Fit a bascule object

Usage

fit(
  counts,
  k_list,
  cluster = NULL,
  reference_cat = list(SBS = COSMIC_sbs_filt, DBS = COSMIC_dbs),
  keep_sigs = c("SBS1", "SBS5"),
  hyperparameters = NULL,
  lr = 0.005,
  n_steps = 3000,
  py = NULL,
  enumer = "parallel",
  nonparametric = TRUE,
  autoguide = FALSE,
  filter_dn = FALSE,
  min_exposure = 0.2,
  CUDA = TRUE,
  store_parameters = FALSE,
  store_fits = TRUE,
  seed_list = c(10)
)

Arguments

counts: List of mutation counts matrices from multiple variant types.
k_list: List of number of denovo signatures to test.
cluster: Maximum number of clusters. If `NULL`, no clustering will be performed.
reference_cat: List of reference catalogues to use for NMF. Names must be the same as input counts.
keep_sigs: List of reference signatures to keep even if found with low exposures.
hyperparameters: List of hyperparameters passed to the NMF and clustering models.
lr: Learning rate used for SVI.
n_steps: Number of iterations for inference.
py: User-installed version of pybascule package
enumer: Enumeration used for clustering (either `parallel` or `sequential`).
nonparametric: Deprecated. The model only works in nonparametric way.
autoguide: Logical. If `TRUE`, the clustering model will use the Pyro autoguide.
filter_dn: Logical. If `TRUE`, all contexts below 0.01 in denovo signatures will be set to 0, provided the filtered signatures remain consistent with the inferred ones.
min_exposure: Reference signatures with an exposures lower than `min_exposure` will be dropped.
CUDA: Logical. If `TRUE` and a GPU is available, the models will run on GPU.
store_parameters: Logical. If `TRUE`, parameters at every step of inference will be stored in the object.
store_fits: Logical. If `TRUE`, all tested fits, i.e., for every value of `K`, will be stored in the object.
seed_list: List of seeds used for every input configuration.

Value

Bascule object.