aggregateData {muscat} | R Documentation

## Aggregation of single-cell to pseudobulk data

### Description

...

### Usage

    aggregateData(
      x,
      assay = NULL,
      by = c("cluster_id", "sample_id"),
      fun = c("sum", "mean", "median"),
      scale = FALSE
    )

### Arguments

`x` | a `SingleCellExperiment`.
`assay` | character string specifying the assay slot to use as input data. Defaults to the 1st available (`assayNames(x)[1]`).
`by` | character vector specifying which `colData(x)` columns to summarize by (at most 2!).
`fun` | a character string. Specifies the function to use as summary statistic.
`scale` | logical. Should pseudo-bulks be scaled with the effective library size & multiplied by 1M?

### Value

a `SingleCellExperiment`.

- If `length(by) == 2`, each sheet (`assay`) contains pseudobulks for each of `by[1]`, e.g., for each cluster when `by = "cluster_id"`. Rows correspond to genes, columns to `by[2]`, e.g., samples when `by = "sample_id"`.

- If `length(by) == 1`, the returned SCE will contain only a single `assay` with rows = genes and colums = `by`.

Aggregation parameters (`assay, by, fun, scaled`) are stored in `metadata()$agg_pars`, and the number of cells that were aggregated are accessible in `metadata()$n_cells`.

### Author(s)

Helena L Crowell & Mark D Robinson

### References

Crowell, HL, Soneson, C, Germain, P-L, Calini, D, Collin, L, Raposo, C, Malhotra, D & Robinson, MD: On the discovery of population-specific state transitions from multi-sample multi-condition single-cell RNA sequencing data. bioRxiv 713412 (2018). doi: https://doi.org/10.1101/713412

### Examples

    data(sce)
    library(SingleCellExperiment)

    # pseudobulk counts by cluster-sample
    pb <- aggregateData(sce)

    assayNames(sce)  # one sheet per cluster
    head(assay(sce)) # n_genes x n_samples

    # scaled CPM
    assays(sce)$cpm <- edgeR::cpm(assay(sce))
    pb <- aggregateData(sce, assay = "cpm", scale = TRUE)
    head(assay(pb))

    # aggregate by cluster only
    pb <- aggregateData(sce, by = "cluster_id")
    length(assays(pb)) # single assay
    head(assay(pb))    # n_genes x n_clusters

[Package muscat version 1.0.1 Index]