logoplot from df and seqs with custom height
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parent
34a2057d29
commit
b5aa524914
3 changed files with 284 additions and 13 deletions
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@ -160,7 +160,6 @@ if (max(mcsm_data2$Dis_lig_Ang) < 10){
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mcsm_data = mcsm_data2
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#!!!!!!!!!!!!!!!!!!!!!
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#=======================================================================
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# clear variables
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rm(mcsm_data2)
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@ -173,13 +172,13 @@ head(mcsm_data$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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setDT(mcsm_data)[, mut_pos_occurrence := .N, by = .(position)]
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cat('Added 1 col: position frequency to see which posn has how many muts'
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cat('Added col: position frequency to see which posn has how many muts'
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, '\nNo. of cols now', ncol(mcsm_data)
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, '\nNo. of cols before: ', orig_col)
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pos_count_check = data.frame(mcsm_data$Position, mcsm_data$occurrence)
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mut_pos_occurrence = data.frame(mcsm_data$id, mcsm_data$Position, mcsm_data$mut_pos_occurrence)
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######################################
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# input file2 meta data with AFandOR
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@ -205,8 +204,21 @@ str(meta_with_afor)
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head(meta_with_afor$Mutationinformation)
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meta_with_afor = meta_with_afor[order(meta_with_afor$Mutationinformation),]
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head(meta_with_afor$Mutationinformation)
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orig_col2 = ncol(meta_with_afor)
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# get freq count of positions and add to the df
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setDT(meta_with_afor)[, sample_pos_occurrence := .N, by = .(Position)]
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cat('Added col: position frequency of samples to check'
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,'how many samples correspond to a partiulcar posn associated with muts'
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, '\nNo. of cols now', ncol(meta_with_afor)
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, '\nNo. of cols before: ', orig_col2)
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sample_pos_occurrence = data.frame(meta_with_afor$id, meta_with_afor$position, meta_with_afor$sample_pos_occurrence)
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#=======================================================================
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cat('Begin merging dfs with NAs',
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cat('Begin merging dfs with NAs'
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, '\n===============================================================')
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###########################
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@ -315,7 +327,7 @@ if (identical(sum(is.na(merged_df3$OR))
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#=======================================================================
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#%% merging without NAs
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cat('Begin merging dfs without NAs',
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cat('Begin merging dfs without NAs'
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, '\n===============================================================')
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cat('Merging dfs without any NAs: big df (1-many relationship b/w id & mut)'
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@ -378,7 +390,7 @@ if(nrow(merged_df3_comp) == nrow(merged_df3)){
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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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rm(pos_count_check)
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rm(mut_pos_occurrence)
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#%% end of script
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#=======================================================================
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@ -155,14 +155,17 @@ head(mcsm_data$Mutationinformation)
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orig_col = ncol(mcsm_data)
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# get freq count of positions and add to the df
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setDT(mcsm_data)[, occurrence := .N, by = .(Position)]
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setDT(mcsm_data)[, mut_pos_occurrence := .N, by = .(Position)]
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cat('Added 1 col: position frequency to see which posn has how many muts'
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cat('Added col: position frequency of muts to see which posn has how many muts'
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, '\nNo. of cols now', ncol(mcsm_data)
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, '\nNo. of cols before: ', orig_col)
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pos_count_check = data.frame(mcsm_data$Position, mcsm_data$occurrence)
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mut_pos_occurrence = data.frame(mcsm_data$Mutationinformation
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, mcsm_data$Position
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, mcsm_data$mut_pos_occurrence)
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colnames(mut_pos_occurrence) = c('Mutationinformation', 'position', 'mut_pos_occurrence')
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#######################################
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# input file 2: meta data with AFandOR
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#######################################
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@ -201,8 +204,25 @@ str(meta_with_afor)
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head(meta_with_afor$Mutationinformation)
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meta_with_afor = meta_with_afor[order(meta_with_afor$Mutationinformation),]
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head(meta_with_afor$Mutationinformation)
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orig_col2 = ncol(meta_with_afor)
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# get freq count of positions and add to the df
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setDT(meta_with_afor)[, sample_pos_occurrence := .N, by = .(position)]
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cat('Added col: position frequency of samples to check'
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,'how many samples correspond to a partiulcar posn associated with muts'
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, '\nNo. of cols now', ncol(meta_with_afor)
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, '\nNo. of cols before: ', orig_col2)
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sample_pos_occurrence = data.frame(meta_with_afor$id
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, meta_with_afor$mutation
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, meta_with_afor$Mutationinformation
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, meta_with_afor$position
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, meta_with_afor$sample_pos_occurrence)
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colnames(sample_pos_occurrence) = c('id', 'mutation', 'Mutationinformation', 'position', 'sample_pos_occurrence')
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#=======================================================================
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cat('Begin merging dfs with NAs',
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cat('Begin merging dfs with NAs'
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, '\n===============================================================')
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###########################
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@ -313,7 +333,7 @@ if (identical(sum(is.na(merged_df3$OR))
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#=======================================================================
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#%% merging without NAs
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cat('Begin merging dfs without NAs',
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cat('Begin merging dfs without NAs'
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, '\n===============================================================')
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cat('Merging dfs without any NAs: big df (1-many relationship b/w id & mut)'
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@ -414,10 +434,28 @@ for (i in outvars){
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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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#%% merging mut_pos_occurrence and sample_pos_occurence
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# FIXME
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#cat('Merging dfs with positional frequency from mcsm and meta_data'
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# , '\nNcol in mut_pos_occurrence:', ncol(mut_pos_occurrence)
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# , '\nncol in sample_pos_occurence:', ncol(sample_pos_occurrence)
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# ,'\nlinking col:', intersect(colnames(sample_pos_occurrence), colnames(mut_pos_occurrence))
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# ,'\nfilename: merged_df4')
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#merged_df4 = merge(sample_pos_occurrence, mut_pos_occurrence
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# , by = 'position'
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# , all = T)
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#out_filename4 = 'mut_and_sample_freq.csv'
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#outfile4 = paste0(outdir, '/', out_filename4)
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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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rm(pos_count_check)
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rm(mut_pos_occurrence, sample_pos_occurrence)
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#rm(merged_df4)
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#%% end of script
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#=======================================================================
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221
plotting_test/logo_plot.R
Normal file
221
plotting_test/logo_plot.R
Normal file
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@ -0,0 +1,221 @@
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#=======================================================================
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# Task: To generate a logo plot or bar plot but coloured
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# aa properties.
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# step1: read mcsm file and OR file
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# step2: plot wild type positions
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# step3: plot mutants per position coloured by aa properties
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# step4: make the size of the letters/bars prop to OR if you can!
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# useful links
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# https://stackoverflow.com/questions/5438474/plotting-a-sequence-logo-using-ggplot2
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# https://omarwagih.github.io/ggseqlogo/
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# https://kkdey.github.io/Logolas-pages/workflow.html
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# A new sequence logo plot to highlight enrichment and depletion.
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# https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6288878/
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#very good: http://www.cbs.dtu.dk/biotools/Seq2Logo-2.0/
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#=======================================================================
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#%% specify curr dir
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getwd()
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setwd('~/git/LSHTM_analysis/plotting_test/')
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getwd()
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#=======================================================================
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#%% load packages
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# header file
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header_dir = '~/git/LSHTM_analysis/'
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source(paste0(header_dir, '/', 'my_header.R'))
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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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# source R script 'combining_two_df.R'
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#indir = paste0(datadir, '/', drug, '/', 'output') # reading files
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indir = '../meta_data_analysis' # sourcing R script
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in_filename = 'combining_df_ps.R'
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infile = paste0(indir, '/', in_filename)
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cat(paste0('Input is a R script: ', '\'', infile, '\'')
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, '\n========================================================')
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#===========
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# output
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#===========
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# 1) lineage dist of all muts
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outdir = paste0('~/git/Data', '/', drug, '/', 'output/plots') #same as indir
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#cat('Output dir: ', outdir, '\n')
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#file_type = '.svg'
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#out_filename1 = paste0(tolower(gene), '_lineage_dist_ps', file_type)
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#outfile1 = paste0(outdir, '/', out_filename1)
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#cat(paste0('Output plot1 :', outfile1)
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# , '\n========================================================')
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#%% end of variable assignment for input and output files
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#=======================================================================
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##%% read input file
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cat('Reading input file(sourcing R script):', in_filename)
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source(infile)
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#==========================
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# This will return:
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# df with NA for pyrazinamide:
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# merged_df2
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# merged_df3
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# df without NA for pyrazinamide:
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# merged_df2_comp
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# merged_df3_comp
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#===========================
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###########################
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# Data for plots
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# you need merged_df2 or merged_df2_comp
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# since this is one-many relationship
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# i.e the same SNP can belong to multiple lineages
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# using the _comp dataset means
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# we lose some muts and at this level, we should use
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# as much info as available, hence use df with NA
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# This will the first plotting df
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# Then subset this to extract dr muts only (second plottig df)
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###########################
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#%%%%%%%%%%%%%%%%%%%%%%%%%
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# uncomment as necessary
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# REASSIGNMENT
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#my_data = merged_df2
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#my_data = merged_df2_comp
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#my_data = merged_df3
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my_data = merged_df3_comp
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#%%%%%%%%%%%%%%%%%%%%%%%%%%
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# delete variables not required
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rm(merged_df2, merged_df2_comp, merged_df3, merged_df3_comp)
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# quick checks
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colnames(my_data)
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str(my_data)
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c1 = unique(my_data$Position)
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nrow(my_data)
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cat('No. of rows in my_data:', nrow(my_data)
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, '\nDistinct positions corresponding to snps:', length(c1)
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, '\n===========================================================')
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#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
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# FIXME: Think and decide what you want to remove
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# mut_pos_occurence < 1 or sample_pos_occurrence <1
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# get freq count of positions so you can subset freq<1
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require(data.table)
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#setDT(my_data)[, mut_pos_occurrence := .N, by = .(Position)] #265, 14
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#extract freq_pos>1
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#my_data_snp = my_data[my_data$occurrence!=1,]
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#u = unique(my_data_snp$Position) #73
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#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
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#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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# REASSIGNMENT to prevent changing code
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my_data_snp = my_data
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#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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#=======================================================================
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#%% logo plots from dataframe
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#############
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# PLOTS: ggseqlogo with custom height
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# https://omarwagih.github.io/ggseqlogo/
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#############
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#require(ggplot2)
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#require(tidyverse)
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library(ggseqlogo)
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foo = my_data_snp[, c("Position", "Mutant_type","ratioDUET", "OR"
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, "mut_prop_polarity", "mut_prop_water") ]
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# log10OR
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# FIXME: at the source script (when calculating AFandOR)
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my_data_snp$log10or = log10(my_data_snp$OR)
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bar = my_data_snp[, c('Position', 'Mutant_type', 'OR', 'logor', 'log10or')]
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bar_or = my_data_snp[, c('Position', 'Mutant_type', 'OR')]
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wide_df_or <- bar_or %>% spread(Position, OR, fill = 0)
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wide_df_or = as.matrix(wide_df_or)
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rownames(wide_df_or) = wide_df_or[,1]
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wide_df_or = wide_df_or[,-1]
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# custom height (OR) logo plot: yayy works
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ggseqlogo(wide_df_or, method='custom', seq_type='aa') + ylab('my custom height') +
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theme(legend.position = "bottom"
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, axis.text.x = element_text(size = 11
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, angle = 90
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, hjust = 1
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, vjust = 0.4)
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, axis.text.y = element_text(size = 15
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, angle = 0
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, hjust = 1
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, vjust = 0))+
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labs(title = "AA logo plot"
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, x = "Wild-type Position"
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, y = "OR")
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#%% end of logo plot with OR as height
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#=======================================================================
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# extracting data with log10OR
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bar_logor = my_data_snp[, c('Position', 'Mutant_type', 'log10or')]
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wide_df_logor <- bar_logor %>% spread(Position, log10or, fill = 0)
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wide_df_logor = as.matrix(wide_df_logor)
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rownames(wide_df_logor) = wide_df_logor[,1]
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wide_df_logor = wide_df_logor[,-1]
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# custom height (log10OR) logo plot: yayy works
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ggseqlogo(wide_df_logor, method='custom', seq_type='aa') + ylab('my custom height') +
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theme(legend.position = "bottom"
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, axis.text.x = element_text(size = 11
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, angle = 90
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, hjust = 1
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, vjust = 0.4)
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, axis.text.y = element_text(size = 15
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, angle = 0
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, hjust = 1
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, vjust = 0))+
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labs(title = "AA logo plot"
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, x = "Wild-type Position"
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, y = "Log10(OR)")
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#=======================================================================
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#%% logo plot from sequence
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#################
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# Plot: LOGOLAS (ED plots)
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# link: https://github.com/kkdey/Logolas
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# on all pncA samples: output of mutate.py
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################
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library(Logolas)
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seqs = read.csv('~/git//Data/pyrazinamide/output/pnca_msa.txt'
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, header = FALSE
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, stringsAsFactors = FALSE)$V1
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# my_data: useful!
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logomaker(seqs, type = "EDLogo", color_type = 'per_symbol'
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, return_heights = TRUE)
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logomaker(seqs, type = "Logo", color_type = 'per_symbol')
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#%% end of script
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#=======================================================================
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