modified ks test to output all stats needed in one script
This commit is contained in:
parent
22be845e1f
commit
f5f1e388c3
3 changed files with 309 additions and 106 deletions
|
|
@ -2,55 +2,26 @@
|
|||
#########################################################
|
||||
# TASK: KS test for PS/DUET lineage distributions
|
||||
#=======================================================================
|
||||
#=======================================================================
|
||||
# working dir and loading libraries
|
||||
getwd()
|
||||
setwd("~/git/LSHTM_analysis/scripts/")
|
||||
getwd()
|
||||
|
||||
#source("~/git/LSHTM_analysis/scripts/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))
|
||||
#!/usr/bin/env Rscript
|
||||
#source("~/git/LSHTM_analysis/config/alr.R")
|
||||
source("~/git/LSHTM_analysis/config/embb.R")
|
||||
#source("~/git/LSHTM_analysis/config/katg.R")
|
||||
#source("~/git/LSHTM_analysis/config/gid.R")
|
||||
#source("~/git/LSHTM_analysis/config/pnca.R")
|
||||
#source("~/git/LSHTM_analysis/config/rpob.R")
|
||||
|
||||
# get plottting dfs
|
||||
source("~/git/LSHTM_analysis/scripts/plotting/get_plotting_dfs.R")
|
||||
|
||||
source("~/git/LSHTM_analysis/scripts/plotting/plotting_colnames.R")
|
||||
#=============
|
||||
# Output
|
||||
#=============
|
||||
outdir_images = paste0("~/git/Writing/thesis/images/results/", tolower(gene), "/")
|
||||
outdir_stats = "~/git/LSHTM_analysis/scripts/plotting/plotting_thesis/stats/"
|
||||
|
||||
# ks test by lineage
|
||||
ks_lineage = paste0(outdir, "/KS_lineage_all_muts.csv")
|
||||
#ks_lineage = paste0(outdir, "/KS_lineage_all_muts.csv")
|
||||
|
||||
###########################
|
||||
# Data for stats
|
||||
|
|
@ -81,14 +52,19 @@ str(my_df)
|
|||
table(my_df$lineage); str(my_df$lineage)
|
||||
|
||||
# subset only lineages1-4
|
||||
sel_lineages = c("lineage1"
|
||||
, "lineage2"
|
||||
, "lineage3"
|
||||
, "lineage4")
|
||||
sel_lineages = c("L1"
|
||||
, "L2"
|
||||
, "L3"
|
||||
, "L4")
|
||||
|
||||
# subset selected lineages
|
||||
df_lin = subset(my_df, subset = lineage %in% sel_lineages)
|
||||
|
||||
table(df_lin$lineage)
|
||||
table(df_lin$sensitivity)
|
||||
|
||||
table(df_lin$lineage, df_lin$sensitivity)
|
||||
|
||||
#==============
|
||||
# dr_muts_col
|
||||
#==============
|
||||
|
|
@ -106,16 +82,16 @@ df_lin = subset(my_df, subset = lineage %in% sel_lineages)
|
|||
|
||||
#=======================================================================
|
||||
# individual: CHECKS
|
||||
lin1 = df_lin[df_lin$lineage == "lineage1",]$duet_scaled
|
||||
lin2 = df_lin[df_lin$lineage == "lineage2",]$duet_scaled
|
||||
lin3 = df_lin[df_lin$lineage == "lineage3",]$duet_scaled
|
||||
lin4 = df_lin[df_lin$lineage == "lineage4",]$duet_scaled
|
||||
lin1 = df_lin[df_lin$lineage == "L1",]$avg_stability_scaled
|
||||
lin2 = df_lin[df_lin$lineage == "L2",]$avg_stability_scaled
|
||||
lin3 = df_lin[df_lin$lineage == "L3",]$avg_stability_scaled
|
||||
lin4 = df_lin[df_lin$lineage == "L4",]$avg_stability_scaled
|
||||
|
||||
ks.test(lin1, lin4)
|
||||
|
||||
|
||||
ks.test(df_lin$duet_scaled[df_lin$lineage == "lineage1"]
|
||||
, df_lin$duet_scaled[df_lin$lineage == "lineage2"])
|
||||
ks.test(df_lin$avg_stability_scaled[df_lin$lineage == "L2"]
|
||||
, df_lin$avg_stability_scaled[df_lin$lineage == "L3"])
|
||||
|
||||
#=======================================================================
|
||||
my_lineages = levels(factor(df_lin$lineage)); my_lineages
|
||||
|
|
@ -124,31 +100,43 @@ my_lineages = levels(factor(df_lin$lineage)); my_lineages
|
|||
#=====================
|
||||
# Lineage 1 comparisons
|
||||
#=====================
|
||||
my_lin1 = "lineage1"
|
||||
#my_lineages_comp_l1 = c("lineage2", "lineage3", "lineage4")
|
||||
my_lin1 = "L1"
|
||||
#my_lineages_comp_l1 = c("L2", "L3", "L4")
|
||||
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)
|
||||
l1_df = data.frame(comparison = NA, method = NA, ks_statistic = NA, ks_pvalue = NA
|
||||
, n_samples = NA
|
||||
, n_samples_total = NA)
|
||||
|
||||
lineage_comp = paste0(my_lin1, " vs ", i)
|
||||
ks_method = ks.test(df_lin$duet_scaled[df_lin$lineage == my_lin1]
|
||||
, df_lin$duet_scaled[df_lin$lineage == i])$method
|
||||
n_samples_lin = nrow(df_lin[df_lin$lineage == my_lin1,])
|
||||
n_samples_i = nrow(df_lin[df_lin$lineage == i,])
|
||||
n_samples_all = paste0(my_lin1, "(", n_samples_lin, ")"
|
||||
, ", "
|
||||
, i, "(", n_samples_i, ")")
|
||||
|
||||
ks_statistic = ks.test(df_lin$duet_scaled[df_lin$lineage == my_lin1]
|
||||
, df_lin$duet_scaled[df_lin$lineage == i])$statistic
|
||||
n_samples_total = n_samples_lin + n_samples_i
|
||||
|
||||
ks_method = ks.test(df_lin$avg_stability_scaled[df_lin$lineage == my_lin1]
|
||||
, df_lin$avg_stability_scaled[df_lin$lineage == i])$method
|
||||
|
||||
ks_pvalue = ks.test(df_lin$duet_scaled[df_lin$lineage == my_lin1]
|
||||
, df_lin$duet_scaled[df_lin$lineage == i])$p.value
|
||||
ks_statistic = ks.test(df_lin$avg_stability_scaled[df_lin$lineage == my_lin1]
|
||||
, df_lin$avg_stability_scaled[df_lin$lineage == i])$statistic
|
||||
|
||||
ks_pvalue = ks.test(df_lin$avg_stability_scaled[df_lin$lineage == my_lin1]
|
||||
, df_lin$avg_stability_scaled[df_lin$lineage == i])$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
|
||||
l1_df$comparison = lineage_comp
|
||||
l1_df$method = ks_method
|
||||
l1_df$ks_statistic = ks_statistic[[1]]
|
||||
l1_df$ks_pvalue = ks_pvalue
|
||||
l1_df$n_samples = n_samples_all
|
||||
l1_df$n_samples_total= n_samples_total
|
||||
|
||||
print(l1_df)
|
||||
ks_df_l1 = rbind(ks_df_l1,l1_df)
|
||||
|
|
@ -159,31 +147,43 @@ for (i in my_lineages_comp_l1){
|
|||
#=====================
|
||||
# Lineage 2 comparisons
|
||||
#=====================
|
||||
my_lin2 = "lineage2"
|
||||
#my_lineages_comp_l2 = c("lineage1", lineage3", "lineage4")
|
||||
my_lin2 = "L2"
|
||||
#my_lineages_comp_l2 = c("L1", lineage3", "L4")
|
||||
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)
|
||||
l2_df = data.frame(comparison = NA, method = NA, ks_statistic = NA, ks_pvalue = NA
|
||||
, n_samples = NA
|
||||
, n_samples_total = NA)
|
||||
|
||||
lineage_comp = paste0(my_lin2, " vs ", i)
|
||||
ks_method = ks.test(df_lin$duet_scaled[df_lin$lineage == my_lin2]
|
||||
, df_lin$duet_scaled[df_lin$lineage == i])$method
|
||||
n_samples_lin = nrow(df_lin[df_lin$lineage == my_lin2,])
|
||||
n_samples_i = nrow(df_lin[df_lin$lineage == i,])
|
||||
n_samples_all = paste0(my_lin2, "(", n_samples_lin, ")"
|
||||
, ", "
|
||||
, i, "(", n_samples_i, ")")
|
||||
|
||||
ks_statistic = ks.test(df_lin$duet_scaled[df_lin$lineage == my_lin2]
|
||||
, df_lin$duet_scaled[df_lin$lineage == i])$statistic
|
||||
n_samples_total = n_samples_lin + n_samples_i
|
||||
|
||||
ks_pvalue = ks.test(df_lin$duet_scaled[df_lin$lineage == my_lin2]
|
||||
, df_lin$duet_scaled[df_lin$lineage == i])$p.value
|
||||
ks_method = ks.test(df_lin$avg_stability_scaled[df_lin$lineage == my_lin2]
|
||||
, df_lin$avg_stability_scaled[df_lin$lineage == i])$method
|
||||
|
||||
ks_statistic = ks.test(df_lin$avg_stability_scaled[df_lin$lineage == my_lin2]
|
||||
, df_lin$avg_stability_scaled[df_lin$lineage == i])$statistic
|
||||
|
||||
ks_pvalue = ks.test(df_lin$avg_stability_scaled[df_lin$lineage == my_lin2]
|
||||
, df_lin$avg_stability_scaled[df_lin$lineage == i])$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
|
||||
l2_df$comparison = lineage_comp
|
||||
l2_df$method = ks_method
|
||||
l2_df$ks_statistic = ks_statistic[[1]]
|
||||
l2_df$ks_pvalue = ks_pvalue
|
||||
l2_df$n_samples = n_samples_all
|
||||
l2_df$n_samples_total = n_samples_total
|
||||
|
||||
print(l2_df)
|
||||
ks_df_l2 = rbind(ks_df_l2, l2_df)
|
||||
|
|
@ -193,38 +193,52 @@ for (i in my_lineages_comp_l2){
|
|||
#=====================
|
||||
# Lineage 3 comparisons
|
||||
#=====================
|
||||
my_lin3 = "lineage3"
|
||||
#my_lineages_comp_l3 = c("lineage1", lineage2", "lineage4")
|
||||
my_lin3 = "L3"
|
||||
#my_lineages_comp_l3 = c("L1", lineage2", "L4")
|
||||
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)
|
||||
l3_df = data.frame(comparison = NA, method = NA, ks_statistic = NA, ks_pvalue = NA
|
||||
, n_samples = NA
|
||||
, n_samples_total = NA)
|
||||
|
||||
lineage_comp = paste0(my_lin3, " vs ", i)
|
||||
ks_method = ks.test(df_lin$duet_scaled[df_lin$lineage == my_lin3]
|
||||
, df_lin$duet_scaled[df_lin$lineage == i])$method
|
||||
n_samples_lin = nrow(df_lin[df_lin$lineage == my_lin3,])
|
||||
n_samples_i = nrow(df_lin[df_lin$lineage == i,])
|
||||
n_samples_all = paste0(my_lin3, "(", n_samples_lin, ")"
|
||||
, ", "
|
||||
, i, "(", n_samples_i, ")")
|
||||
n_samples_total = n_samples_lin + n_samples_i
|
||||
|
||||
ks_statistic = ks.test(df_lin$duet_scaled[df_lin$lineage == my_lin3]
|
||||
, df_lin$duet_scaled[df_lin$lineage == i])$statistic
|
||||
ks_method = ks.test(df_lin$avg_stability_scaled[df_lin$lineage == my_lin3]
|
||||
, df_lin$avg_stability_scaled[df_lin$lineage == i])$method
|
||||
|
||||
ks_pvalue = ks.test(df_lin$duet_scaled[df_lin$lineage == my_lin3]
|
||||
, df_lin$duet_scaled[df_lin$lineage == i])$p.value
|
||||
ks_statistic = ks.test(df_lin$avg_stability_scaled[df_lin$lineage == my_lin3]
|
||||
, df_lin$avg_stability_scaled[df_lin$lineage == i])$statistic
|
||||
|
||||
ks_pvalue = ks.test(df_lin$avg_stability_scaled[df_lin$lineage == my_lin3]
|
||||
, df_lin$avg_stability_scaled[df_lin$lineage == i])$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
|
||||
l3_df$comparison = lineage_comp
|
||||
l3_df$method = ks_method
|
||||
l3_df$ks_statistic = ks_statistic[[1]]
|
||||
l3_df$ks_pvalue = ks_pvalue
|
||||
l3_df$n_samples = n_samples_all
|
||||
l3_df$n_samples_total = n_samples_total
|
||||
|
||||
print(l3_df)
|
||||
ks_df_l3 = rbind(ks_df_l3, l3_df)
|
||||
|
||||
}
|
||||
######################################################################################
|
||||
|
||||
|
||||
####################################################################
|
||||
# combine all three ks_dfs
|
||||
# combine all 4 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)) ){
|
||||
|
|
@ -249,11 +263,11 @@ if ( all.equal(nrow(ks_df_l1), nrow(ks_df_l2), nrow(ks_df_l3)) &&
|
|||
, "\nCheck hardcoded value of n_dfs")
|
||||
}
|
||||
|
||||
#=======================================================================
|
||||
#--------------
|
||||
# formatting
|
||||
#=======================================================================
|
||||
#--------------
|
||||
# add total_n number
|
||||
ks_df_combined$total_samples_analysed = nrow(df_lin)
|
||||
#ks_df_combined$total_samples_analysed = nrow(df_lin)
|
||||
|
||||
# adding pvalue_signif
|
||||
ks_df_combined$pvalue_signif = ks_df_combined$ks_pvalue
|
||||
|
|
@ -268,13 +282,192 @@ ks_df_combined = dplyr::mutate(ks_df_combined
|
|||
, TRUE ~ 'ns'))
|
||||
|
||||
# Remove duplicates
|
||||
rows_to_remove = c("lineage2 vs lineage1", "lineage3 vs lineage1", "lineage3 vs lineage2")
|
||||
rows_to_remove = c("L2 vs L1", "L3 vs L1", "L3 vs L2")
|
||||
|
||||
ks_df_combined_f = ks_df_combined[-match(rows_to_remove, ks_df_combined$group),]
|
||||
ks_df_combined_f = ks_df_combined[-match(rows_to_remove, ks_df_combined$comparison),]
|
||||
#################################################################################
|
||||
#================================
|
||||
# R vs S distribution: Overall
|
||||
#=================================
|
||||
df_lin_R = df_lin[df_lin$sensitivity == "R",]
|
||||
df_lin_S = df_lin[df_lin$sensitivity == "S",]
|
||||
|
||||
#=======================================================================
|
||||
#******************
|
||||
# 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)
|
||||
overall_RS_df = data.frame(comparison = NA, method = NA, ks_statistic = NA, ks_pvalue = NA
|
||||
, n_samples = NA
|
||||
, n_samples_total = NA)
|
||||
|
||||
comp_type = "overall R vs S distributions"
|
||||
|
||||
n_samples_R = nrow(df_lin_R)
|
||||
n_samples_S = nrow(df_lin_S)
|
||||
n_samples_all = paste0("R(n=", n_samples_R, ")" , " ", "S(n=", n_samples_S, ")")
|
||||
n_samples_total = n_samples_R+n_samples_S
|
||||
|
||||
ks.test(df_lin_R$avg_stability_scaled
|
||||
, df_lin_S$avg_stability_scaled)
|
||||
|
||||
ks_method = ks.test(df_lin_R$avg_stability_scaled
|
||||
, df_lin_S$avg_stability_scaled)$method
|
||||
|
||||
ks_statistic = ks.test(df_lin_R$avg_stability_scaled
|
||||
, df_lin_S$avg_stability_scaled)$statistic
|
||||
|
||||
ks_pvalue = ks.test(df_lin_R$avg_stability_scaled
|
||||
, df_lin_S$avg_stability_scaled)$p.value
|
||||
|
||||
overall_RS_df$comparison = comp_type
|
||||
overall_RS_df$method = ks_method
|
||||
overall_RS_df$ks_statistic = ks_statistic
|
||||
overall_RS_df$ks_pvalue = ks_pvalue
|
||||
overall_RS_df$n_samples = n_samples_all
|
||||
overall_RS_df$n_samples_total= n_samples_total
|
||||
|
||||
#--------------
|
||||
# formatting
|
||||
#--------------
|
||||
overall_RS_df$pvalue_signif = overall_RS_df$ks_pvalue
|
||||
overall_RS_df = dplyr::mutate(overall_RS_df
|
||||
, pvalue_signif = case_when(pvalue_signif == 0.05 ~ "."
|
||||
, pvalue_signif <=0.0001 ~ '****'
|
||||
, pvalue_signif <=0.001 ~ '***'
|
||||
, pvalue_signif <=0.01 ~ '**'
|
||||
, pvalue_signif <0.05 ~ '*'
|
||||
, TRUE ~ 'ns'))
|
||||
|
||||
overall_RS_df
|
||||
#####################################################################
|
||||
if (all(colnames(ks_df_combined_f) == colnames(overall_RS_df))){
|
||||
|
||||
cat("\nPASS:combining KS test results")
|
||||
}else{
|
||||
stop("\nAbort: Cannot combine results for KS test")
|
||||
}
|
||||
|
||||
ks_df_combined_f2 = rbind(ks_df_combined_f, overall_RS_df)
|
||||
|
||||
# ADD extra cols
|
||||
ks_df_combined_f2$ks_comp_type = "between_lineages"
|
||||
ks_df_combined_f2$gene_name = tolower(gene)
|
||||
|
||||
# #==============================
|
||||
# # write output file: KS test
|
||||
# #===============================
|
||||
# Out_lineage_bwL = paste0(outdir_stats
|
||||
# , tolower(gene)
|
||||
# , "_ks_lineage_bw.csv")
|
||||
#
|
||||
# cat("Output of KS test bt lineage:", Out_lineage_bwL)
|
||||
# write.csv(ks_df_combined_f2, Out_lineage_bwL, row.names = FALSE)
|
||||
|
||||
###########################################################################
|
||||
#=======================
|
||||
# Within lineage R vs S: MANUAL
|
||||
#=======================
|
||||
lin1 = df_lin[df_lin$lineage == my_lin1,]
|
||||
ks.test(lin1$avg_stability_scaled[lin1$sensitivity == "R"]
|
||||
, lin1$avg_stability_scaled[lin1$sensitivity == "S"])
|
||||
|
||||
lin2 = df_lin[df_lin$lineage == my_lin2,]
|
||||
ks.test(lin2$avg_stability_scaled[lin2$sensitivity == "R"]
|
||||
, lin2$avg_stability_scaled[lin2$sensitivity == "S"])
|
||||
|
||||
|
||||
lin3 = df_lin[df_lin$lineage == "L3",]
|
||||
ks.test(lin3$avg_stability_scaled[lin3$sensitivity == "R"]
|
||||
, lin3$avg_stability_scaled[lin3$sensitivity == "S"])
|
||||
|
||||
lin4 = df_lin[df_lin$lineage == "L4",]
|
||||
ks.test(lin4$avg_stability_scaled[lin4$sensitivity == "R"]
|
||||
, lin4$avg_stability_scaled[lin4$sensitivity == "S"])
|
||||
###################################################################
|
||||
#=======================
|
||||
# Within lineage R vs S: LOOP
|
||||
#=======================
|
||||
all_within_lin_df = data.frame()
|
||||
|
||||
for (i in c(my_lin1
|
||||
, my_lin2
|
||||
, my_lin3
|
||||
)){
|
||||
#cat(i)
|
||||
|
||||
within_lin_df = data.frame(comparison = NA, method = NA, ks_statistic = NA, ks_pvalue = NA
|
||||
, n_samples = NA
|
||||
, n_samples_total = NA)
|
||||
|
||||
within_lineage_comp = paste0(i, ": R vs S")
|
||||
my_lin_df = df_lin[df_lin$lineage == i,]
|
||||
|
||||
lin_R_n = nrow(my_lin_df[my_lin_df$sensitivity == "R",])
|
||||
lin_S_n = nrow(my_lin_df[my_lin_df$sensitivity == "S",])
|
||||
lin_total_n = lin_R_n+lin_S_n
|
||||
n_samples_all = paste0("R(n=", lin_R_n, ")" , ", ", "S(n=", lin_S_n, ")")
|
||||
|
||||
ks_method = ks.test(my_lin_df$avg_stability_scaled[my_lin_df$sensitivity == "R"]
|
||||
, my_lin_df$avg_stability_scaled[my_lin_df$sensitivity == "S"])$method
|
||||
|
||||
ks_statistic = ks.test(my_lin_df$avg_stability_scaled[my_lin_df$sensitivity == "R"]
|
||||
, my_lin_df$avg_stability_scaled[my_lin_df$sensitivity == "S"])$statistic
|
||||
|
||||
ks_pvalue =ks.test(my_lin_df$avg_stability_scaled[my_lin_df$sensitivity == "R"]
|
||||
, my_lin_df$avg_stability_scaled[my_lin_df$sensitivity == "S"])$p.value
|
||||
|
||||
# print(c(lineage_comp, ks_method, ks_statistic[[1]], ks_pval))
|
||||
within_lin_df$comparison = within_lineage_comp
|
||||
within_lin_df$method = ks_method
|
||||
within_lin_df$ks_statistic = ks_statistic[[1]]
|
||||
within_lin_df$ks_pvalue = ks_pvalue
|
||||
within_lin_df$n_samples = n_samples_all
|
||||
within_lin_df$n_samples_total=lin_total_n
|
||||
|
||||
print(my_lin_df)
|
||||
all_within_lin_df = rbind(all_within_lin_df, within_lin_df)
|
||||
|
||||
}
|
||||
all_within_lin_df
|
||||
|
||||
all_within_lin_df$pvalue_signif = all_within_lin_df$ks_pvalue
|
||||
str(all_within_lin_df$pvalue_signif)
|
||||
|
||||
all_within_lin_df = dplyr::mutate(all_within_lin_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'))
|
||||
|
||||
all_within_lin_df
|
||||
|
||||
# ADD extra cols
|
||||
all_within_lin_df$ks_comp_type = "within_lineages"
|
||||
all_within_lin_df$gene_name = tolower(gene)
|
||||
|
||||
# #--------------------
|
||||
# # write output file: KS test within grpup
|
||||
# #----------------------
|
||||
# Out_ks_withinL = paste0(outdir_stats
|
||||
# , tolower(gene)
|
||||
# , "_ks_lineage_within.csv")
|
||||
# cat("Output of KS test within lineage:",Out_ks_withinL )
|
||||
# write.csv(all_within_lin_df, Out_ks_withinL, row.names = FALSE)
|
||||
|
||||
##################################################################
|
||||
|
||||
if (all(colnames(ks_df_combined_f2) == colnames(Out_ks_withinL))){
|
||||
|
||||
cat("\nPASS:combining KS test results")
|
||||
}else{
|
||||
stop("\nAbort: Cannot combine results for KS test")
|
||||
}
|
||||
|
||||
ks_df_combined_all = rbind(ks_df_combined_f2, all_within_lin_df)
|
||||
|
||||
#--------------------
|
||||
# write output file: KS test within grpup
|
||||
#----------------------
|
||||
Out_ks_all = paste0(outdir_stats
|
||||
, tolower(gene)
|
||||
, "_ks_lineage_all_comp.csv")
|
||||
cat("Output of KS test all comparisons:", Out_ks_all )
|
||||
write.csv(ks_df_combined_all, Out_ks_all, row.names = FALSE)
|
||||
|
|
@ -321,14 +321,13 @@ svg('/tmp/foo.svg', width=10, height=10, )
|
|||
|
||||
corr_plotting_df = corr_df_ps
|
||||
|
||||
ggpairs(corr_plotting_df, columns = 1:(ncol(corr_plotting_df)-corr_end)
|
||||
ggpairs(corr_plotting_df, columns = 1:(ncol(corr_plotting_df)-1)
|
||||
, upper = list(continuous = wrap('cor', method = "spearman"))
|
||||
, aes(colour = factor(dst_mode), alpha = 0.5)
|
||||
, title="correlogram with ggpairs()") +
|
||||
scale_colour_manual(values = c("red", "blue")) +
|
||||
scale_fill_manual(values = c("red", "blue"))
|
||||
|
||||
|
||||
dev.off()
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -0,0 +1,11 @@
|
|||
"comparison","method","ks_statistic","ks_pvalue","n_samples","n_samples_total","pvalue_signif","ks_comp_type","gene_name"
|
||||
"L1 vs L2","Two-sample Kolmogorov-Smirnov test",0.562081297123684,0,"L1(4013), L2(3554)",7567,"****","between_lineages","embb"
|
||||
"L1 vs L3","Two-sample Kolmogorov-Smirnov test",0.521960420540757,0,"L1(4013), L3(692)",4705,"****","between_lineages","embb"
|
||||
"L1 vs L4","Two-sample Kolmogorov-Smirnov test",0.414409376879199,0,"L1(4013), L4(5276)",9289,"****","between_lineages","embb"
|
||||
"L2 vs L3","Two-sample Kolmogorov-Smirnov test",0.191637851675715,0,"L2(3554), L3(692)",4246,"****","between_lineages","embb"
|
||||
"L2 vs L4","Two-sample Kolmogorov-Smirnov test",0.177978192411417,0,"L2(3554), L4(5276)",8830,"****","between_lineages","embb"
|
||||
"L3 vs L4","Two-sample Kolmogorov-Smirnov test",0.110182657206589,7.08096017931759e-07,"L3(692), L4(5276)",5968,"****","between_lineages","embb"
|
||||
"overall R vs S distributions","Two-sample Kolmogorov-Smirnov test",0.549577339308325,0,"R(n=6878) S(n=6657)",13535,"****","between_lineages","embb"
|
||||
"L1: R vs S","Two-sample Kolmogorov-Smirnov test",0.654874747810162,0,"R(n=209), S(n=3804)",4013,"****","within_lineages","embb"
|
||||
"L2: R vs S","Two-sample Kolmogorov-Smirnov test",0.368060163680602,0,"R(n=3014), S(n=540)",3554,"****","within_lineages","embb"
|
||||
"L3: R vs S","Two-sample Kolmogorov-Smirnov test",0.473750449478605,0,"R(n=486), S(n=206)",692,"****","within_lineages","embb"
|
||||
|
Loading…
Add table
Add a link
Reference in a new issue