added logo plot
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scripts/plotting/logo_plot.R
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scripts/plotting/logo_plot.R
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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$position) #73
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#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
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#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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# REASSIGNMENT to prevent changing code
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my_data_snp = my_data
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#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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#=======================================================================
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#%% logo plots from dataframe
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#############
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# PLOTS: ggseqlogo with custom height
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# https://omarwagih.github.io/ggseqlogo/
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#############
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#require(ggplot2)
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#require(tidyverse)
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library(ggseqlogo)
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foo = my_data_snp[, c("position", "mutant_type","duet_scaled", "or_mychisq"
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, "mut_prop_polarity", "mut_prop_water") ]
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my_data_snp$log10or = log10(my_data_snp$or_mychisq)
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bar = my_data_snp[, c("position", "mutant_type", "or_mychisq", "log10or")]
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bar_or = my_data_snp[, c("position", "mutant_type", "or_mychisq")]
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wide_df_or <- bar_or %>% spread(position, or_mychisq, 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 log10R
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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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# data was pnca_msa.txt
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seqs = read.csv("~/git//Data/pyrazinamide/snp_seqsfile.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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