removed merging df for AF_OR
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1 changed files with 3 additions and 178 deletions
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@ -481,182 +481,7 @@ cat('End of script: calculated AF, OR, pvalues and saved file')
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#########################################################
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# 3: Merge meta data file + calculated num params
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#########################################################
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df1 = gene_metadata
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df2 = comb_AF_and_OR
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cat('checking commom col of the two dfs before merging:'
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,'\ndf1:', head(df1$mutation)
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, '\ndf2:', head(df2$mutation))
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cat(paste0('merging two dfs: '
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,'\ndf1 (big df i.e. meta data) nrows: ', nrow(df1)
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,'\ndf2 (small df i.e af, or, pval) nrows: ', nrow(df2)
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,'\nexpected rows in merged df: ', nrow(df1)
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,'\nexpected cols in merged_df: ', (ncol(df1) + ncol(df2) - 1)))
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merged_df = merge(df1 # big file
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, df2 # small (afor file)
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, by = "mutation"
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, all.x = T) # because you want all the entries of the meta data
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# sanity check
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if(ncol(merged_df) == (ncol(df1) + ncol(df2) - 1)){
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cat(paste0('PASS: no. of cols is as expected: ', ncol(merged_df)))
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} else{
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cat('FAIL: no.of cols mistmatch')
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}
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# quick check
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i = "pnca_p.ala134gly" # has all NAs in pyrazinamide, should be NA in ors, etc.
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merged_df[merged_df$mutation == i,]
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# count na in each column
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na_count = sapply(merged_df, function(y) sum(length(which(is.na(y))))); na_count
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# check last three cols: should be NA
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if ( identical(na_count[[length(na_count)]], na_count[[length(na_count)-1]], na_count[[length(na_count)-2]])){
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cat('PASS: No. of NAs for OR, AF and Pvals are equal as expected',
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'\nNo. of NA: ', na_count[[length(na_count)]])
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} else {
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cat('FAIL: No. of NAs for OR, AF and Pvals mismatch')
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}
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# reassign custom colnames
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#cat('Assigning custom colnames for the calculated params...')
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#colnames(merged_df)[colnames(merged_df)== "ors"] <- "OR"
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#colnames(merged_df)[colnames(merged_df)== "pvals"] <- "pvalue"
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#colnames(merged_df)[colnames(merged_df)== "afs"] <- "AF"
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colnames(merged_df)
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# add 3 more cols: log OR, neglog pvalue and AF_percent cols
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merged_df$logor = log(merged_df$OR)
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is.numeric(merged_df$logor)
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merged_df$neglog10pvalue = -log10(merged_df$pvalue)
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is.numeric(merged_df$neglog10pvalue)
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merged_df$AF_percent = merged_df$AF*100
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is.numeric(merged_df$AF_percent)
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# check AFs
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#i = 'pnca_p.trp68gly'
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i = 'pnca_p.gln10pro'
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#i = 'pnca_p.leu4ser'
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merged_df[merged_df$mutation == i,]
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# FIXME: harcoding (beware!), NOT FATAL though!
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ncol_added = 3
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cat(paste0('Added', ' ', ncol_added, ' more cols to merged_df:'
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, '\ncols added: logor, neglog10pvalue and AF_percent:'
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, '\nno. of cols in merged_df now: ', ncol(merged_df)))
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#%% write file out: pnca_meta_data_with_AF_OR
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#*********************************************
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cat(paste0('writing output file: '
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, '\nFilename: ', out_filename
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, '\nPath:', outdir))
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write.csv(merged_df, outfile
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, row.names = F)
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cat(paste0('Finished writing:'
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, out_filename
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, '\nNo. of rows: ', nrow(merged_df)
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, '\nNo. of cols: ', ncol(merged_df)))
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#************************************************
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cat('======================================================================')
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rm(out_filename)
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cat('End of script: calculated AF, OR, pvalues and saved file')
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# End of script
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#%%
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# sanity check: Count NA in these four cols.
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# However these need to be numeric else these
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# will be misleading when counting NAs (i.e retrun 0)
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#is.numeric(meta_with_afor$OR)
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na_var = c('AF', 'OR', 'pvalue', 'logor', 'neglog10pvalue')
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# loop through these vars and check if these are numeric.
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# if not, then convert to numeric
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check_all = NULL
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for (i in na_var){
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# cat(i)
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check0 = is.numeric(meta_with_afor[,i])
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if (check0) {
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check_all = c(check0, check_all)
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cat('These are all numeric cols')
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} else{
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cat('First converting to numeric')
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check0 = as.numeric(meta_with_afor[,i])
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check_all = c(check0, check_all)
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}
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}
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# count na now that the respective cols are numeric
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na_count = sapply(meta_with_afor, function(y) sum(length(which(is.na(y))))); na_count
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str(na_count)
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# extract how many NAs:
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# should be all TRUE
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# should be a single number since
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# all the cols should have 'equal' and 'same' no. of NAs
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# compare if the No of 'NA' are the same for all these cols
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na_len = NULL
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for (i in na_var){
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temp = na_count[[i]]
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na_len = c(na_len, temp)
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}
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cat('Checking how many NAs and if these are identical for the selected cols:')
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my_nrows = NULL
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for ( i in 1: (length(na_len)-1) ){
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# cat(compare(na_len[i]), na_len[i+1])
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c = compare(na_len[i], na_len[i+1])
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if ( c$result ) {
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cat('PASS: No. of NAa in selected cols are identical')
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my_nrows = na_len[i] }
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else {
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cat('FAIL: No. of NAa in selected cols mismatch')
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}
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}
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cat('No. of NAs in each of the selected cols: ', my_nrows)
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# yet more sanity checks:
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cat('Check whether the ', my_nrows, 'indices are indeed the same')
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#which(is.na(meta_with_afor$OR))
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# initialise an empty df with nrows as extracted above
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na_count_df = data.frame(matrix(vector(mode = 'numeric'
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# , length = length(na_var)
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)
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, nrow = my_nrows
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# , ncol = length(na_var)
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))
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# populate the df with the indices of the cols that are NA
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for (i in na_var){
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cat(i)
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na_i = which(is.na(meta_with_afor[i]))
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na_count_df = cbind(na_count_df, na_i)
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colnames(na_count_df)[which(na_var == i)] <- i
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}
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# Now compare these indices to ensure these are the same
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check2 = NULL
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for ( i in 1: ( length(na_count_df)-1 ) ) {
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# cat(na_count_df[i] == na_count_df[i+1])
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check_all = identical(na_count_df[[i]], na_count_df[[i+1]])
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check2 = c(check_all, check2)
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if ( all(check2) ) {
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cat('PASS: The indices for AF, OR, etc are all the same\n')
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} else {
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cat ('FAIL: Please check indices which are NA')
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}
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}
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#df1 = gene_metadata
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#df2 = comb_AF_and_OR
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# COMMENT: will do the combining with the other OR and AF (in python)
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