aLFQ (version 1.2)

ALF: Generate ALF report

Description

Estimation of Absolute Protein Quantities of Unlabeled Samples by Targeted Mass Spectrometry.

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, consensus_proteins = TRUE, consensus_peptides = TRUE,
consensus_transitions = TRUE, ...)

Arguments

data

a mandatory data frame containing the columns "run_id", "protein_id", "protein_intensity", and "concentration" for quantification on the protein level. For quantification on the peptide lev

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.

consensus_proteins

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

consensus_peptides

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

consensus_transitions

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

...

future extensions.

Value

• The reports specified in the function call.

Details

The ALF modules enables model selection for TopN transitions and peptides for protein quantification (Ludwig et al., 2012). The workflow is completely automated and a report and prediction (using the best model) is generated.

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

import, ProteinInference, AbsoluteQuantification, APEX, apexFeatures, proteotypic

Examples

data(UPS2MS)

ALF(UPS2_SRM)

data(LUDWIGMS)

ALF(LUDWIG_SRM)