ks test script added
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4 changed files with 211 additions and 117 deletions
211
scripts/ks_test_PS.R
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211
scripts/ks_test_PS.R
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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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@ -1,45 +0,0 @@
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#!/usr/bin/env python3
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#=======================================================================
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#%% useful links
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#https://towardsdatascience.com/autoviz-automatically-visualize-any-dataset-ba2691a8b55a
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#https://pypi.org/project/autoviz/
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#=======================================================================
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import os, sys
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import pandas as pd
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import numpy as np
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import re
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import argparse
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from autoviz.AutoViz_Class import AutoViz_Class
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homedir = os.path.expanduser('~')
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os.chdir(homedir + '/git/LSHTM_analysis/scripts')
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#%%============================================================================
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# variables
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gene = 'pncA'
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drug = 'pyrazinamide'
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#%%============================================================================
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#==============
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# directories
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#==============
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datadir = homedir + '/' + 'git/Data'
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indir = datadir + '/' + drug + '/input'
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outdir = datadir + '/' + drug + '/output'
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#=======
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# input
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#=======
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in_filename_plotting = 'car_design.csv'
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in_filename_plotting = gene.lower() + '_all_params.csv'
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infile_plotting = outdir + '/' + in_filename_plotting
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print('plotting file: ', infile_plotting
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, '\n============================================================')
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#=======================================================================
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plotting_df = pd.read_csv(infile_plotting, sep = ',')
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#Instantiate the AutoViz class
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AV = AutoViz_Class()
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df = AV.AutoViz(infile_plotting)
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#df2 = AV.AutoViz(plotting_df)
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plotting_df.columns[~plotting_df.columns.isin(df.columns)]
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@ -1,4 +1,3 @@
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#!/usr/bin/env Rscript
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#########################################################
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# TASK: Basic lineage barplot showing numbers
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#=============
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# merged_df2
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#=============
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----------------
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# no. of samples
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----------------
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Var1 Freq
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1 8
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2 lineage1 144
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3 lineage1;lineage2 3
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4 lineage1;lineage4 4
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5 lineage2 1886
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6 lineage2;lineage4 19
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7 lineage3 190
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8 lineage3;lineage4 11
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9 lineage4 2213
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10 lineage4;lineage6 1
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11 lineage4;lineage7 1
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12 lineage4;lineageBOV 1
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13 lineage5 31
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14 lineage6 9
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15 lineage7 3
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16 lineageBOV 392
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----------------
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# no. of nsSNPs
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----------------
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sel_lineages num_snps_u total_samples
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1 lineage1 74 144
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2 lineage2 277 1886
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3 lineage3 104 190
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4 lineage4 311 2213
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5 lineage5 18 31
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6 lineage6 8 9
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7 lineage7 1 3
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#=============
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# merged_df2_comp
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#=============
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----------------
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# no. of samples
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----------------
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Var1 Freq
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1 3
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2 lineage1 108
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3 lineage1;lineage2 2
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4 lineage1;lineage4 2
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5 lineage2 1497
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6 lineage2;lineage4 13
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7 lineage3 154
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8 lineage3;lineage4 3
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9 lineage4 1846
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10 lineage4;lineageBOV 1
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11 lineage5 12
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12 lineage6 2
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13 lineageBOV 36
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----------------
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# no. of nsSNPs
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----------------
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sel_lineages num_snps_u total_samples
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1 lineage1 42 108
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2 lineage2 141 1497
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3 lineage3 75 154
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4 lineage4 148 1846
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5 lineage5 9 12
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6 lineage6 2 2
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7 lineage7 0 0
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