added rpob ks test script
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scripts/plotting/plotting_thesis/rpob/rpob_ks_test_all_PS.R
Executable file
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scripts/plotting/plotting_thesis/rpob/rpob_ks_test_all_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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#!/usr/bin/env Rscript
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#source("~/git/LSHTM_analysis/config/rpob.R")
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# get plottting dfs
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#source("~/git/LSHTM_analysis/scripts/plotting/get_plotting_dfs.R")
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#=============
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# Output
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#=============
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outdir_images = paste0("~/git/Writing/thesis/images/results/", tolower(gene), "/")
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outdir_stats = paste0(outdir_images,"stats/")
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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 df2 or df2_comp
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# since this is one-many relationship
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###########################
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# REASSIGNMENT
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df2 = merged_df2[, colnames(merged_df2)%in%plotting_cols]
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# quick checks
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colnames(df2)
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str(df2)
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########################################################################
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table(df2$lineage); str(df2$lineage)
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# subset only lineages1-4
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sel_lineages = c("L1"
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, "L2"
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, "L3"
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, "L4")
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# subset selected lineages
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df_lin = subset(df2, subset = lineage %in% sel_lineages)
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table(df_lin$lineage)
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table(df_lin$sensitivity)
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table(df_lin$lineage, df_lin$sensitivity)
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#ensure lineage is a factor
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#str(df_lin$lineage); str(df_lin$lineage_labels)
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#df_lin$lineage = as.factor(df_lin$lineage)
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#df_lin$lineage_labels = as.factor(df_lin$lineage)
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table(df_lin$lineage); table(df_lin$lineage_labels)
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#==============================
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# Stats for average stability
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#=============================
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# individual: CHECKS
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lin1 = df_lin[df_lin$lineage == "L1",]$avg_stability_scaled
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lin2 = df_lin[df_lin$lineage == "L2",]$avg_stability_scaled
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lin3 = df_lin[df_lin$lineage == "L3",]$avg_stability_scaled
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lin4 = df_lin[df_lin$lineage == "L4",]$avg_stability_scaled
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ks.test(lin1, lin4)
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ks.test(df_lin$avg_stability_scaled[df_lin$lineage == "L1"]
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, df_lin$avg_stability_scaled[df_lin$lineage == "L4"])
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#=======================================================================
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my_lineages = levels(factor(df_lin$lineage)); my_lineages
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#=======================================================================
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# Loop
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#0 : < 2.2e-16
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#=====================
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# Lineage 1 comparisons
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#=====================
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my_lin1 = "L1"
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#my_lineages_comp_l1 = c("L2", "L3", "L4")
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my_lineages_comp_l1 = my_lineages[-match(my_lin1, my_lineages)]
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ks_df_l1 = data.frame()
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for (i in my_lineages_comp_l1){
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cat(i)
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l1_df = data.frame(comparison = NA, method = NA, ks_statistic = NA, ks_pvalue = NA
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, n_samples = NA
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, n_samples_total = NA)
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lineage_comp = paste0(my_lin1, " vs ", i)
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n_samples_lin = nrow(df_lin[df_lin$lineage == my_lin1,])
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n_samples_i = nrow(df_lin[df_lin$lineage == i,])
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n_samples_all = paste0(my_lin1, "(", n_samples_lin, ")"
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, ", "
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, i, "(", n_samples_i, ")")
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n_samples_total = n_samples_lin + n_samples_i
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ks_method = ks.test(df_lin$avg_stability_scaled[df_lin$lineage == my_lin1]
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, df_lin$avg_stability_scaled[df_lin$lineage == i])$method
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ks_statistic = ks.test(df_lin$avg_stability_scaled[df_lin$lineage == my_lin1]
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, df_lin$avg_stability_scaled[df_lin$lineage == i])$statistic
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ks_pvalue = ks.test(df_lin$avg_stability_scaled[df_lin$lineage == my_lin1]
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, df_lin$avg_stability_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$comparison = lineage_comp
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l1_df$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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l1_df$n_samples = n_samples_all
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l1_df$n_samples_total= n_samples_total
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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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ks_df_l1
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# adjusted p-value: bonferroni
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ks_df_l1$p_adj_bonferroni = p.adjust(ks_df_l1$ks_pvalue, method = "bonferroni")
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ks_df_l1$signif_bon = ks_df_l1$p_adj_bonferroni
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ks_df_l1 = dplyr::mutate(ks_df_l1
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, signif_bon = case_when(signif_bon == 0.05 ~ "."
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, signif_bon <=0.0001 ~ '****'
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, signif_bon <=0.001 ~ '***'
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, signif_bon <=0.01 ~ '**'
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, signif_bon <0.05 ~ '*'
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, TRUE ~ 'ns'))
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# adjusted p-value:fdr
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ks_df_l1$p_adj_fdr = p.adjust(ks_df_l1$ks_pvalue, method = "fdr")
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ks_df_l1$signif_fdr = ks_df_l1$p_adj_fdr
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ks_df_l1 = dplyr::mutate(ks_df_l1
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, signif_fdr = case_when(signif_fdr == 0.05 ~ "."
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, signif_fdr <=0.0001 ~ '****'
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, signif_fdr <=0.001 ~ '***'
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, signif_fdr <=0.01 ~ '**'
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, signif_fdr <0.05 ~ '*'
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, TRUE ~ 'ns'))
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ks_df_l1
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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 = "L2"
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#my_lineages_comp_l2 = c("L1", lineage3", "L4")
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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(comparison = NA, method = NA, ks_statistic = NA, ks_pvalue = NA
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, n_samples = NA
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, n_samples_total = NA)
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lineage_comp = paste0(my_lin2, " vs ", i)
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n_samples_lin = nrow(df_lin[df_lin$lineage == my_lin2,])
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n_samples_i = nrow(df_lin[df_lin$lineage == i,])
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n_samples_all = paste0(my_lin2, "(", n_samples_lin, ")"
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, ", "
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, i, "(", n_samples_i, ")")
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n_samples_total = n_samples_lin + n_samples_i
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ks_method = ks.test(df_lin$avg_stability_scaled[df_lin$lineage == my_lin2]
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, df_lin$avg_stability_scaled[df_lin$lineage == i])$method
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ks_statistic = ks.test(df_lin$avg_stability_scaled[df_lin$lineage == my_lin2]
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, df_lin$avg_stability_scaled[df_lin$lineage == i])$statistic
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ks_pvalue = ks.test(df_lin$avg_stability_scaled[df_lin$lineage == my_lin2]
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, df_lin$avg_stability_scaled[df_lin$lineage == i])$p.value
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# print(c(lineage_comp, ks_method, ks_statistic[[1]], ks_pval))
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l2_df$comparison = 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$ks_pvalue = ks_pvalue
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l2_df$n_samples = n_samples_all
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l2_df$n_samples_total = n_samples_total
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print(l2_df)
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ks_df_l2 = rbind(ks_df_l2, l2_df)
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}
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# adjusted p-value: bonferroni
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ks_df_l2$p_adj_bonferroni = p.adjust(ks_df_l2$ks_pvalue, method = "bonferroni")
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ks_df_l2$signif_bon = ks_df_l2$p_adj_bonferroni
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ks_df_l2 = dplyr::mutate(ks_df_l2
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, signif_bon = case_when(signif_bon == 0.05 ~ "."
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, signif_bon <=0.0001 ~ '****'
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, signif_bon <=0.001 ~ '***'
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, signif_bon <=0.01 ~ '**'
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, signif_bon <0.05 ~ '*'
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, TRUE ~ 'ns'))
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# adjusted p-value:fdr
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ks_df_l2$p_adj_fdr = p.adjust(ks_df_l2$ks_pvalue, method = "fdr")
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ks_df_l2$signif_fdr = ks_df_l2$p_adj_fdr
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ks_df_l2 = dplyr::mutate(ks_df_l2
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, signif_fdr = case_when(signif_fdr == 0.05 ~ "."
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, signif_fdr <=0.0001 ~ '****'
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, signif_fdr <=0.001 ~ '***'
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, signif_fdr <=0.01 ~ '**'
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, signif_fdr <0.05 ~ '*'
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, TRUE ~ 'ns'))
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ks_df_l2
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#=====================
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# Lineage 3 comparisons
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#=====================
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my_lin3 = "L3"
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#my_lineages_comp_l3 = c("L1", lineage2", "L4")
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my_lineages_comp_l3 = my_lineages[-match(my_lin3, my_lineages)]
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ks_df_l3 = data.frame()
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for (i in my_lineages_comp_l3){
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cat(i)
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l3_df = data.frame(comparison = NA, method = NA, ks_statistic = NA, ks_pvalue = NA
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, n_samples = NA
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, n_samples_total = NA)
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lineage_comp = paste0(my_lin3, " vs ", i)
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n_samples_lin = nrow(df_lin[df_lin$lineage == my_lin3,])
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n_samples_i = nrow(df_lin[df_lin$lineage == i,])
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n_samples_all = paste0(my_lin3, "(", n_samples_lin, ")"
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, ", "
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, i, "(", n_samples_i, ")")
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n_samples_total = n_samples_lin + n_samples_i
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ks_method = ks.test(df_lin$avg_stability_scaled[df_lin$lineage == my_lin3]
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, df_lin$avg_stability_scaled[df_lin$lineage == i])$method
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ks_statistic = ks.test(df_lin$avg_stability_scaled[df_lin$lineage == my_lin3]
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, df_lin$avg_stability_scaled[df_lin$lineage == i])$statistic
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ks_pvalue = ks.test(df_lin$avg_stability_scaled[df_lin$lineage == my_lin3]
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, df_lin$avg_stability_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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l3_df$comparison = lineage_comp
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l3_df$method = ks_method
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l3_df$ks_statistic = ks_statistic[[1]]
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l3_df$ks_pvalue = ks_pvalue
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l3_df$n_samples = n_samples_all
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l3_df$n_samples_total = n_samples_total
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print(l3_df)
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ks_df_l3 = rbind(ks_df_l3, l3_df)
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}
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# adjusted p-value: bonferroni
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ks_df_l3$p_adj_bonferroni = p.adjust(ks_df_l3$ks_pvalue, method = "bonferroni")
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ks_df_l3$signif_bon = ks_df_l3$p_adj_bonferroni
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ks_df_l3 = dplyr::mutate(ks_df_l3
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, signif_bon = case_when(signif_bon == 0.05 ~ "."
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, signif_bon <=0.0001 ~ '****'
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, signif_bon <=0.001 ~ '***'
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, signif_bon <=0.01 ~ '**'
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, signif_bon <0.05 ~ '*'
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, TRUE ~ 'ns'))
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# adjusted p-value:fdr
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ks_df_l3$p_adj_fdr = p.adjust(ks_df_l3$ks_pvalue, method = "fdr")
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ks_df_l3$signif_fdr = ks_df_l3$p_adj_fdr
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ks_df_l3 = dplyr::mutate(ks_df_l3
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, signif_fdr = case_when(signif_fdr == 0.05 ~ "."
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, signif_fdr <=0.0001 ~ '****'
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, signif_fdr <=0.001 ~ '***'
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, signif_fdr <=0.01 ~ '**'
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, signif_fdr <0.05 ~ '*'
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, TRUE ~ 'ns'))
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ks_df_l3
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####################################################################
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# combine all 3 ks_dfs
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n_dfs = 3
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if ( all.equal(nrow(ks_df_l1), nrow(ks_df_l2), nrow(ks_df_l3)) &&
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all.equal(ncol(ks_df_l1), ncol(ks_df_l2), ncol(ks_df_l3)) ){
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cat("\nPASS: Calculating expected rows and cols for sanity checks on combined_dfs")
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expected_rows = nrow(ks_df_l1) * n_dfs
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expected_cols = ncol(ks_df_l1)
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ks_df_combined = rbind(ks_df_l1, ks_df_l2, ks_df_l3)
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if ( nrow(ks_df_combined) == expected_rows && ncol(ks_df_combined) == expected_cols ){
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cat("\nPASS: combined df successfully created"
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, "\nnrow combined_df:", nrow(ks_df_combined)
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, "\nncol combined_df:", ncol(ks_df_combined))
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}
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else{
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cat("\nFAIL: Dim mismatch"
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, "\nExpected rows:", expected_rows
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, "\nGot:", nrow(ks_df_combined)
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, "\nExpected cols:", expected_cols
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, "\nGot:", ncol(ks_df_combined))
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}
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}else{
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cat("\nFAIL: Could not generate expected_rows and/or expected_cols"
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, "\nCheck hardcoded value of n_dfs")
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}
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#----------------------------
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# ADD extra cols: formatting
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#----------------------------
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# adding pvalue_signif
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ks_df_combined$pvalue_signif = ks_df_combined$ks_pvalue
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str(ks_df_combined$pvalue_signif)
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ks_df_combined = dplyr::mutate(ks_df_combined
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, pvalue_signif = case_when(pvalue_signif == 0.05 ~ "."
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, pvalue_signif <=0.0001 ~ '****'
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, pvalue_signif <=0.001 ~ '***'
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, pvalue_signif <=0.01 ~ '**'
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, pvalue_signif <0.05 ~ '*'
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, TRUE ~ 'ns'))
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# Remove duplicates
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rows_to_remove = c("L2 vs L1", "L3 vs L1", "L3 vs L2")
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ks_df_combined_f = ks_df_combined[-match(rows_to_remove, ks_df_combined$comparison),]
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#################################################################################
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#================================
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# R vs S distribution: Overall
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#=================================
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df_lin_R = df_lin[df_lin$sensitivity == "R",]
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df_lin_S = df_lin[df_lin$sensitivity == "S",]
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overall_RS_df = data.frame(comparison = NA, method = NA, ks_statistic = NA, ks_pvalue = NA
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, n_samples = NA
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, n_samples_total = NA)
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comp_type = "overall R vs S distributions"
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n_samples_R = nrow(df_lin_R)
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n_samples_S = nrow(df_lin_S)
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n_samples_all = paste0("R(n=", n_samples_R, ")" , " ", "S(n=", n_samples_S, ")")
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n_samples_total = n_samples_R+n_samples_S
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ks.test(df_lin_R$avg_stability_scaled
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, df_lin_S$avg_stability_scaled)
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ks_method = ks.test(df_lin_R$avg_stability_scaled
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, df_lin_S$avg_stability_scaled)$method
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ks_statistic = ks.test(df_lin_R$avg_stability_scaled
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, df_lin_S$avg_stability_scaled)$statistic
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ks_pvalue = ks.test(df_lin_R$avg_stability_scaled
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, df_lin_S$avg_stability_scaled)$p.value
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overall_RS_df$comparison = comp_type
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overall_RS_df$method = ks_method
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overall_RS_df$ks_statistic = ks_statistic
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overall_RS_df$ks_pvalue = ks_pvalue
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overall_RS_df$n_samples = n_samples_all
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overall_RS_df$n_samples_total= n_samples_total
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#----------------------------
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# ADD extra cols
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#----------------------------
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# adjusted p-value: bonferroni
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overall_RS_df$p_adj_bonferroni = p.adjust(overall_RS_df$ks_pvalue, method = "bonferroni")
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overall_RS_df$signif_bon = overall_RS_df$p_adj_bonferroni
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overall_RS_df = dplyr::mutate(overall_RS_df
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, signif_bon = case_when(signif_bon == 0.05 ~ "."
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, signif_bon <=0.0001 ~ '****'
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, signif_bon <=0.001 ~ '***'
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, signif_bon <=0.01 ~ '**'
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, signif_bon <0.05 ~ '*'
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, TRUE ~ 'ns'))
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# adjusted p-value:fdr
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overall_RS_df$p_adj_fdr = p.adjust(overall_RS_df$ks_pvalue, method = "fdr")
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overall_RS_df$signif_fdr = overall_RS_df$p_adj_fdr
|
||||
overall_RS_df = dplyr::mutate(overall_RS_df
|
||||
, signif_fdr = case_when(signif_fdr == 0.05 ~ "."
|
||||
, signif_fdr <=0.0001 ~ '****'
|
||||
, signif_fdr <=0.001 ~ '***'
|
||||
, signif_fdr <=0.01 ~ '**'
|
||||
, signif_fdr <0.05 ~ '*'
|
||||
, TRUE ~ 'ns'))
|
||||
# unadjusted p-values
|
||||
overall_RS_df$pvalue_signif = overall_RS_df$ks_pvalue
|
||||
overall_RS_df = dplyr::mutate(overall_RS_df
|
||||
, pvalue_signif = case_when(pvalue_signif == 0.05 ~ "."
|
||||
, pvalue_signif <=0.0001 ~ '****'
|
||||
, pvalue_signif <=0.001 ~ '***'
|
||||
, pvalue_signif <=0.01 ~ '**'
|
||||
, pvalue_signif <0.05 ~ '*'
|
||||
, TRUE ~ 'ns'))
|
||||
#####################################################################
|
||||
if (all(colnames(ks_df_combined_f) == colnames(overall_RS_df))){
|
||||
|
||||
cat("\nPASS:combining KS test results")
|
||||
}else{
|
||||
stop("\nAbort: Cannot combine results for KS test")
|
||||
}
|
||||
|
||||
ks_df_combined_f2 = rbind(ks_df_combined_f, overall_RS_df)
|
||||
|
||||
# ADD extra cols
|
||||
ks_df_combined_f2$ks_comp_type = "between_lineages"
|
||||
ks_df_combined_f2$gene_name = tolower(gene)
|
||||
|
||||
ks_df_combined_f2
|
||||
|
||||
###########################################################################
|
||||
#=================================
|
||||
# Within lineage R vs S: MANUAL
|
||||
#=================================
|
||||
lin1 = df_lin[df_lin$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$n_samples_total=lin_total_n
|
||||
|
||||
print(my_lin_df)
|
||||
all_within_lin_df = rbind(all_within_lin_df, within_lin_df)
|
||||
|
||||
}
|
||||
all_within_lin_df
|
||||
|
||||
#----------------------------
|
||||
# ADD extra cols: formatting
|
||||
#----------------------------
|
||||
# adjusted p-value: bonferroni
|
||||
all_within_lin_df$p_adj_bonferroni = p.adjust(all_within_lin_df$ks_pvalue, method = "bonferroni")
|
||||
all_within_lin_df$signif_bon = all_within_lin_df$p_adj_bonferroni
|
||||
all_within_lin_df = dplyr::mutate(all_within_lin_df
|
||||
, signif_bon = case_when(signif_bon == 0.05 ~ "."
|
||||
, signif_bon <=0.0001 ~ '****'
|
||||
, signif_bon <=0.001 ~ '***'
|
||||
, signif_bon <=0.01 ~ '**'
|
||||
, signif_bon <0.05 ~ '*'
|
||||
, TRUE ~ 'ns'))
|
||||
|
||||
# adjusted p-value:fdr
|
||||
all_within_lin_df$p_adj_fdr = p.adjust(all_within_lin_df$ks_pvalue, method = "fdr")
|
||||
all_within_lin_df$signif_fdr = all_within_lin_df$p_adj_fdr
|
||||
all_within_lin_df = dplyr::mutate(all_within_lin_df
|
||||
, signif_fdr = case_when(signif_fdr == 0.05 ~ "."
|
||||
, signif_fdr <=0.0001 ~ '****'
|
||||
, signif_fdr <=0.001 ~ '***'
|
||||
, signif_fdr <=0.01 ~ '**'
|
||||
, signif_fdr <0.05 ~ '*'
|
||||
, TRUE ~ 'ns'))
|
||||
|
||||
# unadjusted p-value
|
||||
all_within_lin_df$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'))
|
||||
|
||||
# ADD info cols
|
||||
all_within_lin_df$ks_comp_type = "within_lineages"
|
||||
all_within_lin_df$gene_name = tolower(gene)
|
||||
|
||||
##################################################################
|
||||
|
||||
if (all(colnames(ks_df_combined_f2) == colnames(all_within_lin_df))){
|
||||
|
||||
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)
|
||||
Loading…
Add table
Add a link
Reference in a new issue