MetacellsByGroups

This function takes a Seurat object and constructs averaged 'metacells' based on neighboring cells in provided groupings, such as cluster or cell type.

MetacellsByGroups(
  seurat_obj,
  group.by = c("seurat_clusters"),
  ident.group = "seurat_clusters",
  k = 25,
  reduction = "pca",
  dims = NULL,
  assay = NULL,
  slot = "counts",
  layer = "counts",
  mode = "average",
  cells.use = NULL,
  min_cells = 100,
  max_shared = 15,
  target_metacells = 1000,
  max_iter = 5000,
  verbose = FALSE,
  wgcna_name = NULL
)

Arguments

seurat_obj

A Seurat object

group.by

A character vector of Seurat metadata column names representing groups for which metacells will be computed.

k

Number of nearest neighbors to aggregate. Default = 50

reduction

A dimensionality reduction stored in the Seurat object. Default = 'pca'

dims

A vector represnting the dimensions of the reduction to use. Either specify the names of the dimensions or the indices. Default = NULL to include all dims.

assay

Assay to extract data for aggregation. Default = 'RNA'

slot

Slot to extract data for aggregation. Default = 'counts'. Slot is used with Seurat v4 instead of layer.

layer

Layer to extract data for aggregation. Default = 'counts'. Layer is used with Seurat v5 instead of slot.

mode

determines how to make gene expression profiles for metacells from their constituent single cells. Options are "average" or "sum".

min_cells

the minimum number of cells in a particular grouping to construct metacells

max_shared

the maximum number of cells to be shared across two metacells

target_metacells

the maximum target number of metacells to construct

max_iter

the maximum number of iterations in the metacells bootstrapping loop

verbose

logical indicating whether to print additional information

wgcna_name

name of the WGCNA experiment

name

A string appended to resulting metalcells. Default = 'agg'

Value

seurat_obj with a metacell seurat_obj stored in the specified WGCNA experiment

Details

MetacellsByGroups merges transcriptomically similar cells into "metacells". Given a dimensionally-reduced representation of the input dataset, this algorithm first uses KNN to identify similar cells. A bootstrapped sampling procedure is then used to group together similar cells until convergence is reached. Importantly, this procedure is done in a context-specific manner based on the provided group.by parameters. Typically this means that metacells will be constructed separately for each biological replicate, cell type or cell state, disease condition, etc. The metacell representation is considerably less sparse than the original single-cell dataset, which is preferable for co-expression network analysis or othter analyses that rely on correlations.