renamed file and updated logo plot code
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2 changed files with 96 additions and 117 deletions
257
mcsm_analysis/pyrazinamide/scripts/plotting/logolas_logoplot.R
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257
mcsm_analysis/pyrazinamide/scripts/plotting/logolas_logoplot.R
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getwd()
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setwd("~/git/LSHTM_analysis/mcsm_analysis/pyrazinamide/scripts/plotting/")
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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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#source("barplot_colour_function.R")
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library(ggseqlogo)
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#=======
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# input
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#=======
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#############
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# msa file: output of generate_mut_sequences.py
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#############
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homedir = '~'
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indir = 'git/Data/pyrazinamide/output'
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in_filename = "gene_msa.txt"
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infile = paste0(homedir, '/', indir,'/', in_filename)
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print(infile)
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#=======
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# input
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#=======
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#############
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# combined dfs
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#############
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source("../combining_two_df.R")
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###########################
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# Data for Logo plots
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# you need big df i.e
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# merged_df2
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# or
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# merged_df2_comp
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# since these have unique SNPs
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# I prefer to use the merged_df2
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# because 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
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###########################
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# uncomment as necessary
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#%%%%%%%%%%%%%%%%%%%%%%%%
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# REASSIGNMENT
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my_df = merged_df2
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#my_df = merged_df2_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_df)
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str(my_df)
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# doesn't work if you use the big df as it has duplicate snps
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#rownames(my_df) = my_df$Mutationinformation
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# sanity check: should be True
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table(my_df$position == my_df$Position)
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c1 = unique(my_df$Position) # 130
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nrow(my_df) # 3092
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#FIXME
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#!!! RESOLVE !!!
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# get freq count of positions and add to the df
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setDT(my_df)[, occurrence_sample := .N, by = .(id)]
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table(my_df$occurrence_sample)
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my_df2 = my_df %>%
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select(id, Mutationinformation, Wild_type, WildPos, position, Mutant_type, occurrence, occurrence_sample)
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write.csv(my_df2, "my_df2.csv")
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# extract freq_pos>1 since this will not add to much in the logo plot
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# pos 5 has one mutation but coming from atleast 5 samples?
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table(my_df$occurrence)
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foo = my_df[my_df$occurrence ==1,]
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# uncomment as necessary
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my_data_snp = my_df #3092
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#!!! RESOLVE
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# FIXME
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my_data_snp = my_df[my_df$occurrence!=1,] #3072, 36...3019
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u = unique(my_data_snp$Position) #96
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########################################################################
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# end of data extraction and cleaning for plots #
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########################################################################
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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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#########################################################
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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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# matrix for mutant type
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# frequency of mutant type by position
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#==============
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table(my_data_snp$Mutant_type, my_data_snp$Position)
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tab_mt = table(my_data_snp$Mutant_type, my_data_snp$Position)
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class(tab_mt)
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# unclass to convert to matrix
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tab_mt = unclass(tab_mt)
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tab_mt = as.matrix(tab_mt, rownames = T)
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# should be TRUE
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is.matrix(tab_mt)
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rownames(tab_mt) #aa
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colnames(tab_mt) #pos
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#**********************
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# Plot 1: mutant logo
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#**********************
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my_ymax = max(my_data_snp$occurrence); my_ymax
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my_ylim = c(0,my_ymax) # very important
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# axis sizes
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# common: text and label
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my_ats = 15
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my_als = 20
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# individual: text and label
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my_xats = 15
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my_yats = 20
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my_xals = 15
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my_yals = 20
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# legend size: text and label
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my_lts = 20
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#my_lls = 20
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# Color scheme based on chemistry of amino acids
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chemistry = data.frame(
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letter = c('G', 'S', 'T', 'Y', 'C', 'N', 'Q', 'K', 'R', 'H', 'D', 'E', 'P', 'A', 'W', 'F', 'L', 'I', 'M', 'V'),
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group = c(rep('Polar', 5), rep('Neutral', 2), rep('Basic', 3), rep('Acidic', 2), rep('Hydrophobic', 8)),
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col = c(rep('#109648', 5), rep('#5E239D', 2), rep('#255C99', 3), rep('#D62839', 2), rep('#221E22', 8)),
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stringsAsFactors = F
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)
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# uncomment as necessary
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my_type = "EDLogo"
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my_type = "Logo"
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logomaker(tab_mt
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, type = my_type
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, return_heights = T
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# , color_type = "per_row"
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# , colors = chemistry$col
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# , method = 'custom'
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# , seq_type = 'aa'
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# , col_scheme = "taylor"
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# , col_scheme = "chemistry2"
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) +
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theme(legend.position = "bottom"
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, legend.title = element_blank()
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, legend.text = element_text(size = my_lts )
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, axis.text.x = element_text(size = my_ats , angle = 90)
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, axis.text.y = element_text(size = my_ats , angle = 90))
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p0 = logomaker(tab_mt
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, type = my_type
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, return_heights = T
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, color_type = "per_row"
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, colors = chemistry$col
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# , seq_type = 'aa'
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# , col_scheme = "taylor"
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# , col_scheme = "chemistry2"
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) +
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#ylab('my custom height') +
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theme(axis.text.x = element_blank()) +
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# theme_logo()+
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# scale_x_continuous(breaks=1:51, parse (text = colnames(tab)) )
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scale_x_continuous(breaks = 1:ncol(tab_mt)
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, labels = colnames(tab_mt))+
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scale_y_continuous( breaks = 1:my_ymax
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, limits = my_ylim)
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p0
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# further customisation
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p1 = p0 + theme(legend.position = "bottom"
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, legend.title = element_blank()
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, legend.text = element_text(size = my_lts)
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, axis.text.x = element_text(size = my_ats , angle = 90)
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, axis.text.y = element_text(size = my_ats , angle = 90))
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p1
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#=======
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# input
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#=======
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#############
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# msa file: output of generate_mut_sequences.py
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#############
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homedir = '~'
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indir = 'git/Data/pyrazinamide/output'
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in_filename = "gene_msa.txt"
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infile = paste0(homedir, '/', indir,'/', in_filename)
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print(infile)
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##############
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# ggseqlogo: custom matrix of my data
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##############
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snps = read.csv(infile
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, stringsAsFactors = F
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, header = F) #3072,
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class(snps); str(snps) # df and chr
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# turn to a character vector
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snps2 = as.character(snps[1:nrow(snps),])
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class(snps2); str(snps2) #character, chr
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# plot
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logomaker(snps2, type = my_type
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, color_type = "per_row") +
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theme(axis.text.x = element_blank()) +
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theme_logo()+
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# scale_x_continuous(breaks=1:51, parse (text = colnames(tab)) )
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scale_x_continuous(breaks = 1:ncol(tab_mt)
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, labels = colnames(tab_mt))+
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scale_y_continuous( breaks = 0:5
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, limits = my_ylim)
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