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
ModalityWeightsobject generated byFindModalityWeights- verbose
Print progress bars and output