Expected modeling

5C data exhibit a strong distance-dependent background signal which is also influenced by local contact domain structure. lib5c includes a variety of algorithms for modeling this background expected interaction frequency.

Command-line interfaces

The subcommand is

$ lib5c expected

For detailed help, run

$ lib5c expected -h

Exposed functionality

The subpackage responsible for expected modeling is lib5c.algorithms.expected.

The most important exposed function is lib5c.algorithms.expected.make_expected_matrix().

To use a global expected model, you must also use lib5c.algorithms.expected.get_global_distance_expected(), leading to the following workflow:

from lib5c.algorithms.expected import make_expected_matrix, get_global_distance_expected

distance_expected = get_global_distance_expected(observed_counts)
expected_counts = make_expected_matrix(observed_counts, distance_expected=distance_expected)

The following functions are provided for visualizing one-dimensional expected models overlayed over real data:

• lib5c.plotters.expected.plot_bin_expected()

• lib5c.plotters.expected.plot_fragment_expected()