Creates an object of class mvnmm.

Function to fit the input data.

Usage

fit(
  cov.df,
  vaf.df = NULL,
  infer_phylogenies = TRUE,
  infer_growth = TRUE,
  k_interval = c(5, 15),
  n_runs = 1,
  steps = 500,
  min_steps = 20,
  lr = 0.005,
  p = 1,
  min_frac = 0,
  max_IS = NULL,
  check_conv = TRUE,
  covariance = "full",
  hyperparams = list(),
  default_lm = TRUE,
  timepoints_to_int = list(),
  show_progr = FALSE,
  store_grads = TRUE,
  store_losses = TRUE,
  store_params = FALSE,
  seed_optim = TRUE,
  seed = 6,
  seed_init = reticulate::py_none(),
  sample_id = ""
)

Arguments

cov.df: Input coverage dataset. It must have at least the columns coverage, and IS, and additional columns timepoints and lineage, will be added if missing, assuming single timepoint and lineage.
vaf.df: Input VAF dataset. If not NULL, the mutations clustering will be performed. It must have at least the columns mutation, IS, alt, dp, and additional vaf, timepoints, lineage, IS, mutation, with the number of reads for the mutated allele, overall depth, vaf values, timepoint, lineage, IS and mutation, respectively.
infer_phylogenies: A Boolean. If set to TRUE, the function will also compute and attach to the returned object the phylogenetic trees for each cluster.
k_interval: Interval of K values to test.
n_runs: Number of runs to perform for each K.
steps: Maximum number of steps for the inference.
lr: Learning rate used in the inference.
p: Numeric value used to check the convergence of the parameters.
min_frac: add
max_IS: add
check_conv: A Boolean. If set to TRUE, the function will check for early convergence, otherwise it will perform steps iterations.
covariance: Covariance type for the Multivariate Gaussian.
hyperparams: add
default_lm: add
timepoints_to_int: add
show_progr: A Boolean. If TRUE, the progression bar will be shown during inference.
store_grads: A Booolean. If TRUE, the gradient norms for the parameters at each iteration will be stored.
store_losses: A Boolean. If TRUE, the computed losses for the parameters at each iteration will be stored.
store_params: A Boolean. If TRUE, the estimated parameters at each iteration will be stored.
seed_optim: add
seed: Value of the seed.
sample_id: add

Value

A mvnmm object, containing the input dataset, annotated with IS_values, N, K, T specific of the dataset, the input IS and column names, a list params that will contain the inferred parameters, the python object