Identify aggregation outliers

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.

Usage

aggvar(object, groupBy, fun)

Arguments

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.

Value

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

Details

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.

See also

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

Author

Laurent Gatto

Examples

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")