Run the SNF algorithm — run

This function will run the SNF algorithm on top of the adjacency matrices found in the object. You can also optionally specify which adjacency matrices to use via the to_include parameter.

Usage

run_snf(object, to_include = NULL, params = NULL)

Arguments

object: The underlying class, see SimilarityNetworkFusion().
to_include: Optional string, if you wish to only use a subset of the generated adjacency matrices. If NULL all matrices will be used for the fusion process.
params: Optional List. If you wish to overwite the already set up parameters for SNF, see params_snf(). If NULL, the settings from within the object will be used. If not NULL, the new parameters will be used for this modality specifically and only for this modality!

Value

The class with added adjacency matrix based on the SNF algorithm.

Examples

# fuse a continuous and a categorical modality
set.seed(42)
continuous <- matrix(rnorm(120), nrow = 12, ncol = 10)
rownames(continuous) <- sprintf("sample_%02i", 1:12)
colnames(continuous) <- sprintf("feature_%i", 1:10)
clinical <- data.table::data.table(
  sample_id = rownames(continuous),
  sex = factor(sample(c("M", "F"), 12, replace = TRUE)),
  stage = factor(sample(c("I", "II", "III"), 12, replace = TRUE))
)
object <- SimilarityNetworkFusion(
  data = continuous,
  data_name = "continuous",
  snf_params = params_snf(k = 3L)
)
object <- add_snf_data_modality(object, clinical, data_name = "clinical")
object <- run_snf(object)
dim(get_snf_final_mat(object))
#> [1] 12 12