## ---- eval=FALSE-------------------------------------------------------------- # if (!require("BiocManager")) # install.packages("BiocManager") # BiocManager::install("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_len(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()