`R/differential_splicing.R`

`differential_splicing.Rd`

Parallelization across tested clusters is achieved using foreach/doMC, so the number of threads that will be used is determined by the cores argument passed to registerDoMC.

differential_splicing( counts, x, confounders = NULL, max_cluster_size = 10, min_samples_per_intron = 5, min_samples_per_group = 4, min_coverage = 20, timeout = 10, robust = F, debug = F, init = "smart", ... )

counts | An [introns] x [samples] matrix of counts. The rownames must be of the form chr:start:end:cluid. If the counts file comes from the leafcutter clustering code this should be the case already. |
---|---|

x | A [samples] numeric vector, should typically be 0s and 1s, although in principle scaling shouldn't matter. |

confounders | A [samples] x [confounders] numeric matrix to be controlled for in the GLM. Factors should already have been converted to a 1-of-(K-1) encoding, e.g. using model.matrix (see scripts/leafcutter_ds.R for how to do this). Can be NULL, implying no covariates are controlled for. |

max_cluster_size | Don't test clusters with more introns than this |

min_samples_per_intron | Ignore introns used (i.e. at least one supporting read) in fewer than n samples |

min_samples_per_group | Require this many samples in each group to have at least min_coverage reads |

min_coverage | Require min_samples_per_group samples in each group to have at least this many reads |

timeout | Maximum time (in seconds) allowed for a single optimization run |

robust | Whether to use the robust model (explicitly models outliers). Generally not required/recommended for differential splicing. |

debug | If true writes more output |

init | One of 'smart' (default) or 'random'. If 'random' you can pass an additional arg "seed" for reproducibility. |

A per cluster list of results. Clusters that were not tested will be represented by a string saying why.