added dir for embb for consistency and checks and moved others to version1
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19 changed files with 1614 additions and 2 deletions
364
scripts/plotting/plotting_thesis/embb/basic_barplots_embb.R
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364
scripts/plotting/plotting_thesis/embb/basic_barplots_embb.R
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#!/usr/bin/env Rscript
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#########################################################
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# TASK: Barplots
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# basic barplots with outcome
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# basic barplots with frequency of count of mutations
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#########################################################
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#=============
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# Data: Input
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#==============
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#source("~/git/LSHTM_analysis/config/embb.R")
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#source("~/git/LSHTM_analysis/scripts/plotting/get_plotting_dfs.R")
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#cat("\nSourced plotting cols as well:", length(plotting_cols))
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####################################################
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class(merged_df3)
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df3 = subset(merged_df3, select = -c(pos_count))
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#=======
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# output
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#=======
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outdir_images = paste0("~/git/Writing/thesis/images/results/", tolower(gene), "/")
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cat("plots will output to:", outdir_images)
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##########################################################
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# blue, red bp
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sts = 8
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lts = 8
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ats = 8
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als = 8
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ltis = 8
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geom_ls = 2.2
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#pos_count
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subtitle_size = 8
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geom_ls_pc = 2.2
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leg_text_size = 8
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axis_text_size = 8
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axis_label_size = 8
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###########################################################
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#------------------------------
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# plot default sizes
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#------------------------------
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#=========================
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# Affinity outcome
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# check this var: outcome_cols_affinity
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# get from preformatting or put in globals
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#==========================
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DistCutOff
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LigDist_colname # = "ligand_distance" # from globals
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ppi2Dist_colname
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naDist_colname
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###########################################################
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# get plotting data within the distance
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df3_lig = df3[df3[[LigDist_colname]]<DistCutOff,]
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df3_ppi2 = df3[df3[[ppi2Dist_colname]]<DistCutOff,]
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df3_na = df3[df3[[naDist_colname]]<DistCutOff,]
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common_bp_title = paste0("Sites <", DistCutOff, angstroms_symbol)
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#------------------------------
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# barplot for ligand affinity:
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# <10 Ang of ligand
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#------------------------------
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mLigP = stability_count_bp(plotdf = df3_lig
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, df_colname = "ligand_outcome"
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#, leg_title = "mCSM-lig"
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#, bp_plot_title = paste(common_bp_title, "ligand")
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, yaxis_title = "Number of nsSNPs"
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, leg_position = "none"
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, subtitle_text = "mCSM\nLig"
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, bar_fill_values = c("#F8766D", "#00BFC4")
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, subtitle_colour= "black"
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, sts = sts
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, lts = lts
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, ats = ats
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, als = als
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, ltis = ltis
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, geom_ls = geom_ls
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)
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mLigP
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#------------------------------
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# barplot for ligand affinity:
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# <10 Ang of ligand
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# mmCSM-lig: will be the same no. of sites but the effect will be different
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#------------------------------
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mmLigP = stability_count_bp(plotdf = df3_lig
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, df_colname = "mmcsm_lig_outcome"
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#, leg_title = "mmCSM-lig"
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#, label_categories = labels_mmlig
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#, bp_plot_title = paste(common_bp_title, "ligand")
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, yaxis_title = ""
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, leg_position = "none"
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, subtitle_text = "mmCSM\nLig"
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, bar_fill_values = c("#F8766D", "#00BFC4")
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, subtitle_colour= "black"
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, sts = sts
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, lts = lts
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, ats = ats
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, als = als
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, ltis = ltis
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, geom_ls = geom_ls
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)
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mmLigP
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#------------------------------
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# barplot for ppi2 affinity
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# <10 Ang of interface
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#------------------------------
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if (tolower(gene)%in%geneL_ppi2){
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ppi2P = stability_count_bp(plotdf = df3_ppi2
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, df_colname = "mcsm_ppi2_outcome"
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#, leg_title = "mCSM-ppi2"
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#, label_categories = labels_ppi2
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#, bp_plot_title = paste(common_bp_title, "PP-interface")
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, yaxis_title = "Number of nsSNPs"
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, leg_position = "none"
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, subtitle_text = "mCSM\nPPI2"
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, bar_fill_values = c("#F8766D", "#00BFC4")
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, subtitle_colour= "black"
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, sts = sts
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, lts = lts
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, ats = ats
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, als = als
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, ltis = ltis
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, geom_ls = geom_ls
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)
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ppi2P
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}
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#----------------------------
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# barplot for ppi2 affinity
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# <10 Ang of interface
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#------------------------------
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if (tolower(gene)%in%geneL_na){
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nca_distP = stability_count_bp(plotdf = df3_na
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, df_colname = "mcsm_na_outcome"
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#, leg_title = "mCSM-NA"
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#, label_categories =
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#, bp_plot_title = paste(common_bp_title, "Dist to NA")
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, yaxis_title = "Number of nsSNPs"
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, leg_position = "none"
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, subtitle_text = "mCSM\nNA"
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, bar_fill_values = c("#F8766D", "#00BFC4")
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, subtitle_colour= "black"
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, sts = sts
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, lts = lts
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, ats = ats
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, als = als
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, ltis = ltis
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, geom_ls = geom_ls
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)
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nca_distP
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}
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#####################################################################
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# ------------------------------
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# bp site site count: mCSM-lig
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# < 10 Ang ligand
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# ------------------------------
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common_bp_title = paste0("Sites <", DistCutOff, angstroms_symbol)
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posC_lig = site_snp_count_bp(plotdf = df3_lig
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, df_colname = "position"
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, xaxis_title = "Number of nsSNPs"
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, yaxis_title = "Number of Sites"
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, subtitle_colour = "chocolate4"
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, subtitle_text = ""
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, subtitle_size = subtitle_size
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, geom_ls = geom_ls_pc
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, leg_text_size = leg_text_size
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, axis_text_size = axis_text_size
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, axis_label_size = axis_label_size)
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posC_lig
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#------------------------------
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# bp site site count: ppi2
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# < 10 Ang interface
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#------------------------------
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if (tolower(gene)%in%geneL_ppi2){
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posC_ppi2 = site_snp_count_bp(plotdf = df3_ppi2
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, df_colname = "position"
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, xaxis_title = "Number of nsSNPs"
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, yaxis_title = "Number of Sites"
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, subtitle_colour = "chocolate4"
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, subtitle_text = ""
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, subtitle_size = subtitle_size
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, geom_ls = geom_ls_pc
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, leg_text_size = leg_text_size
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, axis_text_size = axis_text_size
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, axis_label_size = axis_label_size)
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posC_ppi2
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}
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#------------------------------
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# bp site site count: NCA dist
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# < 10 Ang nca
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#------------------------------
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if (tolower(gene)%in%geneL_na){
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posC_nca = site_snp_count_bp(plotdf = df3_na
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, df_colname = "position"
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, xaxis_title = "Number of nsSNPs"
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, yaxis_title = "Number of Sites"
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, subtitle_colour = "chocolate4"
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, subtitle_text = ""
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, subtitle_size = subtitle_size
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, geom_ls = geom_ls_pc
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, leg_text_size = leg_text_size
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, axis_text_size = axis_text_size
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, axis_label_size = axis_label_size)
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posC_nca
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}
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#===============================================================
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#------------------------------
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# bp site site count: ALL
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# <10 Ang ligand
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#------------------------------
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posC_all = site_snp_count_bp(plotdf = df3
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, df_colname = "position"
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, xaxis_title = "Number of nsSNPs"
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, yaxis_title = "Number of Sites"
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, subtitle_colour = "chocolate4"
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, subtitle_text = "All mutations sites"
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, subtitle_size = subtitle_size
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, geom_ls = geom_ls_pc
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, leg_text_size = leg_text_size
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, axis_text_size = axis_text_size
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, axis_label_size = axis_label_size)
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posC_all
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##################################################################
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consurfP = stability_count_bp(plotdf = df3
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, df_colname = "consurf_outcome"
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#, leg_title = "ConSurf"
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#, label_categories = labels_consurf
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, yaxis_title = "Number of nsSNPs"
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, leg_position = "top"
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, subtitle_text = "ConSurf"
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, bar_fill_values = consurf_colours # from globals
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, subtitle_colour= "black"
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, sts = sts
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, lts = lts
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, ats = ats
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, als = als
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, ltis = ltis
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, geom_ls = geom_ls)
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consurfP
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##############################################################
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sts_so = 10
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lts_so = 10
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ats_so = 10
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als_so = 10
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ltis_so = 10
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geom_ls_so = 2.5
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#===================
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# Stability
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#===================
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# duetP
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duetP = stability_count_bp(plotdf = df3
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, df_colname = "duet_outcome"
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, leg_title = "mCSM-DUET"
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#, label_categories = labels_duet
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, yaxis_title = "Number of nsSNPs"
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, leg_position = "none"
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, subtitle_text = "mCSM-DUET"
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, bar_fill_values = c("#F8766D", "#00BFC4")
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, subtitle_colour= "black"
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, sts = sts_so
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, lts = lts_so
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, ats = ats_so
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, als = als_so
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, ltis = ltis_so
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, geom_ls = geom_ls_so)
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duetP
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# foldx
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foldxP = stability_count_bp(plotdf = df3
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, df_colname = "foldx_outcome"
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#, leg_title = "FoldX"
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#, label_categories = labels_foldx
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, yaxis_title = ""
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, leg_position = "none"
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, subtitle_text = "FoldX"
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, bar_fill_values = c("#F8766D", "#00BFC4")
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, sts = sts_so
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, lts = lts_so
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, ats = ats_so
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, als = als_so
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, ltis = ltis_so
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, geom_ls = geom_ls_so)
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foldxP
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# deepddg
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deepddgP = stability_count_bp(plotdf = df3
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, df_colname = "deepddg_outcome"
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#, leg_title = "DeepDDG"
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#, label_categories = labels_deepddg
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, yaxis_title = ""
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, leg_position = "none"
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, subtitle_text = "DeepDDG"
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, bar_fill_values = c("#F8766D", "#00BFC4")
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, sts = sts_so
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, lts = lts_so
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, ats = ats_so
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, als = als_so
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, ltis = ltis_so
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, geom_ls = geom_ls_so)
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deepddgP
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# deepddg
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dynamut2P = stability_count_bp(plotdf = df3
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, df_colname = "ddg_dynamut2_outcome"
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#, leg_title = "Dynamut2"
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#, label_categories = labels_ddg_dynamut2_outcome
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, yaxis_title = ""
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, leg_position = "none"
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, subtitle_text = "Dynamut2"
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, bar_fill_values = c("#F8766D", "#00BFC4")
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, sts = sts_so
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, lts = lts_so
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, ats = ats_so
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, als = als_so
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, ltis = ltis_so
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, geom_ls = geom_ls_so)
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dynamut2P
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# provean
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proveanP = stability_count_bp(plotdf = df3
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, df_colname = "provean_outcome"
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#, leg_title = "PROVEAN"
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#, label_categories = labels_provean
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, yaxis_title = "Number of nsSNPs"
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, leg_position = "none" # top
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, subtitle_text = "PROVEAN"
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, bar_fill_values = c("#D01C8B", "#F1B6DA") # light pink and deep
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, sts = sts_so
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, lts = lts_so
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, ats = ats_so
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, als = als_so
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, ltis = ltis_so
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, geom_ls = geom_ls_so)
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proveanP
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# snap2
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snap2P = stability_count_bp(plotdf = df3
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, df_colname = "snap2_outcome"
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#, leg_title = "SNAP2"
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#, label_categories = labels_snap2
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, yaxis_title = ""
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, leg_position = "none" # top
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, subtitle_text = "SNAP2"
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, bar_fill_values = c("#D01C8B", "#F1B6DA") # light pink and deep
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, sts = sts_so
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, lts = lts_so
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, ats = ats_so
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, als = als_so
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, ltis = ltis_so
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, geom_ls = geom_ls_so)
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snap2P
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#####################################################################################
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@ -0,0 +1,310 @@
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#=============
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# Data: Input
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#==============
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source("~/git/LSHTM_analysis/config/embb.R")
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source("~/git/LSHTM_analysis/scripts/plotting/get_plotting_dfs.R")
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source("/home/tanu/git/LSHTM_analysis/scripts/plotting/plotting_thesis/embb/basic_barplots_embb.R")
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source("/home/tanu/git/LSHTM_analysis/scripts/plotting/plotting_thesis/embb/pe_sens_site_count_embb.R")
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if ( tolower(gene)%in%c("embb") ){
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cat("\nPlots available for layout are:")
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duetP
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foldxP
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deepddgP
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dynamut2P
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proveanP
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snap2P
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mLigP
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mmLigP
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posC_lig
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ppi2P
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posC_ppi2
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peP2
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sens_siteP
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peP # not used
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sensP # not used
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}
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#========================
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# Common title settings
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#=========================
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theme_georgia <- function(...) {
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theme_gray(base_family = "sans", ...) +
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theme(plot.title = element_text(face = "bold"))
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}
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title_theme <- calc_element("plot.title", theme_georgia())
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###############################################################
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common_bp_title = paste0("Sites <", DistCutOff, angstroms_symbol)
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# extract common legends
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# lig affinity
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common_legend_outcome = get_legend(mLigP +
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guides(color = guide_legend(nrow = 1)) +
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theme(legend.position = "top"))
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# stability
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common_legend_outcome = get_legend(duetP +
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guides(color = guide_legend(nrow = 1)) +
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theme(legend.position = "top"))
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# conservation
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cons_common_legend_outcome = get_legend(snap2P +
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guides(color = guide_legend(nrow = 1)) +
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theme(legend.position = "top"))
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###################################################################
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#==================================
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# Stability+Conservation: COMBINE
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#==================================
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tt_size = 10
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#----------------------------
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# stability and consv title
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#----------------------------
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tt_stab = ggdraw() +
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draw_label(
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paste0("Stability outcome"),
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fontfamily = title_theme$family,
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fontface = title_theme$face,
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#size = title_theme$size
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size = tt_size
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)
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tt_cons = ggdraw() +
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draw_label(
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paste0("Conservation outcome"),
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fontfamily = title_theme$family,
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fontface = title_theme$face,
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size = tt_size
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)
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#----------------------
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# Output plot
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#-----------------------
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stab_cons_CLP = paste0(outdir_images
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,tolower(gene)
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,"_stab_cons_BP_CLP.png")
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print(paste0("plot filename:", stab_cons_CLP))
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png(stab_cons_CLP, units = "in", width = 10, height = 5, res = 300 )
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cowplot::plot_grid(
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cowplot::plot_grid(
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cowplot::plot_grid(
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tt_stab,
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common_legend_outcome,
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nrow = 2
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),
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cowplot::plot_grid(
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duetP,
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foldxP,
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deepddgP,
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dynamut2P,
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nrow = 1,
|
||||
labels = c("A", "B", "C", "D"),
|
||||
label_size = 12),
|
||||
nrow = 2,
|
||||
rel_heights=c(1,10)
|
||||
),
|
||||
NULL,
|
||||
cowplot::plot_grid(
|
||||
cowplot::plot_grid(
|
||||
cowplot::plot_grid(
|
||||
tt_cons,
|
||||
cons_common_legend_outcome,
|
||||
nrow = 2
|
||||
),
|
||||
cowplot::plot_grid(
|
||||
proveanP,
|
||||
snap2P,
|
||||
nrow=1,
|
||||
labels = c("E", "F"),
|
||||
align = "hv"),
|
||||
nrow = 2,
|
||||
rel_heights = c(1, 10),
|
||||
label_size = 12),
|
||||
nrow=1
|
||||
),
|
||||
rel_widths = c(2,0.15,1),
|
||||
nrow=1
|
||||
)
|
||||
|
||||
dev.off()
|
||||
|
||||
#################################################################
|
||||
#=======================================
|
||||
# Affinity barplots: COMBINE ALL four
|
||||
#========================================
|
||||
ligT = paste0(common_bp_title, " ligand")
|
||||
lig_affT = ggdraw() +
|
||||
draw_label(
|
||||
ligT,
|
||||
fontfamily = title_theme$family,
|
||||
fontface = title_theme$face,
|
||||
#size = title_theme$size
|
||||
size = 8
|
||||
)
|
||||
|
||||
p1 = cowplot::plot_grid(cowplot::plot_grid(lig_affT
|
||||
, common_legend_outcome
|
||||
, nrow=2),
|
||||
cowplot::plot_grid(mLigP, mmLigP, posC_lig
|
||||
, nrow = 1
|
||||
, rel_widths = c(1,0.65,1.8)
|
||||
, align = "h"),
|
||||
nrow = 2,
|
||||
rel_heights = c(1,8)
|
||||
|
||||
)
|
||||
p1
|
||||
|
||||
###########################################################
|
||||
ppi2T = paste0(common_bp_title, " PP-interface")
|
||||
ppi2_affT = ggdraw() +
|
||||
draw_label(
|
||||
ppi2T,
|
||||
fontfamily = title_theme$family,
|
||||
fontface = title_theme$face,
|
||||
size = 8
|
||||
)
|
||||
|
||||
p3 = cowplot::plot_grid(cowplot::plot_grid(ppi2_affT, common_legend_outcome, nrow=2),
|
||||
cowplot::plot_grid(ppi2P, posC_ppi2
|
||||
, nrow = 1
|
||||
, rel_widths = c(1,1.9)
|
||||
, align = "h"),
|
||||
nrow = 2,
|
||||
rel_heights = c(1,8)
|
||||
)
|
||||
p3
|
||||
|
||||
# PE + All position count
|
||||
peT_allT = ggdraw() +
|
||||
draw_label(
|
||||
paste0("All mutation sites"),
|
||||
fontfamily = title_theme$family,
|
||||
fontface = title_theme$face,
|
||||
#size = title_theme$size
|
||||
size = 8
|
||||
)
|
||||
|
||||
p4 = cowplot::plot_grid(cowplot::plot_grid(peT_allT, nrow = 2
|
||||
, rel_widths = c(1,3),axis = "lr"),
|
||||
cowplot::plot_grid(
|
||||
peP2, posC_all,
|
||||
nrow = 2,
|
||||
rel_widths = c(1,1),
|
||||
align = "v",
|
||||
axis = "lr",
|
||||
rel_heights = c(1,8)
|
||||
),
|
||||
rel_heights = c(1,18),
|
||||
nrow = 2,axis = "lr")
|
||||
p4
|
||||
|
||||
|
||||
#### Combine p1+p3+p4 ####
|
||||
w = 11.79
|
||||
h = 3.5
|
||||
mut_impact_CLP = paste0(outdir_images
|
||||
,tolower(gene)
|
||||
,"_mut_impactCLP.png")
|
||||
|
||||
#svg(affP, width = 20, height = 5.5)
|
||||
print(paste0("plot filename:", mut_impact_CLP))
|
||||
png(mut_impact_CLP, units = "in", width = w, height = h, res = 300 )
|
||||
|
||||
cowplot::plot_grid(p1,
|
||||
p3,
|
||||
p4,
|
||||
nrow = 1,
|
||||
labels = "AUTO",
|
||||
label_size = 12,
|
||||
rel_widths = c(2.5,2,2)
|
||||
#, rel_heights = c(1)
|
||||
)
|
||||
|
||||
dev.off()
|
||||
w = 11.79
|
||||
h = 3.5
|
||||
mut_impact_CLP = paste0(outdir_images
|
||||
,tolower(gene)
|
||||
,"_mut_impactCLP.png")
|
||||
|
||||
#svg(affP, width = 20, height = 5.5)
|
||||
print(paste0("plot filename:", mut_impact_CLP))
|
||||
png(mut_impact_CLP, units = "in", width = w, height = h, res = 300 )
|
||||
|
||||
cowplot::plot_grid(p1,
|
||||
p3,
|
||||
p4,
|
||||
nrow = 1,
|
||||
labels = "AUTO",
|
||||
label_size = 12,
|
||||
rel_widths = c(2.5,2,2)
|
||||
#, rel_heights = c(1)
|
||||
)
|
||||
|
||||
dev.off()
|
||||
|
||||
##################################################
|
||||
sensP
|
||||
consurfP
|
||||
#=================
|
||||
#### Combine sensitivity + ConSurf ####
|
||||
# or ConSurf
|
||||
#=================
|
||||
w = 3
|
||||
h = 3
|
||||
# sens_conP = paste0(outdir_images
|
||||
# ,tolower(gene)
|
||||
# ,"_sens_cons_CLP.png")
|
||||
#
|
||||
# print(paste0("plot filename:", sens_conP))
|
||||
# png(sens_conP, units = "in", width = w, height = h, res = 300 )
|
||||
#
|
||||
# cowplot::plot_grid(sensP, consurfP,
|
||||
# nrow = 2,
|
||||
# rel_heights = c(1, 1.5)
|
||||
# )
|
||||
#
|
||||
# dev.off()
|
||||
|
||||
conCLP = paste0(outdir_images
|
||||
,tolower(gene)
|
||||
,"_consurf_BP.png")
|
||||
|
||||
print(paste0("plot filename:", conCLP))
|
||||
png(conCLP, units = "in", width = w, height = h, res = 300 )
|
||||
consurfP
|
||||
|
||||
dev.off()
|
||||
#================================
|
||||
# Sensitivity mutation numbers: geom_tile
|
||||
#================================
|
||||
sensCLP = paste0(outdir_images
|
||||
,tolower(gene)
|
||||
,"_sensN_tile.png")
|
||||
|
||||
print(paste0("plot filename:", sensCLP))
|
||||
png(sensCLP, units = "in", width = 1, height = 1, res = 300 )
|
||||
sensP
|
||||
dev.off()
|
||||
#================================
|
||||
# Sensitivity SITE numbers: geom_tile
|
||||
#================================
|
||||
sens_siteCLP = paste0(outdir_images
|
||||
,tolower(gene)
|
||||
,"_sens_siteC_tile.png")
|
||||
|
||||
print(paste0("plot filename:", sens_siteCLP))
|
||||
png(sens_siteCLP, units = "in", width = 1.2, height = 1, res = 300 )
|
||||
sens_siteP
|
||||
dev.off()
|
||||
|
||||
###########################################################
|
||||
|
||||
176
scripts/plotting/plotting_thesis/embb/dm_om_plots_layout_embb.R
Normal file
176
scripts/plotting/plotting_thesis/embb/dm_om_plots_layout_embb.R
Normal file
|
|
@ -0,0 +1,176 @@
|
|||
# source dm_om_plots.R
|
||||
source("/home/tanu/git/LSHTM_analysis/scripts/plotting/plotting_thesis/dm_om_plots.R")
|
||||
|
||||
##### plots to combine ####
|
||||
duetP
|
||||
foldxP
|
||||
deepddgP
|
||||
dynamut2P
|
||||
genomicsP
|
||||
consurfP
|
||||
proveanP
|
||||
snap2P
|
||||
mcsmligP
|
||||
mcsmlig2P
|
||||
mcsmppi2P
|
||||
|
||||
# Plot labels
|
||||
tit1 = "Stability changes"
|
||||
tit2 = "Genomic measure"
|
||||
tit3 = "Distance to partners"
|
||||
tit4 = "Evolutionary Conservation"
|
||||
tit5 = "Affinity changes"
|
||||
pt_size = 30
|
||||
|
||||
theme_georgia <- function(...) {
|
||||
theme_gray(base_family = "sans", ...) +
|
||||
theme(plot.title = element_text(face = "bold"))
|
||||
}
|
||||
|
||||
|
||||
title_theme <- calc_element("plot.title", theme_georgia())
|
||||
|
||||
pt1 = ggdraw() +
|
||||
draw_label(
|
||||
tit1,
|
||||
fontfamily = title_theme$family,
|
||||
fontface = title_theme$face,
|
||||
#size = title_theme$size
|
||||
size = pt_size
|
||||
)
|
||||
|
||||
pt2 = ggdraw() +
|
||||
draw_label(
|
||||
tit2,
|
||||
fontfamily = title_theme$family,
|
||||
fontface = title_theme$face,
|
||||
size = pt_size
|
||||
)
|
||||
|
||||
pt3 = ggdraw() +
|
||||
draw_label(
|
||||
tit3,
|
||||
fontfamily = title_theme$family,
|
||||
fontface = title_theme$face,
|
||||
size = pt_size
|
||||
)
|
||||
|
||||
pt4 = ggdraw() +
|
||||
draw_label(
|
||||
tit4,
|
||||
fontfamily = title_theme$family,
|
||||
fontface = title_theme$face,
|
||||
size = pt_size
|
||||
)
|
||||
|
||||
|
||||
pt5 = ggdraw() +
|
||||
draw_label(
|
||||
tit5,
|
||||
fontfamily = title_theme$family,
|
||||
fontface = title_theme$face,
|
||||
size = pt_size
|
||||
)
|
||||
|
||||
#======================
|
||||
# Output plot function
|
||||
#======================
|
||||
OutPlot_dm_om = function(x){
|
||||
|
||||
# dist b/w plot title and plot
|
||||
relH_tp = c(0.08, 0.92)
|
||||
|
||||
my_label_size = 25
|
||||
#----------------
|
||||
# Top panel
|
||||
#----------------
|
||||
top_panel = cowplot::plot_grid(
|
||||
cowplot::plot_grid(pt1,
|
||||
cowplot::plot_grid(duetP, foldxP, deepddgP, dynamut2P
|
||||
, nrow = 1
|
||||
, labels = c("A", "B", "C", "D")
|
||||
, label_size = my_label_size)
|
||||
, ncol = 1
|
||||
, rel_heights = relH_tp
|
||||
),
|
||||
NULL,
|
||||
cowplot::plot_grid(pt2,
|
||||
cowplot::plot_grid(genomicsP
|
||||
, nrow = 1
|
||||
, labels = c("E")
|
||||
, label_size = my_label_size)
|
||||
, ncol = 1
|
||||
, rel_heights = relH_tp
|
||||
),
|
||||
NULL,
|
||||
cowplot::plot_grid(pt3,
|
||||
cowplot::plot_grid( #distanceP
|
||||
distanceP_lig
|
||||
, distanceP_ppi2
|
||||
, nrow = 1
|
||||
, labels = c("F", "G")
|
||||
, label_size = my_label_size)
|
||||
, ncol = 1
|
||||
, rel_heights = relH_tp
|
||||
),
|
||||
nrow = 1,
|
||||
rel_widths = c(2/7, 0.1/7, 0.5/7, 0.1/7, 1/7)
|
||||
)
|
||||
|
||||
#----------------
|
||||
# Bottom panel
|
||||
#----------------
|
||||
bottom_panel = cowplot::plot_grid(
|
||||
cowplot::plot_grid(pt4,
|
||||
cowplot::plot_grid(consurfP, proveanP, snap2P
|
||||
, nrow = 1
|
||||
, labels = c("H", "I", "J")
|
||||
, label_size = my_label_size)
|
||||
, ncol = 1
|
||||
, rel_heights =relH_tp
|
||||
),NULL,
|
||||
cowplot::plot_grid(pt5,
|
||||
cowplot::plot_grid(mcsmligP
|
||||
, mcsmlig2P
|
||||
, mcsmppi2P
|
||||
, nrow = 1
|
||||
, labels = c("K", "L", "M")
|
||||
, label_size = my_label_size)
|
||||
, ncol = 1
|
||||
, rel_heights = relH_tp
|
||||
),NULL,
|
||||
nrow = 1,
|
||||
rel_widths = c(3/6,0.1/6,3/6, 0.1/6 )
|
||||
)
|
||||
|
||||
#-------------------------------
|
||||
# combine: Top and Bottom panel
|
||||
#-------------------------------
|
||||
cowplot::plot_grid (top_panel, bottom_panel
|
||||
, nrow =2
|
||||
, rel_widths = c(1, 1)
|
||||
, align = "hv")
|
||||
}
|
||||
|
||||
#=====================
|
||||
# OutPlot: svg and png
|
||||
#======================
|
||||
dm_om_combinedP = paste0(outdir_images
|
||||
,tolower(gene)
|
||||
,"_dm_om_all.svg")
|
||||
|
||||
cat("DM OM plots with stats:", dm_om_combinedP)
|
||||
svg(dm_om_combinedP, width = 32, height = 18)
|
||||
|
||||
OutPlot_dm_om()
|
||||
dev.off()
|
||||
|
||||
|
||||
dm_om_combinedP_png = paste0(outdir_images
|
||||
,tolower(gene)
|
||||
,"_dm_om_all.png")
|
||||
cat("DM OM plots with stats:", dm_om_combinedP_png)
|
||||
png(dm_om_combinedP_png, width = 32, height = 18, units = "in", res = 300)
|
||||
|
||||
OutPlot_dm_om()
|
||||
dev.off()
|
||||
2
scripts/plotting/plotting_thesis/embb/embb_other_plots.R
Normal file
2
scripts/plotting/plotting_thesis/embb/embb_other_plots.R
Normal file
|
|
@ -0,0 +1,2 @@
|
|||
|
||||
source("/home/tanu/git/LSHTM_analysis/scripts/plotting/plotting_thesis/linage_bp_dist_layout.R")
|
||||
203
scripts/plotting/plotting_thesis/embb/gg_pairs_all_embb.R
Normal file
203
scripts/plotting/plotting_thesis/embb/gg_pairs_all_embb.R
Normal file
|
|
@ -0,0 +1,203 @@
|
|||
#source("~/git/LSHTM_analysis/config/embb.R")
|
||||
#source("~/git/LSHTM_analysis/scripts/plotting/plotting_colnames.R")
|
||||
#source("~/git/LSHTM_analysis/scripts/plotting/get_plotting_dfs.R")
|
||||
|
||||
my_gg_pairs=function(plot_df, plot_title
|
||||
, tt_args_size = 2.5
|
||||
, gp_args_size = 2.5){
|
||||
ggpairs(plot_df,
|
||||
columns = 1:(ncol(plot_df)-1),
|
||||
upper = list(
|
||||
continuous = wrap('cor', # ggally_cor()
|
||||
method = "spearman",
|
||||
use = "pairwise.complete.obs",
|
||||
title="ρ",
|
||||
digits=2,
|
||||
justify_labels = "centre",
|
||||
title_args=list(size=tt_args_size, colour="black"),#2.5
|
||||
group_args=list(size=gp_args_size)#2.5
|
||||
)
|
||||
),
|
||||
lower = list(
|
||||
continuous = wrap("points",
|
||||
alpha = 0.7,
|
||||
size=0.125),
|
||||
combo = wrap("dot",
|
||||
alpha = 0.7,
|
||||
size=0.125)
|
||||
),
|
||||
aes(colour = factor(ifelse(dst_mode==0,
|
||||
"S",
|
||||
"R") ),
|
||||
alpha = 0.5),
|
||||
title=plot_title) +
|
||||
|
||||
scale_colour_manual(values = c("red", "blue")) +
|
||||
scale_fill_manual(values = c("red", "blue")) #+
|
||||
# theme(text = element_text(size=7,
|
||||
# face="bold"))
|
||||
}
|
||||
|
||||
DistCutOff = 10
|
||||
###########################################################################
|
||||
geneL_normal = c("pnca")
|
||||
geneL_na = c("gid", "rpob")
|
||||
geneL_ppi2 = c("alr", "embb", "katg", "rpob")
|
||||
|
||||
merged_df3 = as.data.frame(merged_df3)
|
||||
|
||||
corr_plotdf = corr_data_extract(merged_df3
|
||||
, gene = gene
|
||||
, drug = drug
|
||||
, extract_scaled_cols = F)
|
||||
|
||||
aff_dist_cols = colnames(corr_plotdf)[grep("Dist", colnames(corr_plotdf))]
|
||||
static_cols = c("Log10(MAF)"
|
||||
, "Log10(OR)"
|
||||
)
|
||||
############################################################
|
||||
#=============================================
|
||||
# Creating masked df for affinity data
|
||||
#=============================================
|
||||
corr_affinity_df = corr_plotdf
|
||||
|
||||
#----------------------
|
||||
# Mask affinity columns
|
||||
#-----------------------
|
||||
corr_affinity_df[corr_affinity_df["Lig-Dist"]>DistCutOff,"mCSM-lig"]=0
|
||||
corr_affinity_df[corr_affinity_df["Lig-Dist"]>DistCutOff,"mmCSM-lig"]=0
|
||||
|
||||
if (tolower(gene)%in%geneL_ppi2){
|
||||
corr_affinity_df[corr_affinity_df["PPI-Dist"]>DistCutOff,"mCSM-PPI2"]=0
|
||||
}
|
||||
|
||||
if (tolower(gene)%in%geneL_na){
|
||||
corr_affinity_df[corr_affinity_df["NA-Dist"]>DistCutOff,"mCSM-NA"]=0
|
||||
}
|
||||
|
||||
# count 0
|
||||
#res <- colSums(corr_affinity_df==0)/nrow(corr_affinity_df)*100
|
||||
unmasked_vals <- nrow(corr_affinity_df) - colSums(corr_affinity_df==0)
|
||||
unmasked_vals
|
||||
|
||||
##########################################################
|
||||
#================
|
||||
# Stability
|
||||
#================
|
||||
corr_ps_colnames = c(static_cols
|
||||
, "DUET"
|
||||
, "FoldX"
|
||||
, "DeepDDG"
|
||||
, "Dynamut2"
|
||||
, aff_dist_cols
|
||||
, "dst_mode")
|
||||
|
||||
corr_df_ps = corr_plotdf[, corr_ps_colnames]
|
||||
|
||||
# Plot #1
|
||||
plot_corr_df_ps = my_gg_pairs(corr_df_ps, plot_title="Stability estimates")
|
||||
|
||||
##########################################################
|
||||
#================
|
||||
# Conservation
|
||||
#================
|
||||
corr_conservation_cols = c( static_cols
|
||||
, "ConSurf"
|
||||
, "SNAP2"
|
||||
, "PROVEAN"
|
||||
#, aff_dist_cols
|
||||
, "dst_mode"
|
||||
)
|
||||
|
||||
corr_df_cons = corr_plotdf[, corr_conservation_cols]
|
||||
|
||||
# Plot #2
|
||||
plot_corr_df_cons = my_gg_pairs(corr_df_cons, plot_title="Conservation estimates")
|
||||
|
||||
##########################################################
|
||||
#================
|
||||
# Affinity: lig, ppi and na as applicable
|
||||
#================
|
||||
#corr_df_lig = corr_plotdf[corr_plotdf["Lig-Dist"]<DistCutOff,]
|
||||
common_aff_colnames = c("mCSM-lig"
|
||||
, "mmCSM-lig")
|
||||
|
||||
if (tolower(gene)%in%geneL_normal){
|
||||
aff_colnames = common_aff_colnames
|
||||
}
|
||||
if (tolower(gene)%in%geneL_ppi2){
|
||||
aff_colnames = c(common_aff_colnames, "mCSM-PPI2")
|
||||
}
|
||||
|
||||
if (tolower(gene)%in%geneL_na){
|
||||
aff_colnames = c(common_aff_colnames, "mCSM-NA")
|
||||
}
|
||||
|
||||
# building ffinal affinity colnames for correlation
|
||||
corr_aff_colnames = c(static_cols
|
||||
, aff_colnames
|
||||
, "dst_mode") # imp
|
||||
|
||||
corr_df_aff = corr_affinity_df[, corr_aff_colnames]
|
||||
colnames(corr_df_aff)
|
||||
|
||||
# Plot #3
|
||||
plot_corr_df_aff = my_gg_pairs(corr_df_aff
|
||||
, plot_title="Affinity estimates"
|
||||
#, tt_args_size = 4
|
||||
#, gp_args_size = 4
|
||||
)
|
||||
|
||||
#### Combine plots #####
|
||||
# #png("/home/tanu/tmp/gg_pairs_all.png", height = 6, width=11.75, unit="in",res=300)
|
||||
# png(paste0(outdir_images
|
||||
# ,tolower(gene)
|
||||
# ,"_CorrAB.png"), height = 6, width=11.75, unit="in",res=300)
|
||||
#
|
||||
# cowplot::plot_grid(ggmatrix_gtable(plot_corr_df_ps),
|
||||
# ggmatrix_gtable(plot_corr_df_cons),
|
||||
# # ggmatrix_gtable(plot_corr_df_aff),
|
||||
# # nrow=1, ncol=3, rel_heights = 7,7,3
|
||||
# nrow=1,
|
||||
# #rel_heights = 1,1
|
||||
# labels = "AUTO",
|
||||
# label_size = 12)
|
||||
# dev.off()
|
||||
#
|
||||
# # affinity corr
|
||||
# #png("/home/tanu/tmp/gg_pairs_affinity.png", height =7, width=7, unit="in",res=300)
|
||||
# png(paste0(outdir_images
|
||||
# ,tolower(gene)
|
||||
# ,"_CorrC.png"), height =7, width=7, unit="in",res=300)
|
||||
#
|
||||
# cowplot::plot_grid(ggmatrix_gtable(plot_corr_df_aff),
|
||||
# labels = "C",
|
||||
# label_size = 12)
|
||||
# dev.off()
|
||||
|
||||
#### Combine A ####
|
||||
png(paste0(outdir_images
|
||||
,tolower(gene)
|
||||
,"_CorrA.png"), height =8, width=8, unit="in",res=300)
|
||||
|
||||
cowplot::plot_grid(ggmatrix_gtable(plot_corr_df_ps),
|
||||
labels = "A",
|
||||
label_size = 12)
|
||||
dev.off()
|
||||
|
||||
#### Combine B+C ####
|
||||
# B + C
|
||||
png(paste0(outdir_images
|
||||
,tolower(gene)
|
||||
,"_CorrBC.png"), height = 6, width=11.75, unit="in",res=300)
|
||||
|
||||
cowplot::plot_grid(ggmatrix_gtable(plot_corr_df_cons),
|
||||
ggmatrix_gtable(plot_corr_df_aff),
|
||||
# ggmatrix_gtable(plot_corr_df_aff),
|
||||
# nrow=1, ncol=3, rel_heights = 7,7,3
|
||||
nrow=1,
|
||||
#rel_heights = 1,1
|
||||
labels = c("B", "C"),
|
||||
label_size = 12)
|
||||
dev.off()
|
||||
|
||||
173
scripts/plotting/plotting_thesis/embb/pe_sens_site_count_embb.R
Normal file
173
scripts/plotting/plotting_thesis/embb/pe_sens_site_count_embb.R
Normal file
|
|
@ -0,0 +1,173 @@
|
|||
source("/home/tanu/git/LSHTM_analysis/scripts/plotting/plotting_thesis/embb/prominent_effects_embb.R")
|
||||
source("/home/tanu/git/LSHTM_analysis/scripts/plotting/plotting_thesis/embb/sensitivity_count_embb.R")
|
||||
|
||||
##############################################################
|
||||
# PE count
|
||||
#pe_colour_map = c("DD_lig" = "#f0e68c" # khaki
|
||||
# , "SS_lig" = "#ffd700" # gold
|
||||
|
||||
# , "DD_nucleic_acid"= "#d2b48c" # sandybrown
|
||||
# , "SS_nucleic_acid"= "#a0522d" # sienna
|
||||
|
||||
# , "DD_ppi2" = "#da70d6" # orchid
|
||||
# , "SS_ppi2" = "#ff1493" # deeppink
|
||||
|
||||
# , "DD_stability" = "#f8766d" # red
|
||||
# , "SS_stability" = "#00BFC4") # blue
|
||||
table(str_df_plot_cols$pe_effect_outcome)
|
||||
##############################################################
|
||||
#===========
|
||||
#PE count:
|
||||
# lig, ppi2, stability
|
||||
#===========
|
||||
rects <- data.frame(x=1:6,
|
||||
colors = c("#f0e68c" ,
|
||||
"#ffd700" ,
|
||||
|
||||
"#da70d6" ,
|
||||
"#ff1493" ,
|
||||
|
||||
"#f8766d" ,
|
||||
"#00BFC4")
|
||||
)
|
||||
|
||||
rects$text = c("-ve Lig"
|
||||
, "+ve Lig"
|
||||
|
||||
, "-ve PPI2"
|
||||
, "+ve PPI2"
|
||||
|
||||
, "-ve stability"
|
||||
, "+ve stability"
|
||||
)
|
||||
|
||||
cell1 = table(str_df_plot_cols$pe_effect_outcome)[["DD_lig"]]
|
||||
cell2 = 0
|
||||
|
||||
#cell3 = table(str_df_plot_cols$pe_effect_outcome)[["DD_nucleic_acid"]]
|
||||
#cell4 = table(str_df_plot_cols$pe_effect_outcome)[["SS_nucleic_acid"]]
|
||||
|
||||
cell5 = table(str_df_plot_cols$pe_effect_outcome)[["DD_ppi2"]]
|
||||
cell6 = table(str_df_plot_cols$pe_effect_outcome)[["SS_ppi2"]]
|
||||
|
||||
cell7 = table(str_df_plot_cols$pe_effect_outcome)[["DD_stability"]]
|
||||
cell8 = table(str_df_plot_cols$pe_effect_outcome)[["SS_stability"]]
|
||||
|
||||
|
||||
#rects$numbers = c(38, 0, 22, 9, 108, 681) #for embb
|
||||
rects$numbers = c(cell1, cell2,
|
||||
#cell3, cell4,
|
||||
cell5, cell6,
|
||||
cell7, cell8)
|
||||
|
||||
rects$num_labels = paste0("n=", rects$numbers)
|
||||
|
||||
rects
|
||||
#------
|
||||
# Plot
|
||||
#------
|
||||
#https://stackoverflow.com/questions/47986055/create-a-rectangle-filled-with-text
|
||||
peP = ggplot(rects, aes(x, y = 0, fill = colors, label = paste0(text,"\n", num_labels))) +
|
||||
geom_tile(width = 1, height = 1) + # make square tiles
|
||||
geom_text(color = "black", size = 1.7) + # add white text in the middle
|
||||
scale_fill_identity(guide = "none") + # color the tiles with the colors in the data frame
|
||||
coord_fixed() + # make sure tiles are square
|
||||
coord_flip()+ scale_x_reverse() +
|
||||
# theme_void() # remove any axis markings
|
||||
theme_nothing() # remove any axis markings
|
||||
peP
|
||||
|
||||
#------
|
||||
# Plot: this one is better
|
||||
#------
|
||||
peP2 = ggplot(rects, aes(x, y = 0, fill = colors, label = paste0(text,"\n", num_labels))) +
|
||||
geom_tile() + # make square tiles
|
||||
geom_text(color = "black", size = 1.6) + # add white text in the middle
|
||||
scale_fill_identity(guide = "none") + # color the tiles with the colors in the data frame
|
||||
coord_fixed() + # make sure tiles are square
|
||||
theme_nothing() # remove any axis markings
|
||||
peP2
|
||||
|
||||
########################################################
|
||||
# From: script sensitivity_count per gene
|
||||
#===============================
|
||||
# Sensitivity count: SITE
|
||||
#===============================
|
||||
#--------
|
||||
# embb
|
||||
#--------
|
||||
#rsc = 54
|
||||
#ccc = 46
|
||||
#ssc = 470
|
||||
|
||||
rsc = site_Rc; rsc
|
||||
ccc = site_Cc; ccc
|
||||
ssc = site_Sc; ssc
|
||||
|
||||
rect_rs_siteC <- data.frame(x=1:3,
|
||||
colors = c("red",
|
||||
"purple",
|
||||
"blue")
|
||||
)
|
||||
|
||||
rect_rs_siteC
|
||||
rect_rs_siteC$text = c("Resistant",
|
||||
"Common",
|
||||
"Sensitive")
|
||||
|
||||
rect_rs_siteC$numbers = c(rsc,ccc,ssc)
|
||||
rect_rs_siteC$num_labels = paste0("n=", rect_rs_siteC$numbers)
|
||||
rect_rs_siteC
|
||||
|
||||
#------
|
||||
# Plot
|
||||
#------
|
||||
sens_siteP = ggplot(rect_rs_siteC, aes(x, y = 0,
|
||||
fill = colors,
|
||||
label = num_labels
|
||||
#,label = paste0(text,"\n", num_labels)
|
||||
)) +
|
||||
geom_tile(width = 1, height = 1) +
|
||||
#geom_text(color = "black", size = 1.7) +
|
||||
geom_label(color = "black", size = 1.7,fill = "white", alpha=0.7) +
|
||||
scale_fill_identity(guide = "none") +
|
||||
coord_fixed()+
|
||||
theme_nothing() # remove any axis markings
|
||||
sens_siteP
|
||||
|
||||
################################################################
|
||||
#===============================
|
||||
# Sensitivity count: Mutations
|
||||
#===============================
|
||||
table(sensP_df$sensitivity)
|
||||
muts_Rc = table(sensP_df$sensitivity)[["R"]]
|
||||
muts_Sc = table(sensP_df$sensitivity)[["S"]]
|
||||
rect_sens <- data.frame(x=1:2,
|
||||
colors = c("red",
|
||||
"blue")
|
||||
)
|
||||
|
||||
rect_sens$text = c("Resistant",
|
||||
"Sensitive")
|
||||
rect_sens$numbers = c(muts_Rc,muts_Sc)
|
||||
rect_sens$num_labels = paste0("n=", rect_sens$numbers)
|
||||
rect_sens
|
||||
#------
|
||||
# Plot
|
||||
#------
|
||||
sensP = ggplot(rect_sens, aes(x, y = 0,
|
||||
fill = colors,
|
||||
label = paste0(text,"\n", num_labels))) +
|
||||
geom_tile(width = 1, height = 1) +
|
||||
#geom_text(color = "black", size = 1.7) +
|
||||
geom_label(color = "black", size = 1.7,fill = "white", alpha=0.7) +
|
||||
scale_fill_identity(guide = "none") +
|
||||
coord_fixed()+
|
||||
theme_nothing() # remove any axis markings
|
||||
sensP
|
||||
|
||||
sensP2 = sensP +
|
||||
coord_flip() + scale_x_reverse()
|
||||
sensP2
|
||||
|
||||
|
||||
320
scripts/plotting/plotting_thesis/embb/prominent_effects_embb.R
Normal file
320
scripts/plotting/plotting_thesis/embb/prominent_effects_embb.R
Normal file
|
|
@ -0,0 +1,320 @@
|
|||
########################################################
|
||||
pos_colname = "position"
|
||||
|
||||
#-------------
|
||||
# from ~/git/LSHTM_analysis/scripts/plotting/plotting_colnames.R
|
||||
#-------------
|
||||
length(all_stability_cols); length(raw_stability_cols)
|
||||
length(scaled_stability_cols); length(outcome_stability_cols)
|
||||
length(affinity_dist_colnames)
|
||||
|
||||
|
||||
static_cols = c("mutationinformation",
|
||||
#"position",
|
||||
pos_colname,
|
||||
"sensitivity")
|
||||
|
||||
other_cols_all = c(scaled_stability_cols, scaled_affinity_cols, affinity_dist_colnames)
|
||||
|
||||
#omit avg cols and foldx_scaled_signC cols
|
||||
other_cols = other_cols_all[grep("avg", other_cols_all, invert = T)]
|
||||
other_cols = other_cols[grep("foldx_scaled_signC",other_cols, invert = T )]
|
||||
other_cols
|
||||
|
||||
cols_to_extract = c(static_cols, other_cols)
|
||||
cat("\nExtracting cols:", cols_to_extract)
|
||||
expected_ncols = length(static_cols) + length(other_cols)
|
||||
expected_ncols
|
||||
|
||||
str_df = merged_df3[, cols_to_extract]
|
||||
|
||||
if (ncol(str_df) == expected_ncols){
|
||||
cat("\nPASS: successfully extracted cols for calculating prominent effects")
|
||||
}else{
|
||||
stop("\nAbort: Could not extract cols for calculating prominent effects")
|
||||
}
|
||||
|
||||
#=========================
|
||||
# Masking affinity columns
|
||||
#=========================
|
||||
# First make values for affinity cols 0 when their corresponding dist >10
|
||||
head(str_df)
|
||||
|
||||
# replace in place affinity values >10
|
||||
str_df[str_df["ligand_distance"]>10,"affinity_scaled"]=0
|
||||
str_df[str_df["ligand_distance"]>10,"mmcsm_lig_scaled"]=0
|
||||
|
||||
#ppi2 gene: replace in place ppi2 affinity values where ppi2 dist >10
|
||||
if (tolower(gene)%in%geneL_ppi2){
|
||||
str_df[str_df["interface_dist"]>10,"mcsm_ppi2_scaled"]=0
|
||||
}
|
||||
|
||||
# na gene: replace in place na affinity values where na dist >10
|
||||
if (tolower(gene)%in%geneL_na){
|
||||
str_df[str_df["nca_distance"]>10,"mcsm_na_scaled"]=0
|
||||
}
|
||||
|
||||
colnames(str_df)
|
||||
head(str_df)
|
||||
|
||||
scaled_cols_tc = other_cols[grep("scaled", other_cols)]
|
||||
|
||||
|
||||
################################################
|
||||
#===============
|
||||
# whole df
|
||||
#===============
|
||||
give_col=function(x,y,df=str_df){
|
||||
df[df[[pos_colname]]==x,y]
|
||||
}
|
||||
|
||||
for (i in unique(str_df[[pos_colname]]) ){
|
||||
print(i)
|
||||
#cat(length(unique(str_df[[pos_colname]])))
|
||||
|
||||
biggest = max(abs(give_col(i,scaled_cols_tc)))
|
||||
|
||||
str_df[str_df[[pos_colname]]==i,'abs_max_effect'] = biggest
|
||||
str_df[str_df[[pos_colname]]==i,'effect_type']= names(
|
||||
give_col(i,scaled_cols_tc)[which(
|
||||
abs(
|
||||
give_col(i,scaled_cols_tc)
|
||||
) == biggest, arr.ind=T
|
||||
)[, "col"]])[1]
|
||||
|
||||
effect_name = unique(str_df[str_df[[pos_colname]]==i,'effect_type'])#[1] # pick first one in case we have multiple exact values
|
||||
|
||||
# get index/rowname for value of max effect, and then use it to get the original sign
|
||||
# here
|
||||
#ind = rownames(which(abs(str_df[str_df[[pos_colname]]==i,c('position',effect_name)][effect_name])== biggest, arr.ind=T))
|
||||
ind = rownames(which(abs(str_df[str_df[[pos_colname]]==i,c(pos_colname,effect_name)][effect_name])== biggest, arr.ind=T))
|
||||
|
||||
str_df[str_df[[pos_colname]]==i,'effect_sign'] = sign(str_df[effect_name][ind,])[1]
|
||||
}
|
||||
|
||||
# ends with suffix 2 if dups
|
||||
str_df$effect_type = sub("\\.[0-9]+", "", str_df$effect_type) # cull duplicate effect types that happen when there are exact duplicate values
|
||||
colnames(str_df)
|
||||
table(str_df$effect_type)
|
||||
|
||||
# check
|
||||
str_df_check = str_df[str_df[[pos_colname]]%in%c(24, 32, 160, 303, 334),]
|
||||
|
||||
#================
|
||||
# for Plots
|
||||
#================
|
||||
str_df_short = str_df[, c("mutationinformation",
|
||||
#"position",
|
||||
pos_colname,
|
||||
"sensitivity"
|
||||
, "effect_type"
|
||||
, "effect_sign")]
|
||||
|
||||
table(str_df_short$effect_type)
|
||||
table(str_df_short$effect_sign)
|
||||
str(str_df_short)
|
||||
|
||||
# assign pe outcome
|
||||
str_df_short$pe_outcome = ifelse(str_df_short$effect_sign<0, "DD", "SS")
|
||||
table(str_df_short$pe_outcome )
|
||||
table(str_df_short$effect_sign)
|
||||
|
||||
#==============
|
||||
# group effect type:
|
||||
# lig, ppi2, nuc. acid, stability
|
||||
#==============
|
||||
affcols = c("affinity_scaled", "mmcsm_lig_scaled")
|
||||
ppi2_cols = c("mcsm_ppi2_scaled")
|
||||
|
||||
#lig
|
||||
table(str_df_short$effect_type)
|
||||
str_df_short$effect_grouped = ifelse(str_df_short$effect_type%in%affcols
|
||||
, "lig"
|
||||
, str_df_short$effect_type)
|
||||
table(str_df_short$effect_grouped)
|
||||
|
||||
#ppi2
|
||||
str_df_short$effect_grouped = ifelse(str_df_short$effect_grouped%in%ppi2_cols
|
||||
, "ppi2"
|
||||
, str_df_short$effect_grouped)
|
||||
table(str_df_short$effect_grouped)
|
||||
|
||||
|
||||
#stability
|
||||
str_df_short$effect_grouped = ifelse(!str_df_short$effect_grouped%in%c("lig",
|
||||
"ppi2"
|
||||
)
|
||||
, "stability"
|
||||
, str_df_short$effect_grouped)
|
||||
|
||||
table(str_df_short$effect_grouped)
|
||||
|
||||
# create a sign as well
|
||||
str_df_short$pe_effect_outcome = paste0(str_df_short$pe_outcome, "_"
|
||||
, str_df_short$effect_grouped)
|
||||
|
||||
table(str_df_short$pe_effect_outcome)
|
||||
|
||||
#####################################################################
|
||||
# Chimera: for colouring
|
||||
####################################################################
|
||||
|
||||
#-------------------------------------
|
||||
# get df with unique position
|
||||
#--------------------------------------
|
||||
#data[!duplicated(data$x), ]
|
||||
str_df_plot = str_df_short[!duplicated(str_df[[pos_colname]]),]
|
||||
|
||||
if (nrow(str_df_plot) == length(unique(str_df[[pos_colname]]))){
|
||||
cat("\nPASS: successfully extracted df with unique positions")
|
||||
}else{
|
||||
stop("\nAbort: Could not extract df with unique positions")
|
||||
}
|
||||
|
||||
#-------------------------------------
|
||||
# generate colours for effect types
|
||||
#--------------------------------------
|
||||
str_df_plot_cols = str_df_plot[, c(pos_colname,
|
||||
"sensitivity",
|
||||
"pe_outcome",
|
||||
"effect_grouped",
|
||||
"pe_effect_outcome")]
|
||||
head(str_df_plot_cols)
|
||||
|
||||
# colour intensity based on sign
|
||||
#str_df_plot_cols$colour_hue = ifelse(str_df_plot_cols$effect_sign<0, "bright", "light")
|
||||
str_df_plot_cols$colour_hue = ifelse(str_df_plot_cols$pe_outcome=="DD", "bright", "light")
|
||||
|
||||
table(str_df_plot_cols$colour_hue); table(str_df_plot$pe_outcome)
|
||||
head(str_df_plot_cols)
|
||||
|
||||
# colour based on effect
|
||||
table(str_df_plot_cols$pe_effect_outcome)
|
||||
|
||||
pe_colour_map = c("DD_lig" = "#f0e68c" # khaki
|
||||
, "SS_lig" = "#ffd700" # gold
|
||||
|
||||
, "DD_nucleic_acid"= "#d2b48c" # sandybrown
|
||||
, "SS_nucleic_acid"= "#a0522d" # sienna
|
||||
|
||||
, "DD_ppi2" = "#da70d6" # orchid
|
||||
, "SS_ppi2" = "#ff1493" # deeppink
|
||||
|
||||
, "DD_stability" = "#f8766d" # red
|
||||
, "SS_stability" = "#00BFC4") # blue
|
||||
|
||||
#unlist(d[c('a', 'a', 'c', 'b')], use.names=FALSE)
|
||||
|
||||
#map the colours
|
||||
str_df_plot_cols$colour_map= unlist(map(str_df_plot_cols$pe_effect_outcome
|
||||
,function(x){pe_colour_map[[x]]}
|
||||
))
|
||||
head(str_df_plot_cols$colour_map)
|
||||
table(str_df_plot_cols$colour_map)
|
||||
table(str_df_plot_cols$pe_effect_outcome)
|
||||
|
||||
# str_df_plot_cols$colours = paste0(str_df_plot_cols$colour_hue
|
||||
# , "_"
|
||||
# , str_df_plot_cols$colour_map)
|
||||
# head(str_df_plot_cols$colours)
|
||||
# table(str_df_plot_cols$colours)
|
||||
#
|
||||
#
|
||||
# class(str_df_plot_cols$colour_map)
|
||||
# str(str_df_plot_cols)
|
||||
|
||||
# sort by colour
|
||||
head(str_df_plot_cols)
|
||||
str_df_plot_cols = str_df_plot_cols[order(str_df_plot_cols$colour_map), ]
|
||||
head(str_df_plot_cols)
|
||||
|
||||
#======================================
|
||||
# write file with prominent effects
|
||||
#======================================
|
||||
outdir_images = paste0("~/git/Writing/thesis/images/results/", tolower(gene), "/")
|
||||
write.csv(str_df_plot_cols, paste0(outdir_images, tolower(gene), "_prominent_effects.csv"))
|
||||
|
||||
################################
|
||||
# printing for chimera
|
||||
###############################
|
||||
chain_suffix = ".A"
|
||||
str_df_plot_cols$pos_chain = paste0(str_df_plot_cols[[pos_colname]], chain_suffix)
|
||||
table(str_df_plot_cols$colour_map)
|
||||
table(str_df_plot_cols$pe_effect_outcome)
|
||||
|
||||
#===================================================
|
||||
#-------------------
|
||||
# Ligand Affinity
|
||||
#-------------------
|
||||
# -ve Lig Aff
|
||||
dd_lig = str_df_plot_cols[str_df_plot_cols$pe_effect_outcome=="DD_lig",]
|
||||
if (nrow(dd_lig) == table(str_df_plot_cols$pe_effect_outcome)[['DD_lig']]){
|
||||
dd_lig_pos = dd_lig[[pos_colname]]
|
||||
}else{
|
||||
stop("Abort: DD affinity colour numbers mismtatch")
|
||||
}
|
||||
toString(paste0(dd_lig_pos, chain_suffix))
|
||||
|
||||
|
||||
# +ve Lig Aff
|
||||
ss_lig = str_df_plot_cols[str_df_plot_cols$pe_effect_outcome=="SS_lig",]
|
||||
if (!empty(ss_lig)){
|
||||
if (nrow(ss_lig) == table(str_df_plot_cols$pe_effect_outcome)[['SS_lig']]){
|
||||
ss_lig_pos = ss_lig[[pos_colname]]
|
||||
}else{
|
||||
stop("Abort: SS affinity colour numbers mismtatch")
|
||||
}
|
||||
#put in chimera cmd
|
||||
toString(paste0(ss_lig_pos, chain_suffix))
|
||||
}
|
||||
|
||||
|
||||
#===================================================
|
||||
#-------------------
|
||||
# PPI2 Affinity
|
||||
#-------------------
|
||||
# -ve PPI2
|
||||
dd_ppi2 = str_df_plot_cols[str_df_plot_cols$pe_effect_outcome=="DD_ppi2",]
|
||||
if (nrow(dd_ppi2) == table(str_df_plot_cols$pe_effect_outcome)[['DD_ppi2']]){
|
||||
dd_ppi2_pos = dd_ppi2[[pos_colname]]
|
||||
}else{
|
||||
stop("Abort: DD PPI2 colour numbers mismtatch")
|
||||
}
|
||||
|
||||
# +ve PPI2
|
||||
ss_ppi2 = str_df_plot_cols[str_df_plot_cols$pe_effect_outcome=="SS_ppi2",]
|
||||
if (nrow(ss_ppi2) == table(str_df_plot_cols$pe_effect_outcome)[['SS_ppi2']]){
|
||||
ss_ppi2_pos = ss_ppi2[[pos_colname]]
|
||||
}else{
|
||||
stop("Abort: SS PPI2 colour numbers mismtatch")
|
||||
}
|
||||
|
||||
#put in chimera cmd
|
||||
toString(paste0(dd_ppi2_pos,chain_suffix))
|
||||
toString(paste0(ss_ppi2_pos,chain_suffix))
|
||||
|
||||
#=========================================================
|
||||
#------------------------
|
||||
# Stability
|
||||
#------------------------
|
||||
# -ve Stability
|
||||
dd_stability = str_df_plot_cols[str_df_plot_cols$pe_effect_outcome=="DD_stability",]
|
||||
if (nrow(dd_stability) == table(str_df_plot_cols$pe_effect_outcome)[['DD_stability']]){
|
||||
dd_stability_pos = dd_stability[[pos_colname]]
|
||||
}else{
|
||||
stop("Abort: DD Stability colour numbers mismtatch")
|
||||
}
|
||||
|
||||
# +ve Stability
|
||||
ss_stability = str_df_plot_cols[str_df_plot_cols$pe_effect_outcome=="SS_stability",]
|
||||
if (nrow(ss_stability) == table(str_df_plot_cols$pe_effect_outcome)[['SS_stability']]){
|
||||
ss_stability_pos = ss_stability[[pos_colname]]
|
||||
}else{
|
||||
stop("Abort: SS Stability colour numbers mismtatch")
|
||||
}
|
||||
|
||||
#put in chimera cmd
|
||||
toString(paste0(dd_stability_pos, chain_suffix))
|
||||
toString(paste0(ss_stability_pos, chain_suffix))
|
||||
####################################################################
|
||||
|
||||
|
|
@ -0,0 +1,65 @@
|
|||
#=========================
|
||||
# Count Sensitivity
|
||||
# Mutations and positions
|
||||
#=========================
|
||||
pos_colname_c ="position"
|
||||
|
||||
sensP_df = merged_df3[,c("mutationinformation",
|
||||
#"position",
|
||||
pos_colname_c,
|
||||
"sensitivity")]
|
||||
|
||||
head(sensP_df)
|
||||
table(sensP_df$sensitivity)
|
||||
|
||||
#---------------
|
||||
# Total unique positions
|
||||
#----------------
|
||||
tot_mut_pos = length(unique(sensP_df[[pos_colname_c]]))
|
||||
cat("\nNo of Tot muts sites:", tot_mut_pos)
|
||||
|
||||
# resistant mut pos
|
||||
sens_site_allR = sensP_df[[pos_colname_c]][sensP_df$sensitivity=="R"]
|
||||
sens_site_UR = unique(sens_site_allR)
|
||||
length(sens_site_UR)
|
||||
|
||||
# Sensitive mut pos
|
||||
sens_site_allS = sensP_df[[pos_colname_c]][sensP_df$sensitivity=="S"]
|
||||
sens_site_US = unique(sens_site_allS)
|
||||
length(sens_site_UR)
|
||||
|
||||
#---------------
|
||||
# Common Sites
|
||||
#----------------
|
||||
common_pos = intersect(sens_site_UR,sens_site_US)
|
||||
site_Cc = length(common_pos)
|
||||
cat("\nNo of Common sites:", site_Cc
|
||||
, "\nThese are:", common_pos)
|
||||
|
||||
#---------------
|
||||
# Resistant muts
|
||||
#----------------
|
||||
site_R = sens_site_UR[!sens_site_UR%in%common_pos]
|
||||
site_Rc = length(site_R)
|
||||
|
||||
if ( length(sens_site_allR) == table(sensP_df$sensitivity)[['R']] ){
|
||||
cat("\nNo of R muts:", length(sens_site_allR)
|
||||
, "\nNo. of R sites:",site_Rc
|
||||
, "\nThese are:", site_R
|
||||
)
|
||||
}
|
||||
|
||||
#---------------
|
||||
# Sensitive muts
|
||||
#----------------
|
||||
site_S = sens_site_US[!sens_site_US%in%common_pos]
|
||||
site_Sc = length(site_S)
|
||||
|
||||
if ( length(sens_site_allS) == table(sensP_df$sensitivity)[['S']] ){
|
||||
cat("\nNo of S muts:", length(sens_site_allS)
|
||||
, "\nNo. of S sites:", site_Sc
|
||||
, "\nThese are:", site_S)
|
||||
}
|
||||
|
||||
#########################
|
||||
|
||||
|
|
@ -3,7 +3,7 @@ source("/home/tanu/git/LSHTM_analysis/scripts/plotting/plotting_thesis/gid/sensi
|
|||
|
||||
##############################################################
|
||||
# PE count
|
||||
#pe_colour_map = c("DD_lig" = "#f0e68c" # khaki
|
||||
#pe_colour_map = c("DD_lig" = "#f0e68c" # khaki
|
||||
# , "SS_lig" = "#ffd700" # gold
|
||||
|
||||
# , "DD_nucleic_acid"= "#d2b48c" # sandybrown
|
||||
|
|
|
|||
|
|
@ -31,7 +31,6 @@ source("/home/tanu/git/LSHTM_analysis/scripts/plotting/plotting_thesis/linage_bp
|
|||
|
||||
# png
|
||||
my_label_size = 12
|
||||
|
||||
linPlots_combined = paste0(outdir_images
|
||||
, tolower(gene)
|
||||
,"_linP_combined.png")
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue