renamed count_vars_ML previous version as such
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14e655eeeb
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a5d22540e1
9 changed files with 336 additions and 185 deletions
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@ -22,13 +22,12 @@ outfile_merged_df3 = paste0(outdir, '/', tolower(gene), '_merged_df3.csv')
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# Add acticve site indication
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###############################################
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merged_df2$active_site = as.integer(merged_df2$position %in% active_aa_pos)
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#merged_df2_comp$active_site = as.integer(merged_df2_comp$position %in% active_aa_pos)
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merged_df3$active_site = as.integer(merged_df3$position %in% active_aa_pos)
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#merged_df3_comp$active_site = as.integer(merged_df3_comp$position %in% active_aa_pos)
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# check
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cols_sel = c('mutationinformation', 'mutation_info_labels', 'dm_om_numeric', 'dst', 'dst_mode')
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cols_sel = c('mutationinformation', 'mutation_info_labels'
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#, 'dm_om_numeric'
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, 'dst', 'dst_mode')
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check_mdf2 = merged_df2[, cols_sel]
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check_mdf2T = table(check_mdf2$mutationinformation, check_mdf2$dst_mode)
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@ -42,8 +41,8 @@ dst_check = all((ft_mdf2[,1]==0)==(ft_mdf2[,2]!=0)); dst_check
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#=======================
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# CHECK: dst mode labels
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#=======================
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table(merged_df2$mutation_info_labels_orig)
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table(merged_df2$mutation_info_labels_v1)
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#table(merged_df2$mutation_info_labels_orig)
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#table(merged_df2$mutation_info_labels_v1)
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table(merged_df2$mutation_info_labels)
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dst_check1 = table(merged_df2$dst_mode)[1] == table(merged_df2$mutation_info_labels)[2]
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@ -75,184 +74,61 @@ gene
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gene_match
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nrow(merged_df3)
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###########################################
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#========================
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# CHECK: drtype: revised labels [Merged_df2]
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#=========================
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table(merged_df2$drtype) #orig
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table(merged_df2$drtype_mode)
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# mapping 2.1: numeric
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# drtype_map = {'XDR': 5
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# , 'Pre-XDR': 4
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# , 'MDR': 3
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# , 'Pre-MDR': 2
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# , 'Other': 1
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# , 'Sensitive': 0}
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# create a labels col that is mapped based on drtype_mode
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merged_df2$drtype_mode_labels = merged_df2$drtype_mode
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merged_df2$drtype_mode_labels = as.factor(merged_df2$drtype_mode)
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levels(merged_df2$drtype_mode_labels)
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levels(merged_df2$drtype_mode_labels) <- c('Sensitive', 'Other'
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, 'Pre-MDR', 'MDR'
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, 'Pre-XDR', 'XDR')
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levels(merged_df2$drtype_mode_labels)
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# check
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a1 = all(table(merged_df2$drtype_mode) == table(merged_df2$drtype_mode_labels))
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b1 = sum(table(merged_df2$drtype_mode_labels)) == nrow(merged_df2)
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if (all(a1 && b1)){
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cat("\nPASS: added drtype mode labels to merged_df2")
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}else{
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stop("FAIL: could not add drtype mode labels to merged_df2")
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##quit()
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}
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#################################################
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#=======================
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# CHECK: drtype: revised labels [merged_df3]
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#=======================
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table(merged_df3$drtype) #orig
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table(merged_df3$drtype_mode)
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# mapping 2.1: numeric
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# drtype_map = {'XDR': 5
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# , 'Pre-XDR': 4
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# , 'MDR': 3
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# , 'Pre-MDR': 2
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# , 'Other': 1
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# , 'Sensitive': 0}
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# create a labels col that is mapped based on drtype_mode
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merged_df3$drtype_mode_labels = merged_df3$drtype_mode
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merged_df3$drtype_mode_labels = as.factor(merged_df3$drtype_mode)
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levels(merged_df3$drtype_mode_labels)
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levels(merged_df3$drtype_mode_labels) <- c('Sensitive', 'Other'
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, 'Pre-MDR', 'MDR'
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, 'Pre-XDR', 'XDR')
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levels(merged_df3$drtype_mode_labels)
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a2 = all(table(merged_df3$drtype_mode) == table(merged_df3$drtype_mode_labels))
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b2 = sum(table(merged_df3$drtype_mode_labels)) == nrow(merged_df3)
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# check
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if (all(a2 && b2)){
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cat("\nPASS: added drtype mode labels to merged_df3")
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}else{
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stop("FAIL: could not add drtype mode labels to merged_df3")
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##quit()
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}
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#===============
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# CHECK: lineage
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#===============
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l1 = table(merged_df3$lineage) == table(merged_df3$lineage_labels)
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l2 = table(merged_df2$lineage) == table(merged_df2$lineage_labels)
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l3 = sum(table(merged_df2$lineage_labels)) == nrow(merged_df2)
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l4 = sum(table(merged_df3$lineage_labels)) == nrow(merged_df3)
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if (all(l1 && l2 && l3 && l4) ){
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cat("\nPASS: lineage and lineage labels are identical!")
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}else{
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stop("FAIL: could not verify lineage labels")
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##quit()
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}
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###############################################
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# #=============
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# # mutation_info: revised labels
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# #==============
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# table(merged_df3$mutation_info)
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# sum(table(merged_df3$mutation_info))
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# table(merged_df3$mutation_info_orig)
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##############################################
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# #=============
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# # <drug>, dst_mode: revised labels
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# #==============
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# table(merged_df3$dst) # orig
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# sum(table(merged_df3$dst))
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#
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# table(merged_df3$dst_mode)
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# #table(merged_df3[dr_muts_col])
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# sum(table(merged_df3$drtype_mode))
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##############################################
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if ( all( check12 && aa_check1 && aa_check2 && a1 && b1 && a2 && b2 && l1 && l2 && l3 && l4) ){
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cat("\nWriting merged_dfs for:"
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, "\nDrug:", drug
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, "\nGene:", gene)
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write.csv(merged_df3, outfile_merged_df3)
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#write.csv(merged_df2, outfile_merged_df2)
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cat(paste("\nmerged df3 filename:", outfile_merged_df3
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write.csv(merged_df3, outfile_merged_df3)
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#write.csv(merged_df2, outfile_merged_df2)
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cat(paste("\nmerged df3 filename:", outfile_merged_df3
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#, "\nmerged df2 filename:", outfile_merged_df2)
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))
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} else{
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stop("FAIL: Not able to write merged dfs. Please check numbers!")
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#quit()
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}
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#%%###################################################################
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# check merged_df3
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check_mdf3 = merged_df3[, cols_sel]
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check_mdf3T = table(check_mdf3$mutationinformation, check_mdf3$dst_mode)
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ft_mdf3 = as.data.frame.matrix(check_mdf3T)
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#==================
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# CHECK: dst mode
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#===================
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dst_check_mdf3 = all((ft_mdf3[,1]==0)==(ft_mdf3[,2]!=0)); dst_check_mdf3
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sel = c("mutationinformation", "dst", "dst_mode")
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a = merged_df3[, sel]
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str(a)
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###################################################
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###################################################
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###################################################
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source("~/git/LSHTM_analysis/config/alr.R")
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source("~/git/LSHTM_analysis/config/embb.R")
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source("~/git/LSHTM_analysis/config/gid.R")
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source("~/git/LSHTM_analysis/config/katg.R")
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source("~/git/LSHTM_analysis/config/pnca.R")
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source("~/git/LSHTM_analysis/config/rpob.R")
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#
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# source("~/git/LSHTM_analysis/config/alr.R")
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# source("~/git/LSHTM_analysis/config/embb.R")
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# source("~/git/LSHTM_analysis/config/gid.R")
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# source("~/git/LSHTM_analysis/config/katg.R")
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# source("~/git/LSHTM_analysis/config/pnca.R")
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# source("~/git/LSHTM_analysis/config/rpob.R")
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# #
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df3_filename = paste0("/home/tanu/git/Data/", drug, "/output/", tolower(gene), "_merged_df3.csv")
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df3 = read.csv(df3_filename)
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#
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# #
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# mutationinformation
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length(unique((df3$mutationinformation)))
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# #
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# # #dm _om
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# table(df3$mutation_info)
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# #table(df3$mutation_info_orig)
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# #table(df3$mutation_info_labels_orig)
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#
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# #dm _om
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table(df3$mutation_info)
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table(df3$mutation_info_orig)
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table(df3$mutation_info_labels_orig)
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# used in plots and analyses
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table(df3$mutation_info_labels) # different, and matches dst_mode
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table(df3$dst_mode)
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# test_set
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na_count <-sapply(df3, function(y) sum(length(which(is.na(y)))))
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na_count[drug]
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# # used in plots and analyses
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# table(df3$mutation_info_labels) # different, and matches dst_mode
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# table(df3$dst_mode)
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#
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# # test_set
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# na_count <-sapply(df3, function(y) sum(length(which(is.na(y)))))
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# na_count[drug]
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# #
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# # # training set
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# table(df3[drug])
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# #
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# # # drtype: MDR and XDR
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# # #table(df3$drtype) orig i.e. incorrect ones!
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# # table(df3$drtype_mode_labels)
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#
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# # training set
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table(df3[drug])
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#
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# # drtype: MDR and XDR
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# #table(df3$drtype) orig i.e. incorrect ones!
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# table(df3$drtype_mode_labels)
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df3_complete = df3
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table(df3_complete$dst_mode)
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comp_lin_all = df3_complete[df3_complete$lineage_labels%in%c("L1", "L2", "L3", "L4"),]
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table(comp_lin_all$lineage); sum(table(comp_lin_all$lineage))
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df3_actual = df3[!is.na(df3$dst), ]
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table(df3_actual$dst_mode)
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comp_lin_actual = df3_actual[df3_actual$lineage_labels%in%c("L1", "L2", "L3", "L4"),]
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table(comp_lin_actual$lineage); sum(table(comp_lin_actual$lineage))
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# df3_complete = df3
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# table(df3_complete$dst_mode)
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# comp_lin_all = df3_complete[df3_complete$lineage_labels%in%c("L1", "L2", "L3", "L4"),]
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# table(comp_lin_all$lineage); sum(table(comp_lin_all$lineage))
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#
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# df3_actual = df3[!is.na(df3$dst), ]
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# table(df3_actual$dst_mode)
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# comp_lin_actual = df3_actual[df3_actual$lineage_labels%in%c("L1", "L2", "L3", "L4"),]
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# table(comp_lin_actual$lineage); sum(table(comp_lin_actual$lineage))
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#
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