knitr::opts_chunk$set( collapse = TRUE, comment = "#>" )
library(mstools)
## Specify path to merged osw file in_osw <- "../inst/extdata/Synthetic_Dilution_Phosphoproteomics/osw/merged.merged.osw" ## Call getOSWData_ function osw_dt <- mstools::getOSWData_(oswfile = in_osw) head(osw_dt, 5)
## Specify path to merged osw file in_osw <- "/media/justincsing/ExtraDrive1/Documents2/Roest_Lab/Github/PTMs_Project/Synth_PhosoPep/Justin_Synth_PhosPep/results/pyprophet/merged.osw" ## Call getOSWData_ function ### NOTE: peak_group_rank_filter is set to TRUE to only filter for the top peak_group_rank 1 features ### NOTE: decoy_filter is set to FALSE to keep decoy peptides osw_dt <- mstools::getOSWData_(oswfile = in_osw, peak_group_rank_filter = TRUE, decoy_filter = FALSE) ### Call sub-function of osw_reports_visualization to visualize d-score distribution mstools::osw_reports_visualization$d_score_hist( osw = osw_dt, bins = 20, position = 'dodge2' )
library(dplyr) ## Specify path to merged osw file in_osw <- "/media/justincsing/ExtraDrive1/Documents2/Roest_Lab/Github/mstools/inst/extdata/Synthetic_Dilution_Phosphoproteomics/osw/merged.merged.osw" ## Specify path to library in_lib <- "/media/justincsing/ExtraDrive1/Documents2/Roest_Lab/Github/mstools/inst/extdata/Synthetic_Dilution_Phosphoproteomics/pqp/psgs_phospho_optimized_decoys.pqp" ## Specify path to sqMass Chromatogram in_sqmass<- "/media/justincsing/ExtraDrive1/Documents2/Roest_Lab/Github/mstools/inst/extdata/Synthetic_Dilution_Phosphoproteomics/sqmass/chludwig_K150309_013_SW_0.chrom.sqMass" ### OR ## Specify path to mzML Chromatogram in_mzml <- "/media/justincsing/ExtraDrive1/Documents2/Roest_Lab/Github/mstools/inst/extdata/Synthetic_Dilution_Phosphoproteomics/mzml/chludwig_K150309_013_SW_0.chrom.mzML" ## Specify path to a tsv file of manual annotations if you have one. (Optional, if you have manual coordications, that is fine as well) in_manual_annotations <- "/media/justincsing/ExtraDrive1/Documents2/Roest_Lab/Github/mstools/inst/extdata/Synthetic_Dilution_Phosphoproteomics/manual_annotations/chludwig_K150309_013_SW_0_filtered.tsv" ### Read in manual annotations into data.table annotation_dt <- data.table::fread(in_manual_annotations) colnames(annotation_dt) annotation_dt$FullPeptideName <- gsub("unimod", "UniMod", annotation_dt$`Modified Sequence Unimod Ids`) ## Specify peptide sequence and unique modified peptide sequence (optional) and target charge dup_peps = "ANSSPTTNIDHLK"; uni_mod = "ANS(UniMod:21)SPTTNIDHLK(UniMod:259)"; target_charge = 3 ## Get manual annotation coordinates for the given peptide annotation_dt %>% dplyr::filter( FullPeptideName==uni_mod & `Precursor Charge`==target_charge) %>% dplyr::select( `Min Start Time`, `Max End Time`) %>% as.numeric() -> manual_annotation_coordinates ## Convert left and right boundaries to seconds manual_annotation_coordinates <- manual_annotation_coordinates*60 out.plot.h <- mstools::XICMasterPlotFcn_( dup_peps = dup_peps, uni_mod = uni_mod, sqMass_files = in_sqmass, in_lib = in_lib, in_osw = in_osw, plotPrecursor=T, plotIntersectingDetecting=T, plotUniqueDetecting=F, plotIdentifying=T, plotIdentifying.Unique=T, plotIdentifying.Shared=F, plotIdentifying.Against=F, doFacetZoom=F, doPlot=T, Charge_State=target_charge, N_sample = 1, # idx_draw_these = c(8), # store_plots_subdir = paste('/XIC_plots/TP/U', pool, '/', sep=''), printPlot=T, use_top_trans_pep=F, show_n_transitions=6, show_all_pkgrprnk=F, show_manual_annotation = manual_annotation_coordinates, show_legend=T ) out.plot.h ### You can make an interactive plot through plotly. #### out.plot.h returns a ggplot graphics object, which plot coerces into an interative plot # # plotly::ggplotly( (out.plot.h), tooltip = 'all', dynamicTicks = T) %>% # plotly::layout(title = list(text = paste0(out.plot.h$labels$title, # '<br>', # '<sup>', # gsub('\\\n', ' | ', out.plot.h$labels$subtitle), # '</sup>')))
The manual annotation is identified by the shaded blue region
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