aggregateFeatures {QFeatures} | R Documentation

## Aggregate an assay's quantitative features

### Description

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`.

### Usage

    ## 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, ...)

### Arguments

`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`.

### Details

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;

### Value

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

### Missing quantitative values

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

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.

### See Also

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

### Examples

    ## ---------------------------------------
    ## 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]])

[Package QFeatures version 1.0.0 Index]