Metadata-Version: 2.1
Name: hpo_similarity
Version: 0.5.1
Summary: Testing similarity of HPO terms between probands sharing         variants in genes.
Home-page: UNKNOWN
Author: Jeremy McRae
Author-email: jeremy.mcrae@sanger.ac.uk
License: MIT
Description: [![Build Status](https://travis-ci.org/jeremymcrae/hpo_similarity.svg?branch=master)](https://travis-ci.org/jeremymcrae/hpo_similarity)
        
        #### Similarity of phenotypes in patient groups
        This estimates how likely it is for groups of individuals to have similar
        phenotypes.  To estimate this probability, we need three things:
        - a way to quantify phenotypic similarity of two individuals. We use the maximum
          information content of the most informative common ancestor for each pair of
          HPO terms from two probands.
        - a way to quantify similarity across more than two probands. We sum phenotypic
          similarity scores from all pairs of probands.
        - a null distribution of similarity scores for those probands, generated by
          randomly sampled groups of probands
        
        The P value is calculated as the proportion of simulated scores greater than the
        observed probands' score.
        
        ##### Install
        Install the package with:
        ```sh
        pip install hpo_similarity
        ```
        
        ##### Run
        ```sh
        hpo_similarity --genes genes.json --phenotypes phenotypes.json
        ```
        The data directory on github includes example files, one with proband IDs
        per gene ([data/example_genes.json](data/example_genes.json)), and the other
        with HPO terms per proband ([data/example_phenotypes.json](data/example_phenotypes.json)).
        
        Additional options:
        - `--output PATH` to send output gene and P-values to a file.
        - `--ontology PATH` to use a HPO ontology file other than the default.
        - `--iterations INTEGER` to change the number of iterations (default=100000)
        
        You can also explore the HPO graph using the hpo_similarity package within
        python, for example:
        ```python
        from hpo_similarity import open_ontology
        
        graph, alt_ids, obsolete_ids = open_ontology()
        
        # find all descendant terms
        graph.get_descendants('HP:0001249')
        
        # get the text for the phenotypic abnormality
        graph.nodes['HP:0001249']['name']
        ```
        
        ##### Acknowledgments
          This code incorporates the following code and datasets:
          - a [python ontology parser](https://github.com/ntamas/gfam/blob/master/gfam/go/obo.py)
            written by Tamás Nepusz.
          - the [hp.obo file](http://purl.obolibrary.org/obo/hp.obo) from the
            [Human Phenotype Ontology Consortium](http://human-phenotype-ontology.org/).
        
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: License :: OSI Approved :: MIT License
Description-Content-Type: text/markdown
