Become an expert in R — Interactive courses, Cheat Sheets, certificates and more!
Get Started for Free

ALF

Generate ALF report

Description

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

Usage

## Default S3 method:
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.

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.

Value

The reports specified in the function call.

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

## Not run: data(UPS2MS)

## Not run: ALF(UPS2_SRM)

## Not run: data(LUDWIGMS)

## Not run: ALF(LUDWIG_SRM)
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

We don't support your browser anymore

Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.