aLFQ (version 1.3.6)

ALF: Generate ALF report

Description

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

Usage

# S3 method for 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,
cval_method = "boot", cval_mcx = 1000, ...)

Arguments

data

a mandatory data frame containing the columns "run_id", "protein_id", "peptide_id", "peptide_sequence", "precursor_charge", "peptide_intensity" and "concentration" are required. For quantification on the transition level, the columns "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" = "?"))

report_filename

the path and filename of the PDF report.

prediction_filename

the path and filename of the predictions of the optimal model.

peptide_methods

a vecter containing a combination of "top", "all", "iBAQ", "APEX" or "NSAF" peptide to protein intensity estimation methods.

peptide_topx

("top" only:) a positive integer value of the top x peptides to consider for "top" methods.

peptide_strictness

("top" only:) whether peptide_topx should only consider proteins with the minimal peptide number ("strict") or all ("loose").

peptide_summary

("top" and "all" only:) how to summarize the peptide intensities: "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".

fasta

("iBAQ", "APEX" and "NSAF" only:) the path and filename to an amino acid fasta file containing the proteins of interest.

apex_model

("APEX" only:) The "APEX" model to use (see APEX).

combine_precursors

whether to sum all precursors of the same peptide.

combine_peptide_sequences

whether to sum all variants (modifications) of the same peptide sequence.

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.

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.

...

future extensions.

Value

The reports specified in the function call.

Details

The ALF module 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

# NOT RUN {
data(UPS2MS)
# }
# NOT RUN {
# }
# NOT RUN {
ALF(UPS2_SRM)
# }
# NOT RUN {
# }
# NOT RUN {
data(LUDWIGMS)
# }
# NOT RUN {
# }
# NOT RUN {
ALF(LUDWIG_SRM)
# }