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aLFQ

PeptideInference

Peptide inference for aLFQ import data frame

Description

Peptide inference for aLFQ import data frame.

Usage

## Default S3 method:
PeptideInference(data, transition_topx = 3,
transition_strictness = "strict",transition_summary = "sum",
consensus_proteins = TRUE, consensus_transitions = TRUE, ...)

Arguments

data

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" = "?"))
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".
consensus_proteins

if multiple runs are provided, select identical proteins among all runs.
consensus_transitions

if multiple runs are provided, select identical transitions among all runs.
...

future extensions.

Details

The PeptideInference module provides functionality to infer peptide / precursor quantities from the measured precursor or fragment intensities or peptide spectral counts.

Value

A standard aLFQ import data frame on peptide / precursor level.

Author(s)

George Rosenberger gr2578@cumc.columbia.edu

References

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).

See Also

Examples

data(UPS2MS)

data_PI <- PeptideInference(UPS2_SRM)
print(data_PI)
aLFQ

Estimating Absolute Protein Quantities from Label-Free LC-MS/MS Proteomics Data

v1.3.6
GPL (>= 3)
Authors
George Rosenberger, Hannes Roest, Christina Ludwig, Ruedi Aebersold, Lars Malmstroem
Initial release
2020-01-07

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