reran to generate merged_df3 with correct dst for dst muts. modified combining_dfs_plotting.R
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6 changed files with 148 additions and 211 deletions
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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))
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# , "\nNo. of cols: ", ncol(get(i)), "\n")
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#}
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return(list( merged_df2
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, merged_df3
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, merged_df2_comp
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, merged_df3_comp
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, merged_df2_lig
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, merged_df3_lig
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, merged_df2_comp_lig
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, merged_df3_comp_lig))
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, merged_df3
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))
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cat("\nEnd of combining_dfs_plotting.R script")
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}
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