HPOSet¶
HPOSet
instances contains a set of HPO terms. This class is useful to represent a patient’s clinical information.
It provides analytical helper functions to narrow down the actual provided clinical information.
HPOSet
class¶
-
class
pyhpo.set.
HPOSet
(items: Iterable[pyhpo.term.HPOTerm])[source]¶
child_nodes¶
-
HPOSet.
child_nodes
() → pyhpo.set.HPOSet[source]¶ Return a new HPOSet tha contains only the most specific HPO term for each subtree
It basically will return only HPO terms that do not have descendant HPO terms present in the set
- Returns
HPOSet instance that contains only the most specific child nodes of the current HPOSet
- Return type
remove_modifier¶
-
HPOSet.
remove_modifier
() → pyhpo.set.HPOSet[source]¶ Removes all modifier terms. By default, this includes
Mode of inheritance: 'HP:0000005'
Clinical modifier: 'HP:0012823'
Frequency: 'HP:0040279'
Clinical course: 'HP:0031797'
Blood group: 'HP:0032223'
Past medical history: 'HP:0032443'
- Returns
HPOSet instance that contains only
Phenotypic abnormality
HPO terms- Return type
replace_obsolete¶
-
HPOSet.
replace_obsolete
(verbose: bool = False) → pyhpo.set.HPOSet[source]¶ Replaces obsolete terms with the replacement term
Warning
Not all obsolete terms have a replacement
- Parameters
verbose (bool, default:
False
) – Print warnings if an obsolete term does not have a replacement.- Returns
A new HPOSet
- Return type
all_genes¶
-
HPOSet.
all_genes
() → Set[pyhpo.annotations.GeneSingleton][source]¶ Calculates the union of the genes attached to the HPO Terms in this set
- Returns
Set of all genes associated with the HPOTerms in the set
- Return type
set of
annotations.Gene
omim_diseases¶
information_content¶
-
HPOSet.
information_content
(kind: str = '') → Dict[source]¶ Gives back basic information content stats about the HPOTerms within the set
- Parameters
kind (str, default:
omim
) – Which kind of information content should be calculated. Options are [‘omim’, ‘orpha’, ‘decipher’, ‘gene’]- Returns
Dict with the following items
mean - float - Mean information content
max - float - Maximum information content value
total - float - Sum of all information content values
all - list of float - List with all information content values
- Return type
dict
variance¶
-
HPOSet.
variance
() → Tuple[float, int, int, List[int]][source]¶ Calculates the distances between all its term-pairs. It also provides basic calculations for variances among the pairs.
- Returns
Tuple with the variance metrices
float Average distance between pairs
int Smallest distance between pairs
int Largest distance between pairs
list of int List of all distances between pairs
- Return type
tuple of (int, int, int, list of int)
combinations¶
-
HPOSet.
combinations
() → Iterator[Tuple[pyhpo.term.HPOTerm, pyhpo.term.HPOTerm]][source]¶ Helper generator function that returns all possible two-pair combination between all its terms
This function is direction dependent. That means that every pair will appear twice. Once for each direction
- Yields
Tuple of
term.HPOTerm
– Tuple containing the follow itemsHPOTerm instance 1 of the pair
HPOTerm instance 2 of the pair
Examples
ci = HPOSet([term1, term2, term3]) ci.combinations() # Output: [ (term1, term2), (term1, term3), (term2, term1), (term2, term3), (term3, term1), (term3, term2) ]
combinations_one_way¶
-
HPOSet.
combinations_one_way
() → Iterator[Tuple[pyhpo.term.HPOTerm, pyhpo.term.HPOTerm]][source]¶ Helper generator function that returns all possible two-pair combination between all its terms
This methow will report each pair only once
See also
- Yields
Tuple of
term.HPOTerm
– Tuple containing the follow itemsHPOTerm instance 1 of the pair
HPOTerm instance 2 of the pair
Example
ci = HPOSet([term1, term2, term3]) ci.combinations() # Output: [ (term1, term2), (term1, term3), (term2, term3) ]
similarity¶
-
HPOSet.
similarity
(other: pyhpo.set.HPOSet, kind: str = '', method: str = '', combine: str = 'funSimAvg') → float[source]¶ Calculates the similarity to another HPOSet According to Robinson et al, American Journal of Human Genetics, (2008) and Pesquita et al, BMC Bioinformatics, (2008)
- Parameters
other (HPOSet) – Another HPOSet to measure the similarity to
kind (str, default
''
) – Which kind of information content should be calculated. Options are [‘omim’, ‘orpha’, ‘decipher’, ‘gene’] Seepyhpo.HPOTerm.similarity_score()
for optionsmethod (string, default
''
) –The method to use to calculate the similarity. See
pyhpo.HPOTerm.similarity_score()
for optionsAdditional options:
equal - Calculates exact matches between both sets
combine (string, default
funSimAvg
) –The method to combine similarity measures.
Available options:
funSimAvg - Schlicker A, BMC Bioinformatics, (2006)
funSimMax - Schlicker A, BMC Bioinformatics, (2006)
BMA - Deng Y, et. al., PLoS One, (2015)
- Returns
The similarity score to the other HPOSet
- Return type
float
toJSON¶
serialize¶
-
HPOSet.
serialize
() → str[source]¶ Creates a string serialization that can be used to rebuild the same HPOSet via
pyhpo.set.HPOSet.from_serialized()
- Returns
A string representation of the HPOSet
- Return type
str
BasicHPOSet
class¶
-
class
pyhpo.set.
BasicHPOSet
(items: Iterable[pyhpo.term.HPOTerm])[source]¶ Child of
HPOSet
that automatically:removes parent terms
removes modifier terms
replaces obsolete terms
Class methods¶
from_queries¶
-
classmethod
HPOSet.
from_queries
(queries: List[Union[str, int]]) → pyhpo.set.HPOSet[source]¶ Builds an HPO set by specifying a list of queries to run on the
pyhpo.ontology.Ontology
- Parameters
queries (list of (string or int)) – The queries to be run the identify the HPOTerm from the ontology
- Returns
A new HPOset
- Return type
Examples
ci = HPOSet([ 'Scoliosis', 'HP:0001234', 12 ])
from_serialized¶
-
classmethod
HPOSet.
from_serialized
(pickle: str) → pyhpo.set.HPOSet[source]¶ Re-Builds an HPO set from a serialized HPOSet object
- Parameters
pickle (str) – The serialized HPOSet object
- Returns
A new HPOset
- Return type
Examples
ci = HPOSet(ontology, '12+24+66628')