Learn 'prego' models for ATAC difference of a trajectory

Usage

learn_traj_prego(
  peak_intervals,
  atac_diff,
  n_motifs,
  min_diff = 0.2,
  energy_norm_quantile = 1,
  norm_energy_max = 10,
  min_energy = -7,
  sample_for_kmers = FALSE,
  sample_fraction = 0.1,
  sequences = NULL,
  seed = NULL,
  peaks_size = 300,
  additional_features = NULL,
  norm_intervals = peak_intervals,
  ...
)

Arguments

peak_intervals: A data frame, indicating the genomic positions ('chrom', 'start', 'end') of each peak, with an additional column named "const" indicating whether the peak is constitutive and therefore shouldn't be used in the regression. Optionally, a column named "cluster" can be added with indication of the cluster of each peak.
atac_diff: A numeric vector, indicating the ATAC difference of each peak
n_motifs: Number of motifs to learn. Should be at least 2
min_diff: Minimum ATAC difference to include a peak in the training
energy_norm_quantile: quantile of the energy used for normalization. Default: 1
norm_energy_max: maximum value of the normalized energy. Default: 10
min_energy: Minimum energy value after normalization (default: -7)
sample_for_kmers: Whether to sample kmers for training. Default: TRUE
sample_fraction: Fraction of peaks to sample for training. Default: 0.1#'
sequences: A character vector of sequences to learn the motifs on. If NULL, the sequences of the peaks are used.
seed: Random seed
peaks_size: size of the peaks to extract sequences from. Default: 300bp
additional_features: A matrix of additional features to filter out before learning the motifs (e.g. CpG content, dinucleotide content, etc.)
norm_intervals: A data frame, indicating the genomic positions ('chrom', 'start', 'end') of peaks used for energy normalization. If NULL, the function will use peak_intervals for normalization.
...: Additional arguments to be passed to prego::regress_pwm