moved old lineage_basic_barplot.R to redundant
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scripts/plotting/redundant/lineage_basic_barplot.R
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214
scripts/plotting/redundant/lineage_basic_barplot.R
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#!/usr/bin/env Rscript
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getwd()
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setwd("~/git/LSHTM_analysis/scripts/plotting/")
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getwd()
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#########################################################
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# TASK: Basic lineage barplot showing numbers
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# Output: Basic barplot with lineage samples and mut count
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##########################################################
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# Installing and loading required packages
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##########################################################
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source("Header_TT.R")
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require(data.table)
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source("combining_dfs_plotting.R")
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# should return the following dfs, directories and variables
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# PS combined:
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# 1) merged_df2
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# 2) merged_df2_comp
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# 3) merged_df3
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# 4) merged_df3_comp
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# LIG combined:
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# 5) merged_df2_lig
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# 6) merged_df2_comp_lig
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# 7) merged_df3_lig
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# 8) merged_df3_comp_lig
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# 9) my_df_u
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# 10) my_df_u_lig
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cat("Directories imported:"
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, "\n===================="
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, "\ndatadir:", datadir
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, "\nindir:", indir
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, "\noutdir:", outdir
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, "\nplotdir:", plotdir)
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cat("Variables imported:"
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, "\n====================="
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, "\ndrug:", drug
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, "\ngene:", gene
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, "\ngene_match:", gene_match
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, "\nAngstrom symbol:", angstroms_symbol
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, "\nNo. of duplicated muts:", dup_muts_nu
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, "\nNA count for ORs:", na_count
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, "\nNA count in df2:", na_count_df2
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, "\nNA count in df3:", na_count_df3
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, "\ndr_muts_col:", dr_muts_col
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, "\nother_muts_col:", other_muts_col
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, "\ndrtype_col:", resistance_col)
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#===========
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# input
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#===========
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# output of combining_dfs_plotting.R
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#=======
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# output
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#=======
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# plot 1
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basic_bp_lineage = "basic_lineage_barplot.svg"
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plot_basic_bp_lineage = paste0(plotdir,"/", basic_bp_lineage)
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#=======================================================================
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#================
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# Data for plots:
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# you need merged_df2, comprehensive one
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# since this has one-many relationship
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# i.e the same SNP can belong to multiple lineages
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#================
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# REASSIGNMENT as necessary
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my_df = merged_df2
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# clear excess variable
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rm(merged_df2_comp, merged_df3, merged_df3_comp)
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# quick checks
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colnames(my_df)
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str(my_df)
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# Ensure correct data type in columns to plot: need to be factor
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is.factor(my_df$lineage)
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my_df$lineage = as.factor(my_df$lineage)
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is.factor(my_df$lineage)
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#==========================
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# Plot: Lineage barplot
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# x = lineage y = No. of samples
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# col = Lineage
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# fill = lineage
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#============================
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table(my_df$lineage)
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as.data.frame(table(my_df$lineage))
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#=============
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# Data for plots
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#=============
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# REASSIGNMENT
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df <- my_df
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rm(my_df)
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# get freq count of positions so you can subset freq<1
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#setDT(df)[, lineage_count := .N, by = .(lineage)]
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#******************
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# generate plot: barplot of mutation by lineage
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#******************
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sel_lineages = c("lineage1"
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, "lineage2"
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, "lineage3"
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, "lineage4"
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#, "lineage5"
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#, "lineage6"
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#, "lineage7"
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)
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df_lin = subset(df, subset = lineage %in% sel_lineages)
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# Create df with lineage inform & no. of unique mutations
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# per lineage and total samples within lineage
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# this is essentially barplot with two y axis
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bar = bar = as.data.frame(sel_lineages) #4, 1
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total_snps_u = NULL
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total_samples = NULL
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for (i in sel_lineages){
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#print(i)
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curr_total = length(unique(df$id)[df$lineage==i])
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total_samples = c(total_samples, curr_total)
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print(total_samples)
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foo = df[df$lineage==i,]
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print(paste0(i, "======="))
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print(length(unique(foo$mutationinformation)))
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curr_count = length(unique(foo$mutationinformation))
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total_snps_u = c(total_snps_u, curr_count)
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}
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print(total_snps_u)
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bar$num_snps_u = total_snps_u
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bar$total_samples = total_samples
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bar
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#*****************
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# generate plot: lineage barplot with two y-axis
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#https://stackoverflow.com/questions/13035295/overlay-bar-graphs-in-ggplot2
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#*****************
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y1 = bar$num_snps_u
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y2 = bar$total_samples
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x = sel_lineages
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to_plot = data.frame(x = x
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, y1 = y1
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, y2 = y2)
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to_plot
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# FIXME later: will be depricated!
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melted = melt(to_plot, id = "x")
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melted
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svg(plot_basic_bp_lineage)
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my_ats = 20 # axis text size
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my_als = 22 # axis label size
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g = ggplot(melted, aes(x = x
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, y = value
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, fill = variable))
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printFile = g + geom_bar(stat = "identity"
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, position = position_stack(reverse = TRUE)
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, alpha=.75
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, colour='grey75') +
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theme(axis.text.x = element_text(size = my_ats)
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, axis.text.y = element_text(size = my_ats
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#, angle = 30
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, hjust = 1
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, vjust = 0)
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, axis.title.x = element_text(size = my_als
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, colour = 'black')
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, axis.title.y = element_text(size = my_als
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, colour = 'black')
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, legend.position = "top"
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, legend.text = element_text(size = my_als)) +
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#geom_text() +
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geom_label(aes(label = value)
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, size = 5
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, hjust = 0.5
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, vjust = 0.5
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, colour = 'black'
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, show.legend = FALSE
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#, check_overlap = TRUE
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, position = position_stack(reverse = T)) +
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labs(title = ''
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, x = ''
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, y = "Number"
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, fill = 'Variable'
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, colour = 'black') +
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scale_fill_manual(values = c('grey50', 'gray75')
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, name=''
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, labels=c('Mutations', 'Total Samples')) +
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scale_x_discrete(breaks = c('lineage1', 'lineage2', 'lineage3', 'lineage4')
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, labels = c('Lineage 1', 'Lineage 2', 'Lineage 3', 'Lineage 4'))
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print(printFile)
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dev.off()
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301
scripts/plotting/redundant/other_plots_data_SAFEGUARD.R
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301
scripts/plotting/redundant/other_plots_data_SAFEGUARD.R
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#!/usr/bin/env Rscript
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#########################################################
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# TASK: producing boxplots for dr and other muts
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#########################################################
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#=======================================================================
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# working dir and loading libraries
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getwd()
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setwd("~/git/LSHTM_analysis/scripts/plotting")
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getwd()
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#source("Header_TT.R")
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library(ggplot2)
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library(data.table)
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library(dplyr)
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library(tidyverse)
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source("combining_dfs_plotting.R")
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rm(merged_df2, merged_df2_comp, merged_df2_lig, merged_df2_comp_lig
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, merged_df3_comp, merged_df3_comp_lig
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, my_df_u, my_df_u_lig)
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cols_to_select = c("mutation", "mutationinformation"
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, "wild_type", "position", "mutant_type"
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, "mutation_info")
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merged_df3_short = merged_df3[, cols_to_select]
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# write merged_df3 to generate structural figure
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write.csv(merged_df3_short, "merged_df3_short.csv")
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#========================================================================
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#%%%%%%%%%%%%%%%%%%%
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# REASSIGNMENT: PS
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#%%%%%%%%%%%%%%%%%%%%
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df_ps = merged_df3
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#============================
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# adding foldx scaled values
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# scale data b/w -1 and 1
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#============================
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n = which(colnames(df_ps) == "ddg"); n
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my_min = min(df_ps[,n]); my_min
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my_max = max(df_ps[,n]); my_max
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df_ps$foldx_scaled = ifelse(df_ps[,n] < 0
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, df_ps[,n]/abs(my_min)
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, df_ps[,n]/my_max)
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# sanity check
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my_min = min(df_ps$foldx_scaled); my_min
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my_max = max(df_ps$foldx_scaled); my_max
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if (my_min == -1 && my_max == 1){
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cat("PASS: foldx ddg successfully scaled b/w -1 and 1"
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, "\nProceeding with assigning foldx outcome category")
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}else{
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cat("FAIL: could not scale foldx ddg values"
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, "Aborting!")
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}
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#================================
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# adding foldx outcome category
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# ddg<0 = "Stabilising" (-ve)
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#=================================
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c1 = table(df_ps$ddg < 0)
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df_ps$foldx_outcome = ifelse(df_ps$ddg < 0, "Stabilising", "Destabilising")
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c2 = table(df_ps$ddg < 0)
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if ( all(c1 == c2) ){
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cat("PASS: foldx outcome successfully created")
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}else{
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cat("FAIL: foldx outcome could not be created. Aborting!")
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exit()
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}
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#=======================================================================
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# name tidying
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df_ps$mutation_info = as.factor(df_ps$mutation_info)
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df_ps$duet_outcome = as.factor(df_ps$duet_outcome)
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df_ps$foldx_outcome = as.factor(df_ps$foldx_outcome)
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df_ps$ligand_outcome = as.factor(df_ps$ligand_outcome)
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# check
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table(df_ps$mutation_info)
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# further checks to make sure dr and other muts are indeed unique
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dr_muts = df_ps[df_ps$mutation_info == dr_muts_col,]
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dr_muts_names = unique(dr_muts$mutation)
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other_muts = df_ps[df_ps$mutation_info == other_muts_col,]
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other_muts_names = unique(other_muts$mutation)
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if ( table(dr_muts_names%in%other_muts_names)[[1]] == length(dr_muts_names) &&
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table(other_muts_names%in%dr_muts_names)[[1]] == length(other_muts_names) ){
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cat("PASS: dr and other muts are indeed unique")
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}else{
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cat("FAIL: dr adn others muts are NOT unique!")
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quit()
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}
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#%%%%%%%%%%%%%%%%%%%
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# REASSIGNMENT: LIG
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#%%%%%%%%%%%%%%%%%%%%
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df_lig = merged_df3_lig
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# name tidying
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df_lig$mutation_info = as.factor(df_lig$mutation_info)
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df_lig$duet_outcome = as.factor(df_lig$duet_outcome)
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#df_lig$ligand_outcome = as.factor(df_lig$ligand_outcome)
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# check
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table(df_lig$mutation_info)
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#========================================================================
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#===========
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# Data: ps
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#===========
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# keep similar dtypes cols together
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cols_to_select_ps = c("mutationinformation", "mutation", "position", "mutation_info"
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, "duet_outcome"
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, "duet_scaled"
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, "ligand_distance"
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, "asa"
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, "rsa"
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, "rd_values"
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, "kd_values")
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df_wf_ps = df_ps[, cols_to_select_ps]
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pivot_cols_ps = cols_to_select_ps[1:5]; pivot_cols_ps
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expected_rows_lf_ps = nrow(df_wf_ps) * (length(df_wf_ps) - length(pivot_cols_ps))
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expected_rows_lf_ps
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# LF data: duet
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df_lf_ps = gather(df_wf_ps, param_type, param_value, duet_scaled:kd_values, factor_key=TRUE)
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if (nrow(df_lf_ps) == expected_rows_lf_ps){
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cat("PASS: long format data created for duet")
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}else{
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cat("FAIL: long format data could not be created for duet")
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exit()
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}
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str(df_wf_ps)
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str(df_lf_ps)
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# assign pretty labels: param_type
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levels(df_lf_ps$param_type); table(df_lf_ps$param_type)
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ligand_dist_colname = paste0("Distance to ligand (", angstroms_symbol, ")")
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ligand_dist_colname
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duet_stability_name = paste0(delta_symbol, delta_symbol, "G")
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duet_stability_name
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#levels(df_lf_ps$param_type)[levels(df_lf_ps$param_type)=="duet_scaled"] <- "Stability"
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levels(df_lf_ps$param_type)[levels(df_lf_ps$param_type)=="duet_scaled"] <- duet_stability_name
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#levels(df_lf_ps$param_type)[levels(df_lf_ps$param_type)=="ligand_distance"] <- "Ligand Distance"
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levels(df_lf_ps$param_type)[levels(df_lf_ps$param_type)=="ligand_distance"] <- ligand_dist_colname
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levels(df_lf_ps$param_type)[levels(df_lf_ps$param_type)=="asa"] <- "ASA"
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levels(df_lf_ps$param_type)[levels(df_lf_ps$param_type)=="rsa"] <- "RSA"
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levels(df_lf_ps$param_type)[levels(df_lf_ps$param_type)=="rd_values"] <- "RD"
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levels(df_lf_ps$param_type)[levels(df_lf_ps$param_type)=="kd_values"] <- "KD"
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# check
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levels(df_lf_ps$param_type); table(df_lf_ps$param_type)
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# assign pretty labels: mutation_info
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levels(df_lf_ps$mutation_info); table(df_lf_ps$mutation_info)
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sum(table(df_lf_ps$mutation_info)) == nrow(df_lf_ps)
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levels(df_lf_ps$mutation_info)[levels(df_lf_ps$mutation_info)==dr_muts_col] <- "DM"
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levels(df_lf_ps$mutation_info)[levels(df_lf_ps$mutation_info)==other_muts_col] <- "OM"
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# check
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levels(df_lf_ps$mutation_info); table(df_lf_ps$mutation_info)
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############################################################################
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#===========
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# LF data: LIG
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#===========
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# keep similar dtypes cols together
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cols_to_select_lig = c("mutationinformation", "mutation", "position", "mutation_info"
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, "ligand_outcome"
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, "affinity_scaled"
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#, "ligand_distance"
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, "asa"
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, "rsa"
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, "rd_values"
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, "kd_values")
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df_wf_lig = df_lig[, cols_to_select_lig]
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pivot_cols_lig = cols_to_select_lig[1:5]; pivot_cols_lig
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expected_rows_lf_lig = nrow(df_wf_lig) * (length(df_wf_lig) - length(pivot_cols_lig))
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expected_rows_lf_lig
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# LF data: foldx
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df_lf_lig = gather(df_wf_lig, param_type, param_value, affinity_scaled:kd_values, factor_key=TRUE)
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if (nrow(df_lf_lig) == expected_rows_lf_lig){
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cat("PASS: long format data created for foldx")
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}else{
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cat("FAIL: long format data could not be created for foldx")
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exit()
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}
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# assign pretty labels: param_type
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levels(df_lf_lig$param_type); table(df_lf_lig$param_type)
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levels(df_lf_lig$param_type)[levels(df_lf_lig$param_type)=="affinity_scaled"] <- "Ligand Affinity"
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#levels(df_lf_lig$param_type)[levels(df_lf_lig$param_type)=="ligand_distance"] <- "Ligand Distance"
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levels(df_lf_lig$param_type)[levels(df_lf_lig$param_type)=="asa"] <- "ASA"
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levels(df_lf_lig$param_type)[levels(df_lf_lig$param_type)=="rsa"] <- "RSA"
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levels(df_lf_lig$param_type)[levels(df_lf_lig$param_type)=="rd_values"] <- "RD"
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levels(df_lf_lig$param_type)[levels(df_lf_lig$param_type)=="kd_values"] <- "KD"
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#check
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levels(df_lf_lig$param_type); table(df_lf_lig$param_type)
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# assign pretty labels: mutation_info
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levels(df_lf_lig$mutation_info); table(df_lf_lig$mutation_info)
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sum(table(df_lf_lig$mutation_info)) == nrow(df_lf_lig)
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levels(df_lf_lig$mutation_info)[levels(df_lf_lig$mutation_info)==dr_muts_col] <- "DM"
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||||
levels(df_lf_lig$mutation_info)[levels(df_lf_lig$mutation_info)==other_muts_col] <- "OM"
|
||||
# check
|
||||
levels(df_lf_lig$mutation_info); table(df_lf_lig$mutation_info)
|
||||
|
||||
#############################################################################
|
||||
#===========
|
||||
# Data: foldx
|
||||
#===========
|
||||
# keep similar dtypes cols together
|
||||
cols_to_select_foldx = c("mutationinformation", "mutation", "position", "mutation_info"
|
||||
, "foldx_outcome"
|
||||
|
||||
, "foldx_scaled")
|
||||
#, "ligand_distance"
|
||||
#, "asa"
|
||||
#, "rsa"
|
||||
#, "rd_values"
|
||||
#, "kd_values")
|
||||
|
||||
|
||||
df_wf_foldx = df_ps[, cols_to_select_foldx]
|
||||
|
||||
pivot_cols_foldx = cols_to_select_foldx[1:5]; pivot_cols_foldx
|
||||
|
||||
expected_rows_lf_foldx = nrow(df_wf_foldx) * (length(df_wf_foldx) - length(pivot_cols_foldx))
|
||||
expected_rows_lf_foldx
|
||||
|
||||
# LF data: foldx
|
||||
df_lf_foldx = gather(df_wf_foldx, param_type, param_value, foldx_scaled, factor_key=TRUE)
|
||||
|
||||
if (nrow(df_lf_foldx) == expected_rows_lf_foldx){
|
||||
cat("PASS: long format data created for foldx")
|
||||
}else{
|
||||
cat("FAIL: long format data could not be created for foldx")
|
||||
exit()
|
||||
}
|
||||
|
||||
foldx_stability_name = paste0(delta_symbol, delta_symbol, "G")
|
||||
foldx_stability_name
|
||||
|
||||
# assign pretty labels: param type
|
||||
levels(df_lf_foldx$param_type); table(df_lf_foldx$param_type)
|
||||
|
||||
#levels(df_lf_foldx$param_type)[levels(df_lf_foldx$param_type)=="foldx_scaled"] <- "Stability"
|
||||
levels(df_lf_foldx$param_type)[levels(df_lf_foldx$param_type)=="foldx_scaled"] <- foldx_stability_name
|
||||
#levels(df_lf_foldx$param_type)[levels(df_lf_foldx$param_type)=="ligand_distance"] <- "Ligand Distance"
|
||||
#levels(df_lf_foldx$param_type)[levels(df_lf_foldx$param_type)=="asa"] <- "ASA"
|
||||
#levels(df_lf_foldx$param_type)[levels(df_lf_foldx$param_type)=="rsa"] <- "RSA"
|
||||
#levels(df_lf_foldx$param_type)[levels(df_lf_foldx$param_type)=="rd_values"] <- "RD"
|
||||
#levels(df_lf_foldx$param_type)[levels(df_lf_foldx$param_type)=="kd_values"] <- "KD"
|
||||
# check
|
||||
levels(df_lf_foldx$param_type); table(df_lf_foldx$param_type)
|
||||
|
||||
# assign pretty labels: mutation_info
|
||||
levels(df_lf_foldx$mutation_info); table(df_lf_foldx$mutation_info)
|
||||
sum(table(df_lf_foldx$mutation_info)) == nrow(df_lf_foldx)
|
||||
|
||||
levels(df_lf_foldx$mutation_info)[levels(df_lf_foldx$mutation_info)==dr_muts_col] <- "DM"
|
||||
levels(df_lf_foldx$mutation_info)[levels(df_lf_foldx$mutation_info)==other_muts_col] <- "OM"
|
||||
# check
|
||||
levels(df_lf_foldx$mutation_info); table(df_lf_foldx$mutation_info)
|
||||
|
||||
############################################################################
|
||||
|
||||
# clear excess variables
|
||||
rm(cols_to_select_ps, cols_to_select_foldx, cols_to_select_lig
|
||||
, pivot_cols_ps, pivot_cols_foldx, pivot_cols_lig
|
||||
, expected_rows_lf_ps, expected_rows_lf_foldx, expected_rows_lf_lig
|
||||
, my_max, my_min, na_count, na_count_df2, na_count_df3, dup_muts_nu
|
||||
, c1, c2, n)
|
||||
Loading…
Add table
Add a link
Reference in a new issue