
|
||||
# Total unique positions
|
||||
#----------------
|
||||
tot_mut_pos = length(unique(sensP_df[[pos_colname_c]]))
|
||||
cat("\nNo of Tot muts sites:", tot_mut_pos)
|
||||
|
||||
# resistant mut pos
|
||||
sens_site_allR = sensP_df[[pos_colname_c]][sensP_df$sensitivity=="R"]
|
||||
sens_site_UR = unique(sens_site_allR)
|
||||
length(sens_site_UR)
|
||||
|
||||
# Sensitive mut pos
|
||||
sens_site_allS = sensP_df[[pos_colname_c]][sensP_df$sensitivity=="S"]
|
||||
sens_site_US = unique(sens_site_allS)
|
||||
length(sens_site_UR)
|
||||
|
||||
#---------------
|
||||
# Common Sites
|
||||
#----------------
|
||||
common_pos = intersect(sens_site_UR,sens_site_US)
|
||||
site_Cc = length(common_pos)
|
||||
cat("\nNo of Common sites:", site_Cc
|
||||
, "\nThese are:", common_pos)
|
||||
|
||||
#---------------
|
||||
# Resistant muts
|
||||
#----------------
|
||||
site_R = sens_site_UR[!sens_site_UR%in%common_pos]
|
||||
site_Rc = length(site_R)
|
||||
|
||||
if ( length(sens_site_allR) == table(sensP_df$sensitivity)[['R']] ){
|
||||
cat("\nNo of R muts:", length(sens_site_allR)
|
||||
, "\nNo. of R sites:",site_Rc
|
||||
, "\nThese are:", site_R
|
||||
)
|
||||
}
|
||||
|
||||
#---------------
|
||||
# Sensitive muts
|
||||
#----------------
|
||||
site_S = sens_site_US[!sens_site_US%in%common_pos]
|
||||
site_Sc = length(site_S)
|
||||
|
||||
if ( length(sens_site_allS) == table(sensP_df$sensitivity)[['S']] ){
|
||||
cat("\nNo of S muts:", length(sens_site_allS)
|
||||
, "\nNo. of S sites:", site_Sc
|
||||
, "\nThese are:", site_S)
|
||||
}
|
||||
|
||||
#########################
|
||||
|
||||
2
scripts/plotting/plotting_thesis/katg/katg_other_plots.R
Normal file
2
scripts/plotting/plotting_thesis/katg/katg_other_plots.R
Normal file
|
|
@ -0,0 +1,2 @@
|
|||
|
||||
source("/home/tanu/git/LSHTM_analysis/scripts/plotting/plotting_thesis/linage_bp_dist_layout.R")
|
||||
|
|
@ -7,7 +7,6 @@ source("/home/tanu/git/LSHTM_analysis/scripts/plotting/plotting_thesis/linage_bp
|
|||
#linage_dist_ens_stability.R
|
||||
###########################################
|
||||
# svg
|
||||
# my_label_size = 12
|
||||
# linPlots_combined = paste0(outdir_images
|
||||
# , tolower(gene)
|
||||
# ,"_linP_combined.svg")
|
||||
|
|
@ -31,6 +30,8 @@ source("/home/tanu/git/LSHTM_analysis/scripts/plotting/plotting_thesis/linage_bp
|
|||
# dev.off()
|
||||
|
||||
# png
|
||||
my_label_size = 12
|
||||
|
||||
linPlots_combined = paste0(outdir_images
|
||||
, tolower(gene)
|
||||
,"_linP_combined.png")
|
||||
|
|
|
|||
|
|
@ -7,16 +7,9 @@
|
|||
#=============
|
||||
# Data: Input
|
||||
#==============
|
||||
#source("~/git/LSHTM_analysis/config/pnca.R")
|
||||
#source("~/git/LSHTM_analysis/config/embb.R")
|
||||
#source("~/git/LSHTM_analysis/config/gid.R")
|
||||
|
||||
#source("~/git/LSHTM_analysis/config/alr.R")
|
||||
#source("~/git/LSHTM_analysis/config/katg.R")
|
||||
#source("~/git/LSHTM_analysis/config/rpob.R")
|
||||
|
||||
#source("~/git/LSHTM_analysis/scripts/plotting/get_plotting_dfs.R")
|
||||
#source("~/git/LSHTM_analysis/scripts/plotting/plotting_colnames.R") sourced by above
|
||||
|
||||
#cat("\nSourced plotting cols as well:", length(plotting_cols))
|
||||
|
||||
|
|
|
|||
|
|
@ -1,4 +1,18 @@
|
|||
#source("/home/tanu/git/LSHTM_analysis/scripts/plotting/plotting_thesis/pe_sens_site_count_rpob.R")
|
||||
#=============
|
||||
# Data: Input
|
||||
#==============
|
||||
source("~/git/LSHTM_analysis/config/rpob.R")
|
||||
source("~/git/LSHTM_analysis/scripts/plotting/get_plotting_dfs.R")
|
||||
#cat("\nSourced plotting cols as well:", length(plotting_cols))
|
||||
|
||||
#=======
|
||||
# output
|
||||
#=======
|
||||
outdir_images = paste0("~/git/Writing/thesis/images/results/", tolower(gene), "/")
|
||||
cat("plots will output to:", outdir_images)
|
||||
|
||||
source("/home/tanu/git/LSHTM_analysis/scripts/plotting/plotting_thesis/rpob/basic_barplots_rpob.R")
|
||||
source("/home/tanu/git/LSHTM_analysis/scripts/plotting/plotting_thesis/rpob/pe_sens_site_count_rpob.R")
|
||||
|
||||
if ( tolower(gene)%in%c("rpob") ){
|
||||
cat("\nPlots available for layout are:")
|
||||
|
|
@ -1,6 +1,11 @@
|
|||
#source("~/git/LSHTM_analysis/config/embb.R")
|
||||
#source("~/git/LSHTM_analysis/scripts/plotting/plotting_colnames.R")
|
||||
#source("~/git/LSHTM_analysis/scripts/plotting/get_plotting_dfs.R")
|
||||
source("~/git/LSHTM_analysis/config/rpob.R")
|
||||
source("~/git/LSHTM_analysis/scripts/plotting/get_plotting_dfs.R")
|
||||
|
||||
#=======
|
||||
# output
|
||||
#=======
|
||||
outdir_images = paste0("~/git/Writing/thesis/images/results/", tolower(gene), "/")
|
||||
cat("plots will output to:", outdir_images)
|
||||
|
||||
my_gg_pairs=function(plot_df, plot_title
|
||||
, tt_args_size = 2.5
|
||||
|
|
@ -72,7 +77,7 @@ if (tolower(gene)%in%geneL_ppi2){
|
|||
}
|
||||
|
||||
if (tolower(gene)%in%geneL_na){
|
||||
corr_affinity_df[corr_affinity_df["NCA-Dist"]>DistCutOff,"mCSM-NA"]=0
|
||||
corr_affinity_df[corr_affinity_df["NA-Dist"]>DistCutOff,"mCSM-NA"]=0
|
||||
}
|
||||
|
||||
# count 0
|
||||
|
|
|
|||
|
|
@ -1,172 +0,0 @@
|
|||
#!/usr/bin/env Rscript
|
||||
|
||||
#########################################################
|
||||
# TASK: Lineage plots [merged_df2]
|
||||
# Count
|
||||
# Diversity
|
||||
# Average stability dist
|
||||
# Avergae affinity dist: optional
|
||||
#########################################################
|
||||
#=======
|
||||
# output
|
||||
#=======
|
||||
# outdir_images = paste0("~/git/Writing/thesis/images/results/"
|
||||
# , tolower(gene), "/")
|
||||
# cat("plots will output to:", outdir_images)
|
||||
#########################################################
|
||||
|
||||
#===============
|
||||
#Quick numbers checks
|
||||
#===============
|
||||
nsample_lin = merged_df2[merged_df2$lineage%in%c("L1", "L2", "L3", "L4"),]
|
||||
|
||||
if ( all(table(nsample_lin$sensitivity)== table(nsample_lin$mutation_info_labels)) ){
|
||||
cat("\nTotal no. of samples belonging to L1-l4 for", gene,":", nrow(nsample_lin)
|
||||
, "\nCounting R and S samples")
|
||||
if( sum(table(nsample_lin$sensitivity)) == nrow(nsample_lin) ){
|
||||
cat("\nPASSNumbers cross checked:")
|
||||
print(table(nsample_lin$sensitivity))
|
||||
}
|
||||
}else{
|
||||
stop("Abort: Numbers mismatch. Please check")
|
||||
}
|
||||
########################################################################
|
||||
###################################################
|
||||
# Lineage barplots #
|
||||
###################################################
|
||||
|
||||
#===============================
|
||||
# lineage sample and SNP count
|
||||
#===============================
|
||||
lin_countP = lin_count_bp(lf_data = lineage_dfL[['lin_lf']]
|
||||
, all_lineages = F
|
||||
, x_categ = "sel_lineages"
|
||||
, y_count = "p_count"
|
||||
, use_lineages = c("L1", "L2", "L3", "L4")
|
||||
, bar_fill_categ = "count_categ"
|
||||
, display_label_col = "p_count"
|
||||
, bar_stat_stype = "identity"
|
||||
, d_lab_size = 8
|
||||
, d_lab_col = "black"
|
||||
, my_xats = 25 # x axis text size
|
||||
, my_yats = 25 # y axis text sized_lab_size
|
||||
, my_xals = 25 # x axis label size
|
||||
, my_yals = 25 # y axis label size
|
||||
, my_lls = 25 # legend label size
|
||||
, bar_col_labels = c("SNPs", "Total Samples")
|
||||
, bar_col_values = c("grey50", "gray75")
|
||||
, bar_leg_name = ""
|
||||
, leg_location = "top"
|
||||
, y_log10 = F
|
||||
, y_scale_percent = FALSE
|
||||
, y_label = c("Count")
|
||||
)
|
||||
lin_countP
|
||||
#===============================
|
||||
# lineage SNP diversity count
|
||||
#===============================
|
||||
lin_diversityP = lin_count_bp_diversity(lf_data = lineage_dfL[['lin_wf']]
|
||||
, x_categ = "sel_lineages"
|
||||
, y_count = "snp_diversity"
|
||||
#, all_lineages = F
|
||||
, use_lineages = c("L1", "L2", "L3", "L4")
|
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, display_label_col = "snp_diversity_f"
|
||||
, bar_stat_stype = "identity"
|
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, x_lab_angle = 90
|
||||
, d_lab_size =9
|
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, my_xats = 25 # x axis text size
|
||||
, my_yats = 25 # y axis text size
|
||||
, my_xals = 25 # x axis label size
|
||||
, my_yals = 25 # y axis label size
|
||||
, my_lls = 25 # legend label size
|
||||
, y_log10 = F
|
||||
, y_scale_percent = F
|
||||
, leg_location = "top"
|
||||
, y_label = "Percent" #"SNP diversity"
|
||||
, bp_plot_title = "nsSNP diversity"
|
||||
, title_colour = "black" #"chocolate4"
|
||||
, subtitle_text = NULL
|
||||
, sts = 20
|
||||
, subtitle_colour = "#350E20FF")
|
||||
lin_diversityP
|
||||
#=============================================
|
||||
# Output plots: Lineage count and Diversity
|
||||
#=============================================
|
||||
# lineage_bp_CL = paste0(outdir_images
|
||||
# ,tolower(gene)
|
||||
# ,"_lineage_bp_CL_Tall.svg")
|
||||
#
|
||||
# cat("Lineage barplots:", lineage_bp_CL)
|
||||
# svg(lineage_bp_CL, width = 8, height = 15)
|
||||
#
|
||||
# cowplot::plot_grid(lin_countP, lin_diversityP
|
||||
# #, labels = c("(a)", "(b)", "(c)", "(d)")
|
||||
# , nrow = 2
|
||||
# # , ncols = 2
|
||||
# , labels = "AUTO"
|
||||
# , label_size = 25)
|
||||
#
|
||||
# dev.off()
|
||||
########################################################################
|
||||
|
||||
|
||||
###################################################
|
||||
# Stability dist #
|
||||
###################################################
|
||||
# scaled_cols_stability = c("duet_scaled"
|
||||
# , "deepddg_scaled"
|
||||
# , "ddg_dynamut2_scaled"
|
||||
# , "foldx_scaled"
|
||||
# , "avg_stability_scaled")
|
||||
|
||||
my_xlabel = paste0("Average stability ", "(", stability_suffix, ")"); my_xlabel
|
||||
#plotdf = merged_df2[merged_df2$lineage%in%c("L1", "L2", "L3", "L4"),]
|
||||
|
||||
linP_dm_om = lineage_distP(merged_df2
|
||||
, with_facet = F
|
||||
, x_axis = "avg_stability_scaled"
|
||||
, y_axis = "lineage_labels"
|
||||
, x_lab = my_xlabel
|
||||
, use_lineages = c("L1", "L2", "L3", "L4")
|
||||
#, fill_categ = "mutation_info_orig", fill_categ_cols = c("#E69F00", "#999999")
|
||||
, fill_categ = "sensitivity"
|
||||
, fill_categ_cols = c("red", "blue")
|
||||
, label_categories = c("Resistant", "Sensitive")
|
||||
, leg_label = "Mutation group"
|
||||
, my_ats = 22 # axis text size
|
||||
, my_als = 22 # axis label size
|
||||
, my_leg_ts = 22
|
||||
, my_leg_title = 22
|
||||
, my_strip_ts = 22
|
||||
, alpha = 0.56
|
||||
)
|
||||
|
||||
linP_dm_om
|
||||
|
||||
###################################################
|
||||
# Affinity dist [OPTIONAL] #
|
||||
###################################################
|
||||
# scaled_cols_affinity = c("affinity_scaled"
|
||||
# , "mmcsm_lig_scaled"
|
||||
# , "mcsm_ppi2_scaled"
|
||||
# , "mcsm_na_scaled"
|
||||
# , "avg_lig_affinity_scaled")
|
||||
|
||||
# lineage_distP(merged_df2
|
||||
# , with_facet = F
|
||||
# , x_axis = "avg_lig_affinity_scaled"
|
||||
# , y_axis = "lineage_labels"
|
||||
# , x_lab = my_xlabel
|
||||
# , use_lineages = c("L1", "L2", "L3", "L4")
|
||||
# #, fill_categ = "mutation_info_orig", fill_categ_cols = c("#E69F00", "#999999")
|
||||
# , fill_categ = "sensitivity"
|
||||
# , fill_categ_cols = c("red", "blue")
|
||||
# , label_categories = c("Resistant", "Sensitive")
|
||||
# , leg_label = "Mutation group"
|
||||
# , my_ats = 22 # axis text size
|
||||
# , my_als = 22 # axis label size
|
||||
# , my_leg_ts = 22
|
||||
# , my_leg_title = 22
|
||||
# , my_strip_ts = 22
|
||||
# , alpha = 0.56
|
||||
# )
|
||||
|
|
@ -1,30 +0,0 @@
|
|||
#!/usr/bin/env Rscript
|
||||
|
||||
###########################################
|
||||
# TASK: generate plots for lineage
|
||||
# Individual plots in
|
||||
#lineage_bp_both.R
|
||||
#linage_dist_ens_stability.R
|
||||
###########################################
|
||||
my_label_size = 25
|
||||
|
||||
linPlots_combined = paste0(outdir_images
|
||||
, tolower(gene)
|
||||
,"_linP_combined.svg")
|
||||
|
||||
cat("\nOutput plot:", linPlots_combined)
|
||||
svg(linPlots_combined, width = 18, height = 12)
|
||||
|
||||
cowplot::plot_grid(
|
||||
cowplot::plot_grid(lin_countP, lin_diversityP
|
||||
, nrow = 2
|
||||
, labels = "AUTO"
|
||||
, label_size = my_label_size),
|
||||
NULL,
|
||||
linP_dm_om,
|
||||
nrow = 1,
|
||||
labels = c("", "", "C"),
|
||||
label_size = my_label_size,
|
||||
rel_widths = c(35, 3, 52)
|
||||
)
|
||||
dev.off()
|
||||
|
|
@ -1,5 +1,5 @@
|
|||
source("/home/tanu/git/LSHTM_analysis/scripts/plotting/plotting_thesis/prominent_effects_rpob.R")
|
||||
source("/home/tanu/git/LSHTM_analysis/scripts/plotting/plotting_thesis/sensitivity_count_rpob.R")
|
||||
source("/home/tanu/git/LSHTM_analysis/scripts/plotting/plotting_thesis/rpob/prominent_effects_rpob.R")
|
||||
source("/home/tanu/git/LSHTM_analysis/scripts/plotting/plotting_thesis/rpob/sensitivity_count_rpob.R")
|
||||
|
||||
##############################################################
|
||||
# PE count
|
||||
|
|
|
|||
2
scripts/plotting/plotting_thesis/rpob/rpob_other_plots.R
Normal file
2
scripts/plotting/plotting_thesis/rpob/rpob_other_plots.R
Normal file
|
|
@ -0,0 +1,2 @@
|
|||
|
||||
source("/home/tanu/git/LSHTM_analysis/scripts/plotting/plotting_thesis/linage_bp_dist_layout.R")
|
||||
Loading…
Add table
Add a link
Reference in a new issue