Apply scale-free topology transform to shared-nearest neighbor graph

Apply scale-free topology transform to shared-nearest neighbor graph

scaleFreeNet(
  object,
  graph_name = "RNA_snn",
  sf = c(seq(1, 5, by = 0.5), seq(6, 10)),
  sf_threshold = 0.9,
  umap_knn = 10,
  n_dim = 30,
  binary_threshold = 0.9,
  n_workers = 1,
  verbose = T
)

Arguments

object

Seurat object

sf

soft thresholds (sf) that are evaluated in search for optimal soft threshold (used to transform SNN to scale-free topology). Default is sf = c(seq(1, 5, by = 0.5), seq(6, 10)).

sf_threshold

R2 threshold [0,1] for identifying optimal soft threshold. Default is 0.9 (recommended 0.8-0.9).

umap_knn

This determines the number of neighboring points used in local approximations of UMAP manifold structure. Larger values will result in more global structure being preserved at the loss of detailed local structure. In general this parameter should often be in the range 5 to 50. default is 10.

n_dim

Number of PC dimensions to use in generating UMAP. Default is PCA.

binary_threshold

Numeric threshold used to binarize SNN graph. Default is 0.9.

n_workers

Number of workers used for parallel implementation. Default is 1.

verbose

Show progress. Default is T.

graph.name

name of SNN graph in `object`. Default is "RNA_snn".

Value

list containing

  • "object" - scale-free SNN and binarized graph are stored in `object` graph slot, and umap embedding is stored in `object` reduction slot.

  • "st.res" - soft threshold optimization results.

See also

runSSN

Author

Nicholas Mikolajewicz