added ed_pfm_data.R function and its corresponding test
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142
scripts/functions/ed_pfm_data.R
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142
scripts/functions/ed_pfm_data.R
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source("~/git/LSHTM_analysis/scripts/functions/my_logolas.R")
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#####################################################################################
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# DataED_PFM():
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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 [Optional]: MSA chr vector for wt
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# Others params:
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# ED_score = 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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# Returns data for ED plot from MSA
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# Mut matrix:
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# PFM matrix
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# PFM matrix scaled
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# ED matrix
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# Wt matrix [optional]
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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: ED score type (in the actual plot function!)
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# drop down/enter field : bg probability (in the actual plot function!)
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# Make it hover over position and then get the corresponding data table!
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########################a###########################################################
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DataED_PFM <- function(msaSeq_mut
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, msaSeq_wt
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, ED_score = c("log")
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, bg_prob = NULL)
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{
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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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############################################
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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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pfm_mutM = matrix()
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pfm_mut_scaledM = matrix()
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combED_mutM = matrix()
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#--------------------------
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# Getting PFM: mutant MSA
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#--------------------------
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pfm_mutM <- Biostrings::consensusMatrix(msaSeq_mut)
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colnames(pfm_mutM) <- 1:dim(pfm_mutM)[2]
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pfm_mut_scaledM <- do.call(dash, append(list(comp_data = pfm_mutM),
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dash_control))$posmean
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logo_mut_h = get_logo_heights(pfm_mut_scaledM
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, bg = bg_prob
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, score = ED_score)
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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_mutM)[2]){
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cat("\nPASS: pfm calculated successfully including scaled matrix"
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, "\nDim of pfm matrix:", dim(pfm_mutM)[1], dim(pfm_mutM)[2])
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}
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combED_mutM = pos_mutED + neg_mutED
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# initialise the mut list
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names_mutL = c("pfm_mutM", "pfm_mut_scaledM", "combED_mutM")
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EDmutDataL = vector("list", length(names_mutL))
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EDmutDataL = list(pfm_mutM, pfm_mut_scaledM, combED_mutM)
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names(EDmutDataL) = names_mutL
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#---------------------
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# Getting PFM: WT
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#---------------------
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if(!missing(msaSeq_wt)){
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cat("\nLength of WT seq", length(msaSeq_wt))
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pfm_wtM = matrix()
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pfm_wt_scaledM = matrix()
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combED_wtM = matrix()
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pfm_wtM <- Biostrings::consensusMatrix(msaSeq_wt)
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colnames(pfm_wtM) <- 1:dim(pfm_wtM)[2]
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pfm_wt_scaledM <- do.call(dash, append(list(comp_data = pfm_wtM),
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dash_control))$posmean
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logo_wt_h = get_logo_heights(pfm_wt_scaledM
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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_wtM)[2]){
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cat("\nPASS: pfm calculated successfully including scaled matrix"
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, "\nDim of pfm matrix:", dim(pfm_wtM)[1], dim(pfm_wtM)[2])
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}
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combED_wtM = pos_wtED + neg_wtED
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# initialise the wt list
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names_wtL = c("pfm_wtM", "pfm_wt_scaledM", "combED_wtM")
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EDwtDataL = vector("list", length(names_wtL))
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EDwtDataL = list(pfm_wtM, pfm_wt_scaledM, combED_wtM)
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names(EDwtDataL) = names_wtL
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# Combine two lists
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EDallDataL = append(EDmutDataL, EDwtDataL)
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cat("\nReturning output for Mut + WT"
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, "\nLength of all data:", length(EDallDataL))
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return(EDallDataL)
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}else{
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cat("\nReturning output for Mut data only"
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, "\nLength of Mut data:", length(EDmutDataL))
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return(EDmutDataL)
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}
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}
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319
scripts/functions/redundant/logoP_msa_raw.R
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scripts/functions/redundant/logoP_msa_raw.R
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#####################################################################################
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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 [Optional]: MSA chr vector for wt
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# Others params:
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# plot_positions: can choose what positions to plot
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# msa_method : can be "bits" or "probability"
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# my_logo_col : can be "chemistry", "hydrophobicity", "taylor" or "clustalx"
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# Returns data LogoPlot from MSA
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#...
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# TODO: SHINY
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# drop down: my_logo_col i.e the 4 colour choices
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# drop down: for DataED_PFM(), ED score options:
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# c("log", log-odds", "diff", "probKL", "ratio", "unscaled_log", "wKL")
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# drop down/enter field: for DataED_PFM(), background probability
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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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# logo data: OR
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#==================
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LogoPlotMSA <- function(msaSeq_mut
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, msaSeq_wt
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, plot_positions
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, msa_method = 'bits' # or probability
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, my_logo_col = "chemistry"
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, x_lab = "Wild-type position"
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, y_lab = ""
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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 = 0.07 # dist b/w y-axis and plot start
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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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############################################
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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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if(missing(plot_positions)){
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#if(is.null(plot_positions)){
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cat("\n======================="
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, "\nPlotting entire MSA"
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, "\n========================")
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msa_seq_plot = msaSeq_mut
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msa_all_interim = sapply(msa_seq_plot, function(x) unlist(strsplit(x,"")))
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msa_all_interimDF = data.frame(msa_all_interim)
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msa_all_pos = as.numeric(rownames(msa_all_interimDF))
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wt_seq_plot = msaSeq_wt
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wt_all_interim = sapply(wt_seq_plot, function(x) unlist(strsplit(x,"")))
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wt_all_interimDF = data.frame(wt_all_interim)
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wt_all_pos = as.numeric(rownames(wt_all_interimDF))
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} else {
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cat("\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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#-----------
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# MSA: mut
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#-----------
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cat("\n==========================================="
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, "\nGenerating MSA: filtered positions"
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, "\n===========================================")
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msa_interim = sapply(msaSeq_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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if (all(plot_positions%in%msa_pos)){
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cat("\nAll positions within range"
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, "\nProceeding with generating requested position MSA seqs..."
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, "\nNo. of positions in plot:", length(plot_positions))
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i_extract = plot_positions
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dfP1 = msa_interimDF[i_extract,]
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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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#i_extract = plot_positions[plot_positions%in%msa_pos]
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#cat("\nFinal no. of positions being plottted:", length(i_extract)
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# , "\nNo. of positions dropped from request:", length(i_ofr))
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quit()
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}
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#matP1 = msa_interim[i_extract, 1:ncol(msa_interim)]
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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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msa_seq_plot = dfP1$chosen_seq
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#-----------
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# WT: fasta
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#-----------
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cat("\n========================================="
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, "\nGenerating WT fasta: filtered positions"
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,"\n===========================================")
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wt_interim = sapply(msaSeq_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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if (all(plot_positions%in%wt_pos)){
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cat("\nAll positions within range"
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, "\nProceeding with generating requested position MSA seqs..."
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, "\nplot positions:", length(plot_positions))
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i2_extract = plot_positions
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}else{
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cat("\nNo. of positions selected:", length(plot_positions))
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i2_ofr = plot_positions[!plot_positions%in%wt_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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#i2_extract = plot_positions[plot_positions%in%wt_pos]
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#cat("\nFinal no. of positions being plottted:", length(i2_extract)
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# , "\nNo. of positions dropped from request:", length(i2_ofr))
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quit()
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}
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#matP1 = msa_interim[i_extract, 1:ncol(msa_interim)]
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dfP2 = wt_interimDF[i2_extract,]
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dfP2 = data.frame(t(dfP2))
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names(dfP2) = i2_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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wt_seq_plot = dfP2$chosen_seq
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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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LogoPlotMSAL <- list()
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#-------------------
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# Mutant logo plot
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#-------------------
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p0 = ggseqlogo(msa_seq_plot
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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 = 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)
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if (missing(plot_positions)){
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msa_mut_logo_P = p0 +
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scale_x_discrete(breaks = msa_all_pos
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, expand = c(0.02,0)
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, labels = msa_all_pos
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, limits = factor(msa_all_pos))
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}else{
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msa_mut_logo_P = p0 +
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scale_y_continuous(expand = c(0,0.09)) +
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scale_x_discrete(breaks = i_extract
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, expand = c(x_axis_offset,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 mutations')
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#return(msa_mut_logoP)
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LogoPlotMSAL[['msa_mut_logoP']] <- msa_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(wt_seq_plot
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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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msa_wt_logo_P = p1 +
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scale_x_discrete(breaks = wt_all_pos
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, expand = c(0.02,0)
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, labels = wt_all_pos
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, limits = factor(wt_all_pos) )
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}else{
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msa_wt_logo_P = p1 +
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scale_y_continuous(expand = c(0,0.09)) +
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scale_x_discrete(breaks = i2_extract
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, expand = c(x_axis_offset, 0)
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, labels = i2_extract
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, limits = factor(i2_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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LogoPlotMSAL[['msa_wt_logoP']] <- msa_wt_logo_P
|
||||
|
||||
#=========================================
|
||||
# Output
|
||||
# Combined plot: logo_MSA
|
||||
#=========================================
|
||||
|
||||
cat('\nDone: msa_mut_logoP + msa_wt_logoP')
|
||||
|
||||
# colour scheme: https://rdrr.io/cran/ggseqlogo/src/R/col_schemes.r
|
||||
#cat("\nOutput plot:", LogoSNPs_comb, "\n")
|
||||
#svg(LogoSNPs_combined, width = 32, height = 10)
|
||||
|
||||
LogoMSA_comb = cowplot::plot_grid(LogoPlotMSAL[['msa_mut_logoP']]
|
||||
, LogoPlotMSAL[['msa_wt_logoP']]
|
||||
, nrow = 2
|
||||
, align = "v"
|
||||
, rel_heights = c(3/4, 1/4))
|
||||
|
||||
return(LogoMSA_comb)
|
||||
|
||||
}
|
||||
36
scripts/functions/tests/test_ed_pfm_data.R
Normal file
36
scripts/functions/tests/test_ed_pfm_data.R
Normal file
|
|
@ -0,0 +1,36 @@
|
|||
# data msa: mut
|
||||
my_data = read.csv("/home/tanu/git/Misc/practice_plots/pnca_msa_eg2.csv", header = F) #15 cols only
|
||||
msaSeq_mut = my_data$V1
|
||||
msa_seq = msaSeq_mut
|
||||
|
||||
# data msa: wt
|
||||
gene = "pncA"
|
||||
drug = "pyrazinamide"
|
||||
indir = paste0("~/git/Data/", drug , "/input/")
|
||||
|
||||
in_filename_fasta = paste0(tolower(gene), "2_f2.fasta")
|
||||
infile_fasta = paste0(indir, in_filename_fasta)
|
||||
cat("\nInput fasta file for WT: ", infile_fasta, "\n")
|
||||
|
||||
msa2 = read.csv(infile_fasta, header = F)
|
||||
head(msa2)
|
||||
cat("\nLength of WT fasta:", nrow(msa2))
|
||||
wt_seq = msa2$V1
|
||||
head(wt_seq)
|
||||
msaSeq_wt = msa2$V1
|
||||
wt_seq = msaSeq_wt
|
||||
|
||||
################################
|
||||
# DataED_PFM():
|
||||
# script: ed_pfm_data.R
|
||||
source("~/git/LSHTM_analysis/scripts/functions/ed_pfm_data.R")
|
||||
################################
|
||||
|
||||
data_ed = DataED_PFM(msa_seq, wt_seq)
|
||||
names(data_ed)
|
||||
|
||||
#par(mfrow = c(2,1))
|
||||
logomaker(msa_seq, type = "EDLogo")
|
||||
ggseqlogo(data_ed[['combED_mutM']]
|
||||
, method = "custom")
|
||||
|
||||
Loading…
Add table
Add a link
Reference in a new issue