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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