Load a multiple motif regression model from a file

Usage

load_multi_regression(
  fn,
  response = NULL,
  sequences = NULL,
  motif_dataset = all_motif_datasets(),
  parallel = getOption("prego.parallel", FALSE),
  alternative = "two.sided"
)

Arguments

fn: file name or a list with the model
response: A matrix of response variables - number of rows should equal the number of sequences
sequences: A vector of DNA sequences ('A', 'T', 'C' or 'G'. Will go through toupper). Please make sure that the sequences are long enough to cover spat_num_bins * spat_bin_size bp, and that they are centered around the motif/signal.
motif_dataset: a data frame with PSSMs ('A', 'C', 'G' and 'T' columns), with an additional column 'motif' containing the motif name, for example HOMER_motifs, JASPAR_motifs or all_motif_datasets(). By default all_motif_datasets() would be used.
parallel: whether to run optimization in parallel. use set_parallel to set the number of cores to use.
alternative: alternative hypothesis for the p-value calculation when using ks.test. One of "two.sided", "less" or "greater".

Value

a list with the following elements:

models:: a list of models.
model:: the combined model.
spat_min:: the minimum spatial position.
spat_max:: the maximum spatial position.
bidirect:: whether the model is bidirectional.
spat_bin_size:: the spatial bin size.
seq_length:: the sequence length.
motif_num:: the number of motifs.
predict:: a function to predict the response.
predict_multi:: a function to predict the response for each motif.

Examples

if (FALSE) { # \dontrun{
res_multi <- regress_pwm(cluster_sequences_example, cluster_mat_example[, 1],
    final_metric = "ks", spat_bin_size = 40,
    spat_num_bins = 7,
    motif_num = 2
)
tmp <- tempfile()
res_multi$export(tmp)
r <- load_multi_regression(tmp)
} # }