Description Usage Arguments Value Author(s) See Also Examples

View source: R/melissa_gibbs.R

`melissa_gibbs`

implements the Gibbs sampling algorithm
for performing clustering of single cells based on their DNA methylation
profiles, where the observation model is the Bernoulli distributed Probit
Regression likelihood. NOTE: that Gibbs sampling is really slow and we
recommend using the VB implementation: `melissa`

.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 |

`X` |
A list of length I, where I are the total number of cells. Each element of the list contains another list of length N, where N is the total number of genomic regions. Each element of the inner list is an L x 2 matrix of observations, where 1st column contains the locations and the 2nd column contains the methylation level of the corresponding CpGs. |

`K` |
Integer denoting the number of clusters K. |

`pi_k` |
Vector of length K, denoting the mixing proportions. |

`w` |
A N x M x K array, where each column contains the basis function coefficients for the corresponding cluster. |

`basis` |
A 'basis' object. E.g. see create_rbf_object from BPRMeth package |

`w_0_mean` |
The prior mean hyperparameter for w |

`w_0_cov` |
The prior covariance hyperparameter for w |

`dir_a` |
The Dirichlet concentration parameter, prior over pi_k |

`lambda` |
The complexity penalty coefficient for penalized regression. |

`gibbs_nsim` |
Argument giving the number of simulations of the Gibbs sampler. |

`gibbs_burn_in` |
Argument giving the burn in period of the Gibbs sampler. |

`inner_gibbs` |
Logical, indicating if we should perform Gibbs sampling to sample from the augmented BPR model. |

`gibbs_inner_nsim` |
Number of inner Gibbs simulations. |

`is_parallel` |
Logical, indicating if code should be run in parallel. |

`no_cores` |
Number of cores to be used, default is max_no_cores - 1. |

`is_verbose` |
Logical, print results during EM iterations |

An object of class `melissa_gibbs`

.

C.A.Kapourani C.A.Kapourani@ed.ac.uk

`melissa`

, `create_melissa_data_obj`

,
`partition_dataset`

, `filter_regions`

1 2 3 4 5 6 7 8 9 10 11 12 | ```
# Example of running Melissa Gibbs on synthetic data
# Create RBF basis object with 4 RBFs
basis_obj <- BPRMeth::create_rbf_object(M = 4)
set.seed(15)
# Run Melissa Gibbs
melissa_obj <- melissa_gibbs(X = melissa_synth_dt$met, K = 2, basis = basis_obj,
gibbs_nsim = 10, gibbs_burn_in = 5, is_parallel = FALSE, is_verbose = FALSE)
# Extract mixing proportions
print(melissa_obj$pi_k)
``` |

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