Description Usage Arguments Value
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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | 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
)
|
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 |
verbose |
Print progress bars and output |
Seurat object containing a nearest-neighbor object, KNN graph, and SNN graph - each based on a weighted combination of modalities.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.