Description Usage Arguments Details Value Author(s) References See Also Examples
Estimation of Absolute Protein Quantities of Unlabeled Samples by Targeted Mass Spectrometry.
| 1 2 3 4 5 6 7 8 9 | ## 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, ...)
 | 
| data | a mandatory data frame containing the columns  | 
| 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  | 
| peptide_topx | ( | 
| peptide_strictness | ( | 
| peptide_summary | ( | 
| transition_topx | a positive integer value of the top x transitions to consider for transition to peptide intensity estimation methods. | 
| transition_strictness | whether  | 
| transition_summary | how to summarize the transition intensities:  | 
| fasta | ( | 
| apex_model | ( | 
| 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:  | 
| cval_mcx | a positive integer value of the number of folds for cross-validation. | 
| ... | future extensions. | 
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.
The reports specified in the function call.
George Rosenberger gr2578@cumc.columbia.edu
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, ProteinInference, AbsoluteQuantification, APEX, apexFeatures, proteotypic
| 1 2 3 4 5 6 7 | ## Not run: data(UPS2MS)
## Not run: ALF(UPS2_SRM)
## Not run: data(LUDWIGMS)
## Not run: ALF(LUDWIG_SRM)
 | 
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