279 lines
8.6 KiB
R
279 lines
8.6 KiB
R
#!/usr/bin/env Rscript
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#########################################################
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# TASK: producing logo-type plot showing
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# multiple muts per position coloured by aa property
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#########################################################
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#=======================================================================
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# working dir and loading libraries
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getwd()
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setwd("~/git/LSHTM_analysis/scripts/plotting")
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getwd()
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source("Header_TT.R")
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#library(ggplot2)
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#library(data.table)
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#library(dplyr)
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#===========
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# input
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#===========
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source("combining_dfs_plotting.R")
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#===========
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# output
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#===========
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logo_combined_labelled = "logo_combined_labelled.svg"
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plot_logo_combined_labelled = paste0(plotdir,"/", logo_combined_labelled)
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##########################################################################
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#%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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# REASSIGNMENT
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my_df = merged_df3
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#%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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# clear excess variables
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rm(merged_df2, merged_df2_comp, merged_df2_lig, merged_df2_comp_lig
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, merged_df3_comp, merged_df3_comp_lig
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, my_df_u, my_df_u_lig, merged_df3_lig)
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colnames(my_df)
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str(my_df)
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#rownames(my_df) = my_df$mutation
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c1 = unique(my_df$position)
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nrow(my_df)
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# get freq count of positions so you can subset freq<1
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#require(data.table)
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setDT(my_df)[, mut_pos_occurrence := .N, by = .(position)] #189, 36
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table(my_df$position)
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table(my_df$mut_pos_occurrence)
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max_mut = max(table(my_df$position))
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# extract freq_pos>1
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my_df_snp = my_df[my_df$mut_pos_occurrence!=1,]
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u = unique(my_df_snp$position)
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max_mult_mut = max(table(my_df_snp$position))
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if (nrow(my_df_snp) == nrow(my_df) - table(my_df$mut_pos_occurrence)[[1]] ){
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cat("PASS: positions with multiple muts extracted"
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, "\nNo. of mutations:", nrow(my_df_snp)
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, "\nNo. of positions:", length(u)
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, "\nMax no. of muts at any position", max_mult_mut)
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}else{
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cat("FAIL: positions with multiple muts could NOT be extracted"
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, "\nExpected:",nrow(my_df) - table(my_df$mut_pos_occurrence)[[1]]
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, "\nGot:", nrow(my_df_snp) )
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}
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cat("\nNo. of sites with only 1 mutations:", table(my_df$mut_pos_occurrence)[[1]])
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########################################################################
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# end of data extraction and cleaning for_mychisq plots #
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########################################################################
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#==============
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# matrix for_mychisq mutant type
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# frequency of mutant type by position
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#==============
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table(my_df_snp$mutant_type, my_df_snp$position)
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tab_mt = table(my_df_snp$mutant_type, my_df_snp$position)
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class(tab_mt)
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# unclass to convert to matrix
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tab_mt = unclass(tab_mt)
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tab_mt = as.matrix(tab_mt, rownames = T)
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#should be TRUE
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is.matrix(tab_mt)
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rownames(tab_mt) #aa
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colnames(tab_mt) #pos
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#**************
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# Plot 1: mutant logo
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#**************
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p0 = ggseqlogo(tab_mt
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, method = 'custom'
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, seq_type = 'aa') +
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#ylab('my custom height') +
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theme_logo()+
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scale_x_discrete(#breaks = 1:ncol(tab_mt)
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labels = as.numeric(colnames(tab_mt))
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, limits = factor(as.numeric(colnames(tab_mt))))+
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scale_y_continuous( breaks = 1:max_mult_mut
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, limits = c(0, max_mult_mut))
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p0
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# further customisation
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p1 = p0 + theme(axis.text.x = element_text(size = 16
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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_blank()
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, legend.position = "none")
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#, axis.text.y = element_text(size = 20))
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p1
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#==============
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# matrix for wild type
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# frequency of wild type by position
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#==============
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tab_wt = table(my_df_snp$wild_type, my_df_snp$position); tab_wt
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tab_wt = unclass(tab_wt)
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#remove wt duplicates
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wt = my_df_snp[, c("position", "wild_type")]
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wt = wt[!duplicated(wt),]
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tab_wt = table(wt$wild_type, wt$position); tab_wt # should all be 1
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rownames(tab_wt)
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rownames(tab_wt)
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#**************
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# Plot 2: wild_type logo
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#**************
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# sanity check: MUST BE TRUE
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identical(colnames(tab_mt), colnames(tab_wt))
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identical(ncol(tab_mt), ncol(tab_wt))
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p2 = ggseqlogo(tab_wt
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, method = 'custom'
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, seq_type = 'aa'
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#, col_scheme = "taylor"
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#, col_scheme = chemistry2
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) +
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#ylab('my custom height') +
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theme(axis.text.x = element_blank()
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, axis.text.y = element_blank()) +
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#theme_logo() +
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scale_x_discrete(breaks = 1:ncol(tab_wt)
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, labels = colnames(tab_wt))
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p2
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# further customise
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p3 = p2 +
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theme(legend.position = "bottom"
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#, legend.title = element_blank()
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, legend.title = element_text("Amino acid properties", size = 20)
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, legend.text = element_text( size = 20)
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, axis.text.x = element_text(size = 20, angle = 90)
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, axis.text.y = element_blank()
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, axis.title.y = element_blank()
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, axis.title.x = element_text(size = 22))+
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labs(x= "Wild-type Position")
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p3
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#======================================================================
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# logo with OR
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#=======================================================================
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# quick checks
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colnames(my_df)
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str(my_df)
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c1 = unique(my_df$position)
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nrow(my_df)
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cat("No. of rows in my_df:", nrow(my_df)
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, "\nDistinct positions corresponding to snps:", length(c1)
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, "\n===========================================================")
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#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
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# FIXME: Think and decide what you want to remove
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# mut_pos_occurence < 1 or sample_pos_occurrence <1
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# get freq count of positions so you can subset freq<1
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require(data.table)
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#setDT(my_df)[, mut_pos_occurrence := .N, by = .(position)]
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#extract freq_pos>1
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#my_df_snp = my_df[my_df$occurrence!=1,]
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#u = unique(my_df_snp$position)
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#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
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#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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# REASSIGNMENT to prevent changing code
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head(my_df_snp) #(positions with multiple snps) only
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length(unique(my_df_snp$position))
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#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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#=======================================================================
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#%% logo plots from dataframe
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#############
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# PLOTS: ggseqlogo with custom height
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# https://omarwagih.github.io/ggseqlogo/
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#############
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foo = my_df_snp[, c("position", "mutant_type","duet_scaled", "or_mychisq"
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, "mut_prop_polarity", "mut_prop_water") ]
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my_df_snp$log10or = log10(my_df_snp$or_mychisq)
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logo_data = my_df_snp[, c("position", "mutant_type", "or_mychisq", "log10or")]
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logo_data_or = my_df_snp[, c("position", "mutant_type", "or_mychisq")]
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#wide_df_or <- logo_data_or %>% spread(position, or_mychisq, fill = 0.0)
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wide_df_or <- logo_data_or %>% spread(position, or_mychisq, fill = NA)
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wide_df_or = as.matrix(wide_df_or)
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rownames(wide_df_or) = wide_df_or[,1]
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wide_df_or = wide_df_or[,-1]
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str(wide_df_or)
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position_or = as.numeric(colnames(wide_df_or))
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#===========================================
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#custom height (OR) logo plot: CORRECT x-axis labelling
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#============================================
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# custom height (OR) logo plot: yayy works
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logo_or = ggseqlogo(wide_df_or, method="custom", seq_type="aa") + ylab("my custom height") +
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theme(axis.text.x = element_text(size = 16
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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 = 18
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, angle = 0
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, hjust = 1
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, vjust = 0)
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, axis.title.y = element_text(size = 18)
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, axis.title.x = element_blank()
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, legend.position = "none")+
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scale_x_discrete( labels = position_or
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, limits = factor(1:length(position_or))) +
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scale_y_discrete(breaks = c(50, 150, 250, 350)
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, labels = c(50, 150, 250, 350)
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, limits = c(50, 150, 250, 350)
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) +
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xlab("Position") +
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ylab("Odds Ratio")
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logo_or
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########################################################################
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#=============================
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# combine plots for output
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#=============================
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cat("Output plot:", plot_logo_combined_labelled)
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svg(plot_logo_combined_labelled, width = 25, height = 10)
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OutPlot2 = cowplot::plot_grid(logo_or, p1, p3
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, nrow = 3
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, align = "hv"
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#, labels = c("(a)","(b)", "(c)")
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, labels = "AUTO"
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, rel_heights = c(3/8, 3/8, 1.5/8)
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, rel_widths = c(0.85, 1, 1)
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, label_size = 25)
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print(OutPlot2)
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dev.off()
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