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Merge pull request #6 from singjc/master
Stable Release v1.0.0
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‎DESCRIPTION‎

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Package: DrawAlignR
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Type: Package
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Title: Visualizes Aligned Ms2 Chromatograms
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Version: 0.1.0
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Authors@R: c( person("Arshia", "Mahmoodi", email = "[email protected]", role = c("aut")),
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person( "Justin", "Sing", email = "[email protected]", role = c("cre") )
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Version: 1.0.0
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Authors@R: c( person( "Justin", "Sing", email = "[email protected]", role = c("cre") ),
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person("Arshia", "Mahmoodi", email = "[email protected]", role = c("aut"))
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)
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Maintainers: Arshia Mahmoodi <[email protected]>
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Maintainers: c( person( "Justin", "Sing", email = "[email protected]", role = c("cre") ),
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person("Arshia", "Mahmoodi", email = "[email protected]", role = c("aut"))
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)
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Description: A package for the visualization of aligned Ms2 chromatograms. The main intended use of this package is
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to be used downstream of a chromatogram alignment tool for ms2 chromatograms, to draw and visualize
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the newly generated aligned chromatograms.
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Depends: R (>= 3.6.1)
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Depends: R (>= 3.0)
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Imports: shiny,
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shinyjs,
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shinyFiles,

‎LICENSE‎

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MIT License
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Copyright (c) 2019 ArshiaMahmoodi
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Copyright (c) 2019 ArshiaMahmoodi and JustinSing
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal

‎NAMESPACE‎

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# Generated by roxygen2: do not edit by hand
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export(curateXICplot)
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export(getAlignObjs)
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export(getMZMLpointers)
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export(getOswAnalytes)
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export(getOswFiles)
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export(getXIC)
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export(getXICs)
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export(getXICs4AlignObj)
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export(plotAlignedAnalytes)
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export(plotAlignmentPath)
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export(runDrawAlignR)
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import(DIAlignR)
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import(mstools)
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import(plotly)
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import(shiny)
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import(shinyFiles)
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import(shinyjs)
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importFrom(MazamaCoreUtils,logger.error)
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importFrom(MazamaCoreUtils,logger.info)
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importFrom(MazamaCoreUtils,logger.isInitialized)
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importFrom(MazamaCoreUtils,logger.trace)
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importFrom(MazamaCoreUtils,logger.warn)
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importFrom(data.table,set)
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importFrom(crayon,blue)
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importFrom(crayon,bold)
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importFrom(crayon,magenta)
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importFrom(crayon,red)
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importFrom(crayon,underline)
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importFrom(dplyr,"%>%")
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importFrom(dplyr,arrange)
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importFrom(dplyr,bind_rows)
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importFrom(dplyr,distinct)
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importFrom(dplyr,filter)
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importFrom(dplyr,group_by)
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importFrom(dplyr,mutate)
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importFrom(dplyr,select)
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importFrom(dplyr,top_n)
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importFrom(ggplot2,aes)
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importFrom(ggplot2,element_text)
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importFrom(ggplot2,geom_line)
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importFrom(ggplot2,geom_rect)
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importFrom(ggplot2,geom_vline)
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importFrom(ggplot2,ggplot)
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importFrom(ggplot2,ggtitle)
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importFrom(ggplot2,guide_legend)
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importFrom(ggplot2,guides)
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importFrom(ggplot2,labs)
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importFrom(ggplot2,scale_alpha_identity)
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importFrom(ggplot2,scale_fill_manual)
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importFrom(ggplot2,scale_y_continuous)
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importFrom(ggplot2,theme)
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importFrom(ggplot2,xlab)
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importFrom(ggpubr,as_ggplot)
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importFrom(gridExtra,arrangeGrob)
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importFrom(gridExtra,grid.arrange)
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importFrom(gridExtra,tableGrob)
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importFrom(gridExtra,ttheme_default)
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importFrom(plyr,mapvalues)
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importFrom(rlang,sym)
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importFrom(parallel,detectCores)
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importFrom(parallel,mclapply)
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importFrom(scales,scientific_format)
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importFrom(signal,sgolayfilt)
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importFrom(tibble,rownames_to_column)
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importFrom(stringr,str_replace_all)
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importFrom(tictoc,tic)
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importFrom(tictoc,toc)
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importFrom(zoo,na.locf)

‎R/Phosphoproteomics_Dilution_Dataset_filters.R‎

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‎R/curateXICplot.R‎

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#// **********************************************************************************************
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#// curateXICplot.R
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#// **********************************************************************************************
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#//
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#//
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#// **********************************************************************************************
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#// @Maintainer: Justin Sing
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#// @Author: Justin Sing
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#' @export
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#' @title Master Plotting function for plotting XIC's
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#' @description This function can be used to draw XIC's by calling getXIC
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#'
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#' @param pep A character vector of a peptide sequence(s).
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#' @param uni_mod A character vector of a modified peptide sequence. (Default: NULL) If using this argument, pep must be a single peptide
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#' @param in_sqMass A character vector. Full path to chromatogram file.
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#' @param df_lib A dataframe containing library information.
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#' @param in_osw A character vector. Full path to an osw results file.
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#' @param df_osw A dataframe containg OpenSwath Results Information
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#' @param plotPrecursor A logical value. True will plot precursor chromatogram
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#' @param plotDetecting A logcail value. True will plot detecting transitions.
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#' @param plotIdentifying A logical value. True will plot identifying transitions.
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#' @param plotIdentifying.Unique A logical value. True will plot unique identifying transitions.
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#' @param plotIdentifying.Shared A logical value. True will plot shared identifying transitions. (Decaprecated)
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#' @param plotIdentifying.Against A logical value. True will plot against identifying transitions. (Decaprecated)
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#' @param smooth_chromatogram A list containing p (numeric) for the polynomial order for sgolay, and n (numeric) the bandwidth for sgolay. (Defualt: list(p=4, n=9)
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#' @param doFacetZoom A logical valie. Should the plot be zoomed in. The default zooming operation is based on the max int divided by 4. (Default: FALSE)
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#' @param FacetFcnCall A facet_zoom function with user defined parameters. i.e. FacetFcnCall = facet_zoom(xlim = c(7475, 7620), ylim = c(0, 4000) ). (Default: NULL)
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#' @param doPlot A logical value. TRUE will perform steps to save the plot as a pdf.
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#' @param Charge_State A numeric value. The target charge state. (Default: NULL)
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#' @param store_plots_subdir A character vector. The location to store plots.
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#' @param printPlot A logical value. TRUE will print plot in RStudio display.
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#' @param use_top_trans_pep A logical value. TRUE will rank the transitions based on the posterior error probabilities.
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#' @param transition_selection_list A list containing transitions to display for unique identifying. i.e. transition_selection_list <- list( y = c(3), b = c(8:10) )
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#' @param show_n_transitions A numeric value. Show n number of transitions
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#' @param show_transition_scores A logical value. If set to TRUE, will include TRANSITION PEPs as text tag when using interactive plotly.
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#' @param annotate_best_pkgrp A logical value. Annotate Top Peak Group
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#' @param show_all_pkgrprnk A logical value. Show all feature peak-group ranks. Usually 5. (Default: 5)
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#' @param show_manual_annotation A dataframe with leftWidth and rightWidth retention time boundary values of a manually annotated peak. Will draw a transparent blue shaded rectangle indicating manual annotation. I.e data.frame(leftWidth=300, rightWidth=330)
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#' @param show_legend A logical value. Display legend information for transition id, m/z and charge. (Default: TRUE)
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#'
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#' @return A ggplot-grobs table of a XIC
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#'
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#' @author Justin Sing \url{https://github.com/singjc}
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#' @importFrom tictoc tic toc
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#' @importFrom crayon blue red underline magenta bold
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#' @importFrom parallel mclapply detectCores
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#' @importFrom ggplot2 ggplot
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#' @importFrom dplyr %>% filter select distinct arrange
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#' @importFrom stringr str_replace_all
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#' @importFrom MazamaCoreUtils logger.isInitialized logger.info logger.error logger.warn logger.trace
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curateXICplot <- function( pep,
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uni_mod=NULL,
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in_sqMass, df_lib, in_osw, df_osw,
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plotPrecursor=T,
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plotDetecting=T,
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plotIdentifying=F,
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plotIdentifying.Unique=F,
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plotIdentifying.Shared=F,
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plotIdentifying.Against=F,
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smooth_chromatogram=list(p = 4, n = 9),
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doFacetZoom=F,
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FacetFcnCall=NULL,
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doPlot=T,
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Charge_State=NULL,
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store_plots_subdir = NULL,
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printPlot=F,
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use_top_trans_pep=F,
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transition_selection_list=NULL,
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show_n_transitions=NULL,
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show_transition_scores=FALSE,
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annotate_best_pkgrp=TRUE,
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show_all_pkgrprnk=T,
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show_peak_info_tbl=F,
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show_manual_annotation=NULL,
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show_legend=T,
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mzPntrs=NULL
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) {
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# Get XICs for Modified Peptides ---------------------------------------------------------------
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## Check if logging has been initialized
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if( MazamaCoreUtils::logger.isInitialized() ){
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mstools:::log_setup()
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}
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tictoc::tic( paste('XIC plotting for ', pep, ' peptides took: ', sep=' '))
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run_name <- gsub('_osw_chrom[.]sqMass$|[.]chrom.mzML$|[.]chrom.sqMass$', '', basename(in_sqMass))
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run <- gsub('_SW*|_SW_0|(*_-_SW[.]mzML[.]gz|[.]chrom[.]sqMass)', '', gsub('yanliu_I170114_\\d+_|chludwig_K150309_|lgillet_L\\d+_\\d+-Manchester_dirty_phospho_-_', '', run_name))
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MazamaCoreUtils::logger.info( crayon::blue('@ Run: ', run),'\n', sep='' )
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plot_chrom_error <- tryCatch({
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## Charge State
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Isoform_Target_Charge <- Charge_State
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m_score_filter_var <- ifelse( length(grep( "m_score|mss_m_score", colnames(df_osw), value = T))==2, "m_score", "ms2_m_score" )
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df_osw %>%
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dplyr::filter( Sequence==pep ) %>%
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dplyr::filter( FullPeptideName==uni_mod ) %>%
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dplyr::filter( !is.na( !!rlang::sym(m_score_filter_var) ) ) %>%
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dplyr::filter( Charge==Isoform_Target_Charge ) %>%
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dplyr::filter( grepl(run_name, filename) ) -> tmp_osw_df
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if ( any(colnames(tmp_osw_df) %in% "ipf_FullPeptideName") ){
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tmp_osw_df %>%
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dplyr::filter( ipf_FullPeptideName==mstools::unimodTocodename(uni_mod) ) -> tmp_osw_df
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}
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# if ( dim(tmp_osw_df)[1]==0 ){ MazamaCoreUtils::logger.error(crayon::red(pep, ' was not found in osw file!!!, skipping...\n'),sep=''); return(list()) }
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# Filter df_lib based on only the uni modifications with specified charge state found in OSW results
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df_lib %>%
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dplyr::filter( UNMODIFIED_SEQUENCE==pep ) %>%
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dplyr::filter( MODIFIED_SEQUENCE==uni_mod ) %>%
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dplyr::filter( PRECURSOR_CHARGE==Isoform_Target_Charge ) -> df_lib
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# Display other peak group rank features
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if ( show_all_pkgrprnk ){
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RT_pkgrps <- tmp_osw_df$RT
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} else {
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RT_pkgrps <- NULL
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}
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uni_mod_list <- NULL
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##***********************************##
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## Get TRANSITION SCORES INFO TABLE ##
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##***********************************##
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if ( show_transition_scores ){
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transition_dt <- getTransitionScores_( oswfile = in_osw, run_name = run_name, precursor_id = "", peptide_id = pep)
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# transition_dt_test <- getTransitionScores_( oswfile = in_osw, run_name = "chludwig_K150309_007b_SW_1_6", precursor_id = "", peptide_id = pep)
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} else {
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transition_dt <- NULL
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}
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MazamaCoreUtils::logger.info(' ~ Starting Plotting Action\n', sep='')
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plot_list <- list()
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max_Int <- 0
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mod <- uni_mod
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tictoc::tic("Plotting: ")
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MazamaCoreUtils::logger.info( crayon::green(' --- Peptidoform: ', mod), '\n', sep='')
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##***********************##
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## PLOT PRECURSOR ##
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##***********************##
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if ( plotPrecursor==T ){
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g <- ggplot2::ggplot()
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g <- mstools::getXIC( graphic_obj = g,
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df_lib = df_lib,
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mod = mod,
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Isoform_Target_Charge = Isoform_Target_Charge,
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chromatogram_file = in_sqMass,
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transition_type = 'precursor',
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uni_mod_list = NULL,
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max_Int = max_Int,
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in_osw=NULL,
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smooth_chromatogram=smooth_chromatogram,
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doFacetZoom=F,
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top_trans_mod_list=NULL,
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show_n_transitions=show_n_transitions,
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transition_dt=transition_dt,
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mzPntrs=mzPntrs )
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max_Int <- g$max_Int
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g <- g$graphic_obj
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} else {
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g <- ggplot2::ggplot()
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}
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##*****************************##
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## DETECTING TRANSITIONS ##
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##*****************************##
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## INTERSECTING
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if ( plotDetecting==T ){
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g <- mstools::getXIC( graphic_obj = g,
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df_lib = df_lib,
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mod = mod,
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Isoform_Target_Charge = Isoform_Target_Charge,
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chromatogram_file = in_sqMass,
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transition_type = 'detecting',
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uni_mod_list = uni_mod_list,
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max_Int = max_Int,
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in_osw=NULL,
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smooth_chromatogram=smooth_chromatogram,
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doFacetZoom=F,
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top_trans_mod_list=NULL,
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show_n_transitions=show_n_transitions,
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transition_dt=transition_dt,
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mzPntrs=mzPntrs)
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max_Int <- g$max_Int
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g <- g$graphic_obj
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}
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##*******************************##
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## IDENTIFYING TRANSITIONS ##
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##*******************************##
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if ( plotIdentifying==T ){
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g <- mstools:: getXIC( graphic_obj = g,
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df_lib = df_lib,
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mod = mod,
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Isoform_Target_Charge = Isoform_Target_Charge,
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chromatogram_file = in_sqMass,
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transition_type='identifying',
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uni_mod_list = uni_mod_list,
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max_Int = max_Int,
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in_osw=NULL,
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smooth_chromatogram=smooth_chromatogram,
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doFacetZoom=F,
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top_trans_mod_list=NULL,
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transition_selection_list=transition_selection_list,
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show_n_transitions=show_n_transitions,
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plotIdentifying.Unique=plotIdentifying.Unique,
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plotIdentifying.Shared=plotIdentifying.Shared,
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plotIdentifying.Against=plotIdentifying.Against,
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transition_dt=transition_dt,
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mzPntrs=mzPntrs)
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max_Int <- g$max_Int
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g <- g$graphic_obj
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} else {
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MazamaCoreUtils::logger.warn(crayon::red('-- Identifying Transitions were not found for: ', crayon::underline(mod)), '\n', sep='')
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}
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tictoc::toc()
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##*******************************##
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## ADD OSW RESULTS INFO ##
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##*******************************##
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g <- mstools::getXIC( graphic_obj = g,
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df_lib = df_lib,
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mod = mod,
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Isoform_Target_Charge = Isoform_Target_Charge,
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chromatogram_file = in_sqMass,
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transition_type='none',
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uni_mod_list = NULL,
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max_Int = max_Int,
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in_osw = in_osw,
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df_osw = tmp_osw_df,
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annotate_best_pkgrp=annotate_best_pkgrp,
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doFacetZoom=doFacetZoom,
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top_trans_mod_list=NULL,
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RT_pkgrps=RT_pkgrps,
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show_manual_annotation=show_manual_annotation,
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show_peak_info_tbl=show_peak_info_tbl,
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FacetFcnCall=FacetFcnCall,
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show_legend = show_legend )
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max_Int <- g$max_Int
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g <- g$graphic_obj
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graphics.off()
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return( g )
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}, error=function(e){
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MazamaCoreUtils::logger.error(crayon::red('There was an issue trying to process ', crayon::underline(pep), ' from run: '), crayon::underline(run), '\n', sep='')
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stop(e$message)
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})
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}
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