Combining dfs for PS and lig in one
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6 changed files with 464 additions and 621 deletions
193
scripts/plotting/combined_or.R
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193
scripts/plotting/combined_or.R
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#!/usr/bin/env Rscript
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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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# 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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require(cowplot)
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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(paste0("Directories imported:"
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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(paste0("Variables imported:"
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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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#=========================
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#=======
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# output
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#=======
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or_combined = "or_combined_PS_LIG.svg"
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plot_or_combined = paste0(plotdir,"/", or_combined)
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or_kin_combined = "or_kin_combined_PS_LIG.svg"
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plot_or_kin_combined = paste0(plotdir,"/", or_kin_combined)
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#=======================================================================
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###########################
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# Data for OR and stability plots
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# you need merged_df3_comp
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# since these are matched
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# to allow pairwise corr
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###########################
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ps_df = merged_df3_comp
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lig_df = merged_df3_comp_lig
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# Ensure correct data type in columns to plot: should be TRUE
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is.numeric(ps_df$or_mychisq)
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is.numeric(lig_df$or_mychisq)
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# delete variables not required
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rm(merged_df2, merged_df2_comp, merged_df2_lig, merged_df2_comp_lig, my_df_u, my_df_u_lig)
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#%% end of section 1
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# sanity check: should be <10
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if (max(lig_df$ligand_distance) < 10){
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print ("Sanity check passed: lig data is <10Ang")
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}else{
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print ("Error: data should be filtered to be within 10Ang")
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}
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#############
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# Plots: Bubble plot
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# x = Position, Y = stability
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# size of dots = OR
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# col: stability
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#############
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#-----------------
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# Plot 1: DUET vs OR by position as geom_points
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#-------------------
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my_ats = 20 # axis text size
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my_als = 22 # axis label size
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# Spelling Correction: made redundant as already corrected at the source
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#ps_df$duet_outcome[ps_df$duet_outcome=='Stabilizing'] <- 'Stabilising'
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#ps_df$duet_outcome[ps_df$duet_outcome=='Destabilizing'] <- 'Destabilising'
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table(ps_df$duet_outcome) ; sum(table(ps_df$duet_outcome))
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g1 = ggplot(ps_df, aes(x = factor(position)
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, y = duet_scaled))
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p1 = g1 +
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geom_point(aes(col = duet_outcome
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#, size = or_mychisq))+
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, size = or_kin)) +
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theme(axis.text.x = element_text(size = my_ats
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, angle = 90
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, hjust = 1
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, vjust = 0.4)
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, axis.text.y = element_text(size = my_ats
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, angle = 0
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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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, axis.title.y = element_text(size = my_als)
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, legend.text = element_text(size = my_als)
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, legend.title = element_text(size = my_als) ) +
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#, legend.key.size = unit(1, "cm")) +
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labs(title = ""
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, x = "Position"
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, y = "DUET(PS)"
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, size = "Odds Ratio"
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, colour = "DUET Outcome") +
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guides(colour = guide_legend(override.aes = list(size=4)))
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p1
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#-------------------
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# generate plot 2: Lig vs OR by position as geom_points
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#-------------------
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# Spelling Correction: made redundant as already corrected at the source
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#lig_df$ligand_outcome[lig_df$ligand_outcome=='Stabilizing'] <- 'Stabilising'
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#lig_df$ligand_outcome[lig_df$ligand_outcome=='Destabilizing'] <- 'Destabilising'
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table(lig_df$ligand_outcome)
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g2 = ggplot(lig_df, aes(x = factor(position)
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, y = affinity_scaled))
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p2 = g2 +
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geom_point(aes(col = ligand_outcome
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#, size = or_mychisq))+
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, size = or_kin)) +
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theme(axis.text.x = element_text(size = my_ats
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, angle = 90
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, hjust = 1
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, vjust = 0.4)
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, axis.text.y = element_text(size = my_ats
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, angle = 0
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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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, axis.title.y = element_text(size = my_als)
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, legend.text = element_text(size = my_als)
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, legend.title = element_text(size = my_als) ) +
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#, legend.key.size = unit(1, "cm")) +
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labs(title = ""
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, x = "Position"
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, y = "Ligand Affinity"
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, size = "Odds Ratio"
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, colour = "Ligand Outcome"
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) +
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guides(colour = guide_legend(override.aes = list(size=4)))
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p2
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#======================
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# combine using cowplot
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#======================
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svg(plot_or_combined, width = 32, height = 12)
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svg(plot_or_kin_combined, width = 32, height = 12)
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theme_set(theme_gray()) # to preserve default theme
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printFile = cowplot::plot_grid(plot_grid(p1, p2
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, ncol = 1
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, align = 'v'
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, labels = c("", "")
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, label_size = my_als+5))
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print(printFile)
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dev.off()
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@ -1,8 +1,4 @@
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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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#!/usr/bin/env Rscript
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#########################################################
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# TASK: To combine struct params and meta data for plotting
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# Input csv files:
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@ -16,21 +12,22 @@ getwd()
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# 3) small combined df including NAs for AF, OR, etc.
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# Dim: same as mcsm data
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# 4) large combined df excluding NAs
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# Dim: dim(#1) - no. of NAs(AF|OR) + 1
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# Dim: dim(#1) - na_count_df2
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# 5) small combined df excluding NAs
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# Dim: dim(#2) - no. of unique NAs - 1
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# Dim: dim(#2) - na_count_df3
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# This script is sourced from other .R scripts for plotting
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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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##########################################################
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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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require(arsenal)
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require(compare)
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library(tidyverse)
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source("Header_TT.R")
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#require(data.table)
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#require(arsenal)
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#require(compare)
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#library(tidyverse)
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source("plotting_data.R")
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# should return the following dfs, directories and variables
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@ -53,28 +50,13 @@ cat(paste0("Variables imported:"
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, "\nAngstrom symbol:", angstroms_symbol))
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# clear excess variable
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rm(my_df, upos, dup_muts, my_df_u_lig)
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rm(my_df, upos, dup_muts)
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#========================================================
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#========================================================
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#%% variable assignment: input and output paths & filenames
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drug = "pyrazinamide"
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gene = "pncA"
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gene_match = paste0(gene,"_p.")
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cat(gene_match)
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#=============
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# directories
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#=============
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datadir = paste0("~/git/Data")
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indir = paste0(datadir, "/", drug, "/input")
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outdir = paste0("~/git/Data", "/", drug, "/output")
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plotdir = paste0("~/git/Data", "/", drug, "/output/plots")
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#===========
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# input
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#===========
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#in_file1: output of plotting_data.R
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# my_df_u
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# infile 2: gene associated meta data
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#in_filename_gene_metadata = paste0(tolower(gene), "_meta_data_with_AFandOR.csv")
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@ -85,56 +67,22 @@ cat(paste0("Input infile 2:", infile_gene_metadata))
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#===========
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# output
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#===========
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# mutations with opposite effects
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out_filename_opp_muts = paste0(tolower(gene), "_muts_opp_effects.csv")
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outfile_opp_muts = paste0(outdir, "/", out_filename_opp_muts)
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# other variables that you can write
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# primarily called by other scripts for plotting
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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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#%%===============================================================
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table(my_df_u$duet_outcome); sum(table(my_df_u$duet_outcome) )
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# spelling Correction 1: DUET incase American spelling needed!
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#my_df_u$duet_outcome[my_df_u$duet_outcome=="Stabilising"] <- "Stabilizing"
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#my_df_u$duet_outcome[my_df_u$duet_outcome=="Destabilising"] <- "Destabilizing"
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# spelling Correction 2: Ligand incase American spelling needed!
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table(my_df_u$ligand_outcome); sum(table(my_df_u$ligand_outcome) )
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#my_df_u$ligand_outcome[my_df_u$ligand_outcome=="Stabilising"] <- "Stabilizing"
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#my_df_u$ligand_outcome[my_df_u$ligand_outcome=="Destabilising"] <- "Destabilizing"
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# muts with opposing effects on protomer and ligand stability
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table(my_df_u$duet_outcome != my_df_u$ligand_outcome)
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changes = my_df_u[which(my_df_u$duet_outcome != my_df_u$ligand_outcome),]
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# sanity check: redundant, but uber cautious!
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dl_i = which(my_df_u$duet_outcome != my_df_u$ligand_outcome)
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ld_i = which(my_df_u$ligand_outcome != my_df_u$duet_outcome)
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cat("Identifying muts with opposite stability effects")
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if(nrow(changes) == (table(my_df_u$duet_outcome != my_df_u$ligand_outcome)[[2]]) & identical(dl_i,ld_i)) {
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cat("PASS: muts with opposite effects on stability and affinity correctly identified"
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, "\nNo. of such muts: ", nrow(changes))
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}else {
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cat("FAIL: unsuccessful in extracting muts with changed stability effects")
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}
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#***************************
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# write file: changed muts
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write.csv(changes, outfile_opp_muts)
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cat("Finished writing file for muts with opp effects:"
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, "\nFilename: ", outfile_opp_muts
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, "\nDim:", dim(changes))
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# clear variables
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rm(out_filename_opp_muts, outfile_opp_muts)
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rm(changes, dl_i, ld_i)
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# count na in each column
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na_count = sapply(my_df_u, function(y) sum(length(which(is.na(y))))); na_count
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df_ncols = ncol(my_df_u)
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###########################
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# 2: Read file: <gene>_meta data.csv
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@ -146,8 +94,6 @@ gene_metadata <- read.csv(infile_gene_metadata
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, header = T)
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cat("Dim:", dim(gene_metadata))
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#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
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# FIXME: remove
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# counting NAs in AF, OR cols:
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if (identical(sum(is.na(my_df_u$or_mychisq))
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, sum(is.na(my_df_u$pval_fisher))
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@ -176,31 +122,24 @@ if (identical(sum(is.na(my_df_u$or_kin))
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, "\nNA in AF:", sum(is.na(my_df_u$af_kin)))
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}
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#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
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# clear variables
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rm(in_filename_gene_metadata, infile_gene_metadata)
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str(gene_metadata)
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###################################################################
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# combining: PS
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###################################################################
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# sort by position (same as my_df)
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# earlier it was mutationinformation
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#head(gene_metadata$mutationinformation)
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#gene_metadata = gene_metadata[order(gene_metadata$mutationinformation),]
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##head(gene_metadata$mutationinformation)
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head(gene_metadata$position)
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gene_metadata = gene_metadata[order(gene_metadata$position),]
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head(gene_metadata$position)
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###########################
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# Merge 1: two dfs with NA
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# merged_df2
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###########################
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#=========================
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# Merge 1: merged_df2
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# dfs with NAs in ORs
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#=========================
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head(my_df_u$mutationinformation)
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head(gene_metadata$mutationinformation)
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# Find common columns b/w two df
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# FIXME: mutation has empty cell for some muts
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merging_cols = intersect(colnames(my_df_u), colnames(gene_metadata))
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cat(paste0("Merging dfs with NAs: big df (1-many relationship b/w id & mut)"
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@ -214,9 +153,6 @@ table(nchar(my_df_u$wild_type))
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table(nchar(my_df_u$mutant_type))
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table(nchar(my_df_u$position))
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#=============
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# merged_df2: gene_metadata + my_df
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#==============
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# all.y because x might contain non-structural positions!
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merged_df2 = merge(x = gene_metadata
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, y = my_df_u
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@ -226,9 +162,7 @@ merged_df2 = merge(x = gene_metadata
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cat("Dim of merged_df2: ", dim(merged_df2))
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head(merged_df2$position)
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#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
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# FIXME: count how many unique muts have entries in meta data
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# should PASS
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# sanity check
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cat("Checking nrows in merged_df2")
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if(nrow(gene_metadata) == nrow(merged_df2)){
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cat("PASS: nrow(merged_df2) = nrow (gene associated gene_metadata)"
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@ -243,47 +177,23 @@ if(nrow(gene_metadata) == nrow(merged_df2)){
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meta_muts_u = unique(gene_metadata$mutationinformation)
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# find the index where it differs
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unique(meta_muts_u[! meta_muts_u %in% merged_muts_u])
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quit()
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}
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#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
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# sort by position
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head(merged_df2$position)
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merged_df2 = merged_df2[order(merged_df2$position),]
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head(merged_df2$position)
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merged_df2v3 = merge(x = gene_metadata
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, y = my_df_u
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, by = merging_cols
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, all = T)
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merged_df2v2 = merge(x = gene_metadata
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, y = my_df_u
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, by = merging_cols
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, all.x = T)
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#!=!=!=!=!=!=!=!
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#identical(merged_df2, merged_df2v2)
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nrow(merged_df2[merged_df2$position==186,])
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#!=!=!=!=!=!=!=!
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# should be False
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identical(merged_df2, merged_df2v2)
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table(merged_df2$position%in%merged_df2v2$position)
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#!!!!!!!!!!! check why these differ
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#########
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# merge 3b (merged_df3):remove duplicated mutations
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#=========================
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# Merge 2: merged_df3
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# dfs with NAs in ORs
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#
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# Cannot trust lineage, country from this df as the same mutation
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# can have many different lineages
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# but this should be good for the numerical corr plots
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#=========================
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# remove duplicated mutations
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||||
cat("Merging dfs without NAs: small df (removing muts with no AF|OR associated)"
|
||||
,"\nCannot trust lineage info from this"
|
||||
,"\nlinking col: mutationinforamtion"
|
||||
,"\nfilename: merged_df3")
|
||||
|
||||
#==#=#=#=#=#=#
|
||||
# Cannot trust lineage, country from this df as the same mutation
|
||||
# can have many different lineages
|
||||
# but this should be good for the numerical corr plots
|
||||
#=#=#=#=#=#=#=
|
||||
merged_df3 = merged_df2[!duplicated(merged_df2$mutationinformation),]
|
||||
head(merged_df3$position); tail(merged_df3$position) # should be sorted
|
||||
|
||||
|
|
@ -326,12 +236,10 @@ if ( identical( which(is.na(merged_df2$or_mychisq)), which(is.na(merged_df2$or_k
|
|||
quit()
|
||||
}
|
||||
|
||||
###########################
|
||||
# 4: merging two dfs: without NA
|
||||
###########################
|
||||
#########
|
||||
# merge 4a (merged_df2_comp): same as merge 1 but excluding NA
|
||||
#########
|
||||
#=========================
|
||||
# Merge3: merged_df2_comp
|
||||
# same as merge 1 but excluding NAs from ORs, etc.
|
||||
#=========================
|
||||
cat("Merging dfs without any NAs: big df (1-many relationship b/w id & mut)"
|
||||
,"\nlinking col: Mutationinforamtion"
|
||||
,"\nfilename: merged_df2_comp")
|
||||
|
|
@ -357,9 +265,12 @@ if ( identical( which(is.na(merged_df2$af)), which(is.na(merged_df2$af_kin))) ){
|
|||
print('Index mismatch for mychisq and kin ors. Aborting NA ommission')
|
||||
}
|
||||
|
||||
#########
|
||||
# merge 4b (merged_df3_comp): remove duplicate mutation information
|
||||
#########
|
||||
#=========================
|
||||
# Merge4: merged_df3_comp
|
||||
# same as merge 2 but excluding NAs from ORs, etc or
|
||||
# remove duplicate mutation information
|
||||
#=========================
|
||||
|
||||
if ( identical( which(is.na(merged_df3$af)), which(is.na(merged_df3$af_kin))) ){
|
||||
print('mychisq and kin ors missing indices match. Procedding with omitting NAs')
|
||||
na_count_df3 = sum(is.na(merged_df3$af))
|
||||
|
|
@ -388,7 +299,6 @@ bar = merged_df3_comp[!duplicated(merged_df3_comp$mutationinformation),]
|
|||
all.equal(foo, bar)
|
||||
#summary(comparedf(foo, bar))
|
||||
|
||||
#=============== end of combining df
|
||||
#==============================================================
|
||||
#################
|
||||
# OPTIONAL: write ALL 4 output files
|
||||
|
|
@ -416,7 +326,32 @@ all.equal(foo, bar)
|
|||
# clear variables
|
||||
rm(foo, bar, gene_metadata
|
||||
, in_filename_params, infile_params, merging_cols
|
||||
, in_filename_gene_metadata, infile_gene_metadata
|
||||
, merged_df2v2, merged_df2v3)
|
||||
#*************************
|
||||
#####################################################################
|
||||
# Combining: LIG
|
||||
#####################################################################
|
||||
|
||||
#============================= end of script
|
||||
#=========================
|
||||
# Merges 5-8
|
||||
#=========================
|
||||
|
||||
merged_df2_lig = merged_df2[merged_df2$ligand_distance<10,]
|
||||
merged_df2_comp_lig = merged_df2_comp[merged_df2_comp$ligand_distance<10,]
|
||||
|
||||
merged_df3_lig = merged_df3[merged_df3$ligand_distance<10,]
|
||||
merged_df3_comp_lig = merged_df3_comp[merged_df3_comp$ligand_distance<10,]
|
||||
|
||||
# sanity check
|
||||
if (nrow(merged_df3_lig) == nrow(my_df_u_lig)){
|
||||
print("PASS: verified merged_df3_lig")
|
||||
}else{
|
||||
cat(paste0('FAIL: nrow mismatch for merged_df3_lig'
|
||||
, "\nExpected:", nrow(my_df_u_lig)
|
||||
, "\nGot:", nrow(merged_df3_lig)))
|
||||
}
|
||||
|
||||
#==========================================================================
|
||||
# end of script
|
||||
##==========================================================================
|
||||
|
|
@ -1,442 +0,0 @@
|
|||
getwd()
|
||||
setwd("~/git/LSHTM_analysis/scripts/plotting/")
|
||||
getwd()
|
||||
|
||||
#########################################################
|
||||
# TASK: To combine struct params and meta data for plotting
|
||||
# Input csv files:
|
||||
# 1) <gene>_all_params.csv
|
||||
# 2) <gene>_meta_data.csv
|
||||
|
||||
# Output:
|
||||
# 1) muts with opposite effects on stability
|
||||
# 2) large combined df including NAs for AF, OR,etc
|
||||
# Dim: same no. of rows as gene associated meta_data_with_AFandOR
|
||||
# 3) small combined df including NAs for AF, OR, etc.
|
||||
# Dim: same as mcsm data
|
||||
# 4) large combined df excluding NAs
|
||||
# Dim: dim(#1) - no. of NAs(AF|OR) + 1
|
||||
# 5) small combined df excluding NAs
|
||||
# Dim: dim(#2) - no. of unique NAs - 1
|
||||
# This script is sourced from other .R scripts for plotting
|
||||
#########################################################
|
||||
|
||||
##########################################################
|
||||
# Installing and loading required packages
|
||||
##########################################################
|
||||
source("Header_TT.R")
|
||||
#require(data.table)
|
||||
#require(arsenal)
|
||||
#require(compare)
|
||||
#library(tidyverse)
|
||||
|
||||
|
||||
#%% variable assignment: input and output paths & filenames
|
||||
drug = "pyrazinamide"
|
||||
gene = "pncA"
|
||||
gene_match = paste0(gene,"_p.")
|
||||
cat(gene_match)
|
||||
|
||||
#=============
|
||||
# directories
|
||||
#=============
|
||||
datadir = paste0("~/git/Data")
|
||||
indir = paste0(datadir, "/", drug, "/input")
|
||||
outdir = paste0("~/git/Data", "/", drug, "/output")
|
||||
|
||||
#===========
|
||||
# input
|
||||
#===========
|
||||
#in_filename = "mcsm_complex1_normalised.csv"
|
||||
in_filename_params = paste0(tolower(gene), "_all_params.csv")
|
||||
infile_params = paste0(outdir, "/", in_filename_params)
|
||||
cat(paste0("Input file 1:", infile_params) )
|
||||
|
||||
# infile 2: gene associated meta data
|
||||
#in_filename_gene_metadata = paste0(tolower(gene), "_meta_data_with_AFandOR.csv")
|
||||
in_filename_gene_metadata = paste0(tolower(gene), "_metadata.csv")
|
||||
infile_gene_metadata = paste0(outdir, "/", in_filename_gene_metadata)
|
||||
cat(paste0("Input infile 2:", infile_gene_metadata))
|
||||
|
||||
#===========
|
||||
# output
|
||||
#===========
|
||||
# mutations with opposite effects
|
||||
out_filename_opp_muts = paste0(tolower(gene), "_muts_opp_effects.csv")
|
||||
outfile_opp_muts = paste0(outdir, "/", out_filename_opp_muts)
|
||||
|
||||
|
||||
#%%===============================================================
|
||||
###########################
|
||||
# Read file: struct params
|
||||
###########################
|
||||
cat("Reading struct params including mcsm:"
|
||||
, in_filename_params)
|
||||
|
||||
mcsm_data = read.csv(infile_params
|
||||
#, row.names = 1
|
||||
, stringsAsFactors = F
|
||||
, header = T)
|
||||
|
||||
cat("Input dimensions:", dim(mcsm_data)) #416, 86
|
||||
|
||||
# clear variables
|
||||
rm(in_filename_params, infile_params)
|
||||
|
||||
str(mcsm_data)
|
||||
|
||||
table(mcsm_data$duet_outcome); sum(table(mcsm_data$duet_outcome) )
|
||||
|
||||
# spelling Correction 1: DUET incase American spelling needed!
|
||||
#mcsm_data$duet_outcome[mcsm_data$duet_outcome=="Stabilising"] <- "Stabilizing"
|
||||
#mcsm_data$duet_outcome[mcsm_data$duet_outcome=="Destabilising"] <- "Destabilizing"
|
||||
|
||||
# checks: should be the same as above
|
||||
table(mcsm_data$duet_outcome); sum(table(mcsm_data$duet_outcome) )
|
||||
head(mcsm_data$duet_outcome); tail(mcsm_data$duet_outcome)
|
||||
|
||||
# spelling Correction 2: Ligand incase American spelling needed!
|
||||
table(mcsm_data$ligand_outcome); sum(table(mcsm_data$ligand_outcome) )
|
||||
#mcsm_data$ligand_outcome[mcsm_data$ligand_outcome=="Stabilising"] <- "Stabilizing"
|
||||
#mcsm_data$ligand_outcome[mcsm_data$ligand_outcome=="Destabilising"] <- "Destabilizing"
|
||||
|
||||
# checks: should be the same as above
|
||||
table(mcsm_data$ligand_outcome); sum(table(mcsm_data$ligand_outcome) )
|
||||
head(mcsm_data$ligand_outcome); tail(mcsm_data$ligand_outcome)
|
||||
|
||||
# muts with opposing effects on protomer and ligand stability
|
||||
table(mcsm_data$duet_outcome != mcsm_data$ligand_outcome)
|
||||
changes = mcsm_data[which(mcsm_data$duet_outcome != mcsm_data$ligand_outcome),]
|
||||
|
||||
# sanity check: redundant, but uber cautious!
|
||||
dl_i = which(mcsm_data$duet_outcome != mcsm_data$ligand_outcome)
|
||||
ld_i = which(mcsm_data$ligand_outcome != mcsm_data$duet_outcome)
|
||||
|
||||
cat("Identifying muts with opposite stability effects")
|
||||
if(nrow(changes) == (table(mcsm_data$duet_outcome != mcsm_data$ligand_outcome)[[2]]) & identical(dl_i,ld_i)) {
|
||||
cat("PASS: muts with opposite effects on stability and affinity correctly identified"
|
||||
, "\nNo. of such muts: ", nrow(changes))
|
||||
}else {
|
||||
cat("FAIL: unsuccessful in extracting muts with changed stability effects")
|
||||
}
|
||||
|
||||
#***************************
|
||||
# write file: changed muts
|
||||
write.csv(changes, outfile_opp_muts)
|
||||
|
||||
cat("Finished writing file for muts with opp effects:"
|
||||
, "\nFilename: ", outfile_opp_muts
|
||||
, "\nDim:", dim(changes))
|
||||
|
||||
# clear variables
|
||||
rm(out_filename_opp_muts, outfile_opp_muts)
|
||||
rm(changes, dl_i, ld_i)
|
||||
|
||||
#***************************
|
||||
# count na in each column
|
||||
na_count = sapply(mcsm_data, function(y) sum(length(which(is.na(y))))); na_count
|
||||
|
||||
# sort by mutationinformation
|
||||
##mcsm_data = mcsm_data[order(mcsm_data$mutationinformation),]
|
||||
##head(mcsm_data$mutationinformation)
|
||||
|
||||
df_ncols = ncol(mcsm_data)
|
||||
|
||||
# REMOVE as this is dangerous due to dup muts
|
||||
# get freq count of positions and add to the df
|
||||
#setDT(mcsm_data)[, occurrence := .N, by = .(position)]
|
||||
|
||||
#cat("Added 1 col: position frequency to see which posn has how many muts"
|
||||
# , "\nNo. of cols now", ncol(mcsm_data)
|
||||
# , "\nNo. of cols before: ", df_ncols)
|
||||
|
||||
#pos_count_check = data.frame(mcsm_data$position, mcsm_data$occurrence)
|
||||
|
||||
# check duplicate muts
|
||||
if (length(unique(mcsm_data$mutationinformation)) == length(mcsm_data$mutationinformation)){
|
||||
cat("No duplicate mutations in mcsm data")
|
||||
}else{
|
||||
dup_muts = mcsm_data[duplicated(mcsm_data$mutationinformation),]
|
||||
dup_muts_nu = length(unique(dup_muts$mutationinformation))
|
||||
cat(paste0("CAUTION:", nrow(dup_muts), " Duplicate mutations identified"
|
||||
, "\nOf these, no. of unique mutations are:", dup_muts_nu
|
||||
, "\nExtracting df with unique mutations only"))
|
||||
mcsm_data_u = mcsm_data[!duplicated(mcsm_data$mutationinformation),]
|
||||
}
|
||||
|
||||
if (nrow(mcsm_data_u) == length(unique(mcsm_data$mutationinformation))){
|
||||
cat("Df without duplicate mutations successfully extracted")
|
||||
} else{
|
||||
cat("FAIL: could not extract clean df!")
|
||||
quit()
|
||||
}
|
||||
|
||||
###########################
|
||||
# 2: Read file: <gene>_meta data.csv
|
||||
###########################
|
||||
cat("Reading meta data file:", infile_gene_metadata)
|
||||
|
||||
gene_metadata <- read.csv(infile_gene_metadata
|
||||
, stringsAsFactors = F
|
||||
, header = T)
|
||||
cat("Dim:", dim(gene_metadata))
|
||||
|
||||
#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
|
||||
# FIXME: remove
|
||||
# counting NAs in AF, OR cols:
|
||||
if (identical(sum(is.na(gene_metadata$OR))
|
||||
, sum(is.na(gene_metadata$pvalue))
|
||||
, sum(is.na(gene_metadata$AF)))){
|
||||
cat("PASS: NA count match for OR, pvalue and AF\n")
|
||||
na_count = sum(is.na(gene_metadata$AF))
|
||||
cat("No. of NAs: ", sum(is.na(gene_metadata$OR)))
|
||||
} else{
|
||||
cat("FAIL: NA count mismatch"
|
||||
, "\nNA in OR: ", sum(is.na(gene_metadata$OR))
|
||||
, "\nNA in pvalue: ", sum(is.na(gene_metadata$pvalue))
|
||||
, "\nNA in AF:", sum(is.na(gene_metadata$AF)))
|
||||
}
|
||||
#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
|
||||
# clear variables
|
||||
rm(in_filename_gene_metadata, infile_gene_metadata)
|
||||
|
||||
str(gene_metadata)
|
||||
|
||||
# sort by position (same as mcsm_data)
|
||||
# earlier it was mutationinformation
|
||||
#head(gene_metadata$mutationinformation)
|
||||
#gene_metadata = gene_metadata[order(gene_metadata$mutationinformation),]
|
||||
##head(gene_metadata$mutationinformation)
|
||||
|
||||
head(gene_metadata$position)
|
||||
gene_metadata = gene_metadata[order(gene_metadata$position),]
|
||||
head(gene_metadata$position)
|
||||
|
||||
###########################
|
||||
# Merge 1: two dfs with NA
|
||||
# merged_df2
|
||||
###########################
|
||||
head(mcsm_data$mutationinformation)
|
||||
head(gene_metadata$mutationinformation)
|
||||
|
||||
# Find common columns b/w two df
|
||||
merging_cols = intersect(colnames(mcsm_data), colnames(gene_metadata))
|
||||
|
||||
cat(paste0("Merging dfs with NAs: big df (1-many relationship b/w id & mut)"
|
||||
, "\nNo. of merging cols:", length(merging_cols)
|
||||
, "\nMerging columns identified:"))
|
||||
print(merging_cols)
|
||||
|
||||
#=============
|
||||
# merged_df2): gene_metadata + mcsm_data
|
||||
#==============
|
||||
merged_df2 = merge(x = gene_metadata
|
||||
, y = mcsm_data
|
||||
, by = merging_cols
|
||||
, all.y = T)
|
||||
|
||||
cat("Dim of merged_df2: ", dim(merged_df2) #4520, 11
|
||||
)
|
||||
head(merged_df2$position)
|
||||
|
||||
#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
|
||||
# FIXME: count how many unique muts have entries in meta data
|
||||
# sanity check
|
||||
cat("Checking nrows in merged_df2")
|
||||
if(nrow(gene_metadata) == nrow(merged_df2)){
|
||||
cat("nrow(merged_df2) = nrow (gene associated gene_metadata)"
|
||||
,"\nExpected no. of rows: ",nrow(gene_metadata)
|
||||
,"\nGot no. of rows: ", nrow(merged_df2))
|
||||
} else{
|
||||
cat("nrow(merged_df2)!= nrow(gene associated gene_metadata)"
|
||||
, "\nExpected no. of rows after merge: ", nrow(gene_metadata)
|
||||
, "\nGot no. of rows: ", nrow(merged_df2)
|
||||
, "\nFinding discrepancy")
|
||||
merged_muts_u = unique(merged_df2$mutationinformation)
|
||||
meta_muts_u = unique(gene_metadata$mutationinformation)
|
||||
# find the index where it differs
|
||||
unique(meta_muts_u[! meta_muts_u %in% merged_muts_u])
|
||||
}
|
||||
|
||||
#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
|
||||
|
||||
# sort by position
|
||||
head(merged_df2$position)
|
||||
merged_df2 = merged_df2[order(merged_df2$position),]
|
||||
head(merged_df2$position)
|
||||
|
||||
merged_df2v3 = merge(x = gene_metadata
|
||||
, y = mcsm_data
|
||||
, by = merging_cols
|
||||
, all = T)
|
||||
|
||||
merged_df2v2 = merge(x = gene_metadata
|
||||
, y = mcsm_data
|
||||
, by = merging_cols
|
||||
, all.x = T)
|
||||
#!=!=!=!=!=!=!=!
|
||||
# COMMENT: used all.y since position 186 is not part of the struc,
|
||||
# hence doesn"t have a mcsm value
|
||||
# but 186 is associated with mutation
|
||||
#!=!=!=!=!=!=!=!
|
||||
|
||||
# should be False
|
||||
identical(merged_df2, merged_df2v2)
|
||||
table(merged_df2$position%in%merged_df2v2$position)
|
||||
|
||||
rm(merged_df2v2)
|
||||
|
||||
#!!!!!!!!!!! check why these differ
|
||||
|
||||
#########
|
||||
# merge 3b (merged_df3):remove duplicate mutation information
|
||||
#########
|
||||
cat("Merging dfs without NAs: small df (removing muts with no AF|OR associated)"
|
||||
,"\nCannot trust lineage info from this"
|
||||
,"\nlinking col: Mutationinforamtion"
|
||||
,"\nfilename: merged_df3")
|
||||
|
||||
#==#=#=#=#=#=#
|
||||
# Cannot trust lineage, country from this df as the same mutation
|
||||
# can have many different lineages
|
||||
# but this should be good for the numerical corr plots
|
||||
#=#=#=#=#=#=#=
|
||||
merged_df3 = merged_df2[!duplicated(merged_df2$mutationinformation),]
|
||||
head(merged_df3$position); tail(merged_df3$position) # should be sorted
|
||||
|
||||
# sanity check
|
||||
cat("Checking nrows in merged_df3")
|
||||
if(nrow(mcsm_data) == nrow(merged_df3)){
|
||||
cat("PASS: No. of rows match with mcsm_data"
|
||||
,"\nExpected no. of rows: ", nrow(mcsm_data)
|
||||
,"\nGot no. of rows: ", nrow(merged_df3))
|
||||
} else {
|
||||
cat("FAIL: No. of rows mismatch"
|
||||
, "\nNo. of rows mcsm_data: ", nrow(mcsm_data)
|
||||
, "\nNo. of rows merged_df3: ", nrow(merged_df3))
|
||||
}
|
||||
|
||||
# counting NAs in AF, OR cols in merged_df3
|
||||
# this is becuase mcsm has no AF, OR cols,
|
||||
# so you cannot count NAs
|
||||
if (identical(sum(is.na(merged_df3$OR))
|
||||
, sum(is.na(merged_df3$pvalue))
|
||||
, sum(is.na(merged_df3$AF)))){
|
||||
cat("PASS: NA count match for OR, pvalue and AF\n")
|
||||
na_count_df3 = sum(is.na(merged_df3$AF))
|
||||
cat("No. of NAs: ", sum(is.na(merged_df3$OR)))
|
||||
} else{
|
||||
cat("FAIL: NA count mismatch"
|
||||
, "\nNA in OR: ", sum(is.na(merged_df3$OR))
|
||||
, "\nNA in pvalue: ", sum(is.na(merged_df3$pvalue))
|
||||
, "\nNA in AF:", sum(is.na(merged_df3$AF)))
|
||||
}
|
||||
|
||||
###########################
|
||||
# 4: merging two dfs: without NA
|
||||
###########################
|
||||
#########
|
||||
# merge 4a (merged_df2_comp): same as merge 1 but excluding NA
|
||||
#########
|
||||
cat("Merging dfs without any NAs: big df (1-many relationship b/w id & mut)"
|
||||
,"\nlinking col: Mutationinforamtion"
|
||||
,"\nfilename: merged_df2_comp")
|
||||
|
||||
merged_df2_comp = merged_df2[!is.na(merged_df2$AF),]
|
||||
#merged_df2_comp = merged_df2[!duplicated(merged_df2$mutationinformation),]
|
||||
|
||||
# sanity check
|
||||
cat("Checking nrows in merged_df2_comp")
|
||||
if(nrow(merged_df2_comp) == (nrow(merged_df2) - na_count + 1)){
|
||||
cat("PASS: No. of rows match"
|
||||
,"\nDim of merged_df2_comp: "
|
||||
,"\nExpected no. of rows: ", nrow(merged_df2) - na_count + 1
|
||||
, "\nNo. of rows: ", nrow(merged_df2_comp)
|
||||
, "\nNo. of cols: ", ncol(merged_df2_comp))
|
||||
}else{
|
||||
cat("FAIL: No. of rows mismatch"
|
||||
,"\nExpected no. of rows: ", nrow(merged_df2) - na_count + 1
|
||||
,"\nGot no. of rows: ", nrow(merged_df2_comp))
|
||||
}
|
||||
|
||||
#########
|
||||
# merge 4b (merged_df3_comp): remove duplicate mutation information
|
||||
#########
|
||||
merged_df3_comp = merged_df2_comp[!duplicated(merged_df2_comp$mutationinformation),]
|
||||
|
||||
cat("Dim of merged_df3_comp: "
|
||||
, "\nNo. of rows: ", nrow(merged_df3_comp)
|
||||
, "\nNo. of cols: ", ncol(merged_df3_comp))
|
||||
|
||||
# alternate way of deriving merged_df3_comp
|
||||
foo = merged_df3[!is.na(merged_df3$AF),]
|
||||
# compare dfs: foo and merged_df3_com
|
||||
all.equal(foo, merged_df3)
|
||||
|
||||
summary(comparedf(foo, merged_df3))
|
||||
|
||||
# sanity check
|
||||
cat("Checking nrows in merged_df3_comp")
|
||||
if(nrow(merged_df3_comp) == nrow(merged_df3)){
|
||||
cat("NO NAs detected in merged_df3 in AF|OR cols"
|
||||
,"\nNo. of rows are identical: ", nrow(merged_df3))
|
||||
} else{
|
||||
if(nrow(merged_df3_comp) == nrow(merged_df3) - na_count_df3) {
|
||||
cat("PASS: NAs detected in merged_df3 in AF|OR cols"
|
||||
, "\nNo. of NAs: ", na_count_df3
|
||||
, "\nExpected no. of rows in merged_df3_comp: ", nrow(merged_df3) - na_count_df3
|
||||
, "\nGot no. of rows: ", nrow(merged_df3_comp))
|
||||
}
|
||||
}
|
||||
|
||||
#=============== end of combining df
|
||||
#*********************
|
||||
# writing 1 file in the style of a loop: merged_df3
|
||||
# print(output dir)
|
||||
#i = "merged_df3"
|
||||
#out_filename = paste0(i, ".csv")
|
||||
#outfile = paste0(outdir, "/", out_filename)
|
||||
|
||||
#cat("Writing output file: "
|
||||
# ,"\nFilename: ", out_filename
|
||||
# ,"\nPath: ", outdir)
|
||||
|
||||
#template: write.csv(merged_df3, "merged_df3.csv")
|
||||
#write.csv(get(i), outfile, row.names = FALSE)
|
||||
#cat("Finished writing: ", outfile
|
||||
# , "\nNo. of rows: ", nrow(get(i))
|
||||
# , "\nNo. of cols: ", ncol(get(i)))
|
||||
|
||||
#%% write_output files; all 4 files:
|
||||
outvars = c("merged_df2"
|
||||
, "merged_df3"
|
||||
, "merged_df2_comp"
|
||||
, "merged_df3_comp")
|
||||
|
||||
cat("Writing output files: "
|
||||
, "\nPath:", outdir)
|
||||
|
||||
for (i in outvars){
|
||||
# cat(i, "\n")
|
||||
out_filename = paste0(i, ".csv")
|
||||
# cat(out_filename, "\n")
|
||||
# cat("getting value of variable: ", get(i))
|
||||
outfile = paste0(outdir, "/", out_filename)
|
||||
# cat("Full output path: ", outfile, "\n")
|
||||
cat("Writing output file:"
|
||||
,"\nFilename: ", out_filename,"\n")
|
||||
write.csv(get(i), outfile, row.names = FALSE)
|
||||
cat("Finished writing: ", outfile
|
||||
, "\nNo. of rows: ", nrow(get(i))
|
||||
, "\nNo. of cols: ", ncol(get(i)), "\n")
|
||||
}
|
||||
|
||||
# alternate way to replace with implicit loop
|
||||
# FIXME
|
||||
#sapply(outvars, function(x, y) write.csv(get(outvars), paste0(outdir, "/", outvars, ".csv")))
|
||||
#*************************
|
||||
# clear variables
|
||||
rm(mcsm_data, gene_metadata, foo, drug, gene, gene_match, indir, merged_muts_u, meta_muts_u, na_count, df_ncols, outdir)
|
||||
rm(pos_count_check)
|
||||
#============================= end of script
|
||||
|
||||
|
|
@ -5,27 +5,30 @@ getwd()
|
|||
#########################################################
|
||||
# TASK: Basic lineage barplot showing numbers
|
||||
|
||||
# Output:
|
||||
# Output: Basic barplot with lineage samples and mut count
|
||||
|
||||
##########################################################
|
||||
# Installing and loading required packages
|
||||
##########################################################
|
||||
source("Header_TT.R")
|
||||
require(data.table)
|
||||
source("combining_two_df.R")
|
||||
|
||||
#==========================
|
||||
source("combining_dfs_plotting.R")
|
||||
# should return the following dfs, directories and variables
|
||||
|
||||
# df with NA:
|
||||
# merged_df2
|
||||
# merged_df3
|
||||
# PS combined:
|
||||
# 1) merged_df2
|
||||
# 2) merged_df2_comp
|
||||
# 3) merged_df3
|
||||
# 4) merged_df3_comp
|
||||
|
||||
# df without NA:
|
||||
# merged_df2_comp
|
||||
# merged_df3_comp
|
||||
# LIG combined:
|
||||
# 5) merged_df2_lig
|
||||
# 6) merged_df2_comp_lig
|
||||
# 7) merged_df3_lig
|
||||
# 8) merged_df3_comp_lig
|
||||
|
||||
# my_df_u
|
||||
# 9) my_df_u
|
||||
# 10) my_df_u_lig
|
||||
|
||||
cat(paste0("Directories imported:"
|
||||
, "\ndatadir:", datadir
|
||||
|
|
@ -38,13 +41,16 @@ cat(paste0("Variables imported:"
|
|||
, "\ngene:", gene
|
||||
, "\ngene_match:", gene_match
|
||||
, "\nAngstrom symbol:", angstroms_symbol
|
||||
, "\nNo. of cols:", df_ncols
|
||||
, "\nNo. of duplicated muts:", dup_muts_nu
|
||||
, "\nNA count for ORs:", na_count
|
||||
, "\nNA count in df2:", na_count_df2
|
||||
, "\nNA count in df3:", na_count_df3))
|
||||
|
||||
#=========================
|
||||
#===========
|
||||
# input
|
||||
#===========
|
||||
# output of combining_dfs_plotting.R
|
||||
|
||||
#=======
|
||||
# output
|
||||
#=======
|
||||
|
|
@ -82,15 +88,11 @@ is.factor(my_df$lineage)
|
|||
# fill = lineage
|
||||
#============================
|
||||
table(my_df$lineage)
|
||||
|
||||
#****************
|
||||
# Plot: Lineage Barplot
|
||||
#****************
|
||||
as.data.frame(table(my_df$lineage))
|
||||
|
||||
#=============
|
||||
# Data for plots
|
||||
#=============
|
||||
|
||||
# REASSIGNMENT
|
||||
df <- my_df
|
||||
|
||||
|
|
@ -111,18 +113,7 @@ sel_lineages = c("lineage1"
|
|||
#, "lineage7"
|
||||
)
|
||||
|
||||
df_lin = subset(df, subset = lineage %in% sel_lineages )
|
||||
|
||||
#FIXME; add sanity check for numbers.
|
||||
# Done this manually
|
||||
|
||||
############################################################
|
||||
|
||||
#########
|
||||
# Data for barplot: Lineage barplot
|
||||
# to show total samples and number of unique mutations
|
||||
# within each linege
|
||||
##########
|
||||
df_lin = subset(df, subset = lineage %in% sel_lineages)
|
||||
|
||||
# Create df with lineage inform & no. of unique mutations
|
||||
# per lineage and total samples within lineage
|
||||
|
|
@ -193,7 +184,7 @@ printFile = g + geom_bar(stat = "identity"
|
|||
, axis.title.y = element_text(size = my_als
|
||||
, colour = 'black')
|
||||
, legend.position = "top"
|
||||
, legend.text = element_text(size = my_als) +
|
||||
, legend.text = element_text(size = my_als)) +
|
||||
#geom_text() +
|
||||
geom_label(aes(label = value)
|
||||
, size = 5
|
||||
|
|
@ -212,7 +203,7 @@ printFile = g + geom_bar(stat = "identity"
|
|||
, name=''
|
||||
, labels=c('Mutations', 'Total Samples')) +
|
||||
scale_x_discrete(breaks = c('lineage1', 'lineage2', 'lineage3', 'lineage4')
|
||||
, labels = c('Lineage 1', 'Lineage 2', 'Lineage 3', 'Lineage 4')))
|
||||
, labels = c('Lineage 1', 'Lineage 2', 'Lineage 3', 'Lineage 4'))
|
||||
|
||||
print(printFile)
|
||||
dev.off()
|
||||
|
|
|
|||
71
scripts/plotting/lineage_count.txt
Normal file
71
scripts/plotting/lineage_count.txt
Normal file
|
|
@ -0,0 +1,71 @@
|
|||
#=============
|
||||
# merged_df2
|
||||
#=============
|
||||
----------------
|
||||
# no. of samples
|
||||
----------------
|
||||
Var1 Freq
|
||||
1 8
|
||||
2 lineage1 144
|
||||
3 lineage1;lineage2 3
|
||||
4 lineage1;lineage4 4
|
||||
5 lineage2 1886
|
||||
6 lineage2;lineage4 19
|
||||
7 lineage3 190
|
||||
8 lineage3;lineage4 11
|
||||
9 lineage4 2213
|
||||
10 lineage4;lineage6 1
|
||||
11 lineage4;lineage7 1
|
||||
12 lineage4;lineageBOV 1
|
||||
13 lineage5 31
|
||||
14 lineage6 9
|
||||
15 lineage7 3
|
||||
16 lineageBOV 392
|
||||
|
||||
----------------
|
||||
# no. of nsSNPs
|
||||
----------------
|
||||
|
||||
sel_lineages num_snps_u total_samples
|
||||
1 lineage1 74 144
|
||||
2 lineage2 277 1886
|
||||
3 lineage3 104 190
|
||||
4 lineage4 311 2213
|
||||
5 lineage5 18 31
|
||||
6 lineage6 8 9
|
||||
7 lineage7 1 3
|
||||
|
||||
|
||||
#=============
|
||||
# merged_df2_comp
|
||||
#=============
|
||||
----------------
|
||||
# no. of samples
|
||||
----------------
|
||||
|
||||
Var1 Freq
|
||||
1 3
|
||||
2 lineage1 108
|
||||
3 lineage1;lineage2 2
|
||||
4 lineage1;lineage4 2
|
||||
5 lineage2 1497
|
||||
6 lineage2;lineage4 13
|
||||
7 lineage3 154
|
||||
8 lineage3;lineage4 3
|
||||
9 lineage4 1846
|
||||
10 lineage4;lineageBOV 1
|
||||
11 lineage5 12
|
||||
12 lineage6 2
|
||||
13 lineageBOV 36
|
||||
|
||||
----------------
|
||||
# no. of nsSNPs
|
||||
----------------
|
||||
sel_lineages num_snps_u total_samples
|
||||
1 lineage1 42 108
|
||||
2 lineage2 141 1497
|
||||
3 lineage3 75 154
|
||||
4 lineage4 148 1846
|
||||
5 lineage5 9 12
|
||||
6 lineage6 2 2
|
||||
7 lineage7 0 0
|
||||
95
scripts/plotting/opp_mcsm_muts.R
Normal file
95
scripts/plotting/opp_mcsm_muts.R
Normal file
|
|
@ -0,0 +1,95 @@
|
|||
#!/usr/bin/env Rscript
|
||||
#########################################################
|
||||
# TASK: To write muts with opposite effects on
|
||||
# protomer and ligand stability
|
||||
#########################################################
|
||||
# working dir and loading libraries
|
||||
|
||||
getwd()
|
||||
setwd("~/git/LSHTM_analysis/scripts/plotting/")
|
||||
getwd()
|
||||
|
||||
source("plotting_data.R")
|
||||
|
||||
# should return the following dfs, directories and variables
|
||||
# my_df
|
||||
# my_df_u
|
||||
# my_df_u_lig
|
||||
# dup_muts
|
||||
|
||||
cat(paste0("Directories imported:"
|
||||
, "\ndatadir:", datadir
|
||||
, "\nindir:", indir
|
||||
, "\noutdir:", outdir
|
||||
, "\nplotdir:", plotdir))
|
||||
|
||||
cat(paste0("Variables imported:"
|
||||
, "\ndrug:", drug
|
||||
, "\ngene:", gene
|
||||
, "\ngene_match:", gene_match
|
||||
, "\nLength of upos:", length(upos)
|
||||
, "\nAngstrom symbol:", angstroms_symbol))
|
||||
|
||||
# clear excess variable
|
||||
rm(my_df, upos, dup_muts)
|
||||
#========================================================
|
||||
#===========
|
||||
# input
|
||||
#===========
|
||||
#in_file1: output of plotting_data.R
|
||||
# my_df_u
|
||||
|
||||
# output
|
||||
#===========
|
||||
# mutations with opposite effects
|
||||
out_filename_opp_muts = paste0(tolower(gene), "_muts_opp_effects.csv")
|
||||
outfile_opp_muts = paste0(outdir, "/", out_filename_opp_muts)
|
||||
|
||||
#%%===============================================================
|
||||
|
||||
# spelling Correction 1: DUET incase American spelling needed!
|
||||
table(my_df_u$duet_outcome); sum(table(my_df_u$duet_outcome) )
|
||||
#my_df_u$duet_outcome[my_df_u$duet_outcome=="Stabilising"] <- "Stabilizing"
|
||||
#my_df_u$duet_outcome[my_df_u$duet_outcome=="Destabilising"] <- "Destabilizing"
|
||||
|
||||
|
||||
# spelling Correction 2: Ligand incase American spelling needed!
|
||||
table(my_df_u$ligand_outcome); sum(table(my_df_u$ligand_outcome) )
|
||||
#my_df_u$ligand_outcome[my_df_u$ligand_outcome=="Stabilising"] <- "Stabilizing"
|
||||
#my_df_u$ligand_outcome[my_df_u$ligand_outcome=="Destabilising"] <- "Destabilizing"
|
||||
|
||||
|
||||
# muts with opposing effects on protomer and ligand stability
|
||||
table(my_df_u$duet_outcome != my_df_u$ligand_outcome)
|
||||
changes = my_df_u[which(my_df_u$duet_outcome != my_df_u$ligand_outcome),]
|
||||
|
||||
# sanity check: redundant, but uber cautious!
|
||||
dl_i = which(my_df_u$duet_outcome != my_df_u$ligand_outcome)
|
||||
ld_i = which(my_df_u$ligand_outcome != my_df_u$duet_outcome)
|
||||
|
||||
cat("Identifying muts with opposite stability effects")
|
||||
if(nrow(changes) == (table(my_df_u$duet_outcome != my_df_u$ligand_outcome)[[2]]) & identical(dl_i,ld_i)) {
|
||||
cat("PASS: muts with opposite effects on stability and affinity correctly identified"
|
||||
, "\nNo. of such muts: ", nrow(changes))
|
||||
}else {
|
||||
cat("FAIL: unsuccessful in extracting muts with changed stability effects")
|
||||
}
|
||||
|
||||
#==========================
|
||||
# write file: changed muts
|
||||
#==========================
|
||||
write.csv(changes, outfile_opp_muts)
|
||||
|
||||
cat("Finished writing file for muts with opp effects:"
|
||||
, "\nFilename: ", outfile_opp_muts
|
||||
, "\nDim:", dim(changes))
|
||||
|
||||
# clear variables
|
||||
rm(out_filename_opp_muts, outfile_opp_muts)
|
||||
rm(changes, dl_i, ld_i)
|
||||
|
||||
# count na in each column
|
||||
na_count = sapply(my_df_u, function(y) sum(length(which(is.na(y))))); na_count
|
||||
df_ncols = ncol(my_df_u)
|
||||
|
||||
#===================================== end of script
|
||||
Loading…
Add table
Add a link
Reference in a new issue