renamed file and updated logo plot code
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2 changed files with 96 additions and 117 deletions
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@ -1,38 +1,37 @@
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getwd()
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setwd("~/git/LSHTM_Y1_PNCA/mcsm_analysis/pyrazinamide/Scripts/Plotting")
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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("../Header_TT.R")
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#source("barplot_colour_function.R")
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#library(ggseqlogo)
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library(ggseqlogo)
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########################################################################
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# Read file: call script for combining df for lig #
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########################################################################
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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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#---------------------- PAY ATTENTION
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# the above changes the working dir
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#[1] "/home/tanu/git/LSHTM_Y1_PNCA/mcsm_analysis/pyrazinamide/Scripts"
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#---------------------- PAY ATTENTION
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#==========================
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# This will return:
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#merged_df2 # 3092, 35
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#merged_df2_comp #3012, 35
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#merged_df3 #335, 35
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#merged_df3_comp #293, 35
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#==========================
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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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@ -69,10 +68,40 @@ 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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@ -94,79 +123,6 @@ u = unique(my_data_snp$Position) #96
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##very good: http://www.cbs.dtu.dk/biotools/Seq2Logo-2.0/
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#############
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#PLOTS: Bar plot with aa properties
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#using gglogo
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#useful links: https://stackoverflow.com/questions/5438474/plotting-a-sequence-logo-using-ggplot2
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#############
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#following example
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require(ggplot2)
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require(reshape2)
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library(gglogo)
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library(ggrepel)
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#lmf <- melt(logodf, id.var='pos')
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foo = my_data_snp[, c("Position"
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, "Mutant_type"
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, "ratioDUET"
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, "OR"
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, "mut_prop_polarity"
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, "mut_prop_water") ]
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head(foo) #3019, 6
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foo = foo[order(foo$Position),]
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head(foo)
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##############
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# ggseqlogo
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#https://stackoverflow.com/questions/1439513/creating-a-sequential-list-of-letters-with-r
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##############
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# Some sample data for aa
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data(ggseqlogo_sample)
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seqs_aa = seqs_aa$AKT1
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class(seqs_aa); str(seqs_aa)
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# seq logo with custom x-axis
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ggseqlogo( seqs_aa$AKT1, seq_type='aa'
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, col_scheme = "hydrophobicity")+
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theme(legend.position = "top")
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#theme(axis.text.x = element_blank()) +
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theme_logo()#+
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#scale_x_continuous(breaks= 1:15
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#, expand = c(0.105, 0)
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# , labels = LETTERS[1:15]
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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(#'../Data/snps_msa2.txt'
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# '../Data/snps_msa.txt'
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'../Data/gene_msa.txt'
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, stringsAsFactors = F
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, header = F) #3072,
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class(snps)
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snps2 = as.character(snps[1:nrow(snps),])
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class(snps2); str(snps2)
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ggseqlogo(snps2) # COMPLAINS about length of each sequence if snps_msa2 is used
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#### NOT WORKING
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#source("http://bioconductor.org/biocLite.R")
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#install.packages("BiocManager")
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#library(BiocManager)
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BiocManager::install("Logolas")
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#biocLite("Logolas")
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library("Logolas")
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#https://kkdey.github.io/Logolas-pages/workflow.html
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# partially working
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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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@ -188,7 +144,7 @@ colnames(tab_mt) #pos
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# Plot 1: mutant logo
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#**********************
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my_ymax = max(my_data_snp$occurrence); my_ymax
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my_ylim = c(0,my_ymax)
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my_ylim = c(0,my_ymax) # very important
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# axis sizes
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# common: text and label
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@ -213,38 +169,38 @@ chemistry = data.frame(
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stringsAsFactors = F
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)
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# uncomment as necessary
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my_type = "EDLogo"
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my_type = "Logo"
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# EDlogo
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logomaker(tab_mt
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, type = "EDLogo"
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# , type = "Logo"
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, type = my_type
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, return_heights = T
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, color_type = "per_row"
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, colors = chemistry$col
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# , color_type = "per_row"
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# , colors = chemistry$col
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# , method = 'custom'
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# , seq_type = 'aa'
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# , col_scheme = "taylor"
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# , col_scheme = "chemistry2"
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) +
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theme(legend.position = "bottom"
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, legend.title = element_blank()
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, legend.text = element_text(size = my_lts )
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, axis.text.x = element_text(size = my_xats , angle = 90)
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# , axis.text.y = element_text(size = my_yats , angle = 90)
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)
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, axis.text.x = element_text(size = my_ats , angle = 90)
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, axis.text.y = element_text(size = my_ats , angle = 90))
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p0 = logomaker(tab_mt
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, type = "EDLogo"
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, type = my_type
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, return_heights = T
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# , method = 'custom'
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, color_type = "per_row"
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, colors = chemistry$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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# 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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@ -256,23 +212,46 @@ 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 = leg_size)
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, axis.text.x = element_text(size = x_size , angle = 90)
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, axis.text.y = element_text(size = y_size , angle = 90))
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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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##############
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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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logomaker(snps2, type = "EDLogo"
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, color_type = "per_symbol") +
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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 = 1:my_ymax
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scale_y_continuous( breaks = 0:5
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, limits = my_ylim)
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@ -251,7 +251,7 @@ p2
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p3 = p2 +
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theme(legend.position = "bottom"
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, legend.text = element_text(size = my_lts)
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, axis.text.x = element_text(size = my_ats-
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, axis.text.x = element_text(size = my_ats
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, angle = 90)
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, axis.text.y = element_blank())
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