output from comb script & electrostatic mut changes calculated
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96ebb85069
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de1822f491
4 changed files with 250 additions and 167 deletions
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@ -1,6 +1,19 @@
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#########################################################
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# TASK: To combine mcsm and meta data with af and or
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# This script doesn't output anything, but can do if needed.
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# TASK: To combine mcsm and meta data with af and or files
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# Input csv files:
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# 1) mcsm output formatted
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# 2) gene associated meta_data_with_AFandOR
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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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# Dim: same as mcsm data
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# 4) large combined df excluding NAs
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# Dim: dim(#1) - no. of NAs(AF|OR) + 1
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# 5) small combined df excluding NAs
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# Dim: dim(#2) - no. of unique NAs - 1
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# This script is sourced from other .R scripts for plotting
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#########################################################
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getwd()
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@ -10,7 +23,6 @@ getwd()
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##########################################################
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# Installing and loading required packages
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##########################################################
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source('Header_TT.R')
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#require(data.table)
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#require(arsenal)
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@ -21,19 +33,23 @@ source('Header_TT.R')
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# Read file: normalised file
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# output of step 4 mcsm_pipeline
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#################################
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#%% variable assignment: input and output paths & filenames
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drug = 'pyrazinamide'
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gene = 'pncA'
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gene_match = paste0(gene,'_p.')
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cat(gene_match)
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#===========
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# data dir
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#===========
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datadir = paste0('~/git/Data')
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#===========
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# input
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#===========
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# infile1: mCSM data
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#indir = '~/git/Data/pyrazinamide/input/processed/'
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indir = paste0('~/git/Data', '/', drug, '/', 'output') # revised {TODO: change in mcsm pipeline}
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indir = paste0(datadir, '/', drug, '/', 'output') # revised {TODO: change in mcsm pipeline}
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in_filename = 'mcsm_complex1_normalised.csv'
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infile = paste0(indir, '/', in_filename)
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cat(paste0('Reading infile1: mCSM output file', ' ', infile) )
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@ -105,8 +121,9 @@ changes = mcsm_data[which(mcsm_data$DUET_outcome != mcsm_data$Lig_outcome),]
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dl_i = which(mcsm_data$DUET_outcome != mcsm_data$Lig_outcome)
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ld_i = which(mcsm_data$Lig_outcome != mcsm_data$DUET_outcome)
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cat('Identifying muts with opposite stability effects')
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if(nrow(changes) == (table(mcsm_data$DUET_outcome != mcsm_data$Lig_outcome)[[2]]) & identical(dl_i,ld_i)) {
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cat('PASS: muts with opposite effects on stability and affinity identified correctly'
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cat('PASS: muts with opposite effects on stability and affinity correctly identified'
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, '\nNo. of such muts: ', nrow(changes))
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}else {
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cat('FAIL: unsuccessful in extracting muts with changed stability effects')
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@ -134,6 +151,7 @@ mcsm_data = mcsm_data[order(mcsm_data$Mutationinformation),]
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head(mcsm_data$Mutationinformation)
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orig_col = ncol(mcsm_data)
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# get freq count of positions and add to the df
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setDT(mcsm_data)[, occurrence := .N, by = .(Position)]
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@ -158,6 +176,20 @@ cat('Read mcsm_data file:'
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, '\nNo.of rows: ', nrow(meta_with_afor)
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, '\nNo. of cols:', ncol(meta_with_afor))
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# counting NAs in AF, OR cols
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if (identical(sum(is.na(meta_with_afor$OR))
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, sum(is.na(meta_with_afor$pvalue))
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, sum(is.na(meta_with_afor$AF)))){
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cat('PASS: NA count match for OR, pvalue and AF\n')
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na_count = sum(is.na(meta_with_afor$AF))
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cat('No. of NAs: ', sum(is.na(meta_with_afor$OR)))
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} else{
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cat('FAIL: NA count mismatch'
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, '\nNA in OR: ', sum(is.na(meta_with_afor$OR))
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, '\nNA in pvalue: ', sum(is.na(meta_with_afor$pvalue))
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, '\nNA in AF:', sum(is.na(meta_with_afor$AF)))
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}
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# clear variables
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rm(in_filename_comb, infile_comb)
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@ -172,15 +204,15 @@ head(meta_with_afor$Mutationinformation)
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# 3: merging two dfs: with NA
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###########################
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# link col name = 'Mutationinforamtion'
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head(mcsm_data$Mutationinformation)
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head(meta_with_afor$Mutationinformation)
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cat('Merging dfs with NAs: big df (1-many relationship b/w id & mut)'
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,'\nlinking col: Mutationinforamtion'
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,'\nfilename: merged_df2')
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head(mcsm_data$Mutationinformation)
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head(meta_with_afor$Mutationinformation)
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#########
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# merge 3a: meta data with mcsm
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# merge 3a (merged_df2): meta data with mcsm
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#########
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merged_df2 = merge(x = meta_with_afor
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,y = mcsm_data
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@ -192,6 +224,8 @@ cat('Dim of merged_df2: '
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, '\nNo. of cols: ', ncol(merged_df2))
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head(merged_df2$Position)
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# sanity check
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cat('Checking nrows in merged_df2')
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if(nrow(meta_with_afor) == nrow(merged_df2)){
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cat('nrow(merged_df2) = nrow (gene associated metadata)'
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,'\nExpected no. of rows: ',nrow(meta_with_afor)
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@ -229,9 +263,9 @@ table(merged_df2$Position%in%merged_df2v2$Position)
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rm(merged_df2v2)
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#########
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# merge 3b:remove duplicate mutation information
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# merge 3b (merged_df3):remove duplicate mutation information
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#########
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cat('Merging dfs with NAs: small df (removing duplicate muts)'
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cat('Merging 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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@ -244,8 +278,8 @@ cat('Merging dfs with NAs: small df (removing duplicate muts)'
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merged_df3 = merged_df2[!duplicated(merged_df2$Mutationinformation),]
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head(merged_df3$Position); tail(merged_df3$Position) # should be sorted
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# sanity checks
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# nrows of merged_df3 should be the same as the nrows of mcsm_data
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# sanity check
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cat('Checking nrows in merged_df3')
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if(nrow(mcsm_data) == nrow(merged_df3)){
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cat('PASS: No. of rows match with mcsm_data'
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,'\nExpected no. of rows: ', nrow(mcsm_data)
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@ -256,41 +290,51 @@ if(nrow(mcsm_data) == nrow(merged_df3)){
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, '\nNo. of rows merged_df3: ', nrow(merged_df3))
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}
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#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
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# uncomment as necessary
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# only need to run this if merged_df2v2 i.e non structural pos included
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#mcsm = mcsm_data$Mutationinformation
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#my_merged = merged_df3$Mutationinformation
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# find the index where it differs
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#diff_n = which(!my_merged%in%mcsm)
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#check if it is indeed pos 186
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#merged_df3[diff_n,]
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# remove this entry
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#merged_df3 = merged_df3[-diff_n,]]
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#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
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# counting NAs in AF, OR cols in merged_df3
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# this is becuase mcsm has no AF, OR cols,
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# so you cannot count NAs
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if (identical(sum(is.na(merged_df3$OR))
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, sum(is.na(merged_df3$pvalue))
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, sum(is.na(merged_df3$AF)))){
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cat('PASS: NA count match for OR, pvalue and AF\n')
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na_count_df3 = sum(is.na(merged_df3$AF))
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cat('No. of NAs: ', sum(is.na(merged_df3$OR)))
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} else{
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cat('FAIL: NA count mismatch'
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, '\nNA in OR: ', sum(is.na(merged_df3$OR))
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, '\nNA in pvalue: ', sum(is.na(merged_df3$pvalue))
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, '\nNA in AF:', sum(is.na(merged_df3$AF)))
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}
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###########################
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# 4: merging two dfs: without NA
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###########################
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#########
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# merge 4a (merged_df2_comp): same as merge 1 but excluding NA
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#########
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cat('Merging dfs without any NAs: big df (1-many relationship b/w id & mut)'
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,'\nlinking col: Mutationinforamtion'
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,'\nfilename: merged_df2_comp')
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#########
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# merge 4a: same as merge 1 but excluding NA
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#########
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merged_df2_comp = merged_df2[!is.na(merged_df2$AF),]
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#merged_df2_comp = merged_df2[!duplicated(merged_df2$Mutationinformation),]
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cat('Dim of merged_df2_comp: '
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, '\nNo. of rows: ', nrow(merged_df2_comp)
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, '\nNo. of cols: ', ncol(merged_df2_comp))
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# sanity check
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cat('Checking nrows in merged_df2_comp')
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if(nrow(merged_df2_comp) == (nrow(merged_df2) - na_count + 1)){
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cat('PASS: No. of rows match'
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,'\nDim of merged_df2_comp: '
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,'\nExpected no. of rows: ', nrow(merged_df2) - na_count + 1
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, '\nNo. of rows: ', nrow(merged_df2_comp)
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, '\nNo. of cols: ', ncol(merged_df2_comp))
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}else{
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cat('FAIL: No. of rows mismatch'
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,'\nExpected no. of rows: ', nrow(merged_df2) - na_count + 1
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,'\nGot no. of rows: ', nrow(merged_df2_comp))
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}
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#########
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# merge 4b: remove duplicate mutation information
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# merge 4b (merged_df3_comp): remove duplicate mutation information
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#########
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merged_df3_comp = merged_df2_comp[!duplicated(merged_df2_comp$Mutationinformation),]
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@ -305,38 +349,65 @@ all.equal(foo, merged_df3)
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summary(comparedf(foo, merged_df3))
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# sanity check
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cat('Checking nrows in merged_df3_comp')
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if(nrow(merged_df3_comp) == nrow(merged_df3)){
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cat('NO NAs detected in merged_df3 in AF|OR cols'
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,'\nNo. of rows are identical: ', nrow(merged_df3))
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} else{
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if(nrow(merged_df3_comp) == nrow(merged_df3) - na_count_df3) {
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cat('PASS: NAs detected in merged_df3 in AF|OR cols'
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, '\nNo. of NAs: ', na_count_df3
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, '\nExpected no. of rows in merged_df3_comp: ', 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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}
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#=============== end of combining df
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#*********************
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# write_output files
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# output dir
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#outdir = '~/git/Data/pyrazinamide/output/'
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#uncomment as necessary
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#FIXME
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#out_filenames = c('merged_df2'
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# , 'merged_df3'
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# , 'meregd_df2_comp'
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# , 'merged_df3_comp'
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#)
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# writing 1 file in the style of a loop: merged_df3
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# print(output dir)
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#i = 'merged_df3'
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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 files: '
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# , '\nPath:', outdir)
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#cat('Writing output file: '
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# ,'\nFilename: ', out_filename
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# ,'\nPath: ', outdir)
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#for (i in out_filenames){
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# print(i)
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# print(get(i))
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# outvar = get(i)
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# print(outvar)
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# outfile = paste0(outdir, '/', outvar, '.csv')
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# cat('Writing output file:'
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# ,'\nFilename: ', outfile
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# ,'\n')
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# write.csv(outvar, outfile)
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# cat('Finished writing file:'
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# ,'\nNo. of rows:', nrow(outvar)
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# , '\nNo. of cols:', ncol(outvar))
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#}
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#template: write.csv(merged_df3, 'merged_df3.csv')
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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)))
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#sapply(out_filenames, function(x) write.csv(x, 'x.csv'))
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#%% write_output files; all 4 files:
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outvars = c('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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cat('Writing output files: '
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, '\nPath:', outdir)
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for (i in outvars){
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# cat(i, '\n')
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out_filename = paste0(i, '.csv')
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# cat(out_filename, '\n')
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# cat('getting value of variable: ', get(i))
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outfile = paste0(outdir, '/', out_filename)
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# cat('Full output path: ', outfile, '\n')
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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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# alternate way to replace with implicit loop
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# FIXME
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#sapply(outvars, function(x, y) write.csv(get(outvars), paste0(outdir, '/', outvars, '.csv')))
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#*************************
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# clear variables
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rm(mcsm_data, meta_with_afor, foo, drug, gene, gene_match, indir, merged_muts_u, meta_muts_u, na_count, orig_col, outdir)
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@ -1,8 +1,8 @@
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#########################################################
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# TASK: To combine mcsm and meta data with af and or
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# by filtering for distance to ligand (<10Ang).
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# This script doesn't output anything, but can do if needed.
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# This script is sourced from other .R scripts for plotting
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# This script doesn't output anything.
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# This script is sourced from other .R scripts for plotting ligand plots
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#########################################################
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getwd()
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setwd('~/git/LSHTM_analysis/mcsm_analysis/pyrazinamide/scripts/')
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