Project single cell gene expression data to developing mouse brain data (LaManno & Siletti et al. 2020)

Calculates correlation between single-cell gene expression and clusters from LaManno & Siletti et al. 2020

mousebrain_map(object, ...)

# S3 method for default
mousebrain_map(
  object,
  groups = NULL,
  method = "pearson",
  genes_use = NULL,
  allow_neg = FALSE,
  pseudobulk_groups = TRUE
)

# S3 method for Seurat
mousebrain_map(
  object,
  group_name = NULL,
  method = "pearson",
  genes_use = NULL,
  allow_neg = FALSE,
  pseudobulk_groups = TRUE
)

Arguments

groups	A character or factor vector or for grouping of cells, e.g. clusters, cell types.
method	A character string indicating which correlation coefficient to compute.
genes_use	A character vector with genes to use for computing the correlation. We recommend to use 150 - 500 genes.
allow_neg	Logical. Whether to allow negative correlations or set them to 0.
pseudobulk_groups	Logical. Whether to summarizse the group expression before computing the correlation.
group_name	A string indicating the metadata column for grouping the cells, e.g. clusters, cell types.

Value

A MousebrainMap object with a cell x ref correlation matrix and metadata.