Usage
## S3 method for class 'default':
ALF(data, report_filename="ALF_report.pdf",prediction_filename="ALF_prediction.csv",
peptide_method = "top", peptide_topx = c(1,2,3,4,5,6), peptide_strictness = "loose",
peptide_summary = "sum", transition_topx= c(1,2,3,4,5,6), transition_strictness = "loose",
transition_summary = "sum", cval_method = "boot", cval_mcx = 1000,
combine_precursors = TRUE, ...)
Arguments
data
a mandatory data frame containing the columns "protein_id", "peptide_id", "transition_id", "peptide_sequence",
"precursor_charge", "transition_intensity" and "concentr
report_filename
the path and filename of the PDF report.
prediction_filename
the path and filename of the predictions of the optimal model.
peptide_method
one of "top", "all", "iBAQ", "APEX" or "NSAF" peptide to protein intensity estimation methods.
peptide_topx
a positive integer value of the top x peptides to consider for "top" methods.
peptide_strictness
whether peptide_topx should only consider proteins with the minimal peptide
number ("strict") or all ("loose").
peptide_summary
how to summarize the peptide intensities for the "top" methods: "mean",
"median", "sum".
transition_topx
a positive integer value of the top x transitions to consider for transition to
peptide intensity estimation methods.
transition_strictness
whether transition_topx should only consider peptides with the minimal transition
number ("strict") or all ("loose").
transition_summary
how to summarize the transition intensities: "mean",
"median", "sum".
cval_method
a method for doing crossvalidation: "boot" (bootstrapping), "mc" (monte carlo cross-validation), "loo" (leaving-one-out).
cval_mcx
a positive integer value of the number of folds for cross-validation.
combine_precursors
whether to pool all precursors of the same peptide.