211 lines
6.1 KiB
R
211 lines
6.1 KiB
R
#!/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("/plotting/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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# 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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# 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$mutation_info); str(my_df$mutation_info)
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# subset df with dr muts only
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my_df_dr = subset(my_df, mutation_info == "dr_mutations_pyrazinamide")
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table(my_df_dr$mutation_info)
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# stats for all muts and dr_muts
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# 1) for all muts
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# 2) for dr_muts
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#===========================
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table(my_df$lineage); str(my_df$lineage)
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table(my_df_dr$lineage); str(my_df_dr$lineage)
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# subset only lineages1-4
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sel_lineages = c("lineage1"
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, "lineage2"
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, "lineage3"
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, "lineage4")
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# subset and refactor: all muts
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df_lin = subset(my_df, subset = lineage %in% sel_lineages)
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df_lin$lineage = factor(df_lin$lineage)
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# subset and refactor: dr muts
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df_lin_dr = subset(my_df_dr, subset = lineage %in% sel_lineages)
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df_lin_dr$lineage = factor(df_lin_dr$lineage)
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#{RESULT: No of samples within lineage}
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table(df_lin$lineage)
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table(df_lin_dr$lineage)
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#{Result: No. of unique mutations the 4 lineages contribute to}
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length(unique(df_lin$mutationinformation))
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length(unique(df_lin_dr$mutationinformation))
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# COMPARING DISTRIBUTIONS
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#================
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# ALL mutations
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#=================
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head(df_lin$lineage)
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df_lin$lineage = as.character(df_lin$lineage)
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lin1 = df_lin[df_lin$lineage == "lineage1",]$duet_scaled
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lin2 = df_lin[df_lin$lineage == "lineage2",]$duet_scaled
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lin3 = df_lin[df_lin$lineage == "lineage3",]$duet_scaled
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lin4 = df_lin[df_lin$lineage == "lineage4",]$duet_scaled
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# ks test
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lin12 = ks.test(lin1,lin2)
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lin12_df = as.data.frame(cbind(lin12$data.name, lin12$p.value))
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lin13 = ks.test(lin1,lin3)
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lin13_df = as.data.frame(cbind(lin13$data.name, lin13$p.value))
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lin14 = ks.test(lin1,lin4)
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lin14_df = as.data.frame(cbind(lin14$data.name, lin14$p.value))
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lin23 = ks.test(lin2,lin3)
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lin23_df = as.data.frame(cbind(lin23$data.name, lin23$p.value))
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lin24 = ks.test(lin2,lin4)
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lin24_df = as.data.frame(cbind(lin24$data.name, lin24$p.value))
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lin34 = ks.test(lin3,lin4)
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lin34_df = as.data.frame(cbind(lin34$data.name, lin34$p.value))
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ks_results_all = rbind(lin12_df
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, lin13_df
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, lin14_df
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, lin23_df
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, lin24_df
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, lin34_df)
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#p-value < 2.2e-16
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rm(lin12, lin12_df
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, lin13, lin13_df
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, lin14, lin14_df
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, lin23, lin23_df
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, lin24, lin24_df
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, lin34, lin34_df)
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#================
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# DRUG mutations
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#=================
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head(df_lin_dr$lineage)
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df_lin_dr$lineage = as.character(df_lin_dr$lineage)
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lin1_dr = df_lin_dr[df_lin_dr$lineage == "lineage1",]$duet_scaled
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lin2_dr = df_lin_dr[df_lin_dr$lineage == "lineage2",]$duet_scaled
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lin3_dr = df_lin_dr[df_lin_dr$lineage == "lineage3",]$duet_scaled
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lin4_dr = df_lin_dr[df_lin_dr$lineage == "lineage4",]$duet_scaled
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# ks test: dr muts
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lin12_dr = ks.test(lin1_dr,lin2_dr)
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lin12_df_dr = as.data.frame(cbind(lin12_dr$data.name, lin12_dr$p.value))
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lin13_dr = ks.test(lin1_dr,lin3_dr)
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lin13_df_dr = as.data.frame(cbind(lin13_dr$data.name, lin13_dr$p.value))
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lin14_dr = ks.test(lin1_dr,lin4_dr)
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lin14_df_dr = as.data.frame(cbind(lin14_dr$data.name, lin14_dr$p.value))
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lin23_dr = ks.test(lin2_dr,lin3_dr)
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lin23_df_dr = as.data.frame(cbind(lin23_dr$data.name, lin23_dr$p.value))
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lin24_dr = ks.test(lin2_dr,lin4_dr)
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lin24_df_dr = as.data.frame(cbind(lin24_dr$data.name, lin24_dr$p.value))
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lin34_dr = ks.test(lin3_dr,lin4_dr)
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lin34_df_dr = as.data.frame(cbind(lin34_dr$data.name, lin34_dr$p.value))
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ks_results_dr = rbind(lin12_df_dr
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, lin13_df_dr
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, lin14_df_dr
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, lin23_df_dr
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, lin24_df_dr
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, lin34_df_dr)
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ks_results_combined = cbind(ks_results_all, ks_results_dr)
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my_colnames = c("Lineage_comparisons"
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, paste0("All_mutations n=", nrow(df_lin))
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, paste0("Drug_associated_mutations n=", nrow(df_lin_dr)))
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my_colnames
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# select the output columns
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ks_results_combined_f = ks_results_combined[,c(1,2,4)]
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colnames(ks_results_combined_f) = my_colnames
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ks_results_combined_f
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#=============
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# write output file
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#=============
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ks_results = paste0(outdir,"/results/ks_results.csv")
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write.csv(ks_results_combined_f, ks_results, row.names = F)
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