226 lines
7.8 KiB
R
226 lines
7.8 KiB
R
#!/usr/bin/env Rscript
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#########################################################
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# TASK: producing logoplot
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# from data and/or from sequence
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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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source("../functions/plotting_globals.R")
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source("../functions/plotting_data.R")
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source("../functions/combining_dfs_plotting.R")
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###########################################################
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# command line args
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#********************
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drug = 'streptomycin'
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gene = 'gid'
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#===========
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# input
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#===========
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#---------------------
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# call: import_dirs()
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#---------------------
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import_dirs(drug, gene)
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#---------------------------
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# call: plotting_data()
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#---------------------------
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if (!exists("infile_params") && exists("gene")){
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#if (!is.character(infile_params) && exists("gene")){
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#in_filename_params = paste0(tolower(gene), "_all_params.csv")
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in_filename_params = paste0(tolower(gene), "_comb_afor.csv") # part combined for gid
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infile_params = paste0(outdir, "/", in_filename_params)
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cat("\nInput file for mcsm comb data not specified, assuming filename: ", infile_params, "\n")
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}
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# Input 1: read <gene>_comb_afor.csv
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pd_df = plotting_data(infile_params)
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my_df_u = pd_df[[1]] # this forms one of the input for combining_dfs_plotting()
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#--------------------------------
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# call: combining_dfs_plotting()
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#--------------------------------
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if (!exists("infile_metadata") && exists("gene")){
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#if (!is.character(infile_params) && exists("gene")){{
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in_filename_metadata = paste0(tolower(gene), "_metadata.csv") # part combined for gid
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infile_metadata = paste0(outdir, "/", in_filename_metadata)
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cat("\nInput file for gene metadata not specified, assuming filename: ", infile_metadata, "\n")
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}
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# Input 2: read <gene>_meta data.csv
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cat("\nReading meta data file:", infile_metadata)
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gene_metadata <- read.csv(infile_metadata
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, stringsAsFactors = F
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, header = T)
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all_plot_dfs = combining_dfs_plotting(my_df_u
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, gene_metadata
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, lig_dist_colname = 'ligand_distance'
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, lig_dist_cutoff = 10)
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merged_df2 = all_plot_dfs[[1]]
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merged_df3 = all_plot_dfs[[2]]
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#merged_df2_comp = all_plot_dfs[[3]]
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#merged_df3_comp = all_plot_dfs[[4]]
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#merged_df2_lig = all_plot_dfs[[5]]
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#merged_df3_lig = all_plot_dfs[[6]]
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#===========
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# output
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#===========
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logo_plot = "logo_plot.svg"
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plot_logo_plot = paste0(plotdir,"/", logo_plot)
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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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# This will the first plotting df
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# Then subset this to extract dr muts only (second plottig df)
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###########################
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#%%%%%%%%%%%%%%%%%%%%%%%%%
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# uncomment as necessary
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# REASSIGNMENT
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#my_data = merged_df2
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#my_data = merged_df2_comp
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my_data = merged_df3
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#my_data = merged_df3_comp
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#%%%%%%%%%%%%%%%%%%%%%%%%%%
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# quick checks
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colnames(my_data)
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str(my_data)
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c1 = unique(my_data$position)
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nrow(my_data)
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cat("No. of rows in my_data:", nrow(my_data)
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, "\nDistinct positions corresponding to snps:", length(c1)
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, "\n===========================================================")
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#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
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# FIXME: Think and decide what you want to remove
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# mut_pos_occurence < 1 or sample_pos_occurrence <1
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# get freq count of positions so you can subset freq<1
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#require(data.table)
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#setDT(my_data)[, mut_pos_occurrence := .N, by = .(position)] #265, 14
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#extract freq_pos>1
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#my_data_snp = my_data[my_data$occurrence!=1,]
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#u = unique(my_data_snp$position) #73
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#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
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#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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# REASSIGNMENT to prevent changing code
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my_data_snp = my_data
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#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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#=======================================================================
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#%% logo plots from dataframe
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#############
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# PLOTS: ggseqlogo with custom height
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# https://omarwagih.github.io/ggseqlogo/
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#############
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foo = my_data_snp[, c("position"
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, "mutant_type","duet_scaled", "or_mychisq"
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, "mut_prop_polarity", "mut_prop_water")]
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my_data_snp$log10or = log10(my_data_snp$or_mychisq)
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logo_data = my_data_snp[, c("position"
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, "mutant_type", "or_mychisq", "log10or")]
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logo_data_or = my_data_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 = 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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cat("Logo plot with OR as y axis:", plot_logo_plot)
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svg(plot_logo_plot, width = 30 , height = 6)
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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 = 12
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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 = 22
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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 = 25)
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, axis.title.x = element_text(size = 20)
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#, legend.position = "bottom") +
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, legend.position = "none")+
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#, legend.text = element_text(size = 15)
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#, legend.title = element_text(size = 15))+
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scale_x_discrete("Position"
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#, breaks
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, labels = position_or
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, limits = factor(1:length(position_or))) +
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ylab("Odds Ratio")
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print(logo_or)
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dev.off()
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#%% end of logo plot with OR as height
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#=======================================================================
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# extracting data with log10R
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logo_data_logor = my_data_snp[, c("position", "mutant_type", "log10or")]
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wide_df_logor <- logo_data_logor %>% spread(position, log10or, fill = 0.0)
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wide_df_logor = as.matrix(wide_df_logor)
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rownames(wide_df_logor) = wide_df_logor[,1]
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wide_df_logor = subset(wide_df_logor, select = -c(1) )
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colnames(wide_df_logor)
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wide_df_logor_m = data.matrix(wide_df_logor)
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rownames(wide_df_logor_m)
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colnames(wide_df_logor_m)
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# FIXME
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#my_ylim_up = as.numeric(max(wide_df_logor_m)) * 5
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#my_ylim_low = as.numeric(min(wide_df_logor_m))
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position_logor = as.numeric(colnames(wide_df_logor_m))
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# custom height (log10OR) logo plot: yayy works
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ggseqlogo(wide_df_logor_m, method="custom", seq_type="aa") + ylab("my custom height") +
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theme(legend.position = "bottom"
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, axis.text.x = element_text(size = 11
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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 = 15
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, angle = 0
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, hjust = 1
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, vjust = 0))+
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scale_x_discrete("Position"
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#, breaks
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, labels = position_logor
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, limits = factor(1:length(position_logor)))+
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ylab("Log (Odds Ratio)") +
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scale_y_continuous(limits = c(0, 9))
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#=======================================================================
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#%% end of script
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#=======================================================================
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