LACE | R Documentation |

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

LACE( D, lik_w = NULL, alpha = NULL, beta = NULL, initialization = NULL, random_tree = FALSE, 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 = "", show = TRUE )

`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` |
Binary matrix representing a perfect philogeny clonal tree; clones are rows and mutations are columns. This parameter overrides "random_tree". |

`random_tree` |
Boolean. Shall I start MCMC search from a random tree? If FALSE (default) and initialization is NULL, search is started from a TRaIT tree (BMC Bioinformatics . 2019 Apr 25;20(1):210. doi: 10.1186/s12859-019-2795-4). |

`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. |

`show` |
Boolean. Show the interactive interface to explore the output. |

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 = TRUE, num_rs = 5, num_iter = 10, n_try_bs = 5, num_processes = NA, seed = 12345, verbose = FALSE, show = FALSE)

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