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_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. 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_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