saving recovered combining_dfs_plotting.R after editing
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1 changed files with 39 additions and 130 deletions
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@ -6,7 +6,6 @@
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# 2) <gene>_meta_data.csv
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# Output:
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# 1) muts with opposite effects on stability
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# 2) large combined df including NAs for AF, OR,etc
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# Dim: same no. of rows as gene associated meta_data_with_AFandOR
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# 3) small combined df including NAs for AF, OR, etc.
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@ -36,18 +35,18 @@ source("plotting_data.R")
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# my_df_u_lig
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# dup_muts
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cat(paste0("Directories imported:"
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, "\ndatadir:", datadir
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, "\nindir:", indir
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, "\noutdir:", outdir
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, "\nplotdir:", plotdir))
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cat("Directories imported:"
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, "\ndatadir:", datadir
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, "\nindir:", indir
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, "\noutdir:", outdir
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, "\nplotdir:", plotdir)
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cat(paste0("Variables imported:"
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, "\ndrug:", drug
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, "\ngene:", gene
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, "\ngene_match:", gene_match
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, "\nLength of upos:", length(upos)
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, "\nAngstrom symbol:", angstroms_symbol))
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cat("Variables imported:"
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, "\ndrug:", drug
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, "\ngene:", gene
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, "\ngene_match:", gene_match
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, "\nLength of upos:", length(upos)
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, "\nAngstrom symbol:", angstroms_symbol)
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# clear excess variable
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rm(my_df, upos, dup_muts)
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@ -58,24 +57,6 @@ rm(my_df, upos, dup_muts)
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#in_file1: output of plotting_data.R
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# my_df_u
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# quick checks
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head(my_df_u[, c("mutation")])
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cols_to_extract = c("mutationinformation", "mutation", "or_mychisq", "or_kin", "af", "af_kin")
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foo = my_df_u[, colnames(my_df_u)%in% cols_to_extract]
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table(which(is.na(my_df_u$af_kin)) == which(is.na(my_df_u$af)))
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baz = read.csv(file.choose())
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baz = cbind(my_df_u$mutation, my_df_u$or_mychisq, bar$mutation, bar$or_mychisq)
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baz = as.data.frame(baz)
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colnames(baz) = c("my_df_u_muts", "my_df_u_or", "real_muts", "real_or")
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sum(is.na(baz$my_df_u_or)) == sum(is.na(my_df_u$or_mychisq))
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cat("\nNo. of with NA in or_mychisq:", sum(is.na(my_df_u$or_mychisq))
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,"\nNo. of NA in or_kin:" , sum(is.na(my_df_u$or_kin)))
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# infile 2: gene associated meta data
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#in_filename_gene_metadata = paste0(tolower(gene), "_meta_data_with_AFandOR.csv")
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@ -145,67 +126,6 @@ if (identical(sum(is.na(my_df_u$or_kin))
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str(gene_metadata)
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# change category of ambiguos mutations
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table(gene_metadata$mutation_info)
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cols_to_extract2 = c("mutationinformation", "mutation", "mutation_info")
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foo2 = gene_metadata[, colnames(gene_metadata)%in% cols_to_extract2]
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dr_muts = foo2[foo2$mutation_info == dr_muts_col,]
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other_muts = foo2[foo2$mutation_info == other_muts_col,]
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common_muts = dr_muts[dr_muts$mutation%in%other_muts$mutation,]
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#write.csv(common_muts, 'common_muts.csv')
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rm(common_muts)
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# FIXME read properly
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# "ambiguous_mut_names.csv"
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#"pnca_p.gly108arg", "pnca_p.gly132ala", "pnca_p.val180phe"
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ambiguous_muts = read.csv(file.choose())
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ambiguous_muts_names = ambiguous_muts$mutation
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common_muts_all = gene_metadata[gene_metadata$mutation%in%ambiguous_muts_names,]
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if (gene_metadata$mutation_info[gene_metadata$mutation%in%ambiguous_muts_names] == other_muts_col){
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print('change me')
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}
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# make a copy
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gene_metadata2 = gene_metadata
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table(gene_metadata$mutation_info)
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count_check = as.data.frame(cbind(table(gene_metadata$mutationinformation, gene_metadata$mutation_info)))
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#count_check$checks = ifelse(count_check$dr_mutations_pyrazinamide&&count_check$other_mutations_pyrazinamide>0, "ambi", "pass")
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table(count_check$checks)
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poo = c("V180F", "G132A", "D49G")
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poo2 = count_check[rownames(count_check)%in%poo,]
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poo2[[dr_muts_col]]&& poo2[[other_muts_col]]>0
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poo2$checks = ifelse(all(poo2$checkspoo2[[dr_muts_col]]&& poo2[[other_muts_col]])>0, "ambi", "pass")
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# remove common_muts_all
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ids = gene_metadata$mutation%in%common_muts_all$mutation; table(ids)
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gene_metadata_unambiguous = gene_metadata2[!ids,]
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# sanity checks: should be true
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table(gene_metadata_unambiguous$mutation%in%common_muts_all$mutation)[[1]] == nrow(gene_metadata_unambiguous)
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nrow(gene_metadata_unambiguous) + nrow(common_muts_all) == nrow(gene_metadata)
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# correct common muts
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table(common_muts_all$mutation_info)
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common_muts_all$mutation_info = as.factor(common_muts_all$mutation_info)
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# change the other_muts to dr_muts
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common_muts_all$mutation_info[common_muts_all$mutation_info==other_muts_col] <- dr_muts_col
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table(common_muts_all$mutation_info)
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common_muts_all$mutation_info = factor(common_muts_all$mutation_info)
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table(common_muts_all$mutation_info)
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# add it back to
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gene_metadata2 = rbind(gene_metadata_unambiguous, common_muts_all)
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nrow(gene_metadata2) == nrow(gene_metadata)
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###################################################################
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# combining: PS
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###################################################################
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@ -326,53 +246,43 @@ cat("Merging dfs without any NAs: big df (1-many relationship b/w id & mut)"
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,"\nlinking col: Mutationinforamtion"
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,"\nfilename: merged_df2_comp")
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if ( identical( which(is.na(merged_df2$af)), which(is.na(merged_df2$af_kin))) ){
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print("mychisq and kin ors missing indices match. Procedding with omitting NAs")
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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("Checking 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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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("Checking 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("FAIL: 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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}else{
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print("Index mismatch for mychisq and kin ors. Aborting NA ommission")
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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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if ( identical( which(is.na(merged_df3$af)), which(is.na(merged_df3$af_kin))) ){
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print("mychisq and kin ors missing indices match. Procedding with omitting NAs")
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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$af),] # another way
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cat("Checking nrows in merged_df3_comp")
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if(nrow(merged_df3_comp) == (nrow(merged_df3) - na_count_df3)){
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cat("\nPASS: No. of rows match"
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,"\nDim of merged_df3_comp: "
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,"\nExpected no. of rows: ", nrow(merged_df3) - na_count_df3
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, "\nNo. of rows: ", nrow(merged_df3_comp)
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, "\nNo. of cols: ", ncol(merged_df3_comp))
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}else{
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cat("FAIL: No. of rows mismatch"
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,"\nExpected no. of rows: ", nrow(merged_df3) - na_count_df3
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,"\nGot no. of rows: ", nrow(merged_df3_comp))
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}
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} else{
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print("Index mismatch for mychisq and kin ors. Aborting NA ommission")
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}
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na_count_df3 = sum(is.na(merged_df3$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("Checking 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("FAIL: 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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@ -408,8 +318,7 @@ all.equal(foo, bar)
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# clear variables
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rm(foo, bar, gene_metadata
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, in_filename_params, infile_params, merging_cols
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, in_filename_gene_metadata, infile_gene_metadata
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, merged_df2v2, merged_df2v3)
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, in_filename_gene_metadata, infile_gene_metadata)
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#*************************
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#####################################################################
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# Combining: LIG
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