QFeatures-aggregate: Aggregate an assay's quantitative features
In QFeatures: Quantitative features for mass spectrometry data

Description Usage Arguments Details Value Missing quantitative values Missing values in the row data See Also Examples

This function aggregates the quantitative features of an assay, applying a summarisation function (fun) to sets of features as defined by the fcol feature variable. The new assay's features will be named based on the unique fcol values.

In addition to the results of the aggregation, the newly aggregated SummarizedExperiment assay also contains a new aggcounts assay containing the aggregation counts matrix, i.e. the number of features that were aggregated, which can be accessed with the aggcounts() accessor.

The rowData of the aggregated SummarizedExperiment assay contains a .n variable that provides the number of features that were aggregated. This .n value is always >= that the sample-level aggcounts.

## S4 method for signature 'QFeatures'
aggregateFeatures(
  object,
  i,
  fcol,
  name = "newAssay",
  fun = MsCoreUtils::robustSummary,
  ...
)

## S4 method for signature 'SummarizedExperiment'
aggregateFeatures(object, fcol, fun = MsCoreUtils::robustSummary, ...)

## S4 method for signature 'SummarizedExperiment'
aggcounts(object, ...)

`object`	An instance of class QFeatures or SummarizedExperiment.
`i`	The index or name of the assay which features will be aggregated the create the new assay.
`fcol`	The feature variable of assay `i` defining how to summarise the features.
`name`	A `character(1)` naming the new assay. Default is `newAssay`. Note that the function will fail if there's already an assay with `name`.
`fun`	A function used for quantitative feature aggregation. See Details for examples.
`...`	Additional parameters passed the `fun`.

Aggregation is performed by a function that takes a matrix as input and returns a vector of length equal to ncol(x). Examples thereof are

MsCoreUtils::medianPolish() to fits an additive model (two way decomposition) using Tukey's median polish_ procedure using stats::medpolish();
MsCoreUtils::robustSummary() to calculate a robust aggregation using MASS::rlm() (default);
base::colMeans() to use the mean of each column;
matrixStats::colMedians() to use the median of each column.
base::colSums() to use the sum of each column;

A QFeatures object with an additional assay or a SummarizedExperiment object (or subclass thereof).

Missing quantitative values have different effect based on the aggregation method employed:

The aggregation functions should be able to deal with missing values by either ignoring them, and propagating them. This is often done with an na.rm argument, that can be passed with .... For example, rowSums, rowMeans, rowMedians, ... will ignore NA values with na.rm = TRUE, as illustrated below.
Missing values will result in an error when using medpolish, unless na.rm = TRUE is used. Note that this option relies on implicit assumptions and/or performes an implicit imputation: when summing, the values are implicitly imputed by 0, assuming that the NA represent a trully absent features; when averaging, the assumption is that the NA represented a genuinely missing value.
When using robust summarisation, individual missing values are excluded prior to fitting the linear model by robust regression. To remove all values in the feature containing the missing values, use filterNA().

More generally, missing values often need dedicated handling such as filtering (see filterNA()) or imputation (see impute()).

Missing values in the row data of an assay will also impact the resulting (aggregated) assay row data, as illustrated in the example below. Any feature variables (a column in the row data) containing NA values will be dropped from the aggregated row data. The reasons underlying this drop are detailed in the reduceDataFrame() manual page: only invariant aggregated rows, i.e. rows resulting from the aggregation from identical variables, are preserved during aggregations.

The situation illustrated below should however only happen in rare cases and should often be imputable using the value of the other aggregation rows before aggregation to preserve the invariant nature of that column. In cases where an NA is present in an otherwise variant column, the column would be dropped anyway.

The QFeatures vignette provides an extended example and the Processing vignette, for a complete quantitative proteomics data processing pipeline.

## ---------------------------------------
## An example QFeatures with PSM-level data
## ---------------------------------------
data(feat1)
feat1

## Aggregate PSMs into peptides
feat1 <- aggregateFeatures(feat1, "psms", "Sequence", name = "peptides")
feat1

## Aggregate peptides into proteins
feat1 <- aggregateFeatures(feat1, "peptides", "Protein", name = "proteins")
feat1

assay(feat1[[1]])
assay(feat1[[2]])
aggcounts(feat1[[2]])
assay(feat1[[3]])
aggcounts(feat1[[3]])

## --------------------------------------------
## Aggregation with missing quantitative values
## --------------------------------------------
data(ft_na)
ft_na

assay(ft_na[[1]])
rowData(ft_na[[1]])

## By default, missing values are propagated
ft2 <- aggregateFeatures(ft_na, 1, fcol = "X", fun = colSums)
assay(ft2[[2]])
aggcounts(ft2[[2]])

## The rowData .n variable tallies number of initial rows that
## were aggregated (irrespective of NAs) for all the samples.
rowData(ft2[[2]])

## Ignored when setting na.rm = TRUE
ft3 <- aggregateFeatures(ft_na, 1, fcol = "X", fun = colSums, na.rm = TRUE)
assay(ft3[[2]])
aggcounts(ft3[[2]])

## -----------------------------------------------
## Aggregation with missing values in the row data
## -----------------------------------------------
## Row data results without any NAs, which includes the
## Y variables
rowData(ft2[[2]])

## Missing value in the Y feature variable
rowData(ft_na[[1]])[1, "Y"] <- NA
rowData(ft_na[[1]])

ft3 <- aggregateFeatures(ft_na, 1, fcol = "X", fun = colSums)
## The Y feature variable has been dropped!
assay(ft3[[2]])
rowData(ft3[[2]])