aggvar: Identify aggregation outliers
In MSnbase: Base Functions and Classes for Mass Spectrometry and Proteomics

Description Usage Arguments Details Value Author(s) See Also Examples

This function evaluates the variability within all protein group of an MSnSet. If a protein group is composed only of a single feature, NA is returned.

1	aggvar(object, groupBy, fun)

`object`	An object of class `MSnSet`.
`groupBy`	A `character` containing the protein grouping feature variable name.
`fun`	A function the summarise the distance between features within protein groups, typically `max` or `mean`.`median`.

This function can be used to identify protein groups with incoherent feature (petides or PSMs) expression patterns. Using max as a function, one can identify protein groups with single extreme outliers, such as, for example, a mis-identified peptide that was erroneously assigned to that protein group. Using mean identifies more systematic inconsistencies where, for example, the subsets of peptide (or PSM) feautres correspond to proteins with different expression patterns.

A matrix providing the number of features per protein group (nb_feats column) and the aggregation summarising distance (agg_dist column).

Laurent Gatto

combineFeatures to combine PSMs quantitation into peptides and/or into proteins.

library("pRolocdata")
data(hyperLOPIT2015ms3r1psm)
groupBy <- "Protein.Group.Accessions"
res1 <- aggvar(hyperLOPIT2015ms3r1psm, groupBy, fun = max)
res2 <- aggvar(hyperLOPIT2015ms3r1psm, groupBy, fun = mean)
par(mfrow = c(1, 3))
plot(res1, log = "y", main = "Single outliers (max)")
plot(res2, log = "y", main = "Overall inconsistency (mean)")
plot(res1[, "agg_dist"], res2[, "agg_dist"],
     xlab = "max", ylab = "mean")