added dir scratch_plots/ to practice extra plots
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scripts/plotting/scratch_plots/lineage_dist_combined_PS_style.R
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274
scripts/plotting/scratch_plots/lineage_dist_combined_PS_style.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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#=======
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# output
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#=======
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#lineage_dist_dm_om = "lineage_dist_dm_om_PS.svg"
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#plot_lineage_dist_dm_om = paste0(plotdir,"/", lineage_dist_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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# Ensure correct data type in columns to plot: need to be factor
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is.factor(my_df$lineage)
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my_df$lineage = as.factor(my_df$lineage)
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is.factor(my_df$lineage)
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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$lineage)
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# select lineages 1-4
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sel_lineages = c("lineage1"
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, "lineage2"
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, "lineage3"
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, "lineage4"
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#, "lineage5"
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#, "lineage6"
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#, "lineage7"
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)
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my_labels = c('Lineage 1', 'Lineage 2', 'Lineage 3', 'Lineage 4'
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#, 'Lineage 5', 'Lineage 6', 'Lineage 7'
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)
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names(my_labels) = c('lineage1', 'lineage2', 'lineage3', 'lineage4'
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# , 'lineage5', 'lineage6', 'lineage7'
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)
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# subset selected lineages
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df_lin = subset(my_df, subset = lineage %in% sel_lineages )
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table(df_lin$lineage)
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# refactor
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df_lin$lineage = factor(df_lin$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$mutationinformation)
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#{Result:Muts not present within selected lineages}
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check = u2[!u2%in%u]; print(check)
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#=======================
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# subset dr muts only
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#=======================
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my_df_dr = subset(df_lin, mutation_info == dr_muts_col)
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table(my_df_dr$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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# Plot 1: ALL Muts
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# x = mcsm_values, y = dist
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# fill = stability
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#============================
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#my_plot_name_dm_om = 'lineage_dist_DM_OM_PS.svg'
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#plot_lineage_dm_om_duet = paste0(plotdir,"/", my_plot_name_dm_om)
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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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#=======================
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# Plot 1: lineage dist
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#=======================
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# check plot name
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plot_lineage_duet
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n_colours = length(unique(df$duet_scaled))
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#my_palette <- colorRampPalette(c(mcsm_red, "#f8766d", "white","#00bfc4", mcsm_blue))(n = 400)
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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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# output svg
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#svg(plot_lineage_duet)
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p1 = 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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#, jittered_points = TRUE
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, scale = 3
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, size = 0.3 ) +
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facet_wrap( ~lineage
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, scales = "free"
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#, switch = 'x'
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, labeller = labeller(lineage = my_labels)) +
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coord_cartesian( xlim = c(-1, 1)) +
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#scale_fill_gradientn(colours = c("#f8766d", "white", "#00bfc4")
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# , name = "DUET" ) +
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#scale_fill_gradientn(colours = c(mcsm_red, "#f8766d", "white","#00bfc4", mcsm_blue)
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# , name = "DUET" ) +
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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_blank()
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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-5)
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, legend.title = element_text(size = my_als)
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)
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print(p1)
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#dev.off()
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#=======================================
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# Plot 2: lineage dist by mutation_info
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#=======================================
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mutation_info_labels = c("DM", "OM")
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names(mutation_info_labels) = c(dr_muts_col, other_muts_col)
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p3 = ggplot(df, aes(x = duet_scaled
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, y = mutation_info))+
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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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facet_wrap(~lineage
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# , scales = "free"
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# #, switch = 'x'
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, labeller = labeller(lineage = my_labels, mutation_info = mutation_info_labels)) +
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coord_cartesian( xlim = c(-1, 1)) +
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#scale_fill_gradientn(colours = c("#ae301e", "white", "#007d85")
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# , name = "DUET" ) +
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#scale_fill_gradientn(colours = c("mcsm_red", "white", "mcsm_blue")
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# , name = "DUET" ) +
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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_blank()
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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-5)
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, legend.title = element_text(size = my_als)
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)
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print(p3)
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#==============================
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########################################################################
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#==============
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# combine plot
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#===============
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svg(plot_lineage_dist_combined, width = 12, height = 6)
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printFile = cowplot::plot_grid(p1, p3
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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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