Find Multi-Modal Nearest Neighbors

A wrapper function that finds Multi-Modal weights and generate a Multi-Modal NN object.

Usage

FindmmNN(
  object,
  sketch.assay = NULL,
  reduction.list,
  dims.list,
  k.nn = 20,
  knn.range = 200,
  l2.norm = TRUE,
  fix.wnn.weights = NULL,
  nn.name = "weighted.nn",
  weighted.nn.name = NULL,
  verbose = TRUE,
  ...
)

Arguments

object: A Seurat object
sketch.assay: The name of the sketched assay of the landmark cells.
reduction.list: A list of dimensional reductions, one for each modality
dims.list: A list containing the dimensions for each reduction to use
k.nn: The number of nearest neighbors to compute for each modality. Internally clamped to a minimum of 20 (i.e. values below 20 are silently raised to 20).
knn.range: Range parameter for nearest neighbor search
l2.norm: Perform L2 normalization on the cell embeddings
fix.wnn.weights: Pre-specified modality weights. If provided, skips the calculation and uses these weights directly. Should be a list with the same length as reduction.list.
nn.name: Name under which the resulting Neighbor object is stored.
weighted.nn.name: Deprecated. Use nn.name instead.
verbose: Print progress bars and output
...: Additional arguments for Seurat:::FindModalityWeights (for example sigma.idx, sd.scale or prune.SNN) or Seurat:::MultiModalNN (for example kernel.power). Each is routed to whichever of the two declares it; arguments matching neither are reported.

Value

return a Seurat object that contains a weighted.nn Neighbor object between the landmark cells and all the other cells.

Details

This function is essentially a wrapper function that perform the following 2 procedures:

Seurat:::FindModalityWeights
Seurat:::MultiModalNN