summarizeAssayByGroup {scuttle} | R Documentation

## Summarize an assay by group

### Description

From an assay matrix, compute summary statistics for groups of cells. A typical example would be to compute various summary statistics for clusters.

### Usage

    summarizeAssayByGroup(x, ...)

    ## S4 method for signature 'ANY'
    summarizeAssayByGroup(
      x,
      ids,
      subset.row = NULL,
      subset.col = NULL,
      statistics = c("mean", "sum", "num.detected", "prop.detected", "median"),
      store.number = "ncells",
      threshold = 0,
      BPPARAM = SerialParam()
    )

    ## S4 method for signature 'SummarizedExperiment'
    summarizeAssayByGroup(x, ..., assay.type = "counts")

### Arguments

`x` | A numeric matrix containing features in rows and cells in columns. Alternatively, a SummarizedExperiment object containing such a matrix.
`...` | For the generics, further arguments to be passed to specific methods. For the SummarizedExperiment method, further arguments to be passed to the ANY method.
`ids` | A factor (or vector coercible into a factor) specifying the group to which each cell in `x` belongs. Alternatively, a DataFrame of such vectors or factors, in which case each unique combination of levels defines a group.
`subset.row` | An integer, logical or character vector specifying the features to use. If `NULL`, defaults to all features.
`subset.col` | An integer, logical or character vector specifying the cells to use. Defaults to all cells with non-`NA` entries of `ids`.
`statistics` | Character vector specifying the type of statistics to be computed, see Details.
`store.number` | String specifying the field of the output `colData` to store the number of cells in each group. If `NULL`, nothing is stored.
`threshold` | A numeric scalar specifying the threshold above which a gene is considered to be detected.
`BPPARAM` | A BiocParallelParam object specifying whether summation should be parallelized.
`assay.type` | A string or integer scalar specifying the assay of `x` containing the assay to be summarized.

### Details

These functions provide a convenient method for summing or averaging expression values across multiple columns for each feature. A typical application would be to sum counts across all cells in each cluster to obtain “pseudo-bulk” samples for further analyses, e.g., differential expression analyses between conditions.

For each feature, the chosen assay can be aggregated by:

- `"sum"`, the sum of all values in each group. This makes the most sense for raw counts, to allow models to account for the mean-variance relationship.

- `"mean"`, the mean of all values in each group. This makes the most sense for normalized and/or transformed assays.

- `"median"`, the median of all values in each group. This makes the most sense for normalized and/or transformed assays, usually generated from large counts where discreteness is less of an issue.

- `"num.detected"` and `"prop.detected"`, the number and proportion of values in each group that are non-zero.# This makes the most sense for raw counts or sparsity-preserving transformations.

Any `NA` values in `ids` are implicitly ignored and will not be considered during summation. This may be useful for removing undesirable cells by setting their entries in `ids` to `NA`. Alternatively, we can explicitly select the cells of interest with `subset_col`.

If `ids` is a factor and contains unused levels, they will not be represented as columns in the output.

### Value

A SummarizedExperiment is returned with one column per level of `ids`. Each entry of the assay contains the sum or average across all cells in a given group (column) for a given feature (row). Columns are ordered by `levels(ids)` and the number of cells per level is reported in the `"ncells"` column metadata. For DataFrame `ids`, each column corresponds to a unique combination of levels (recorded in the `colData`).

### Author(s)

Aaron Lun

### See Also

`aggregateAcrossCells`, which also combines information in the `colData` of `x`.

### Examples

    example_sce <- mockSCE()
    ids <- sample(LETTERS[1:5], ncol(example_sce), replace=TRUE)

    out <- summarizeAssayByGroup(example_sce, ids)
    out

    batches <- sample(1:3, ncol(example_sce), replace=TRUE)
    out2 <- summarizeAssayByGroup(example_sce,
          DataFrame(label=ids, batch=batches))
    head(out2)

[Package scuttle version 1.4.0 Index]