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¶
child_nodes¶

HPOSet.
child_nodes
()[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: HPOSet
remove_modifier¶

HPOSet.
remove_modifier
()[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 termsReturn type: HPOSet
replace_obsolete¶
all_genes¶
omim_diseases¶
information_content¶

HPOSet.
information_content
(kind=None)[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
()[source]¶ Calculates the distances between all its termpairs. It also provides basic calculations for variances among the pairs.
Returns: Tuple with the variance metrices  int 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
()[source]¶ Helper generator function that returns all possible twopair 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 items HPOTerm 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
()[source]¶ Helper generator function that returns all possible twopair combination between all its terms
This methow will report each pair only once
See also
Yields: Tuple of
term.HPOTerm
– Tuple containing the follow items HPOTerm 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, kind='omim', method=None)[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
omim
) – Which kind of information content should be calculated. Options are [‘omim’, ‘orpha’, ‘decipher’, ‘gene’]  method (string, default
resnik
) –The method to use to calculate the similarity.
Available options:
 resnik  Resnik P, Proceedings of the 14th IJCAI, (1995)
 lin  Lin D, Proceedings of the 15th ICML, (1998)
 jc  Jiang J, Conrath D, ROCLING X, (1997) Implementation according to R source code
 jc2  Jiang J, Conrath D, ROCLING X, (1997)
Implementation according to paper from R
hposim
library Deng Y, et. al., PLoS One, (2015)  rel  Relevance measure  Schlicker A, et.al., BMC Bioinformatics, (2006)
 ic  Information coefficient  Li B, et. al., arXiv, (2010)
 graphic  Graph based Information coefficient  Deng Y, et. al., PLoS One, (2015)
 dist  Distance between terms
 equal  Calculates exact matches between both sets
Returns: The similarity score to the other HPOSet
Return type: float
toJSON¶
serialize¶

HPOSet.
serialize
()[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 methods¶
from_queries¶

classmethod
HPOSet.
from_queries
(queries)[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: pyhpo.set.HPOSet
Examples
ci = HPOSet([ 'Scoliosis', 'HP:0001234', 12 ])
from_serialized¶

classmethod
HPOSet.
from_serialized
(pickle)[source]¶ ReBuilds an HPO set from a serialized HPOSet object
Parameters: pickle (str) – The serialized HPOSet object Returns: A new HPOset Return type: pyhpo.set.HPOSet
Examples
ci = HPOSet(ontology, '12+24+66628')