LACE: LACE
In LACE: Longitudinal Analysis of Cancer Evolution (LACE)

Description Usage Arguments Value Examples

View source: R/frontend.R

Perform inference of the maximum likelihood clonal tree from longitudinal data.

LACE(
  D,
  lik_w = NULL,
  alpha = NULL,
  beta = NULL,
  initialization = NULL,
  keep_equivalent = TRUE,
  check_indistinguishable = TRUE,
  num_rs = 50,
  num_iter = 10000,
  n_try_bs = 500,
  learning_rate = 1,
  marginalize = FALSE,
  error_move = FALSE,
  num_processes = Inf,
  seed = NULL,
  verbose = TRUE,
  log_file = ""
)

`D`	Mutation data from multiple experiments for a list of driver genes. It can be either a list with a data matrix per time point or a SummarizedExperiment object. In this latter, the object must contain two fields: assays and colData. Assays stores one unique data matrix pooling all single cells observed at each time point and colData stores a vector of labels reporting the time point when each single cell was sequenced. Ordering of cells in assays field and colData field must be the same.
`lik_w`	Weight for each data point. If not provided, weights to correct for sample sizes are used.
`alpha`	False positive error rate provided as list of elements; if a vector of alpha (and beta) is provided, the inference is performed for multiple values and the solution at maximum-likelihood is returned.
`beta`	False negative error rate provided as list of elements; if a vector of beta (and alpha) is provided, the inference is performed for multiple values and the solution at maximum-likelihood is returned.
`initialization`	Starting point of the mcmc; if not provided, a random starting point is used.
`keep_equivalent`	Boolean. Shall I return results (B and C) at equivalent likelihood with the best returned solution?
`check_indistinguishable`	Boolean. Shall I remove any indistinguishable event from input data prior inference?
`num_rs`	Number of restarts during mcmc inference.
`num_iter`	Maximum number of mcmc steps to be performed during the inference.
`n_try_bs`	Number of steps without change in likelihood of best solution after which to stop the mcmc.
`learning_rate`	Parameter to tune the probability of accepting solutions at lower values during mcmc. Value of learning_rate = 1 (default), set a probability proportional to the difference in likelihood; values of learning_rate greater than 1 inclease the chance of accepting solutions at lower likelihood during mcmc while values lower than 1 decrease such probability.
`marginalize`	Boolean. Shall I marginalize C when computing likelihood?
`error_move`	Boolean. Shall I include estimation of error rates in the MCMC moves?
`num_processes`	Number of processes to be used during parallel execution. To execute in single process mode, this parameter needs to be set to either NA or NULL.
`seed`	Seed for reproducibility.
`verbose`	Boolean. Shall I print to screen information messages during the execution?
`log_file`	log file where to print outputs when using parallel. If parallel execution is disabled, this parameter is ignored.

A list of 9 elements: B, C, clones_prevalence, relative_likelihoods, joint_likelihood, clones_summary and error_rates. Here, B returns the maximum likelihood longitudinal clonal tree, C the attachment of cells to clones, corrected_genotypes the corrected genotypes and clones_prevalence clones' prevalence; relative_likelihoods and joint_likelihood are respectively the likelihood of the solutions at each individual time points and the joint likelihood; clones_summary provide a summary of association of mutations to clones. In equivalent_solutions, solutions (B and C) with likelihood equivalent to the best solution are returned. Finally error_rates provides the best values of alpha and beta among the considered ones.

data(longitudinal_sc_variants)
inference = LACE(D = longitudinal_sc_variants,
                 lik_w = c(0.2308772,0.2554386,0.2701754,0.2435088),
                 alpha = list(c(0.10,0.05,0.05,0.05)),
                 beta = list(c(0.10,0.05,0.05,0.05)),
                 keep_equivalent = FALSE,
                 num_rs = 5,
                 num_iter = 10,
                 n_try_bs = 5,
                 num_processes = NA,
                 seed = 12345,
                 verbose = FALSE)