299 lines
11 KiB
R
Executable file
299 lines
11 KiB
R
Executable file
#!/usr/bin/env Rscript
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#########################################################
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# TASK: KS test for PS/DUET lineage distributions
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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/")
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getwd()
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#source("~/git/LSHTM_analysis/scripts/Header_TT.R")
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#source("../barplot_colour_function.R")
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#require(data.table)
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source("plotting/combining_dfs_plotting.R")
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# should return the following dfs, directories and variables
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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(paste0("Directories imported:"
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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(paste0("Variables imported:"
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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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#=============
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# Output
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#=============
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# ks test by lineage
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ks_lineage_drug_muts = paste0(outdir, "/KS_lineage_drug_muts.csv")
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###########################
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# Data for stats
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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(merged_df2, merged_df2_comp, merged_df3, merged_df3_comp)
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rm(merged_df2_lig, merged_df2_comp_lig, merged_df3_lig, merged_df3_comp_lig, my_df_u, my_df_u_lig)
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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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########################################################################
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table(my_df$lineage); str(my_df$lineage)
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# subset only lineages1-4
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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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# subset selected lineages
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df_lin = subset(my_df, subset = lineage %in% sel_lineages)
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#==============
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# dr_muts_col
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#==============
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table(df_lin$mutation_info); str(df_lin$mutation_info)
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# subset df with dr muts only
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df_lin_dr = subset(df_lin, mutation_info == dr_muts_col)
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table(df_lin_dr$mutation_info)
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#==============
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# other_muts_col
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#==============
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#df_lin_other = subset(df_lin, mutation_info == other_muts_col)
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#table(df_lin_other$mutation_info)
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#=======================================================================
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# individual: CHECKS
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ks.test(df_lin_dr$duet_scaled[df_lin_dr$lineage == "lineage1"]
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, df_lin_dr$duet_scaled[df_lin_dr$lineage == "lineage2"])
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#=======================================================================
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my_lineages = levels(factor(df_lin_dr$lineage)); my_lineages
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#=======================================================================
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# Loop
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#=====================
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# Lineage 1 comparisons
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#=====================
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my_lin1 = "lineage1"
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#my_lineages_comp_l1 = c("lineage2", "lineage3", "lineage4")
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my_lineages_comp_l1 = my_lineages[-match(my_lin1, my_lineages)]
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ks_df_l1 = data.frame()
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for (i in my_lineages_comp_l1){
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cat(i)
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l1_df = data.frame(group = NA, method = NA, ks_statistic = NA, ks_pvalue = NA)
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lineage_comp = paste0(my_lin1, " vs ", i)
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ks_method = ks.test(df_lin_dr$duet_scaled[df_lin_dr$lineage == my_lin1]
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, df_lin_dr$duet_scaled[df_lin_dr$lineage == i])$method
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ks_statistic = ks.test(df_lin_dr$duet_scaled[df_lin_dr$lineage == my_lin1]
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, df_lin_dr$duet_scaled[df_lin_dr$lineage == i])$statistic
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ks_pvalue = ks.test(df_lin_dr$duet_scaled[df_lin_dr$lineage == my_lin1]
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, df_lin_dr$duet_scaled[df_lin_dr$lineage == i])$p.value
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# print(c(lineage_comp, ks_method, ks_statistic[[1]], ks_pval))
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l1_df$group = lineage_comp
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l1_df$method = ks_method
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l1_df$ks_statistic = ks_statistic[[1]]
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l1_df$ks_pvalue = ks_pvalue
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print(l1_df)
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ks_df_l1 = rbind(ks_df_l1,l1_df)
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}
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#####################################################################
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#=====================
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# Lineage 2 comparisons
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#=====================
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my_lin2 = "lineage2"
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#my_lineages_comp_l2 = c("lineage1", lineage3", "lineage4")
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my_lineages_comp_l2 = my_lineages[-match(my_lin2, my_lineages)]
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ks_df_l2 = data.frame()
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for (i in my_lineages_comp_l2){
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cat(i)
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l2_df = data.frame(group = NA, method = NA, ks_statistic = NA, ks_pvalue = NA)
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lineage_comp = paste0(my_lin2, " vs ", i)
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ks_method = ks.test(df_lin_dr$duet_scaled[df_lin_dr$lineage == my_lin2]
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, df_lin_dr$duet_scaled[df_lin_dr$lineage == i])$method
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ks_statistic = ks.test(df_lin_dr$duet_scaled[df_lin_dr$lineage == my_lin2]
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, df_lin_dr$duet_scaled[df_lin_dr$lineage == i])$statistic
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ks_pvalue = ks.test(df_lin_dr$duet_scaled[df_lin_dr$lineage == my_lin2]
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, df_lin_dr$duet_scaled[df_lin_dr$lineage == i])$p.value
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# print(c(lineage_comp, ks_method, ks_statistic[[1]], ks_pval))
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l2_df$group = lineage_comp
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l2_df$method = ks_method
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l2_df$ks_statistic = ks_statistic[[1]]
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l2_df$ks_pvalue = ks_pvalue
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print(l2_df)
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ks_df_l2 = rbind(ks_df_l2, l2_df)
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}
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#=====================
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# Lineage 3 comparisons
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#=====================
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my_lin3 = "lineage3"
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#my_lineages_comp_l3 = c("lineage1", lineage2", "lineage4")
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my_lineages_comp_l3 = my_lineages[-match(my_lin3, my_lineages)]
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ks_df_l3 = data.frame()
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for (i in my_lineages_comp_l3){
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cat(i)
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l3_df = data.frame(group = NA, method = NA, ks_statistic = NA, ks_pvalue = NA)
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lineage_comp = paste0(my_lin3, " vs ", i)
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ks_method = ks.test(df_lin_dr$duet_scaled[df_lin_dr$lineage == my_lin3]
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, df_lin_dr$duet_scaled[df_lin_dr$lineage == i])$method
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ks_statistic = ks.test(df_lin_dr$duet_scaled[df_lin_dr$lineage == my_lin3]
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, df_lin_dr$duet_scaled[df_lin_dr$lineage == i])$statistic
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ks_pvalue = ks.test(df_lin_dr$duet_scaled[df_lin_dr$lineage == my_lin3]
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, df_lin_dr$duet_scaled[df_lin_dr$lineage == i])$p.value
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# print(c(lineage_comp, ks_method, ks_statistic[[1]], ks_pval))
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l3_df$group = lineage_comp
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l3_df$method = ks_method
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l3_df$ks_statistic = ks_statistic[[1]]
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l3_df$ks_pvalue = ks_pvalue
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print(l3_df)
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ks_df_l3 = rbind(ks_df_l3, l3_df)
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}
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####################################################################
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# combine all three ks_dfs
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n_dfs = 3
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if ( all.equal(nrow(ks_df_l1), nrow(ks_df_l2), nrow(ks_df_l3)) &&
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all.equal(ncol(ks_df_l1), ncol(ks_df_l2), ncol(ks_df_l3)) ){
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cat("\nPASS: Calculating expected rows and cols for sanity checks on combined_dfs")
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expected_rows = nrow(ks_df_l1) * n_dfs
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expected_cols = ncol(ks_df_l1)
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ks_df_combined = rbind(ks_df_l1, ks_df_l2, ks_df_l3)
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if ( nrow(ks_df_combined) == expected_rows && ncol(ks_df_combined) == expected_cols ){
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cat("\nPASS: combined df successfully created"
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, "\nnrow combined_df:", nrow(ks_df_combined)
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, "\nncol combined_df:", ncol(ks_df_combined))
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}
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else{
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cat("\nFAIL: Dim mismatch"
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, "\nExpected rows:", expected_rows
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, "\nGot:", nrow(ks_df_combined)
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, "\nExpected cols:", expected_cols
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, "\nGot:", ncol(ks_df_combined))
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}
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}else{
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cat("\nFAIL: Could not generate expected_rows and/or expected_cols"
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, "\nCheck hardcoded value of n_dfs")
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}
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#=======================================================================
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# formatting
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#=======================================================================
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# add total_n number
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ks_df_combined$n_drug_muts = nrow(df_lin_dr)
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# adding pvalue_signif
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ks_df_combined$pvalue_signif = ks_df_combined$ks_pvalue
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str(ks_df_combined$pvalue_signif)
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ks_df_combined = dplyr::mutate(ks_df_combined
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, pvalue_signif = case_when(pvalue_signif == 0.05 ~ "."
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, pvalue_signif <=0.0001 ~ '****'
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, pvalue_signif <=0.001 ~ '***'
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, pvalue_signif <=0.01 ~ '**'
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, pvalue_signif <0.05 ~ '*'
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, TRUE ~ 'ns'))
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# Remove duplicates
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rows_to_remove = c("lineage2 vs lineage1", "lineage3 vs lineage1", "lineage3 vs lineage2")
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ks_df_combined_f = ks_df_combined[-match(rows_to_remove, ks_df_combined$group),]
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#======================
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# P-value adjustments
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#=======================
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#%%
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# add bonferroni adjustment as well
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ks_df_combined_f$p_adj_bonferroni = p.adjust(ks_df_combined_f$ks_pvalue, method = "bonferroni")
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ks_df_combined_f$signif_bon = ks_df_combined_f$p_adj_bonferroni
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ks_df_combined_f = dplyr::mutate(ks_df_combined_f
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, signif_bon = case_when(signif_bon == 0.05 ~ "."
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, signif_bon <=0.0001 ~ '****'
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, signif_bon <=0.001 ~ '***'
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, signif_bon <=0.01 ~ '**'
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, signif_bon <0.05 ~ '*'
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, TRUE ~ 'ns'))
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#add fdr as well
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ks_df_combined_f$p_adj_fdr = p.adjust(ks_df_combined_f$ks_pvalue, method = "hommel")
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ks_df_combined_f$signif_fdr = ks_df_combined_f$p_adj_fdr
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ks_df_combined_f = dplyr::mutate(ks_df_combined_f
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, signif_fdr = case_when(signif_fdr == 0.05 ~ "."
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, signif_fdr <=0.0001 ~ '****'
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, signif_fdr <=0.001 ~ '***'
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, signif_fdr <=0.01 ~ '**'
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, signif_fdr <0.05 ~ '*'
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, TRUE ~ 'ns'))
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#========================================================================================
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#******************
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# write output file: KS test
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#******************
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cat("Output of KS test by lineage for dr_muts:", ks_lineage_drug_muts)
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write.csv(ks_df_combined_f, ks_lineage_drug_muts, row.names = FALSE)
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