added ks_test_all_PS.R, ks_test_dr_PS.R, ks_test_dr_others_PS.R
This commit is contained in:
parent
be8fa7e639
commit
2297617af2
4 changed files with 722 additions and 211 deletions
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@ -1,211 +0,0 @@
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#!/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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280
scripts/ks_test_all_PS.R
Executable file
280
scripts/ks_test_all_PS.R
Executable file
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@ -0,0 +1,280 @@
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#!/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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# Output
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#=============
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# ks test by lineage
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ks_lineage = paste0(outdir, "/KS_lineage_all_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$mutation_info)
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#=======================================================================
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# individual: CHECKS
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lin1 = df_lin[df_lin$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",]$duet_scaled
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ks.test(lin1, lin4)
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ks.test(df_lin$duet_scaled[df_lin$lineage == "lineage1"]
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, df_lin$duet_scaled[df_lin$lineage == "lineage2"])
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#=======================================================================
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my_lineages = levels(factor(df_lin$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$duet_scaled[df_lin$lineage == my_lin1]
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, df_lin$duet_scaled[df_lin$lineage == i])$method
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ks_statistic = ks.test(df_lin$duet_scaled[df_lin$lineage == my_lin1]
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, df_lin$duet_scaled[df_lin$lineage == i])$statistic
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ks_pvalue = ks.test(df_lin$duet_scaled[df_lin$lineage == my_lin1]
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, df_lin$duet_scaled[df_lin$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$ks_pvalue = ks_pvalue
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print(l1_df)
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ks_df_l1 = rbind(ks_df_l1,l1_df)
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}
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#####################################################################
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#=====================
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# Lineage 2 comparisons
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#=====================
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my_lin2 = "lineage2"
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#my_lineages_comp_l2 = c("lineage1", lineage3", "lineage4")
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my_lineages_comp_l2 = my_lineages[-match(my_lin2, my_lineages)]
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ks_df_l2 = data.frame()
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for (i in my_lineages_comp_l2){
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cat(i)
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l2_df = data.frame(group = NA, method = NA, ks_statistic = NA, ks_pvalue = NA)
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lineage_comp = paste0(my_lin2, " vs ", i)
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ks_method = ks.test(df_lin$duet_scaled[df_lin$lineage == my_lin2]
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, df_lin$duet_scaled[df_lin$lineage == i])$method
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ks_statistic = ks.test(df_lin$duet_scaled[df_lin$lineage == my_lin2]
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, df_lin$duet_scaled[df_lin$lineage == i])$statistic
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ks_pvalue = ks.test(df_lin$duet_scaled[df_lin$lineage == my_lin2]
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, df_lin$duet_scaled[df_lin$lineage == i])$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$duet_scaled[df_lin$lineage == my_lin3]
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, df_lin$duet_scaled[df_lin$lineage == i])$method
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ks_statistic = ks.test(df_lin$duet_scaled[df_lin$lineage == my_lin3]
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, df_lin$duet_scaled[df_lin$lineage == i])$statistic
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ks_pvalue = ks.test(df_lin$duet_scaled[df_lin$lineage == my_lin3]
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, df_lin$duet_scaled[df_lin$lineage == i])$p.value
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|
||||
# print(c(lineage_comp, ks_method, ks_statistic[[1]], ks_pval))
|
||||
l3_df$group = lineage_comp
|
||||
l3_df$method = ks_method
|
||||
l3_df$ks_statistic = ks_statistic[[1]]
|
||||
l3_df$ks_pvalue = ks_pvalue
|
||||
|
||||
print(l3_df)
|
||||
ks_df_l3 = rbind(ks_df_l3, l3_df)
|
||||
|
||||
}
|
||||
####################################################################
|
||||
# combine all three ks_dfs
|
||||
n_dfs = 3
|
||||
if ( all.equal(nrow(ks_df_l1), nrow(ks_df_l2), nrow(ks_df_l3)) &&
|
||||
all.equal(ncol(ks_df_l1), ncol(ks_df_l2), ncol(ks_df_l3)) ){
|
||||
cat("\nPASS: Calculating expected rows and cols for sanity checks on combined_dfs")
|
||||
expected_rows = nrow(ks_df_l1) * n_dfs
|
||||
expected_cols = ncol(ks_df_l1)
|
||||
ks_df_combined = rbind(ks_df_l1, ks_df_l2, ks_df_l3)
|
||||
if ( nrow(ks_df_combined) == expected_rows && ncol(ks_df_combined) == expected_cols ){
|
||||
cat("\nPASS: combined df successfully created"
|
||||
, "\nnrow combined_df:", nrow(ks_df_combined)
|
||||
, "\nncol combined_df:", ncol(ks_df_combined))
|
||||
}
|
||||
else{
|
||||
cat("\nFAIL: Dim mismatch"
|
||||
, "\nExpected rows:", expected_rows
|
||||
, "\nGot:", nrow(ks_df_combined)
|
||||
, "\nExpected cols:", expected_cols
|
||||
, "\nGot:", ncol(ks_df_combined))
|
||||
}
|
||||
}else{
|
||||
cat("\nFAIL: Could not generate expected_rows and/or expected_cols"
|
||||
, "\nCheck hardcoded value of n_dfs")
|
||||
}
|
||||
|
||||
#=======================================================================
|
||||
# formatting
|
||||
#=======================================================================
|
||||
# add total_n number
|
||||
ks_df_combined$total_samples_analysed = nrow(df_lin)
|
||||
|
||||
# adding pvalue_signif
|
||||
ks_df_combined$pvalue_signif = ks_df_combined$ks_pvalue
|
||||
str(ks_df_combined$pvalue_signif)
|
||||
|
||||
ks_df_combined = dplyr::mutate(ks_df_combined
|
||||
, pvalue_signif = case_when(pvalue_signif == 0.05 ~ "."
|
||||
, pvalue_signif <=0.0001 ~ '****'
|
||||
, pvalue_signif <=0.001 ~ '***'
|
||||
, pvalue_signif <=0.01 ~ '**'
|
||||
, pvalue_signif <0.05 ~ '*'
|
||||
, TRUE ~ 'ns'))
|
||||
|
||||
# Remove duplicates
|
||||
rows_to_remove = c("lineage2 vs lineage1", "lineage3 vs lineage1", "lineage3 vs lineage2")
|
||||
|
||||
ks_df_combined_f = ks_df_combined[-match(rows_to_remove, ks_df_combined$group),]
|
||||
|
||||
#=======================================================================
|
||||
#******************
|
||||
# write output file: KS test
|
||||
#******************
|
||||
cat("Output of KS test bt lineage:", ks_lineage)
|
||||
write.csv(ks_df_combined_f, ks_lineage, row.names = FALSE)
|
||||
272
scripts/ks_test_dr_PS.R
Executable file
272
scripts/ks_test_dr_PS.R
Executable file
|
|
@ -0,0 +1,272 @@
|
|||
#!/usr/bin/env Rscript
|
||||
#########################################################
|
||||
# TASK: KS test for PS/DUET lineage distributions
|
||||
#=======================================================================
|
||||
#=======================================================================
|
||||
# working dir and loading libraries
|
||||
getwd()
|
||||
setwd("~/git/LSHTM_analysis/scripts/")
|
||||
getwd()
|
||||
|
||||
#source("/plotting/Header_TT.R")
|
||||
#source("../barplot_colour_function.R")
|
||||
#require(data.table)
|
||||
source("plotting/combining_dfs_plotting.R")
|
||||
# should return the following dfs, directories and variables
|
||||
|
||||
# PS combined:
|
||||
# 1) merged_df2
|
||||
# 2) merged_df2_comp
|
||||
# 3) merged_df3
|
||||
# 4) merged_df3_comp
|
||||
|
||||
# LIG combined:
|
||||
# 5) merged_df2_lig
|
||||
# 6) merged_df2_comp_lig
|
||||
# 7) merged_df3_lig
|
||||
# 8) merged_df3_comp_lig
|
||||
|
||||
# 9) my_df_u
|
||||
# 10) my_df_u_lig
|
||||
|
||||
cat(paste0("Directories imported:"
|
||||
, "\ndatadir:", datadir
|
||||
, "\nindir:", indir
|
||||
, "\noutdir:", outdir
|
||||
, "\nplotdir:", plotdir))
|
||||
|
||||
cat(paste0("Variables imported:"
|
||||
, "\ndrug:", drug
|
||||
, "\ngene:", gene
|
||||
, "\ngene_match:", gene_match
|
||||
, "\nAngstrom symbol:", angstroms_symbol
|
||||
, "\nNo. of duplicated muts:", dup_muts_nu
|
||||
, "\nNA count for ORs:", na_count
|
||||
, "\nNA count in df2:", na_count_df2
|
||||
, "\nNA count in df3:", na_count_df3))
|
||||
|
||||
|
||||
#=============
|
||||
# Output
|
||||
#=============
|
||||
# ks test by lineage
|
||||
ks_lineage_drug_muts = paste0(outdir, "/KS_lineage_drug_muts.csv")
|
||||
|
||||
###########################
|
||||
# Data for stats
|
||||
# you need merged_df2 or merged_df2_comp
|
||||
# since this is one-many relationship
|
||||
# i.e the same SNP can belong to multiple lineages
|
||||
# using the _comp dataset means
|
||||
# we lose some muts and at this level, we should use
|
||||
# as much info as available, hence use df with NA
|
||||
###########################
|
||||
|
||||
# REASSIGNMENT
|
||||
my_df = merged_df2
|
||||
|
||||
# delete variables not required
|
||||
rm(merged_df2, merged_df2_comp, merged_df3, merged_df3_comp)
|
||||
rm(merged_df2_lig, merged_df2_comp_lig, merged_df3_lig, merged_df3_comp_lig, my_df_u, my_df_u_lig)
|
||||
|
||||
# quick checks
|
||||
colnames(my_df)
|
||||
str(my_df)
|
||||
|
||||
# Ensure correct data type in columns to plot: need to be factor
|
||||
#is.factor(my_df$lineage)
|
||||
#my_df$lineage = as.factor(my_df$lineage)
|
||||
#is.factor(my_df$lineage)
|
||||
########################################################################
|
||||
table(my_df$lineage); str(my_df$lineage)
|
||||
|
||||
# subset only lineages1-4
|
||||
sel_lineages = c("lineage1"
|
||||
, "lineage2"
|
||||
, "lineage3"
|
||||
, "lineage4")
|
||||
|
||||
# subset selected lineages
|
||||
df_lin = subset(my_df, subset = lineage %in% sel_lineages)
|
||||
|
||||
#==============
|
||||
# dr_muts_col
|
||||
#==============
|
||||
table(df_lin$mutation_info); str(df_lin$mutation_info)
|
||||
|
||||
# subset df with dr muts only
|
||||
df_lin_dr = subset(df_lin, mutation_info == dr_muts_col)
|
||||
table(df_lin_dr$mutation_info)
|
||||
|
||||
#==============
|
||||
# other_muts_col
|
||||
#==============
|
||||
#df_lin_other = subset(df_lin, mutation_info == other_muts_col)
|
||||
#table(df_lin_other$mutation_info)
|
||||
|
||||
#=======================================================================
|
||||
# individual: CHECKS
|
||||
ks.test(df_lin_dr$duet_scaled[df_lin_dr$lineage == "lineage1"]
|
||||
, df_lin_dr$duet_scaled[df_lin_dr$lineage == "lineage2"])
|
||||
|
||||
#=======================================================================
|
||||
my_lineages = levels(factor(df_lin_dr$lineage)); my_lineages
|
||||
#=======================================================================
|
||||
# Loop
|
||||
#=====================
|
||||
# Lineage 1 comparisons
|
||||
#=====================
|
||||
my_lin1 = "lineage1"
|
||||
#my_lineages_comp_l1 = c("lineage2", "lineage3", "lineage4")
|
||||
my_lineages_comp_l1 = my_lineages[-match(my_lin1, my_lineages)]
|
||||
|
||||
ks_df_l1 = data.frame()
|
||||
|
||||
for (i in my_lineages_comp_l1){
|
||||
cat(i)
|
||||
l1_df = data.frame(group = NA, method = NA, ks_statistic = NA, ks_pvalue = NA)
|
||||
|
||||
lineage_comp = paste0(my_lin1, " vs ", i)
|
||||
ks_method = ks.test(df_lin_dr$duet_scaled[df_lin_dr$lineage == my_lin1]
|
||||
, df_lin_dr$duet_scaled[df_lin_dr$lineage == i])$method
|
||||
|
||||
ks_statistic = ks.test(df_lin_dr$duet_scaled[df_lin_dr$lineage == my_lin1]
|
||||
, df_lin_dr$duet_scaled[df_lin_dr$lineage == i])$statistic
|
||||
|
||||
ks_pvalue = ks.test(df_lin_dr$duet_scaled[df_lin_dr$lineage == my_lin1]
|
||||
, df_lin_dr$duet_scaled[df_lin_dr$lineage == i])$p.value
|
||||
|
||||
# print(c(lineage_comp, ks_method, ks_statistic[[1]], ks_pval))
|
||||
l1_df$group = lineage_comp
|
||||
l1_df$method = ks_method
|
||||
l1_df$ks_statistic = ks_statistic[[1]]
|
||||
l1_df$ks_pvalue = ks_pvalue
|
||||
|
||||
print(l1_df)
|
||||
ks_df_l1 = rbind(ks_df_l1,l1_df)
|
||||
|
||||
}
|
||||
#####################################################################
|
||||
|
||||
#=====================
|
||||
# Lineage 2 comparisons
|
||||
#=====================
|
||||
my_lin2 = "lineage2"
|
||||
#my_lineages_comp_l2 = c("lineage1", lineage3", "lineage4")
|
||||
my_lineages_comp_l2 = my_lineages[-match(my_lin2, my_lineages)]
|
||||
|
||||
ks_df_l2 = data.frame()
|
||||
|
||||
for (i in my_lineages_comp_l2){
|
||||
cat(i)
|
||||
l2_df = data.frame(group = NA, method = NA, ks_statistic = NA, ks_pvalue = NA)
|
||||
|
||||
lineage_comp = paste0(my_lin2, " vs ", i)
|
||||
ks_method = ks.test(df_lin_dr$duet_scaled[df_lin_dr$lineage == my_lin2]
|
||||
, df_lin_dr$duet_scaled[df_lin_dr$lineage == i])$method
|
||||
|
||||
ks_statistic = ks.test(df_lin_dr$duet_scaled[df_lin_dr$lineage == my_lin2]
|
||||
, df_lin_dr$duet_scaled[df_lin_dr$lineage == i])$statistic
|
||||
|
||||
ks_pvalue = ks.test(df_lin_dr$duet_scaled[df_lin_dr$lineage == my_lin2]
|
||||
, df_lin_dr$duet_scaled[df_lin_dr$lineage == i])$p.value
|
||||
|
||||
# print(c(lineage_comp, ks_method, ks_statistic[[1]], ks_pval))
|
||||
l2_df$group = lineage_comp
|
||||
l2_df$method = ks_method
|
||||
l2_df$ks_statistic = ks_statistic[[1]]
|
||||
l2_df$ks_pvalue = ks_pvalue
|
||||
|
||||
print(l2_df)
|
||||
ks_df_l2 = rbind(ks_df_l2, l2_df)
|
||||
|
||||
}
|
||||
|
||||
#=====================
|
||||
# Lineage 3 comparisons
|
||||
#=====================
|
||||
my_lin3 = "lineage3"
|
||||
#my_lineages_comp_l3 = c("lineage1", lineage2", "lineage4")
|
||||
my_lineages_comp_l3 = my_lineages[-match(my_lin3, my_lineages)]
|
||||
|
||||
ks_df_l3 = data.frame()
|
||||
|
||||
for (i in my_lineages_comp_l3){
|
||||
cat(i)
|
||||
l3_df = data.frame(group = NA, method = NA, ks_statistic = NA, ks_pvalue = NA)
|
||||
|
||||
lineage_comp = paste0(my_lin3, " vs ", i)
|
||||
ks_method = ks.test(df_lin_dr$duet_scaled[df_lin_dr$lineage == my_lin3]
|
||||
, df_lin_dr$duet_scaled[df_lin_dr$lineage == i])$method
|
||||
|
||||
ks_statistic = ks.test(df_lin_dr$duet_scaled[df_lin_dr$lineage == my_lin3]
|
||||
, df_lin_dr$duet_scaled[df_lin_dr$lineage == i])$statistic
|
||||
|
||||
ks_pvalue = ks.test(df_lin_dr$duet_scaled[df_lin_dr$lineage == my_lin3]
|
||||
, df_lin_dr$duet_scaled[df_lin_dr$lineage == i])$p.value
|
||||
|
||||
# print(c(lineage_comp, ks_method, ks_statistic[[1]], ks_pval))
|
||||
l3_df$group = lineage_comp
|
||||
l3_df$method = ks_method
|
||||
l3_df$ks_statistic = ks_statistic[[1]]
|
||||
l3_df$ks_pvalue = ks_pvalue
|
||||
|
||||
print(l3_df)
|
||||
ks_df_l3 = rbind(ks_df_l3, l3_df)
|
||||
|
||||
}
|
||||
####################################################################
|
||||
# combine all three ks_dfs
|
||||
n_dfs = 3
|
||||
if ( all.equal(nrow(ks_df_l1), nrow(ks_df_l2), nrow(ks_df_l3)) &&
|
||||
all.equal(ncol(ks_df_l1), ncol(ks_df_l2), ncol(ks_df_l3)) ){
|
||||
cat("\nPASS: Calculating expected rows and cols for sanity checks on combined_dfs")
|
||||
expected_rows = nrow(ks_df_l1) * n_dfs
|
||||
expected_cols = ncol(ks_df_l1)
|
||||
ks_df_combined = rbind(ks_df_l1, ks_df_l2, ks_df_l3)
|
||||
if ( nrow(ks_df_combined) == expected_rows && ncol(ks_df_combined) == expected_cols ){
|
||||
cat("\nPASS: combined df successfully created"
|
||||
, "\nnrow combined_df:", nrow(ks_df_combined)
|
||||
, "\nncol combined_df:", ncol(ks_df_combined))
|
||||
}
|
||||
else{
|
||||
cat("\nFAIL: Dim mismatch"
|
||||
, "\nExpected rows:", expected_rows
|
||||
, "\nGot:", nrow(ks_df_combined)
|
||||
, "\nExpected cols:", expected_cols
|
||||
, "\nGot:", ncol(ks_df_combined))
|
||||
}
|
||||
}else{
|
||||
cat("\nFAIL: Could not generate expected_rows and/or expected_cols"
|
||||
, "\nCheck hardcoded value of n_dfs")
|
||||
}
|
||||
|
||||
#=======================================================================
|
||||
# formatting
|
||||
#=======================================================================
|
||||
# add total_n number
|
||||
ks_df_combined$n_drug_muts = nrow(df_lin_dr)
|
||||
|
||||
# adding pvalue_signif
|
||||
ks_df_combined$pvalue_signif = ks_df_combined$ks_pvalue
|
||||
str(ks_df_combined$pvalue_signif)
|
||||
|
||||
ks_df_combined = dplyr::mutate(ks_df_combined
|
||||
, pvalue_signif = case_when(pvalue_signif == 0.05 ~ "."
|
||||
, pvalue_signif <=0.0001 ~ '****'
|
||||
, pvalue_signif <=0.001 ~ '***'
|
||||
, pvalue_signif <=0.01 ~ '**'
|
||||
, pvalue_signif <0.05 ~ '*'
|
||||
, TRUE ~ 'ns'))
|
||||
|
||||
# Remove duplicates
|
||||
rows_to_remove = c("lineage2 vs lineage1", "lineage3 vs lineage1", "lineage3 vs lineage2")
|
||||
|
||||
ks_df_combined_f = ks_df_combined[-match(rows_to_remove, ks_df_combined$group),]
|
||||
|
||||
#=======================================================================
|
||||
#******************
|
||||
# write output file: KS test
|
||||
#******************
|
||||
cat("Output of KS test by lineage for dr_muts:", ks_lineage_drug_muts)
|
||||
write.csv(ks_df_combined_f, ks_lineage_drug_muts, row.names = FALSE)
|
||||
170
scripts/ks_test_dr_others_PS.R
Executable file
170
scripts/ks_test_dr_others_PS.R
Executable file
|
|
@ -0,0 +1,170 @@
|
|||
#!/usr/bin/env Rscript
|
||||
#########################################################
|
||||
# TASK: KS test for PS/DUET lineage distributions
|
||||
#=======================================================================
|
||||
#=======================================================================
|
||||
# working dir and loading libraries
|
||||
getwd()
|
||||
setwd("~/git/LSHTM_analysis/scripts/")
|
||||
getwd()
|
||||
|
||||
#source("/plotting/Header_TT.R")
|
||||
#source("../barplot_colour_function.R")
|
||||
#require(data.table)
|
||||
source("plotting/combining_dfs_plotting.R")
|
||||
# should return the following dfs, directories and variables
|
||||
|
||||
# PS combined:
|
||||
# 1) merged_df2
|
||||
# 2) merged_df2_comp
|
||||
# 3) merged_df3
|
||||
# 4) merged_df3_comp
|
||||
|
||||
# LIG combined:
|
||||
# 5) merged_df2_lig
|
||||
# 6) merged_df2_comp_lig
|
||||
# 7) merged_df3_lig
|
||||
# 8) merged_df3_comp_lig
|
||||
|
||||
# 9) my_df_u
|
||||
# 10) my_df_u_lig
|
||||
|
||||
cat(paste0("Directories imported:"
|
||||
, "\ndatadir:", datadir
|
||||
, "\nindir:", indir
|
||||
, "\noutdir:", outdir
|
||||
, "\nplotdir:", plotdir))
|
||||
|
||||
cat(paste0("Variables imported:"
|
||||
, "\ndrug:", drug
|
||||
, "\ngene:", gene
|
||||
, "\ngene_match:", gene_match
|
||||
, "\nAngstrom symbol:", angstroms_symbol
|
||||
, "\nNo. of duplicated muts:", dup_muts_nu
|
||||
, "\nNA count for ORs:", na_count
|
||||
, "\nNA count in df2:", na_count_df2
|
||||
, "\nNA count in df3:", na_count_df3))
|
||||
|
||||
|
||||
#=============
|
||||
# Output
|
||||
#=============
|
||||
# ks test by lineage
|
||||
ks_dr_others_lineage = paste0(outdir, "/KS_lineage_dr_vs_others.csv")
|
||||
|
||||
###########################
|
||||
# Data for stats
|
||||
# you need merged_df2 or merged_df2_comp
|
||||
# since this is one-many relationship
|
||||
# i.e the same SNP can belong to multiple lineages
|
||||
# using the _comp dataset means
|
||||
# we lose some muts and at this level, we should use
|
||||
# as much info as available, hence use df with NA
|
||||
###########################
|
||||
|
||||
# REASSIGNMENT
|
||||
my_df = merged_df2
|
||||
|
||||
# delete variables not required
|
||||
rm(merged_df2, merged_df2_comp, merged_df3, merged_df3_comp)
|
||||
rm(merged_df2_lig, merged_df2_comp_lig, merged_df3_lig, merged_df3_comp_lig, my_df_u, my_df_u_lig)
|
||||
|
||||
# quick checks
|
||||
colnames(my_df)
|
||||
str(my_df)
|
||||
|
||||
# Ensure correct data type in columns to plot: need to be factor
|
||||
#is.factor(my_df$lineage)
|
||||
#my_df$lineage = as.factor(my_df$lineage)
|
||||
#is.factor(my_df$lineage)
|
||||
########################################################################
|
||||
table(my_df$lineage); str(my_df$lineage)
|
||||
|
||||
# subset only lineages1-4
|
||||
sel_lineages = c("lineage1"
|
||||
, "lineage2"
|
||||
, "lineage3"
|
||||
, "lineage4")
|
||||
|
||||
# subset selected lineages
|
||||
df_lin = subset(my_df, subset = lineage %in% sel_lineages)
|
||||
|
||||
#==============
|
||||
# dr_muts_col
|
||||
#==============
|
||||
table(df_lin$mutation_info); str(df_lin$mutation_info)
|
||||
|
||||
# subset df with dr muts only
|
||||
df_lin_dr = subset(df_lin, mutation_info == dr_muts_col)
|
||||
table(df_lin_dr$mutation_info)
|
||||
|
||||
#==============
|
||||
# other_muts_col
|
||||
#==============
|
||||
df_lin_other = subset(df_lin, mutation_info == other_muts_col)
|
||||
table(df_lin_other$mutation_info)
|
||||
|
||||
#=======================================================================
|
||||
# individual: CHECKS
|
||||
ks.test(df_lin_dr$duet_scaled[df_lin_dr$lineage == "lineage1"]
|
||||
, df_lin_other$duet_scaled[df_lin_other$lineage == "lineage1"])
|
||||
|
||||
#=======================================================================
|
||||
my_lineages = levels(factor(df_lin$lineage)); my_lineages
|
||||
|
||||
ks_df = data.frame()
|
||||
|
||||
for (i in my_lineages){
|
||||
cat(i)
|
||||
df = data.frame(lineage = NA, method = NA, ks_statistic = NA, ks_pvalue = NA)
|
||||
|
||||
lineage_comp = i
|
||||
ks_method = ks.test(df_lin_dr$duet_scaled[df_lin_dr$lineage == i]
|
||||
, df_lin_other$duet_scaled[df_lin_other$lineage == i])$method
|
||||
|
||||
ks_statistic = ks.test(df_lin_dr$duet_scaled[df_lin_dr$lineage == i]
|
||||
, df_lin_other$duet_scaled[df_lin_other$lineage == i])$statistic
|
||||
|
||||
ks_pvalue = ks.test(df_lin_dr$duet_scaled[df_lin_dr$lineage == i]
|
||||
, df_lin_other$duet_scaled[df_lin_other$lineage == i])$p.value
|
||||
|
||||
# print(c(lineage_comp, ks_method, ks_statistic[[1]], ks_pval))
|
||||
df$lineage = lineage_comp
|
||||
df$method = ks_method
|
||||
df$ks_statistic = ks_statistic[[1]]
|
||||
df$ks_pvalue = ks_pvalue
|
||||
|
||||
print(df)
|
||||
ks_df = rbind(ks_df,df)
|
||||
|
||||
}
|
||||
|
||||
#=======================================================================
|
||||
# formatting
|
||||
#=======================================================================
|
||||
# add total_n number
|
||||
ks_df$n_drug = nrow(df_lin_dr)
|
||||
ks_df$n_others = nrow(df_lin_other)
|
||||
|
||||
# add group
|
||||
ks_df$group = "Drug vs Others"
|
||||
|
||||
str(ks_df)
|
||||
ks_df$pvalue_signif = ks_df$ks_pvalue
|
||||
str(ks_df$pvalue_signif)
|
||||
|
||||
# adding pvalue_signif
|
||||
ks_df = dplyr::mutate(ks_df, pvalue_signif = case_when(pvalue_signif == 0.05 ~ "."
|
||||
, pvalue_signif <=0.0001 ~ '****'
|
||||
, pvalue_signif <=0.001 ~ '***'
|
||||
, pvalue_signif <=0.01 ~ '**'
|
||||
, pvalue_signif <0.05 ~ '*'
|
||||
, TRUE ~ 'ns'))
|
||||
|
||||
|
||||
#=======================================================================
|
||||
#******************
|
||||
# write output file: KS test
|
||||
#******************
|
||||
cat("Output of KS test bt lineage:", ks_dr_others_lineage )
|
||||
write.csv(ks_df, ks_dr_others_lineage, row.names = FALSE)
|
||||
Loading…
Add table
Add a link
Reference in a new issue