iPQF: iTRAQ (and TMT) Protein Quantification based on Features
The iPQF spectra-to-protein summarisation method integrates
peptide spectra characteristics and quantitative values for protein
quantitation estimation. Spectra features, such as charge state,
sequence length, identification score and others, contain valuable
information concerning quantification accuracy. The iPQF algorithm
assigns weights to spectra according to their overall feature reliability
and computes a weighted mean to estimate protein quantities.
combineFeatures for a more
general overview of feature aggregation and examples.
iPQF(object, groupBy, low.support.filter = FALSE, ratio.calc = "sum", method.combine = FALSE, feature.weight = c(7, 6, 4, 3, 2, 1, 5)^2)
- An instance of class
MSnSetcontaining absolute ion intensities.
- Vector defining spectra to protein
matching. Generally, this is a feature variable such as
logicalspecifying if proteins being supported by only 1-2 peptide spectra should be filtered out. Default is
"none"(don't calculate any ratios),
"sum"(default), or a specific channel (one of
sampleNames(object)) defining how to calculate relative peptides intensities.
logicaldefining whether to further use median polish to combine features.
"numeric"giving weight to the different features. Default is the squared order of the features redundant -unique-distance metric, charge state, ion intensity, sequence length, identification score, modification state, and mass based on a robustness analysis.
matrixwith estimated protein ratios.
iPQF: A new peptide-to-protein summarization method using peptide characteristics to improve iTRAQ quantification Martina Fischer and Bernhard Y. Renard, in prep.
data(msnset2) head(exprs(msnset2)) prot <- combineFeatures(msnset2, groupBy = fData(msnset2)$accession, fun = "iPQF") head(exprs(prot))