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: