added 2 logo plot scripts
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278
mcsm_analysis/pyrazinamide/scripts/plotting/logo_plot_logolas.R
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278
mcsm_analysis/pyrazinamide/scripts/plotting/logo_plot_logolas.R
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getwd()
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setwd("~/git/LSHTM_Y1_PNCA/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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# Read file: call script for combining df for lig #
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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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# 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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# extract freq_pos>1 since this will not add to much in the logo plot
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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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#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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#==============
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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)
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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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# EDlogo
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logomaker(tab_mt
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, type = "EDLogo"
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# , type = "Logo"
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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_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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p0 = logomaker(tab_mt
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, type = "EDLogo"
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, return_heights = T
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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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#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 = 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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p1
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#####
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logomaker(snps2, type = "EDLogo"
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, color_type = "per_symbol") +
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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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272
mcsm_analysis/pyrazinamide/scripts/plotting/snp_logo_plot.R
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272
mcsm_analysis/pyrazinamide/scripts/plotting/snp_logo_plot.R
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@ -0,0 +1,272 @@
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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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# TASK: Multiple mutations per site
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# as aa symbol coloured by aa property
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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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# Read file: call script for combining df for lig #
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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 small df i.e
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# merged_df3
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# or
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# merged_df3_comp? possibly
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# since these have unique SNPs
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# I prefer to use the merged_df3
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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_df3 # to show multiple mutations per site
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#%%%%%%%%%%%%%%%%%%%%%%%%
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rm(merged_df2, merged_df2_comp, merged_df3, merged_df3_comp)
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colnames(my_df)
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str(my_df)
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rownames(my_df) = my_df$Mutationinformation
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c1 = unique(my_df$Position) #130
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nrow(my_df) #335
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table(my_df$occurrence)
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#1 2 3 4 5 6
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#34 76 63 104 40 18
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# get freq count of positions so you can subset freq<1
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#: already done in teh combining script
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#require(data.table)
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#setDT(my_df)[, occurrence := .N, by = .(Position)] #189, 36
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table(my_df$Position); table(my_df$occurrence)
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# extract freq_pos>1
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my_data_snp = my_df[my_df$occurrence!=1,] #301, 36
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u_pos = unique(my_data_snp$Position) #96
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# sanity check
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exp_dim = nrow(my_df) - table(my_df$occurrence)[[1]]; exp_dim
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if ( nrow(my_data_snp) == exp_dim ){
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print("Sanity check passed: Data filtered correctly, dim match")
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} else {
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print("Error: Please Debug")
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}
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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 data 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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# 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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##############
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# ggseqlogo: custom matrix of my data
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##############
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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$Position)
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class(tab_mt)
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# unclass to convert to matrix
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tab_mt = unclass(tab_mt)
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tab_mt = as.matrix(tab_mt, rownames = T)
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# should be TRUE
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is.matrix(tab_mt)
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rownames(tab_mt) #aa
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colnames(tab_mt) #pos
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#==============
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# matrix for wild type
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# frequency of wild type by position
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#==============
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# remove wt duplicates
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wt = my_data_snp[, c("Position", "Wild_type")] #301, 2
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wt = wt[!duplicated(wt),]#96, 2
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table(wt$Wild_type) # contains duplicates
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tab_wt = table(wt$Wild_type, wt$Position); tab_wt # should all be 1
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tab_wt = unclass(tab_wt) #important
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class(tab_wt); rownames(tab_wt)
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#tab_wt = as.matrix(tab_wt, rownames = T)
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rownames(tab_wt)
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rownames(tab_mt)
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# sanity check
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if (ncol(tab_wt) == length(u_pos) ){
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print("Sanity check passed: wt data dim match")
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} else {
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print("Error: Please debug")
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}
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#**************
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# Plot 1: mutant logo
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#**************
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#install.packages("digest")
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#library(digest)
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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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library(ggseqlogo)
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# generate seq logo for mutant type
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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_yrange = 1:my_ymax; my_yrange
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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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p0 = ggseqlogo(tab_mt
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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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# 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_mt)) )
|
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scale_x_continuous(breaks = 1:ncol(tab_mt)
|
||||
, labels = colnames(tab_mt))+
|
||||
scale_y_continuous( breaks = 1:my_ymax
|
||||
, limits = my_ylim)
|
||||
|
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p0
|
||||
|
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# further customisation
|
||||
p1 = p0 + theme(legend.position = "none"
|
||||
, legend.title = element_blank()
|
||||
, legend.text = element_text(size = my_lts)
|
||||
, axis.text.x = element_text(size = my_xats, angle = 90)
|
||||
, axis.text.y = element_text(size = my_yats, angle = 90))
|
||||
p1
|
||||
|
||||
#**************
|
||||
# Plot 2: for wild_type
|
||||
# with custom x axis to reflect my aa positions
|
||||
#**************
|
||||
# sanity check: MUST BE TRUE
|
||||
# for the correctnes of the x axis
|
||||
identical(colnames(tab_mt), colnames(tab_wt))
|
||||
identical(ncol(tab_mt), ncol(tab_wt))
|
||||
|
||||
p2 = ggseqlogo(tab_wt
|
||||
, method = 'custom'
|
||||
, seq_type = 'aa'
|
||||
# , col_scheme = "taylor"
|
||||
# , col_scheme = chemistry2
|
||||
) +
|
||||
# ylab('my custom height') +
|
||||
theme(axis.text.x = element_blank()
|
||||
, axis.text.y = element_blank()) +
|
||||
theme_logo() +
|
||||
scale_x_continuous(breaks = 1:ncol(tab_wt)
|
||||
, labels = colnames(tab_wt)) +
|
||||
scale_y_continuous( breaks = 0:1
|
||||
, limits = my_ylim )
|
||||
|
||||
p2
|
||||
|
||||
# further customise
|
||||
|
||||
p3 = p2 +
|
||||
theme(legend.position = "bottom"
|
||||
, legend.text = element_text(size = my_lts)
|
||||
, axis.text.x = element_text(size = my_ats-
|
||||
, angle = 90)
|
||||
, axis.text.y = element_blank())
|
||||
|
||||
p3
|
||||
|
||||
|
||||
# Now combine using cowplot, which ensures the plots are aligned
|
||||
suppressMessages( require(cowplot) )
|
||||
|
||||
plot_grid(p1, p3, ncol = 1, align = 'v') #+
|
||||
# background_grid(minor = "xy"
|
||||
# , size.minor = 1
|
||||
# , colour.minor = "grey86")
|
||||
|
||||
|
||||
#colour scheme
|
||||
#https://rdrr.io/cran/ggseqlogo/src/R/col_schemes.r
|
||||
|
||||
Loading…
Add table
Add a link
Reference in a new issue