combined logolas and raw data msa plots into 1 script and called it the same as before logoP_msa.R
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1 changed files with 196 additions and 170 deletions
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@ -1,14 +1,17 @@
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source("~/git/LSHTM_analysis/scripts/plotting/Header_TT.R")
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source("~/git/LSHTM_analysis/scripts/functions/ed_pfm_data.R")
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###########################################
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PlotLogolasMSA <- function(msaSeq_mut # chr vector
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, msaSeq_wt # chr vector
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, msa_method = c("custom") # will be c("EDLogo", "Logo)#
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, ED_score = c("log")# can be: "log-odds", "diff", "probKL", "ratio", "unscaled_log", "wKL"
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#, msa_method = c("custom") # can be "bits", "probability" or "custom"
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, logo_type = c("EDLogo") #"bits_pfm", "probability_pfm", "bits_raw", "probability_raw") # can be "bits", "probability" or "custom"
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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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, 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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@ -25,115 +28,148 @@ PlotLogolasMSA <- function(msaSeq_mut # chr vector
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)
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{
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#''' Can be put into a separate EDData plot function'''
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dash_control = list()
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dash_control_default <- list(concentration = NULL, mode = NULL,
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optmethod = "mixEM", sample_weights = NULL, verbose = FALSE,
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bf = TRUE, pi_init = NULL, squarem_control = list(),
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dash_control = list(), reportcov = FALSE)
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dash_control <- modifyList(dash_control_default, dash_control)
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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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############################################
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# Data processing for logo plot for nsSNPS
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###########################################
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cat("\nLength of MSA", length(msaSeq_mut)
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, "\nlength of WT seq:", length(msaSeq_wt))
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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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cat("\n======================="
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, "\nPlotting entire MSA"
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, "\n========================")
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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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#--------------------------
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# Getting PFM: mutant MSA
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#--------------------------
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pfm_mut <- Biostrings::consensusMatrix(msaSeq_mut)
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colnames(pfm_mut) <- 1:dim(pfm_mut)[2]
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pfm_mut_scaled <- do.call(dash, append(list(comp_data = pfm_mut),
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dash_control))$posmean
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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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logo_mut_h = get_logo_heights(pfm_mut_scaled
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, bg = bg_prob
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, score = ED_score)
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ylim_up <- ceiling(max(data_logo_mut)) + 4; 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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cat("\nGetting logo_heights from Logolas package...")
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pos_mutM = logo_mut_h[['table_mat_pos_norm']]; pos_mutM
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pos_mutS = logo_mut_h[['pos_ic']]; pos_mutS
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pos_mutED = t(pos_mutS*t(pos_mutM)); pos_mutED
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neg_mutM = logo_mut_h[['table_mat_neg_norm']]*(-1)
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neg_mutS = logo_mut_h[['neg_ic']]; neg_mutS
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neg_mutED = t(neg_mutS*t(neg_mutM)); neg_mutED
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if (length(pos_mutS) && length(neg_mutS) == dim(pfm_mut)[2]){
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cat("\nPASS: pfm calculated successfully including scaled matrix"
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, "\nDim of pfm matrix:", dim(pfm_mut)[1], dim(pfm_mut)[2])
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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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combED_mutM = pos_mutED + neg_mutED
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# Construct the x-axis: mutant MSA
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msa_all_pos = as.numeric(colnames(combED_mutM))
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#---------------------
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# Getting PFM: WT
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#---------------------
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pfm_wt <- Biostrings::consensusMatrix(msaSeq_wt)
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colnames(pfm_wt) <- 1:dim(pfm_wt)[2]
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pfm_wt_scaled <- do.call(dash, append(list(comp_data = pfm_wt),
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dash_control))$posmean
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logo_wt_h = get_logo_heights(pfm_wt_scaled
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, bg = bg_prob
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, score = ED_score)
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pos_wtM = logo_wt_h[['table_mat_pos_norm']]; pos_wtM
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pos_wtS = logo_wt_h[['pos_ic']]; pos_wtS
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pos_wtED = t(pos_wtS*t(pos_wtM)); pos_wtED
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neg_wtM = logo_wt_h[['table_mat_neg_norm']]*(-1)
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neg_wtS = logo_wt_h[['neg_ic']]; neg_wtS
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neg_wtED = t(neg_wtS*t(neg_wtM)); neg_wtED
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if (length(pos_wtS) && length(neg_wtS) == dim(pfm_wt)[2]){
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cat("\nPASS: pfm calculated successfully including scaled matrix"
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, "\nDim of pfm matrix:", dim(pfm_wt)[1], dim(pfm_wt)[2])
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}
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combED_wtM = pos_wtED + neg_wtED
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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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# Construct the x-axis: mutant MSA
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wt_all_pos = as.numeric(colnames(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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}
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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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# MSA mut: All, no filtering
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#-------------------------------
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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 = combED_mutM
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plot_mut_edM = data_logo_mut
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#------------------------------
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# MSA WT: All, no filtering
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#-------------------------------
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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 = combED_wtM
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plot_wt_edM = data_logo_wt
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}else{
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#------------------------------
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# PFM mut: Filtered positions
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#-------------------------------
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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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@ -146,17 +182,56 @@ PlotLogolasMSA <- function(msaSeq_mut # chr vector
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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_all_pos) && all(plot_positions%in%wt_all_pos) ){
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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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plot_mut_edM = combED_mutM[, i_extract]
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plot_wt_edM = combED_wtM[, i_extract]
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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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# 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_all_pos]
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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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@ -164,60 +239,10 @@ PlotLogolasMSA <- function(msaSeq_mut # chr vector
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}
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}
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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(combED_mutM)); 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(combED_mutM)) + 4; 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(combED_mutM)) + 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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# 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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@ -252,7 +277,6 @@ PlotLogolasMSA <- function(msaSeq_mut # chr vector
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# Mutant logo plot
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#-------------------
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p0 = ggseqlogo(plot_mut_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 = 'auto') +
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@ -281,30 +305,26 @@ PlotLogolasMSA <- function(msaSeq_mut # chr vector
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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) + ylab(y_lab_mut)
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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_all_pos
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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_all_pos
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, limits = factor(msa_all_pos))+
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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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, 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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#scale_y_continuous(expand = c(0,0.09)) +
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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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@ -314,6 +334,12 @@ PlotLogolasMSA <- function(msaSeq_mut # chr vector
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, size = 1)
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}
|
||||
|
||||
if (missing(y_lab_mut)){
|
||||
ed_mut_logo_P = ed_mut_logo_P + ylab(y_label)
|
||||
} else{
|
||||
ed_mut_logo_P = ed_mut_logo_P + ylab(y_lab_mut)
|
||||
}
|
||||
|
||||
cat('\nDone: MSA plot for mutations')
|
||||
#return(msa_mut_logoP)
|
||||
PlotlogolasL[['ed_mut_logoP']] <- ed_mut_logo_P
|
||||
|
|
@ -354,10 +380,10 @@ PlotLogolasMSA <- function(msaSeq_mut # chr vector
|
|||
|
||||
# No y-axis needed
|
||||
ed_wt_logo_P = p1 +
|
||||
scale_x_discrete(breaks = wt_all_pos
|
||||
scale_x_discrete(breaks = wt_pos
|
||||
, expand = c(x_axis_offset, 0)
|
||||
, labels = wt_all_pos
|
||||
, limits = factor(wt_all_pos))
|
||||
, labels = wt_pos
|
||||
, limits = factor(wt_pos))
|
||||
}else{
|
||||
|
||||
ed_wt_logo_P = p1 +
|
||||
Loading…
Add table
Add a link
Reference in a new issue