protiq (version 1.2)

runScampi: SCAMPI protein quantification function

Description

Estimate a protein abundance score for each protein in the dataset, based on the input peptide abundance scores and the connectivity information between peptides and proteins. Optionally, the peptide abundances can be estimated as well to compare the predicted values with the input measurements.

Usage

runScampi(peptides, proteins, edgespp, rescaling = TRUE, method = "all",  quantifyPeptides = TRUE, numIter = 10, verbose = FALSE)

Arguments

peptides

Data frame with peptide information. The following columns are required: pepId (unique identification number for each distinct peptide sequence, numbering from 1:n where n=number of distinct peptide sequences), pepSeq (peptide sequence, optionally including modifications and charge states), and pepQty (peptide abundance score). An additional column pepObs (peptide observability or identification score) is used if provided. Each row in the data frame describes one observed distinct peptide sequence.

proteins

Data frame with the protein information. The following columns are required: protId (unique identification number for each distinct protein sequence, numbering from (n+1):(n+m) where m=number of distinct protein sequences), protName (protein identifier or protein sequence). Each row describes a distinct protein sequence to which at least one of the observed peptides is matching.

edgespp

Data frame with two mandatory columns: pepId and protId. Each row defines an edge of the bipartite graph.

rescaling

If TRUE, the peptide abundance scores are logarithmized (log10). If this transformation has not yet been done during preprocessing, it is strongly recommended to stick to the default: rescaling=TRUE.

method

Describes which method should be used for the parameter estimation. Available: method="LSE", method="MLE" and method="all" (default).

quantifyPeptides

If TRUE (default) do also re-quantify the peptides and assess the peptide abundance scores.

numIter

Only used with method="MLE", see details. Default: numIter=10.

verbose

If TRUE, detailed output is provided.

Value

scampiVal containing estimates for the model parameters, protein abundances and, optionally, for the peptide abundances/residuals.

Details

To use method="MLE" the inverses of the covariance matrices (of the connected components) are needed. Depending on the chosen parameters, this can lead to stability issues. To avoid the function from crashing, a try(...) bolck is used: the parameter estimation is performed until it was successful numIter times. Among these numIter sets, the one with the lowest negative log-likelihood value is returned.

See Also

Function iterateScampi tuns the model iteratively, by removing outlying peptides in each step.

Examples

data("leptoSRM")
scampiOut <- runScampi(peptides=leptoSRMpeptides, 
                       proteins=leptoSRMproteins, 
                       edgespp=leptoSRMedgespp, 
                       rescaling=FALSE, method="LSE")