Peptide inference for aLFQ import data frame.
# S3 method for default
PeptideInference(data, transition_topx = 3,
transition_strictness = "strict",transition_summary = "sum",
consensus_proteins = TRUE, consensus_transitions = TRUE, ...)a mandatory data frame containing the "protein_id", "peptide_id", "transition_id", "peptide_sequence", "precursor_charge", "transition_intensity" and "concentration" are required. The id columns can be defined in any format, while the "_intensity" and "concentration" columns need to be numeric and in non-log form. The data may contain calibration data (with numeric "concentration" and test data (with "concentration" = "?"))
a positive integer value of the top x transitions to consider for transition to peptide intensity estimation methods.
whether transition_topx should only consider peptides with the minimal transition number ("strict") or all ("loose").
how to summarize the transition intensities: "mean", "median", "sum".
if multiple runs are provided, select identical proteins among all runs.
if multiple runs are provided, select identical transitions among all runs.
future extensions.
A standard aLFQ import data frame on peptide / precursor level.
The PeptideInference module provides functionality to infer peptide / precursor quantities from the measured precursor or fragment intensities or peptide spectral counts.
Ludwig, C., Claassen, M., Schmidt, A. & Aebersold, R. Estimation of Absolute Protein Quantities of Unlabeled Samples by Selected Reaction Monitoring Mass Spectrometry. Molecular & Cellular Proteomics 11, M111.013987-M111.013987 (2012).
import, AbsoluteQuantification, ALF, APEX, apexFeatures, proteotypic
# NOT RUN {
data(UPS2MS)
data_PI <- PeptideInference(UPS2_SRM)
print(data_PI)
# }
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