FindMultiModalNeighbors: Construct weighted nearest neighbor graph

In satijalab/seurat: Tools for Single Cell Genomics

Construct weighted nearest neighbor graph

Description

This function will construct a weighted nearest neighbor (WNN) graph. For each cell, we identify the nearest neighbors based on a weighted combination of two modalities. Takes as input two dimensional reductions, one computed for each modality.Other parameters are listed for debugging, but can be left as default values.

Usage

FindMultiModalNeighbors(
  object,
  reduction.list,
  dims.list,
  k.nn = 20,
  l2.norm = TRUE,
  knn.graph.name = "wknn",
  snn.graph.name = "wsnn",
  weighted.nn.name = "weighted.nn",
  modality.weight.name = NULL,
  knn.range = 200,
  prune.SNN = 1/15,
  sd.scale = 1,
  cross.contant.list = NULL,
  smooth = FALSE,
  return.intermediate = FALSE,
  modality.weight = NULL,
  verbose = TRUE
)

Arguments

object	A Seurat object
reduction.list	A list of two dimensional reductions, one for each of the modalities to be integrated
dims.list	A list containing the dimensions for each reduction to use
k.nn	the number of multimodal neighbors to compute. 20 by default
l2.norm	Perform L2 normalization on the cell embeddings after dimensional reduction. TRUE by default.
knn.graph.name	Multimodal knn graph name
snn.graph.name	Multimodal snn graph name
weighted.nn.name	Multimodal neighbor object name
modality.weight.name	Variable name to store modality weight in object meta data
knn.range	The number of approximate neighbors to compute
prune.SNN	Cutoff not to discard edge in SNN graph
sd.scale	The scaling factor for kernel width. 1 by default
cross.contant.list	Constant used to avoid divide-by-zero errors. 1e-4 by default
smooth	Smoothing modality score across each individual modality neighbors. FALSE by default
return.intermediate	Store intermediate results in misc
modality.weight	A ModalityWeights object generated by FindModalityWeights
verbose	Print progress bars and output

Value

Seurat object containing a nearest-neighbor object, KNN graph, and SNN graph - each based on a weighted combination of modalities.

satijalab/seurat documentation built on May 11, 2024, 4:04 a.m.