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scripts/plotting/scratch_plots/lineage_dist_dm_om_PS.R
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scripts/plotting/scratch_plots/lineage_dist_dm_om_PS.R
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#!/usr/bin/env Rscript
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#########################################################
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# TASK: Lineage dist plots: ggridges
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# Output: 2 SVGs for PS stability
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# 1) all muts
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# 2) dr_muts
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##########################################################
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# Installing and loading required packages
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##########################################################
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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(ggridges)
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source("combining_dfs_plotting.R")
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# PS combined:
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# 1) merged_df2
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# 2) merged_df2_comp
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# 3) merged_df3
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# 4) merged_df3_comp
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# LIG combined:
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# 5) merged_df2_lig
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# 6) merged_df2_comp_lig
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# 7) merged_df3_lig
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# 8) merged_df3_comp_lig
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# 9) my_df_u
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# 10) my_df_u_lig
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cat("Directories imported:"
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, "\n===================="
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, "\ndatadir:", datadir
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, "\nindir:", indir
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, "\noutdir:", outdir
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, "\nplotdir:", plotdir)
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cat("Variables imported:"
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, "\n====================="
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, "\ndrug:", drug
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, "\ngene:", gene
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, "\ngene_match:", gene_match
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, "\nAngstrom symbol:", angstroms_symbol
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, "\nNo. of duplicated muts:", dup_muts_nu
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, "\nNA count for ORs:", na_count
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, "\nNA count in df2:", na_count_df2
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, "\nNA count in df3:", na_count_df3
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, "\ndr_muts_col:", dr_muts_col
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, "\nother_muts_col:", other_muts_col
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, "\ndrtype_col:", resistance_col)
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cat("cols imported:"
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, mcsm_red2, mcsm_red1, mcsm_mid, mcsm_blue1, mcsm_blue2)
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#=======
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# output
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#=======
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lineage_dist_combined_dm_om = "lineage_dist_combined_dm_om_PS.svg"
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plot_lineage_dist_combined_dm_om = paste0(plotdir,"/", lineage_dist_combined_dm_om)
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#========================================================================
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###########################
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# Data for plots
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# you need merged_df2 or merged_df2_comp
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# since this is one-many relationship
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# i.e the same SNP can belong to multiple lineages
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# using the _comp dataset means
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# we lose some muts and at this level, we should use
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# as much info as available, hence use df with NA
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###########################
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# REASSIGNMENT
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my_df = merged_df2
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# delete variables not required
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rm(my_df_u, merged_df2, merged_df2_comp, merged_df3, merged_df3_comp)
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# quick checks
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colnames(my_df)
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str(my_df)
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table(my_df$mutation_info)
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#===================
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# Data for plots
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#===================
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table(my_df$lineage); str(my_df$lineage)
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# select lineages 1-4
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sel_lineages = c("lineage1"
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, "lineage2"
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, "lineage3"
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, "lineage4")
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#, "lineage5"
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#, "lineage6"
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#, "lineage7")
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# works nicely with facet wrap using labeller, but not otherwise
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#my_labels = c('Lineage 1'
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# , 'Lineage 2'
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# , 'Lineage 3'
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# , 'Lineage 4')
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# #, 'Lineage 5'
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# #, 'Lineage 6'
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# #, 'Lineage 7')
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#names(my_labels) = c('lineage1'
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# , 'lineage2'
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# , 'lineage3'
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# , 'lineage4')
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# #, 'lineage5'
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# #, 'lineage6'
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# #, 'lineage7')
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#==========================
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# subset selected lineages
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#==========================
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df_lin = subset(my_df, subset = lineage %in% sel_lineages)
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table(df_lin$lineage)
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#{RESULT: Total number of samples for lineage}
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sum(table(df_lin$lineage))
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#{RESULT: No of samples within lineage}
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table(df_lin$lineage)
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#{Result: No. of unique mutations the 4 lineages contribute to}
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length(unique(df_lin$mutationinformation))
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u2 = unique(my_df$mutationinformation)
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u = unique(df_lin$mutationinformation)
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#{Result:Muts not present within selected lineages}
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check = u2[!u2%in%u]; print(check)
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# workaround to make labels appear nicely for in otherwise cases
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#==================
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# lineage: labels
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# from "plyr"
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#==================
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table(df_lin$lineage)
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df_lin$lineage_labels = mapvalues(df_lin$lineage
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, from = c("lineage1","lineage2", "lineage3", "lineage4")
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, to = c("Lineage 1", "Lineage 2", "Lineage 3", "Lineage 4"))
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table(df_lin$lineage_labels)
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table(df_lin$lineage_labels) == table(df_lin$lineage)
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#========================
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# mutation_info: labels
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#========================
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table(df_lin$mutation_info)
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df_lin$mutation_info_labels = ifelse(df_lin$mutation_info == dr_muts_col, "DM", "OM")
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table(df_lin$mutation_info_labels)
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table(df_lin$mutation_info) == table(df_lin$mutation_info_labels)
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#=======================
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# subset dr muts only
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#=======================
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my_df_dr = subset(df_lin, mutation_info == dr_muts_col)
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table(my_df_dr$mutation_info)
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table(my_df_dr$lineage)
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#=========================
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# subset other muts only
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#=========================
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my_df_other = subset(df_lin, mutation_info == other_muts_col)
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table(my_df_other$mutation_info)
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table(my_df_other$lineage)
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########################################################################
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# end of data extraction and cleaning for plots #
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########################################################################
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#==========================
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# Distribution plots
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#============================
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#%%%%%%%%%%%%%%%%%%%%%%%%%
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# REASSIGNMENT
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df <- df_lin
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#%%%%%%%%%%%%%%%%%%%%%%%%%
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rm(df_lin)
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#******************
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# generate distribution plot of lineages
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#******************
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# 2 : ggridges (good!)
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my_ats = 15 # axis text size
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my_als = 20 # axis label size
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n_colours = length(unique(df$duet_scaled))
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my_palette <- colorRampPalette(c(mcsm_red2, mcsm_red1, mcsm_mid, mcsm_blue1, mcsm_blue2))(n = n_colours+1)
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#=======================================
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# Plot 1: lineage dist: geom_density_ridges_gradient (allows aesthetics to vary along ridgeline, no alpha setting!)
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# else same as geom_density_ridges)
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# x = duet_scaled
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# y = lineage_labels
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# fill = duet_scaled
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# NO FACET (nf)
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#=======================================
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# output individual svg
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#plot_lineage_dist_duet_nf = paste0(plotdir,"/", "lineage_dist_duet_nf.svg")
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#plot_lineage_dist_duet_nf
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#svg(plot_lineage_dist_duet_nf)
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p1 = ggplot(df, aes(x = duet_scaled
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, y = lineage_labels))+
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geom_density_ridges_gradient(aes(fill = ..x..)
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#, jittered_points = TRUE
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, scale = 3
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, size = 0.3 ) +
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coord_cartesian( xlim = c(-1, 1)) +
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scale_fill_gradientn(colours = my_palette, name = "DUET") +
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theme(axis.text.x = element_text(size = my_ats
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, angle = 90
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, hjust = 1
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, vjust = 0.4)
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, axis.text.y = element_text(size = my_ats)
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, axis.title.x = element_text(size = my_ats)
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, axis.title.y = element_blank()
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, axis.ticks.y = element_blank()
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, plot.title = element_blank()
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, strip.text = element_text(size = my_als)
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, legend.text = element_text(size = my_als-10)
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, legend.title = element_text(size = my_als-3)
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, legend.position = c(0.8, 0.8)) +
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#, legend.direction = "horizontal")+
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#, legend.position = "top", )+
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labs(x = "DUET")
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p1
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#p1_copy = p1 + guides(fill = guide_colourbar(label = FALSE))
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#p1_copy= p1_copy + guides(size=guide_legend("Source", override.aes=list(shape=15, size = 10)))
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#p1_copy
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#=======================================
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# Plot 2: lineage dist: geom_density_ridges, allows alpha to be set
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# x = duet_scaled
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# y = lineage_labels
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# fill = mutation_info
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# NO FACET
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#=======================================
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# output svg
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#plot_lineage_dist_duet_dm_om = paste0(plotdir,"/", "lineage_dist_duet_dm_om.svg")
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#plot_lineage_dist_duet_dm_om
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#svg(plot_lineage_dist_duet_dm_om)
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p2 = ggplot(df, aes(x = duet_scaled
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, y = lineage_labels))+
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geom_density_ridges(aes(fill = factor(mutation_info_labels))
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, scale = 3
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, size = 0.3
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, alpha = 0.8) +
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coord_cartesian( xlim = c(-1, 1)) +
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scale_fill_manual(values = c("#E69F00", "#999999")) +
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theme(axis.text.x = element_text(size = my_ats
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, angle = 90
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, hjust = 1
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, vjust = 0.4)
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, axis.text.y = element_text(size = my_ats)
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, axis.title.x = element_text(size = my_ats)
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, axis.title.y = element_blank()
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, axis.ticks.y = element_blank()
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, plot.title = element_blank()
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, strip.text = element_text(size = my_als)
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, legend.text = element_text(size = my_als-2)
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, legend.title = element_text(size = my_als-3)
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, legend.position = c(0.8, 0.9)) +
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labs(x = "DUET"
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, fill = "Mutation class") # legend title
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p2
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########################################################################
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#==============
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# combine plot
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#===============
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plot_lineage_dist_combined_dm_om
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svg(plot_lineage_dist_combined_dm_om, width = 12, height = 6)
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printFile = cowplot::plot_grid(p1, p2
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, rel_widths = c(0.5/2, 0.5/2)
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, label_size = my_als+10)
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print(printFile)
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dev.off()
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########################################################################
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# alternate combination
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########################################################################
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#=======================
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# Plot 3: lineage dist: geom_density_ridges_gradient (allows aesthetics to vary along ridgeline, no alpha setting!)
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# else same as geom_density_ridges)
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# x = duet_scaled
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# y = duet_outcome
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# FACET (f) = lineage
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#=======================
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# output individual svg
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#plot_lineage_dist_duet = paste0(plotdir,"/", "lineage_dist_duet_f.svg")
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#plot_lineage_dist_duet
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#svg(plot_lineage_dist_duet)
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p3 = ggplot(df, aes(x = duet_scaled
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, y = duet_outcome))+
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geom_density_ridges_gradient(aes(fill = ..x..)
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, scale = 3
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, size = 0.3) +
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facet_wrap( ~lineage_labels
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, scales = "free"
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#, labeller = labeller(lineage = my_labels) # sorted by lineage_labels
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) +
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coord_cartesian( xlim = c(-1, 1)) +
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scale_fill_gradientn(colours = my_palette, name = "DUET") +
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theme(axis.text.x = element_text(size = my_ats
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, angle = 90
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, hjust = 1
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, vjust = 0.4)
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, axis.text.y = element_blank()
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, axis.title.x = element_text(size = my_ats)
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, axis.title.y = element_blank()
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, axis.ticks.y = element_blank()
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, plot.title = element_blank()
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, strip.text = element_text(size = my_als)
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, legend.text = element_text(size = my_als-6)
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, legend.title = element_text(size = my_als-3))+
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labs(x = "DUET")
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print(p3)
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#dev.off()
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#==============
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# combine plot: alt version
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#===============
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plot_lineage_dist_duet_fandnf = paste0(plotdir,"/", "lineage_dist_duet_fandnf.svg")
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plot_lineage_dist_duet_fandnf
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svg(plot_lineage_dist_duet_fandnf, width = 12, height = 6)
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printFile = cowplot::plot_grid(p3, p2
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, rel_widths = c(0.5/2, 0.5/2)
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, label_size = my_als+10)
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print(printFile)
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dev.off()
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