reran to generate merged_df3 with correct dst for dst muts. modified combining_dfs_plotting.R
This commit is contained in:
parent
289c8913d0
commit
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6 changed files with 148 additions and 211 deletions
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@ -4,7 +4,7 @@
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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/pnca.R")
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source("~/git/LSHTM_analysis/config/rpob.R")
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#############################
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@ -16,19 +16,19 @@ source("~/git/LSHTM_analysis/scripts/plotting/get_plotting_dfs.R")
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# Output files: merged data
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#############################
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outfile_merged_df3 = paste0(outdir, '/', tolower(gene), '_merged_df3.csv')
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outfile_merged_df2 = paste0(outdir, '/', tolower(gene), '_merged_df2.csv')
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#outfile_merged_df2 = paste0(outdir, '/', tolower(gene), '_merged_df2.csv')
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################################################
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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_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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#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', 'dst', 'mutation_info_labels', 'dm_om_numeric', 'dst_mode')
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cols_sel = c('mutationinformation', 'mutation_info_labels', 'dm_om_numeric', '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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@ -57,6 +57,8 @@ if (check12) {
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stop('FAIL: Something is wrong with the dst_mode column. Quitting!')
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}
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table(is.na(merged_df3$dst))
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#==========================
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# CHECK: active site labels
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#==========================
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@ -137,7 +139,6 @@ if (all(a2 && b2)){
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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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#===============
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# CHECK: lineage
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#===============
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@ -179,10 +180,11 @@ if ( all( check12 && aa_check1 && aa_check2 && a1 && b1 && a2 && b2 && l1 && l2
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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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#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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#, "\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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@ -207,15 +209,6 @@ a = merged_df3[, sel]
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str(a)
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# write file
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# outfile_merged_df3 = paste0(outdir, '/', tolower(gene), '_merged_df3.csv')
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# outfile_merged_df3
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# write.csv(merged_df3, outfile_merged_df3)
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#
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# outfile_merged_df2 = paste0(outdir, '/', tolower(gene), '_merged_df2.csv')
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# outfile_merged_df2
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# write.csv(merged_df2, outfile_merged_df2)
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###################################################
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###################################################
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###################################################
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@ -204,6 +204,9 @@ combining_dfs_plotting <- function( my_df_u
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merged_df2$lineage_labels = merged_df2$lineage
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#merged_df2$lineage_labels = as.factor(merged_df2$lineage_labels)
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#merged_df2$lineage_labels = factor(merged_df2$lineage_labels)
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table(merged_df2$mutation_info_labels_orig) # original
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table(merged_df2$mutation_info_labels_v1) # intermediate
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table(merged_df2$mutation_info_labels) # revised, corresponding to dst_mode
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#=================================================================
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# Merge 2: merged_df3
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@ -214,17 +217,123 @@ combining_dfs_plotting <- function( my_df_u
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# but this should be good for the numerical corr plots
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#==================================================================
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# remove duplicated mutations
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cat("\nMerging dfs without NAs: small df (removing muts with no AF|OR associated)"
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,"\nCannot trust lineage info from this"
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,"\nlinking col: mutationinforamtion"
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,"\nfilename: merged_df3")
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# cat("\nMerging dfs without NAs: small df (removing muts with no AF|OR associated)"
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# ,"\nCannot trust lineage info from this"
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# ,"\nlinking col: mutationinforamtion"
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# ,"\nfilename: merged_df3")
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#
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# merged_df3 = merged_df2[!duplicated(merged_df2$mutationinformation),]
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#
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#
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# head(merged_df3$position); tail(merged_df3$position) # should be sorted
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#
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# # sanity check
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# cat("\nChecking nrows in merged_df3")
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#
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# if( nrow(my_df_u) == nrow(merged_df3) ){
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# cat("\nPASS: No. of rows match with my_df"
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# ,"\nExpected no. of rows: ", nrow(my_df_u)
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# ,"\nGot no. of rows: ", nrow(merged_df3))
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# } else {
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# cat("\nFAIL: No. of rows mismatch"
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# , "\nNo. of rows my_df: ", nrow(my_df_u)
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# , "\nNo. of rows merged_df3: ", nrow(merged_df3))
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# quit()
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# }
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#
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# counting NAs in AF, OR cols in merged_df3
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# this is because mcsm has no AF, OR cols,
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# so you cannot count NAs
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# if (identical(sum(is.na(merged_df3$or_kin))
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# , sum(is.na(merged_df3$pwald_kin))
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# , sum(is.na(merged_df3$af_kin)))){
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# cat("\nPASS: NA count match for OR, pvalue and AF\n")
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# na_count_df3 = sum(is.na(merged_df3$af_kin))
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# cat("\nNo. of NAs: ", sum(is.na(merged_df3$or_kin)))
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# } else{
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# cat("\nFAIL: NA count mismatch"
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# , "\nNA in OR: ", sum(is.na(merged_df3$or_kin))
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# , "\nNA in pvalue: ", sum(is.na(merged_df3$pwald_kin))
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# , "\nNA in AF:", sum(is.na(merged_df3$af_kin)))
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# }
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#
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# ===================================
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# Revised way to generate merged_df3
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# ===================================
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#%% Getting merged_df3: VERY important and careful subsetting merging
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# dst mode column as carefully curated dst based on knowledge based approach.
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# so now we want to get the
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na_muts = merged_df2[is.na(merged_df2$dst),]
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no_na_muts = merged_df2[!is.na(merged_df2$dst),]
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muts_na_U = na_muts[!duplicated(na_muts[c('mutationinformation')]), ]
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muts_no_na_U = no_na_muts[!duplicated(no_na_muts[c('mutationinformation')]), ]
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# get muts from no_na that are NOT present in muts with na from dplyr
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dst_muts = dplyr::anti_join(muts_no_na_U, muts_na_U, by = 'mutationinformation')
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#dst_muts = anti_join(muts_no_na_U, muts_na_U, by = 'mutationinformation')
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# ALL good muts are NOT in na muts unique i.e dst muts should NOT exist in na_muts
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if (all(dst_muts$mutationinformation%in%muts_na_U$mutationinformation) == FALSE){
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cat("\nPASS: checked length for dst tested muts"
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, "\nNo. of dst testetd muts:", nrow(dst_muts))
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}else{
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stop("Dst muts are not correctly identified")
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}
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if ( class(dst_muts) != "data.frame" ){
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dst_muts = as.data.frame(dst_muts)
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} else{
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cat("\ndst_muts is a df")
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}
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# ALL bad muts are in na muts unique
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bad_muts = dplyr::semi_join(muts_no_na_U, muts_na_U, by = "mutationinformation")
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#bad_muts = semi_join(muts_no_na_U, muts_na_U, by = "mutationinformation")
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if (all(bad_muts$mutationinformation%in%muts_na_U$mutationinformation) == TRUE){
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cat("\nPASS: checked length of NOT-dst tested muts"
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, "\nNo. of NOT dst-tested_muts:", nrow(bad_muts))
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}else{
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stop("Non-dst muts are not correctly identified")
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}
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if ( class(bad_muts) != "data.frame" ){
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bad_muts = as.data.frame(bad_muts)
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} else{
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cat("\nbad_muts is a df")
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}
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cat("\nNo. of muts with dst:", nrow(dst_muts)
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, "\nNo. of muts without dst:", nrow(muts_na_U) - nrow(dst_muts) )
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# now merge
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if ( all(colnames(muts_na_U) == colnames(dst_muts)) ){
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cat("\nPASS: rowbind to get merged_df3")
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merged_df3 = dplyr::bind_rows(muts_na_U, dst_muts)
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#merged_df3 = bind_rows(muts_na_U, dst_muts)
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} else{
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stop("Quitting: merged_df3 could not be generated")
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}
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if ( nrow(merged_df3) == length(unique(merged_df2$mutationinformation)) ){
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cat("\nPASS: merged_df3 sucessfully generated..."
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, "\nnrow merged_df3:", nrow(merged_df3)
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, "\nncol merged_df3:", ncol(merged_df3))
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}else{
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stop("Cannot generate merged_df3")
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}
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##################################################################
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merged_df3 = merged_df2[!duplicated(merged_df2$mutationinformation),]
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head(merged_df3$position); tail(merged_df3$position) # should be sorted
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# sanity check
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cat("\nChecking nrows in merged_df3")
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if(nrow(my_df_u) == nrow(merged_df3)){
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if( nrow(my_df_u) == nrow(merged_df3) ){
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cat("\nPASS: No. of rows match with my_df"
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,"\nExpected no. of rows: ", nrow(my_df_u)
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,"\nGot no. of rows: ", nrow(merged_df3))
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@ -234,166 +343,9 @@ combining_dfs_plotting <- function( my_df_u
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, "\nNo. of rows merged_df3: ", nrow(merged_df3))
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quit()
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}
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# counting NAs in AF, OR cols in merged_df3
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# this is because mcsm has no AF, OR cols,
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# so you cannot count NAs
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if (identical(sum(is.na(merged_df3$or_kin))
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, sum(is.na(merged_df3$pwald_kin))
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, sum(is.na(merged_df3$af_kin)))){
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cat("\nPASS: NA count match for OR, pvalue and AF\n")
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na_count_df3 = sum(is.na(merged_df3$af_kin))
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cat("\nNo. of NAs: ", sum(is.na(merged_df3$or_kin)))
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} else{
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cat("\nFAIL: NA count mismatch"
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, "\nNA in OR: ", sum(is.na(merged_df3$or_kin))
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, "\nNA in pvalue: ", sum(is.na(merged_df3$pwald_kin))
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, "\nNA in AF:", sum(is.na(merged_df3$af_kin)))
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}
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#===================================================
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# Merge3: merged_df2_comp
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# same as merge 1 but excluding NAs from ORs, etc.
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#====================================================
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cat("\nMerging dfs without any NAs: big df (1-many relationship b/w id & mut)"
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,"\nfilename: merged_df2_comp")
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na_count_df2 = sum(is.na(merged_df2$af))
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merged_df2_comp = merged_df2[!is.na(merged_df2$af),]
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# sanity check: no +-1 gymnastics
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cat("\nChecking nrows in merged_df2_comp")
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if(nrow(merged_df2_comp) == (nrow(merged_df2) - na_count_df2)){
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cat("\nPASS: No. of rows match"
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,"\nDim of merged_df2_comp: "
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,"\nExpected no. of rows: ", nrow(merged_df2) - na_count_df2
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, "\nNo. of rows: ", nrow(merged_df2_comp)
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, "\nNo. of cols: ", ncol(merged_df2_comp))
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}else{
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cat("\nFAIL: No. of rows mismatch"
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,"\nExpected no. of rows: ", nrow(merged_df2) - na_count_df2
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,"\nGot no. of rows: ", nrow(merged_df2_comp))
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}
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#======================================================
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# Merge4: merged_df3_comp
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# same as merge 2 but excluding NAs from ORs, etc or
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# remove duplicate mutation information
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#=======================================================
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na_count_df3 = sum(is.na(merged_df3$af))
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#merged_df3_comp = merged_df3_comp[!duplicated(merged_df3_comp$mutationinformation),] # a way
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merged_df3_comp = merged_df3[!is.na(merged_df3$af),] # another way
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cat("\nChecking nrows in merged_df3_comp")
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if(nrow(merged_df3_comp) == (nrow(merged_df3) - na_count_df3)){
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cat("\nPASS: No. of rows match"
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,"\nDim of merged_df3_comp: "
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,"\nExpected no. of rows: ", nrow(merged_df3) - na_count_df3
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, "\nNo. of rows: ", nrow(merged_df3_comp)
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, "\nNo. of cols: ", ncol(merged_df3_comp))
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}else{
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cat("\nFAIL: No. of rows mismatch"
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,"\nExpected no. of rows: ", nrow(merged_df3) - na_count_df3
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,"\nGot no. of rows: ", nrow(merged_df3_comp))
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}
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# alternate way of deriving merged_df3_comp
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foo = merged_df3[!is.na(merged_df3$af),]
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bar = merged_df3_comp[!duplicated(merged_df3_comp$mutationinformation),]
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# compare dfs: foo and merged_df3_com
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all.equal(foo, bar)
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#summary(comparedf(foo, bar))
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cat("\n------------------------"
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, "\nSummary of created dfs:"
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, "\n------------------------"
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, "\n1) Dim of merged_df2: " , nrow(merged_df2), "," , ncol(merged_df2)
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, "\n2) Dim of merged_df2_comp: " , nrow(merged_df2_comp), "," , ncol(merged_df2_comp)
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, "\n3) Dim of merged_df3: " , nrow(merged_df3), "," , ncol(merged_df3)
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, "\n4) Dim of merged_df3_comp: " , nrow(merged_df3_comp), "," , ncol(merged_df3_comp))
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#####################################################################
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# Combining: LIG
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#####################################################################
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#============
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# Merges 5-8
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#============
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cat("\n=========================================="
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, "\nStarting filtering for mcsm ligand df"
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, "\n===========================================")
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if (lig_dist_colname%in%names(my_df_u)){
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cat("\nFiltering column: ", lig_dist_colname
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, "\nCut off criteria: ", lig_dist_cutoff, "Angstroms")
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df_lig = my_df_u[my_df_u[[lig_dist_colname]] < lig_dist_cutoff,]
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#merged_df2_lig = merged_df2[merged_df2$ligand_distance<lig_dist_cutoff,]
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merged_df2_lig = merged_df2[merged_df2[[lig_dist_colname]] < lig_dist_cutoff,]
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dim(merged_df2_lig)
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merged_df2_comp_lig = merged_df2_comp[merged_df2_comp[[lig_dist_colname]] < lig_dist_cutoff,]
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merged_df3_lig = merged_df3[merged_df3[[lig_dist_colname]] < lig_dist_cutoff,]
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merged_df3_comp_lig = merged_df3_comp[merged_df3_comp[[lig_dist_colname]] < lig_dist_cutoff,]
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cat("\n------------------------"
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, "\nSummary of created ligand dfs:"
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, "\n------------------------"
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, "\n1) Dim of merged_df2_lig: " , nrow(merged_df2_lig), "," , ncol(merged_df2_lig)
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, "\n2) Dim of merged_df2_comp_lig: " , nrow(merged_df2_comp_lig), "," , ncol(merged_df2_comp_lig)
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, "\n3) Dim of merged_df3_lig: " , nrow(merged_df3_lig), "," , ncol(merged_df3_lig)
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, "\n4) Dim of merged_df3_comp_lig: " , nrow(merged_df3_comp_lig), "," , ncol(merged_df3_comp_lig))
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} else {
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cat("\nFiltering column: ", lig_dist_colname, " not found\n")
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}
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#quit()
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# sanity check
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if (nrow(merged_df3_lig) == nrow(df_lig)){
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print("\nPASS: verified merged_df3_lig")
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}else{
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cat(paste0("\nFAIL: nrow mismatch for merged_df3_lig"
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, "\nExpected:", nrow(df_lig)
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, "\nGot:", nrow(merged_df3_lig)))
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}
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#==============================================================
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############################################
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# OPTIONAL: write output files in one go
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############################################
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#outvars = c(#"merged_df2",
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#"merged_df2_comp",
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#"merged_df2_lig",
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#"merged_df2_comp_lig",
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#"meregd_df3_comp"
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#"merged_df3_comp_lig",
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#"merged_df3",
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#"merged_df3_lig")
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#cat("Writing output files: "
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#, "\nPath:", outdir)
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#for (i in outvars){
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#out_filename = paste0(i, ".csv")
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#outfile = paste0(outdir, "/", out_filename)
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#cat("Writing output file:"
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# ,"\nFilename: ", out_filename,"\n")
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#write.csv(get(i), outfile, row.names = FALSE)
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#cat("Finished writing: ", outfile
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# , "\nNo. of rows: ", nrow(get(i))
|
||||
# , "\nNo. of cols: ", ncol(get(i)), "\n")
|
||||
#}
|
||||
|
||||
return(list( merged_df2
|
||||
, merged_df3
|
||||
, merged_df2_comp
|
||||
, merged_df3_comp
|
||||
, merged_df2_lig
|
||||
, merged_df3_lig
|
||||
, merged_df2_comp_lig
|
||||
, merged_df3_comp_lig))
|
||||
))
|
||||
|
||||
cat("\nEnd of combining_dfs_plotting.R script")
|
||||
}
|
||||
|
|
@ -168,7 +168,7 @@ def MultModelsCl(input_df, target
|
|||
@param skv_cv: stratifiedK fold int or object to allow shuffle and random state to pass
|
||||
@type: int or StratifiedKfold()
|
||||
|
||||
@var_type: numerical, categorical and mixed to determine what col_transform to apply (MinMaxScalar and/or one-ho t encoder)
|
||||
@var_type: numerical, categorical and mixed to determine what col_transform to apply (MinMaxScalar and/or one-hot encoder)
|
||||
@type: list
|
||||
|
||||
returns
|
||||
|
|
|
|||
|
|
@ -168,7 +168,7 @@ def MultModelsCl(input_df, target
|
|||
@param skv_cv: stratifiedK fold int or object to allow shuffle and random state to pass
|
||||
@type: int or StratifiedKfold()
|
||||
|
||||
@var_type: numerical, categorical and mixed to determine what col_transform to apply (MinMaxScalar and/or one-ho t encoder)
|
||||
@var_type: numerical, categorical and mixed to determine what col_transform to apply (MinMaxScalar and/or one-hot encoder)
|
||||
@type: list
|
||||
|
||||
returns
|
||||
|
|
@ -239,8 +239,8 @@ def MultModelsCl(input_df, target
|
|||
# , ('Gaussian NB' , GaussianNB() )
|
||||
# , ('Gaussian Process' , GaussianProcessClassifier(**rs) )
|
||||
# , ('K-Nearest Neighbors' , KNeighborsClassifier() )
|
||||
# , ('LDA' , LinearDiscriminantAnalysis() )
|
||||
# , ('Logistic Regression' , LogisticRegression(**rs) )
|
||||
, ('LDA' , LinearDiscriminantAnalysis() )
|
||||
, ('Logistic Regression' , LogisticRegression(**rs) )
|
||||
# , ('Logistic RegressionCV' , LogisticRegressionCV(cv = 3, **rs))
|
||||
# , ('MLP' , MLPClassifier(max_iter = 500, **rs) )
|
||||
#, ('Multinomial' , MultinomialNB() )
|
||||
|
|
@ -259,7 +259,7 @@ def MultModelsCl(input_df, target
|
|||
# , ('Ridge ClassifierCV' , RidgeClassifierCV(cv = 3) )
|
||||
# , ('SVC' , SVC(**rs) )
|
||||
# , ('Stochastic GDescent' , SGDClassifier(**rs, **njobs) )
|
||||
# , ('XGBoost' , XGBClassifier(**rs, verbosity = 0, use_label_encoder =False, **njobs) )
|
||||
, ('XGBoost' , XGBClassifier(**rs, verbosity = 0, use_label_encoder =False, **njobs) )
|
||||
#
|
||||
]
|
||||
|
||||
|
|
|
|||
|
|
@ -14,23 +14,8 @@ sys.path
|
|||
# import
|
||||
from GetMLData import *
|
||||
from SplitTTS import *
|
||||
#from MultClfs_fi import *
|
||||
from MultClfs import *
|
||||
|
||||
#%%
|
||||
# X,y = load_boston(return_X_y=True)
|
||||
# features = load_boston()['feature_names']
|
||||
|
||||
# X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.33, random_state=42)
|
||||
|
||||
# rf = RandomForestRegressor(random_state=0)
|
||||
# rf.fit(X_train,y_train)
|
||||
|
||||
|
||||
# f_i = list(zip(features,rf.feature_importances_))
|
||||
# f_i.sort(key = lambda x : x[1])
|
||||
# plt.barh([x[0] for x in f_i],[x[1] for x in f_i])
|
||||
# plt.show()
|
||||
#from MultClfs import *
|
||||
from MultClfs_SIMPLE import *
|
||||
|
||||
#%%
|
||||
|
||||
|
|
@ -54,9 +39,8 @@ df = getmldata('katG', 'isoniazid' , **gene_model_paramD)
|
|||
df = getmldata('rpoB', 'rifampicin' , **gene_model_paramD)
|
||||
df = getmldata('gid' , 'streptomycin' , **gene_model_paramD)
|
||||
#df = getmldata('alr' , 'cycloserine' , **combined_model_paramD)
|
||||
|
||||
all(df.columns.isin(['gene_name'])) # should be False
|
||||
|
||||
|
||||
spl_type = '70_30'
|
||||
#spl_type = '80_20'
|
||||
#spl_type = 'sl'
|
||||
|
|
@ -73,6 +57,16 @@ df2 = split_tts(df
|
|||
|
||||
all(df2['X'].columns.isin(['gene_name'])) # should be False
|
||||
|
||||
df['dst'].value_counts()
|
||||
df['dst'].isna().sum()
|
||||
df['dst_mode'].value_counts()
|
||||
|
||||
len(df)
|
||||
|
||||
Counter(df2['y'])
|
||||
Counter(df2['y_bts'])
|
||||
|
||||
|
||||
fooD = MultModelsCl(input_df = df2['X']
|
||||
, target = df2['y']
|
||||
, sel_cv = skf_cv
|
||||
|
|
|
|||
|
|
@ -7,11 +7,9 @@
|
|||
getwd()
|
||||
source("~/git/LSHTM_analysis/scripts/Header_TT.R")
|
||||
|
||||
#********************
|
||||
# cmd args passed
|
||||
# in from other scripts
|
||||
# to call this
|
||||
#********************
|
||||
# set drug and gene name
|
||||
|
||||
#==========================================
|
||||
|
|
@ -85,8 +83,8 @@ all_plot_dfs = combining_dfs_plotting(my_df_u
|
|||
|
||||
merged_df2 = all_plot_dfs[[1]]
|
||||
merged_df3 = all_plot_dfs[[2]]
|
||||
merged_df2_comp = all_plot_dfs[[3]]
|
||||
merged_df3_comp = all_plot_dfs[[4]]
|
||||
#merged_df2_comp = all_plot_dfs[[3]]
|
||||
#merged_df3_comp = all_plot_dfs[[4]]
|
||||
|
||||
#======================================================================
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue