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", ... )
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.
A [samples] numeric vector, should typically be 0s and 1s, although in principle scaling shouldn't matter.
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.
Don't test clusters with more introns than this
Ignore introns used (i.e. at least one supporting read) in fewer than n samples
Require this many samples in each group to have at least min_coverage reads
Require min_samples_per_group samples in each group to have at least this many reads
Maximum time (in seconds) allowed for a single optimization run
Whether to use the robust model (explicitly models outliers). Generally not required/recommended for differential splicing.
If true writes more output
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.