273 lines
7.2 KiB
R
273 lines
7.2 KiB
R
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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my_df = read.csv(file.choose())
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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)
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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 = "none"
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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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p1
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#**************
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# Plot 2: for wild_type
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# with custom x axis to reflect my aa positions
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#**************
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# sanity check: MUST BE TRUE
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# for the correctnes of the x axis
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identical(colnames(tab_mt), colnames(tab_wt))
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identical(ncol(tab_mt), ncol(tab_wt))
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p2 = ggseqlogo(tab_wt
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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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, axis.text.y = element_blank()) +
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theme_logo() +
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scale_x_continuous(breaks = 1:ncol(tab_wt)
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, labels = colnames(tab_wt)) +
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scale_y_continuous( breaks = 0:1
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, limits = my_ylim )
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p2
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# further customise
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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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, angle = 90)
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, axis.text.y = element_blank())
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p3
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# Now combine using cowplot, which ensures the plots are aligned
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suppressMessages( require(cowplot) )
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plot_grid(p1, p3, ncol = 1, align = 'v') #+
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# background_grid(minor = "xy"
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# , size.minor = 1
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# , colour.minor = "grey86")
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#colour scheme
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#https://rdrr.io/cran/ggseqlogo/src/R/col_schemes.r
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