pheno_tool package

Example usage of PhenoTool class

First some imports:

from DAE import *
from pheno_tool.tool import *

Then prepare studies to work with:

studies = vDB.get_studies('ALL SSC')
studies.append(vDB.get_study('w1202s766e611'))

Now we can create an instance of PhenoTool class to work with given set of individuals:

tool = PhenoTool(
         phdb, studies, roles=['prb', 'mom', 'dad'],
         measure_id='ssc_core_descriptive.ssc_diagnosis_nonverbal_iq',
)

We can check the list of individuals that are subject to computation by this particular instance of PhenoTool by using list_of_subjects() method:

tool.list_of_subjects()

will output following dictionary:

{
 u'14566.p1': Person(14566.p1; prb; M),
 u'14398.p1': Person(14398.p1; prb; M),
 u'14524.p1': Person(14524.p1; prb; M),
 u'12607.p1': Person(12607.p1; prb; M),
 ...
}

We also have access to a Pandas data frame representing these subjects:

persons = tool.list_of_subject_df()
persons.head()

will output initial rows of the subjects dataframe:

   person_id family_id role gender  ssc_core_descriptive.ssc_diagnosis_nonverbal_iq  normalized
2   11000.p1     11000  prb      M                                             78.0        78.0
5   11001.p1     11001  prb      M                                            123.0       123.0
8   11002.p1     11002  prb      F                                            111.0       111.0
11  11003.p1     11003  prb      M                                            108.0       108.0
14  11004.p1     11004  prb      M                                             74.0        74.0

Now we are ready for the actual computations. Let first get some set of gene symbols:

from DAE import get_gene_sets_symNS
gt = get_gene_sets_symNS('main')
gene_syms = gt.t2G['autism candidates from Iossifov PNAS 2015'].keys()

To perform the actual computation we use calc method of PhenoTool. We need to pass to this method the specification of the variants:

res = tool.calc(
    VT(
     effect_types=['LGDs'],
     gene_syms=gene_syms,
     present_in_child=['autism only', 'autism and unaffected'],
     present_in_parent=['neither'],
    ))

The returned object is of type PhenoResult. This class could be queried about the results of the calculation:

In [23]: res.positive_count
Out[23]: 135

In [24]: res.negative_count
Out[24]: 2626

In [25]: res.pvalue
Out[25]: 1.5345885574583403e-07

We also have access to phenotypes and genotypes as dataframes:

In [26]: res.genotypes_df.head()
Out[26]:
   person_id gender role  variants
2   11000.p1      M  prb         0
5   11001.p1      M  prb         0
8   11002.p1      F  prb         0
11  11003.p1      M  prb         0
14  11004.p1      M  prb         0

In [27]: res.phenotypes_df.head()
Out[27]:
   person_id role gender  ssc_core_descriptive.ssc_diagnosis_nonverbal_iq  normalized
2   11000.p1  prb      M                                             78.0        78.0
5   11001.p1  prb      M                                            123.0       123.0
8   11002.p1  prb      F                                            111.0       111.0
11  11003.p1  prb      M                                            108.0       108.0
14  11004.p1  prb      M                                             74.0        74.0

and as dictionaries:

In [28]: res.genotypes
Out[28]:
Counter({
         u'11046.p1': 0,
         u'14398.p1': 0,
         u'12547.p1': 1,
         u'14021.p1': 0,
         u'11264.p1': 0,
         u'11925.p1': 1,
         u'11249.p1': 0,
         ....
         })

In [29]: res.phenotypes
Out[29]:
{u'14566.p1': {'gender': u'M',
  'normalized': 44.0,
  'person_id': u'14566.p1',
  'role': u'prb',
  'ssc_core_descriptive.ssc_diagnosis_nonverbal_iq': 44.0},
 u'14398.p1': {'gender': u'M',
  'normalized': 111.0,
  'person_id': u'14398.p1',
  'role': u'prb',
  'ssc_core_descriptive.ssc_diagnosis_nonverbal_iq': 111.0},
 u'14524.p1': {'gender': u'M',
  'normalized': 76.0,
  'person_id': u'14524.p1',
  'role': u'prb',
  'ssc_core_descriptive.ssc_diagnosis_nonverbal_iq': 76.0},
  ...
  }

Example usage of PhenoTool class with pheno filters

The PhenoTool class supports different ways to specify which individuals are subject to the calculations. We can use roles, measure_id, normalize_by and pheno_filters arguments in the PhenoTool constructor. For example if we specify roles and measure_id the tool will use individuals of the specified roles and has the specified measurement:

tool = PhenoTool(phdb, studies, roles=['prb', 'sib'],
            measure_id="ssc_commonly_used.head_circumference")

and if whe check list of subjects:

In [41]: len(tool.list_of_subjects())
Out[41]: 5166

If we specify a normalization, then the list of individuals should have measurements for normalization too:

tool = PhenoTool(phdb, studies, roles=['prb', 'sib'],
            measure_id="ssc_commonly_used.head_circumference",
            normalize_by=['pheno_common.age'])

In [45]: len(tool.list_of_subjects())
Out[45]: 4869

Additionally we can use pheno_filters argument. Let us specify only individuals that are of white race:

tool = PhenoTool(phdb, studies, roles=['prb', 'sib'],
            measure_id="ssc_commonly_used.head_circumference",
            pheno_filters={
                'pheno_common.race': set(['white'])
            })

In [47]: len(tool.list_of_subjects())
Out[47]: 3983

or:

tool = PhenoTool(phdb, studies, roles=['prb', 'sib'],
     measure_id="ssc_commonly_used.head_circumference",
     pheno_filters={
        'pheno_common.race': set(['white', 'asian']),
     })

In [51]: len(tool.list_of_subjects())
Out[51]: 4174

Using pheno_filters we can also specify ranges of values for given measurement:

tool = PhenoTool(phdb, studies, roles=['prb', 'sib'],
      measure_id="ssc_commonly_used.head_circumference",
      pheno_filters={
         'pheno_common.race': set(['white', 'asian']),
         'pheno_common.age': (200, 220),
      })

In [53]: len(tool.list_of_subjects())
Out[53]: 145

Example with vineland_ii

Example for iterating on many measures:

from DAE import *
from pheno_tool.tool import *
from pheno_tool.genotype_helper import GenotypeHelper


studies = vDB.get_studies('IossifovWE2014')
genotype_helper = GenotypeHelper(studies)

effect_types = ['LGDs', 'missense', 'nonsynonymous']

genotypes = {}
for et in effect_types:
    variants_type = VT(
        effect_types=[et],
        present_in_parent=['neither'],
        present_in_child=['autism only',
                          'autism and unaffected',
                          'unaffected only']
    )
    persons_variants = genotype_helper.get_persons_variants_df(variants_type)
    genotypes[et] = persons_variants

result = {}

for measure_id in phdb.get_instrument_measures('vineland_ii'):
    measure = phdb.get_measure(measure_id)
    if measure.measure_type != "continuous":
        continue

    cols = [measure_id]
    for role in ['prb', 'sib']:
        tool = PhenoTool(phdb, studies, roles=[role], measure_id=measure_id)
        for et in effect_types:
            print("working on: {}, {}, {}".format(measure_id, role, et))
            res = tool.calc(genotypes[et])
            cols.append("%.3f" % res.pvalue)
    result[measure_id] = cols

dae.pheno_tool.tool module

Created on Nov 9, 2016

@author: lubo

class dae.pheno_tool.tool.PhenoTool(pheno_db, measure_id, person_ids=None, normalize_by=[])[source]

Bases: object

Tool to estimate dependency between variants and phenotype measrues.

Arguments of the constructor are:

pheno_db – an instance of PhenoDB

measure_id – a phenotype measure ID

person_ids – an optional list of person IDs to filter the phenotype database with

normalize_by – list of continuous measure names. Default value is an empty list

calc(variants, sex_split=False)[source]

variants – an instance of Counter, matching personIds to an amount of variants

sex_split – should we split the result by sex or not. Default is False.

static join_pheno_df_with_variants(pheno_df, variants)[source]
class dae.pheno_tool.tool.PhenoToolHelper(study)[source]

Bases: object

Helper class for PhenoTool. Collects variants and person ids from a study.

Arguments of the constructor are:

study – an instance of StudyWrapper or DatasetWrapper

pheno_filter_persons(pheno_filters)[source]
study_persons(family_ids=[], roles=[prb])[source]
study_variants(data)[source]

dae.pheno_tool.pheno_common.PhenoResult class

class dae.pheno_tool.pheno_common.PhenoResult[source]

Bases: object