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Run DGRDL with the specified parameters

dgrdl_result.Rd

Runs the DGRDL algorithm from Pan et al., with the specified hyperparamters. To determine the hyperparameters, you can use dgrdl_grid_search().

Usage

dgrdl_result(
  object,
  dgrdl_params = params_dgrdl(),
  membership_params = params_module_membership(),
  seed = 42L,
  .verbose = TRUE
)

Arguments

object

The class, see BulkCoExp(). Ideally, you should run preprocess_bulk_coexp() before applying this function.

dgrdl_params

List. Output of params_dgrdl():

  • sparsity - Integer. Sparsity constraint (max non-zero coefficients per signal)

  • dict size - Integer. The dictionary size.

  • alpha - Float. Sample context regularisation weight.

  • beta - Float. Feature effect regularisation weight.

  • max_iter - Integer. Maximum number of iterations for the main algorithm.

  • k_neighbours - Integer. Number of neighbours for the KNN graph for the feature and sample Laplacian.

  • admm_iter - Integer. ADMM iterations for sparse coding.

  • rho - Float. ADMM step size.

membership_params

List. Controls how the atom loadings are turned into module membership, see params_module_membership(). Membership is not exclusive: a gene active in several atoms appears in several modules, and a gene in no tail appears in none.

seed

Integer. Seed for the initialisation of the dictionary.

.verbose

Boolean. Controls verbosity of the function.

References

Pan et al., Cell Syst, 2022

Examples

# fit DGRDL with a six-atom dictionary
syn <- generate_gene_module_data(n_samples = 24L, n_genes = 60L)
obj <- BulkCoExp(syn$data, syn$meta_data)
obj <- preprocess_bulk_coexp(obj, hvg = NULL, .verbose = FALSE)
obj <- dgrdl_result(
  obj,
  dgrdl_params = params_dgrdl(dict_size = 6L, k_neighbours = 3L),
  .verbose = FALSE
)
head(get_modules(get_results(obj)))
#>          gene module_id  loading   sign        z
#>        <char>    <char>    <num> <char>    <num>
#> 1: feature_27    dict_2 1.889059    pos 3.077097
#> 2: feature_24    dict_2 1.885384    pos 3.070073
#> 3: feature_22    dict_2 1.882083    pos 3.063763
#> 4: feature_28    dict_2 1.881407    pos 3.062472
#> 5: feature_25    dict_2 1.878301    pos 3.056535
#> 6: feature_23    dict_2 1.877537    pos 3.055074