mavis.summary.summary
mavis.summary.summary.filter_by_call_method()
Filters a set of breakpoint pairs to returns the call with the most evidence. Prefers contig evidence over spanning over split over flanking, etc.
def filter_by_call_method(bpp_list):
Args
- bpp_list
mavis.summary.summary.group_events()
group events together and join data attributes
def group_events(events):
Args
- events
mavis.summary.summary.group_by_distance()
groups a set of calls based on their proximity. Returns a new list of calls where close calls have been merged
def group_by_distance(calls, distances):
Args
- calls
- distances
mavis.summary.summary.annotate_dgv()
given a list of bpps and a dgv reference, annotate the events that are within the set distance of both breakpoints
def annotate_dgv(bpps, dgv_regions_by_reference_name, distance=0):
Args
- bpps
- dgv_regions_by_reference_name
- distance
mavis.summary.summary.get_pairing_state()
given two libraries, returns the appropriate descriptor for their matched state
def get_pairing_state(
current_protocol,
current_disease_state,
other_protocol,
other_disease_state,
is_matched=False,
inferred_is_matched=False,
):
Args
- current_protocol (
PROTOCOL
): the protocol of the current library - current_disease_state (
DISEASE_STATUS
): the disease status of the current library - other_protocol (
PROTOCOL
): protocol of the library being comparing to - other_disease_state (
DISEASE_STATUS
): disease status of the library being compared to - is_matched (
bool
): True if the libraries are paired - inferred_is_matched
Returns
(PAIRING_STATE)
: descriptor of the pairing of the two libraries