Pcut <- 0.01 # optional pval cutoff - select either rcut or pcut for "cor_filter" caution: pval can reflect either positive of negative correlational relationships Rcut <- 0.5 # r value cutoff for idMSMS spectral reconstruction - MS or MSMS Library <- "pivus_small " # name your spectral library - ensure quotes on either sideĬor_filter <- "rval " # can be either "rval" or "pval" - ensure quotes on either side Rt_dev <- 2 # retention time window (seconds) on either side of feature rt for idMSMS spectral reconstruction Maxlabel <- 12 # maximum number of features labeled on spectrum plots (10 default) Write.table(paste(formatC( M, format = "f ", digits = 3), "_ ",formatC( T, format = "f ", digits = 3), sep = " "), file = "features_for_spectra.csv ", row.names = F, quote = F, col.names = F, sep = ", ") # Write the list of all the features with a spectra Write.table( Ld_cdf1_n5, file = "final_dataset.txt ", row.names = F, col.names = T, quote = F, sep = " \t ") Feature-level correlation between duplicates Calculate total intesity for each sample and check for outlier Phenotypic data to identify variables of unwanted variabilty that need to be adjusted. # 123_2 refers to individual 123, injection 2. # For example: 123_1 refers to individual 123, injection one # # each individual with this format: id_inj where inj is the injection number (1,2). ID to identify samples from same individual (XXXnames_injXXX): it should be an id for #
Real data are not shared, but the skelthon of the program can be applied # # This program describes feature detection, quality control and spectra generation for #