shaman_score_hic_mat_for_track: generate a score matrix for observed data based on the...

In tanaylab/shaman: shaman - Sampling HiC contAct Matrices for Aparametric Normalization

generate a score matrix for observed data based on the expected

Description

shaman_score_hic_mat_for_track

Usage

shaman_score_hic_mat_for_track(
  track_db,
  work_dir,
  obs_track_nms,
  exp_track_nms,
  points_track_nms,
  chrom,
  start1,
  end1,
  start2,
  end2,
  expand = 2000000,
  k = 100,
  min_dist = 1024
)

Arguments

track_db	Directory of the misha database.
work_dir	Centralized directory to store temporary files.
obs_track_nms	Names of observed 2D genomic tracks for the hic data. Pooling of multiple observed tracks is supported.
exp_track_nms	Names of expected (shuffled) 2D genomic tracks. Pooling of multiple expected tracks is supported.
points_track_nms	Names of 2D genomic tracks that contain points on which to compute normalized score. Pooling points from multiple tracks is supported.
chrom	The chormosome of the matrix.
start1	The start coordinate of the first dimension.
end1	The end coordinate of the first dimension.
start2	The start coordinate of the second dimension.
end2	The end coordinate of the second dimension.
expand	Size of expansion, points to include outside the matrix for accurate computing of the score. Note that for each observed point, its k-nearest neighbors must be included in the expanded matrix.
k	The number of neighbor distances used for the score. For higher resolution maps, increase k. For lower resolution maps, decrease k.
min_dist	The minimum distance between points.

Details

This function extracts observed data and expected data in an expanded matrix and computes The score for a point is the KS D-statistic of the distances to the points k-nearest-neighbors

tanaylab/shaman documentation built on April 2, 2022, 1:32 a.m.