462 lines
16 KiB
R
462 lines
16 KiB
R
#####################################################################################
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# LogoPlotMSA():
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# Input:
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# Data:
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# msaSeq_mut: MSA chr vector for muts
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# msaSeq_wt: MSA chr vector for wt
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# Logo type params:
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# logo_type = c("EDLogo", "bits_pfm", "probability_pfm", "bits_raw", "probability_raw")
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# EDLogo: calculated from the Logolas package based on PFM matrix (scaled).
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#The required content from the package is sourced locally within 'my_logolas.R'
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# bits_pfm: Information Content based on PFM scaled matrix (my_logolas.R)
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# probability_pfm: Probability based on PFM scaled matrix (my_logolas.R)
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# bits_raw: Information Content based on Raw MSA (ggseqlogo)
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# probability_raw: Probability based on Raw MSA (ggseqlogo)
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# EDScore_type = c("log", log-odds", "diff", "probKL", "ratio", "unscaled_log", "wKL")
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# bg_prob: background probability, default is equal i.e NULL.
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# This is used by the internal call to DataED_PFM(). This func takes thse args. I have used it here for
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# completeness and allow nuanced plot control
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# my_logo_col = c("chemistry", "hydrophobicity", "clustalx", "taylor")
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# --> if clustalx and taylor, set variable to black bg + white font
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# --> if chemistry and hydrophobicity, then grey bg + black font
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# ...other params
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# Returns: Logo plots from MSA both mutant and wt (for comparability)
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# For my case, I always use it as it helps see what is at the wild-type already!
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# TODO: SHINY
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# drop down: logo_type
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# drop down: ED score type
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# drop down/enter field : bg probability (in the actual plot function!)
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# drop down: my_logo_col
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# Make it hover over position and then get the corresponding data table!
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###################################################################################
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###########################################
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#LogoPlotMSA <- function(msaSeq_mut # chr vector
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# , msaSeq_wt # chr vector
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LogoPlotMSA <- function(unified_msa
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, logo_type = c("EDLogo") #"bits_pfm", "probability_pfm", "bits_raw", "probability_raw")
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, EDScore_type = c("log") # see if this relevant, or source function should have it!
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, bg_prob = NULL
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, my_logo_col = "chemistry"
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, plot_positions
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, y_breaks
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, x_lab_mut = "nsSNP-position"
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, y_lab_mut
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, x_ats = 13 # text size
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, x_tangle = 90 # text angle
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, x_axis_offset = 0.05 # dist b/w y-axis and plot start
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, x_axis_offset_filtered = 0.2
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, y_axis_offset = 0.05
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, y_ats = 13
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, y_tangle = 0
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, x_tts = 13 # title size
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, y_tts = 13
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, leg_pos = "top" # can be top, left, right and bottom or c(0.8, 0.9)
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, leg_dir = "horizontal" #can be vertical or horizontal
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, leg_ts = 16 # leg text size
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, leg_tts = 16 # leg title size
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)
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{
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# FIXME: Hack!
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# msaSeq_mut=unified_msa[[1]]
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# msaSeq_wt=unified_msa[[2]]
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msaSeq_mut=unified_msa[['msa_seq']]
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msaSeq_wt=unified_msa[['wt_seq']]
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# Get PFM matrix for mut and wt MSA provided
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data_ed = DataED_PFM(msaSeq_mut
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, msaSeq_wt
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, ED_score = EDScore_type)
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names(data_ed)
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#"pfm_mutM" "pfm_mut_scaledM" "combED_mutM" "pfm_wtM" "pfm_wt_scaledM" "combED_wtM"
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if (logo_type == "EDLogo"){
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msa_method = "custom"
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y_label = "Enrichment Score"
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data_logo_mut = data_ed[['combED_mutM']]
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data_logo_wt = data_ed[['combED_wtM']]
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msa_pos = as.numeric(colnames(data_logo_mut))
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wt_pos = as.numeric(colnames(data_logo_wt))
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# Construct Y-axis for MSA mut plot:
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cat("\nCalculating y-axis for MSA mut plot")
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if ( missing(y_breaks) ){
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# Y-axis: Calculating
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cat("\n----------------------------------------"
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, "\nY-axis being generated from data"
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, "\n-----------------------------------------")
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ylim_low <- floor(min(data_logo_mut)); ylim_low
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if( ylim_low == 0){
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ylim_low = ylim_low
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cat("\nY-axis lower limit:", ylim_low)
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y_rlow = seq(0, ylim_low, length.out = 3); y_rlow
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ylim_up <- ceiling(max(data_logo_mut)) + 5; ylim_up
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cat("\nY-axis upper limit:", ylim_up)
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y_rup = seq(0, ylim_up, by = 2); y_rup
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}else{
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ylim_low = ylim_low + (-0.5)
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cat("\nY-axis lower limit is <0:", ylim_low)
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y_rlow = seq(0, ylim_low, length.out = 3); y_rlow
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ylim_up <- ceiling(max(data_logo_mut)) + 3; ylim_up
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cat("\nY-axis upper limit:", ylim_up)
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y_rup = seq(0, ylim_up, by = 3); y_rup
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}
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#ylim_scale <- unique(sort(c(y_rlow, y_rup, ylim_up))); ylim_scale
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ylim_scale <- unique(sort(c(y_rlow, y_rup))); ylim_scale
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cat("\nY-axis generated: see below\n"
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, ylim_scale)
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}else{
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# Y-axis: User provided
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cat("\n--------------------------------"
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, "\nUsing y-axis:: User provided"
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,"\n---------------------------------")
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ylim_scale = sort(y_breaks)
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ylim_low = min(ylim_scale); ylim_low
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ylim_up = max(ylim_scale); ylim_up
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}
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}
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if (logo_type == "bits_pfm"){
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msa_method = "bits"
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y_label = "Bits (PFM)"
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data_logo_mut = data_ed[['pfm_mut_scaledM']]
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data_logo_wt = data_ed[['pfm_wtM']]
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msa_pos = as.numeric(colnames(data_logo_mut))
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wt_pos = as.numeric(colnames(data_logo_wt))
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}
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if (logo_type == "probability_pfm"){
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msa_method = "probability"
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y_label = "Probability (PFM)"
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data_logo_mut = data_ed[['pfm_mut_scaledM']]
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data_logo_wt = data_ed[['pfm_wtM']]
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msa_pos = as.numeric(colnames(data_logo_mut))
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wt_pos = as.numeric(colnames(data_logo_wt))
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}
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if (logo_type == "bits_raw"){
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msa_method = "bits"
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y_label = "Bits"
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data_logo_mut = msaSeq_mut
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msa_interim = sapply(data_logo_mut, function(x) unlist(strsplit(x,"")))
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msa_interimDF = data.frame(msa_interim)
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msa_pos = as.numeric(rownames(msa_interimDF))
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data_logo_wt = msaSeq_wt
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wt_interim = sapply(data_logo_wt, function(x) unlist(strsplit(x,"")))
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wt_interimDF = data.frame(wt_interim)
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wt_pos = as.numeric(rownames(wt_interimDF))
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}
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if (logo_type == "probability_raw"){
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msa_method = "probability"
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y_label = "Probability"
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data_logo_mut = msaSeq_mut
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msa_interim = sapply(data_logo_mut, function(x) unlist(strsplit(x,"")))
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msa_interimDF = data.frame(msa_interim)
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msa_pos = as.numeric(rownames(msa_interimDF))
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data_logo_wt = msaSeq_wt
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wt_interim = sapply(data_logo_wt, function(x) unlist(strsplit(x,"")))
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wt_interimDF = data.frame(wt_interim)
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wt_pos = as.numeric(rownames(wt_interimDF))
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}
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#################################################################################
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# param: plot_position
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#################################################################################
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if(missing(plot_positions)){
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#================================
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# NO filtering of positions
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#================================
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#---------
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# MSA mut
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#---------
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cat("\n==========================================="
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, "\nGenerated PFM mut: No filtering"
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, "\n===========================================")
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plot_mut_edM = data_logo_mut
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#---------
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# MSA WT
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#---------
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cat("\n==========================================="
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, "\nGenerated PFM WT: No filtering"
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, "\n===========================================")
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plot_wt_edM = data_logo_wt
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}else{
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#================================
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# Filtering of positions
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#================================
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cat("\n==========================================="
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, "\nGenerating PFM MSA: filtered positions"
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, "\n==========================================="
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, "\nUser specified plotting positions for MSA:"
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, "\nThese are:\n", plot_positions
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, "\nSorting plot positions...")
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plot_positions = sort(plot_positions)
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cat("\nPlotting positions sorted:\n"
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, plot_positions)
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if ( all(plot_positions%in%msa_pos) && all(plot_positions%in%wt_pos) ){
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cat("\nAll positions within range"
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, "\nFiltering positions as specified..."
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, "\nNo. of positions in plot:", length(plot_positions))
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i_extract = plot_positions
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#-----------------
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# PFM: mut + wt
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#------------------
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if (logo_type%in%c("EDLogo", "bits_pfm", "probability_pfm")){
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plot_mut_edM = data_logo_mut[, i_extract]
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plot_wt_edM = data_logo_wt[, i_extract]
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}
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if (logo_type%in%c("bits_raw", "probability_raw")){
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#--------
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# Mut
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#--------
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dfP1 = msa_interimDF[i_extract,]
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dfP1 = data.frame(t(dfP1))
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names(dfP1) = i_extract
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cols_to_paste = names(dfP1)
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dfP1['chosen_seq'] = apply(dfP1[, cols_to_paste]
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, 1
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, paste, sep = ''
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, collapse = "")
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plot_mut_edM = dfP1$chosen_seq
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#--------
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# WT
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#--------
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dfP2 = wt_interimDF[i_extract,]
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dfP2 = data.frame(t(dfP2))
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names(dfP2) = i_extract
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cols_to_paste2 = names(dfP2)
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dfP2['chosen_seq'] = apply( dfP2[, cols_to_paste2]
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, 1
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, paste, sep = ''
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, collapse = "")
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plot_wt_edM = dfP2$chosen_seq
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}
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}else{
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cat("\nNo. of positions selected:", length(plot_positions))
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i_ofr = plot_positions[!plot_positions%in%msa_pos]
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cat("\n1 or more plot_positions out of range..."
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, "\nThese are:\n", i_ofr
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, "\nQuitting! Resubmit with correct plot_positions")
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quit()
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}
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}
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######################################
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# Generating plots for muts and wt
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#####################################
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if (my_logo_col %in% c('clustalx','taylor')) {
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cat("\nSelected colour scheme:", my_logo_col
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, "\nUsing black theme\n")
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theme_bgc = "black"
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xfont_bgc = "white"
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yfont_bgc = "white"
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xtt_col = "white"
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ytt_col = "white"
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}
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if (my_logo_col %in% c('chemistry', 'hydrophobicity')) {
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cat("\nstart of MSA"
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, '\nSelected colour scheme:', my_logo_col
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, "\nUsing grey theme")
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theme_bgc = "grey"
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xfont_bgc = "black"
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yfont_bgc = "black"
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xtt_col = "black"
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ytt_col = "black"
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}
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#####################################
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# Generating logo plots for nsSNPs
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#####################################
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PlotlogolasL <- list()
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#-------------------
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# Mutant logo plot
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#-------------------
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p0 = ggseqlogo(plot_mut_edM
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, method = msa_method
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, col_scheme = my_logo_col
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, seq_type = 'auto') +
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theme(legend.position = leg_pos
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, legend.direction = leg_dir
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#, legend.title = element_blank()
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, legend.title = element_text(size = leg_tts
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, colour = ytt_col)
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, legend.text = element_text(size = leg_ts)
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, axis.text.x = element_text(size = x_ats
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, angle = x_tangle
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, hjust = 1
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, vjust = 0.4
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, colour = xfont_bgc)
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#, axis.text.y = element_blank()
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, axis.text.y = element_text(size = y_ats
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, angle = y_tangle
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, hjust = 1
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, vjust = -1.0
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, colour = yfont_bgc)
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, axis.title.x = element_text(size = x_tts
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, colour = xtt_col)
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, axis.title.y = element_text(size = y_tts
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, colour = ytt_col)
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, plot.background = element_rect(fill = theme_bgc)) +
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xlab(x_lab_mut)
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if (missing(plot_positions)){
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ed_mut_logo_P = p0 +
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scale_x_discrete(breaks = msa_pos
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, expand = c(x_axis_offset, 0)
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, labels = msa_pos
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, limits = factor(msa_pos))
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}else{
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ed_mut_logo_P = p0 +
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scale_x_discrete(breaks = i_extract
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, expand = c(x_axis_offset_filtered, 0)
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, labels = i_extract
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, limits = factor(i_extract))
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}
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if (logo_type == "EDLogo"){
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ed_mut_logo_P = ed_mut_logo_P +
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scale_y_continuous(limits = c(ylim_low, ylim_up)
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, breaks = ylim_scale
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, expand = c(0, y_axis_offset)) +
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geom_hline(yintercept = 0
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, linetype = "solid"
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, color = "grey"
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, size = 1)
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}
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if (missing(y_lab_mut)){
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ed_mut_logo_P = ed_mut_logo_P + ylab(y_label)
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} else{
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ed_mut_logo_P = ed_mut_logo_P + ylab(y_lab_mut)
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}
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cat('\nDone: MSA plot for mutations')
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#return(msa_mut_logoP)
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PlotlogolasL[['ed_mut_logoP']] <- ed_mut_logo_P
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#---------------------------------
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# Wild-type MSA: gene_fasta file
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#---------------------------------
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p1 = ggseqlogo(plot_wt_edM
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#, facet = "grid"
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, method = msa_method
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, col_scheme = my_logo_col
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, seq_type = 'aa') +
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theme(legend.position = "none"
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, legend.direction = leg_dir
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#, legend.title = element_blank()
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, legend.title = element_text(size = leg_tts
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, colour = ytt_col)
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, legend.text = element_text(size = leg_ts)
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, axis.text.x = element_text(size = x_ats
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, angle = x_tangle
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, hjust = 1
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, vjust = 0.4
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, colour = xfont_bgc)
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, axis.text.y = element_blank()
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, axis.title.x = element_text(size = x_tts
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, colour = xtt_col)
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, axis.title.y = element_text(size = y_tts
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, colour = ytt_col)
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, plot.background = element_rect(fill = theme_bgc)) +
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ylab("") + xlab("Wild-type position")
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if (missing(plot_positions)){
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# No y-axis needed
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ed_wt_logo_P = p1 +
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scale_x_discrete(breaks = wt_pos
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, expand = c(x_axis_offset, 0)
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, labels = wt_pos
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, limits = factor(wt_pos))
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}else{
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ed_wt_logo_P = p1 +
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scale_x_discrete(breaks = i_extract
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, expand = c(x_axis_offset_filtered, 0)
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, labels = i_extract
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, limits = factor(i_extract))
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}
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cat('\nDone: MSA plot for WT')
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#return(msa_wt_logoP)
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PlotlogolasL[['ed_wt_logoP']] <- ed_wt_logo_P
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#=========================================
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# Output
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# Combined plot: logo ED/other logo plot
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# customised for ggseqlogo
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#=========================================
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cat('\nDone: ed_mut_logoP + ed_wt_logoP')
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# colour scheme: https://rdrr.io/cran/ggseqlogo/src/R/col_schemes.r
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#cat("\nOutput plot:", LogoSNPs_comb, "\n")
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#svg(LogoSNPs_combined, width = 32, height = 10)
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LogoED_comb = cowplot::plot_grid(PlotlogolasL[['ed_mut_logoP']]
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, PlotlogolasL[['ed_wt_logoP']]
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, nrow = 2
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, align = "v"
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, rel_heights = c(3/4, 1/4))
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return(LogoED_comb)
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}
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#LogoPlotMSA(unified_msa)
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