GenerateSampleObject: Generate a sample-level count matrix based on landmark assay and its weighted.nn object

Description

Returns summed counts of weighted.nn for each landmark cell within each sample.

Usage

GenerateSampleObject(
  object,
  nn.name = NULL,
  k.nn = 5,
  sketch.assay = "LANDMARK",
  return.seurat = TRUE,
  new_assay_name = "LMC",
  group.by = "ident",
  normalization.method = "ChiSquared",
  scale.factor = 10000,
  rename.group.by = NULL,
  add.meta.data = TRUE,
  cells.use = NULL,
  verbose = TRUE,
  ...
)

Value

return a Seurat object that contains a count matrix with number of landmark as rows, sample as columns

Arguments

object: Seurat object
nn.name: Name of the Neighbor object to use for the calculation (e.g. "weighted.nn" as produced by FindmmNN / PrepareSampleObject, or "single.nn" as produced by FindsmNN). If NULL (default), the function auto-detects a Neighbor object named "weighted.nn" or "single.nn"; if both are present it errors and asks you to specify this parameter.
k.nn: the number of nearest neighbors to perform the summing
sketch.assay: the name of the sketch.assay you used to perform the FindmmNN()
return.seurat: Whether to return the data as a Seurat object. Default is TRUE
new_assay_name: Name for the new assay containing landmark counts
group.by: A single meta-data column to group cells by (e.g. Donor ID); 'ident' by default. To group on a combination of columns, add a merged column to the object beforehand and pass that, e.g. object$batch_rep <- paste(object$batch, object$replicate, sep = "_").
normalization.method: Method for normalization. Supports LogNormalize and ChiSquared. see details at NormalizeData and NormalizeChiSquared
scale.factor: Scale factor for Log-Normalization, see NormalizeData
rename.group.by: if rename.group.by is NULL, the rownames of the landmark matrix will used the original cell IDs. But, user can indicate which meta-data column to use to rename the rows. A suffix of "_LM" + order number will be added automatically.
add.meta.data: if TRUE, the function will automatically detect sample-level meta-data (based on 'group.by') and append it to the sample-level object; if FALSE, it will not do so.
cells.use: An optional character vector of cell names to subset before building the sample-level matrix. When non-NULL, only cells in this vector (that are also present in the NN object) are retained. Default is NULL (use all cells).
verbose: Print progress and diagnostic messages
...: Arguments to be passed to methods such as CreateSeuratObject