## ---- eval=FALSE--------------------------------------------------------- # ## try http:// if https:// URLs are not supported # source('https://bioconductor.org/biocLite.R') # biocLite('SWATH2stats') ## ------------------------------------------------------------------------ library(SWATH2stats) library(data.table) data('Spyogenes', package = 'SWATH2stats') ## ---- eval=FALSE--------------------------------------------------------- # data <- data.frame(fread('rawOpenSwathResults_1pcnt_only.tsv', sep='\t', header=TRUE)) ## ---- tidy=TRUE---------------------------------------------------------- Study_design <- data.frame(Filename = unique(data$align_origfilename)) Study_design$Filename <- gsub('.*strep_align/(.*)_all_peakgroups.*', '\\1', Study_design$Filename) Study_design$Condition <- gsub('(Strep.*)_Repl.*', '\\1', Study_design$Filename) Study_design$BioReplicate <- gsub('.*Repl([[:digit:]])_.*', '\\1', Study_design$Filename) Study_design$Run <- seq(1:nrow(Study_design)) head(Study_design) ## ------------------------------------------------------------------------ data.annotated <- sample_annotation(data, Study_design, column.file = "align_origfilename") ## ------------------------------------------------------------------------ data.annotated.nodecoy <- subset(data.annotated, decoy==FALSE) ## ------------------------------------------------------------------------ count_analytes(data.annotated.nodecoy) ## ---- fig.height=2.5, fig.width = 6-------------------------------------- correlation <- plot_correlation_between_samples(data.annotated.nodecoy, column.values = 'Intensity') ## ---- fig.height=2.5, fig.width = 6-------------------------------------- correlation <- plot_correlation_between_samples(data.annotated.nodecoy, column.values = 'delta_rt') ## ---- fig.height=2.5, fig.width = 5.5------------------------------------ variation <- plot_variation(data.annotated.nodecoy) variation[[2]] ## ---- fig.height=2.5, fig.width = 5.5------------------------------------ variation_total <- plot_variation_vs_total(data.annotated.nodecoy) variation_total[[2]] ## ------------------------------------------------------------------------ peptide_signal <- write_matrix_peptides(data.annotated.nodecoy) protein_signal <- write_matrix_proteins(data.annotated.nodecoy) head(protein_signal) ## ---- fig.height = 3.5--------------------------------------------------- par(mfrow = c(1, 3)) fdr_target_decoy <- assess_fdr_overall(data.annotated, n.range = 10, FFT = 0.25, output = 'Rconsole') ## ------------------------------------------------------------------------ mscore4protfdr(data, FFT = 0.25, fdr_target = 0.05) ## ------------------------------------------------------------------------ data.filtered <- filter_mscore_condition(data.annotated, 0.001, n.replica = 2) ## ------------------------------------------------------------------------ data.filtered2 <- filter_on_max_peptides(data.filtered, n_peptides = 10) ## ------------------------------------------------------------------------ data.filtered3 <- filter_on_min_peptides(data.filtered2, n_peptides = 2) ## ------------------------------------------------------------------------ data.transition <- disaggregate(data.filtered3) ## ------------------------------------------------------------------------ MSstats.input <- convert4MSstats(data.transition) head(MSstats.input) ## ------------------------------------------------------------------------ mapDIA.input <- convert4mapDIA(data.transition) head(mapDIA.input) ## ------------------------------------------------------------------------ aLFQ.input <- convert4aLFQ(data.transition) head(aLFQ.input) ## ------------------------------------------------------------------------ sessionInfo()