aggvar {MSnbase} | R Documentation

## Identify aggregation outliers

### Description

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`.

### 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.

### Value

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

### Author(s)

Laurent Gatto

### See Also

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

### 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")

[Package MSnbase version 2.6.4 Index]