## S3 method for class 'default':
ALF(data, report_filename="ALF_report.pdf",
prediction_filename="ALF_prediction.csv", peptide_methods = c("top"),
peptide_topx = c(1,2,3), peptide_strictness = "loose",
peptide_summary = "mean", transition_topx = c(1,2,3),
transition_strictness = "loose", transition_summary = "sum", fasta = NA,
apex_model = NA, combine_precursors = FALSE, combine_peptide_sequences = FALSE,
consensus_proteins = TRUE, consensus_peptides = TRUE, consensus_transitions = TRUE,
scampi_method = "LSE", scampi_iterations = 10, scampi_outliers = FALSE,
scampi_outliers_iterations = 2, scampi_outliers_threshold = 2,
cval_method = "boot", cval_mcx = 1000, ...)"run_id", "protein_id", "peptide_id", "peptide_sequence", "precursor_charge", "peptide_intensity" and "concentration" "top", "all", "iBAQ", "APEX", "NSAF" or code{"SCAMPI"} peptide to protein intensity estimation methods."top" only:) a positive integer value of the top x peptides to consider for "top" methods."top" only:) whether peptide_topx should only consider proteins with the minimal peptide number ("strict") or all ("loose")."top" and "all" only:) how to summarize the peptide intensities: "mean", "median", "sum".transition_topx should only consider peptides with the minimal transition number ("strict") or all ("loose")."mean", "median", "sum"."iBAQ", "APEX", "NSAF" and "SCAMPI" only:) the path and filename to an amino acid fasta file containing the proteins of interest."APEX" only:) The "APEX" model to use (see APEX)."boot" (bootstrapping), "mc" (monte carlo cross-validation), "loo" (leaving-one-out).import, ProteinInference, AbsoluteQuantification, APEX, apexFeatures, proteotypicdata(UPS2MS)
ALF(UPS2_SRM)
data(LUDWIGMS)
ALF(LUDWIG_SRM)Run the code above in your browser using DataLab