ISA create mode
¶This example creates ISA study descriptor
for study with sequential treatments organized in an arm. This shows how to use objects from the isatools.create
component in a granular fashion. It creates each Element
of the Study Arm
at a time.
Finally, the study design plan
is shown by serializing the ISA Study Design Model
content as an ISA_design
JSON document, which can be rendered in various ways (tables, figures).
# If executing the notebooks on `Google Colab`,uncomment the following command
# and run it to install the required python libraries. Also, make the test datasets available.
# !pip install -r requirements.txt
import os
import datetime
import json
from collections import OrderedDict
from isatools.model import (
Investigation,
Study,
Sample,
OntologyAnnotation,
StudyFactor,
FactorValue,
Characteristic,
Source,
Protocol,
Process
)
from isatools.create.model import (
Treatment,
NonTreatment,
StudyDesign,
StudyCell,
StudyArm,
ProductNode,
SampleAndAssayPlan,
AssayGraph
)
from isatools.create.constants import (
BASE_FACTORS,
SCREEN,
RUN_IN,
WASHOUT,
FOLLOW_UP,
SAMPLE,
EXTRACT,
LABELED_EXTRACT,
DATA_FILE
)
from isatools.isatab import dumps
from isatools.isajson import ISAJSONEncoder
investigation = Investigation()
investigation1 = Investigation() # to be used with the study create function
study = Study(filename="s_study_xover.txt")
study.identifier = "elifesprint2019-1"
study.title = "elifesprint2019-1: light sensitivity"
study.description = "a study about light sensitivity difference between a control population (n=10) and a genotype A population (n=10)."
study.submission_date = str(datetime.datetime.today())
study.public_release_date = str(datetime.datetime.today())
study.sources = [Source(name="source1")]
study.samples = [Sample(name="sample1")]
study.protocols = [Protocol(name="sample collection")]
study.process_sequence = [Process(executes_protocol=study.protocols[-1], inputs=[study.sources[-1]], outputs=[study.samples[-1]])]
investigation.studies = [study]
# Let's see the object :
investigation
isatools.model.Investigation(identifier='', filename='', title='', submission_date='', public_release_date='', ontology_source_references=[], publications=[], contacts=[], studies=[isatools.model.Study(filename='s_study_xover.txt', identifier='elifesprint2019-1', title='elifesprint2019-1: light sensitivity', description='a study about light sensitivity difference between a control population (n=10) and a genotype A population (n=10).', submission_date='2021-07-21 17:43:54.131318', public_release_date='2021-07-21 17:43:54.131358', contacts=[], design_descriptors=[], publications=[], factors=[], protocols=[isatools.model.Protocol(name='sample collection', protocol_type=isatools.model.OntologyAnnotation(term='', term_source=None, term_accession='', comments=[]), uri='', version='', parameters=[], components=[], comments=[])], assays=[], sources=[isatools.model.Source(name='source1', characteristics=[], comments=[])], samples=[isatools.model.Sample(name='sample1', characteristics=[], factor_values=[], derives_from=[], comments=[])], process_sequence=[isatools.model.Process(id="". name="None", executes_protocol=Protocol( name=sample collection protocol_type= uri= version= parameters=0 ProtocolParameter objects components=0 OntologyAnnotation objects comments=0 Comment objects ), date="None", performer="None", inputs=[isatools.model.Source(name='source1', characteristics=[], comments=[])], outputs=[isatools.model.Sample(name='sample1', characteristics=[], factor_values=[], derives_from=[], comments=[])])], other_material=[], characteristic_categories=[], comments=[], units=[])], comments=[])
# print(dumps(investigation))
# print(json.dumps(investigation, cls=ISAJSONEncoder, sort_keys=True, indent=4, separators=(',', ': ')))
ISA Study Design Element
and setting both element_type
AND duration_unit
attributes¶# IMPORTANT: note how duration_unit value is supplied as an OntologyAnnotation object
nte1 = NonTreatment(element_type='screen', duration_unit=OntologyAnnotation(term="days"))
print(nte1)
NonTreatment( type='screen', duration=isatools.model.FactorValue(factor_name=isatools.model.StudyFactor(name='DURATION', factor_type=isatools.model.OntologyAnnotation(term='time', term_source=None, term_accession='', comments=[]), comments=[]), value=0.0, unit=isatools.model.OntologyAnnotation(term='days', term_source=None, term_accession='', comments=[])) )
ISA Study Design Element
, of type Treatment
¶te1 = Treatment()
te1.type='radiological intervention'
print(te1)
"Treatment (type=radiological intervention, factor_values=[])
Under "ISA Study Design Create mode", a Study Design Element
of type Treatment
needs to be defined by a vector of Factors
and their respective associated Factor Values
. This is done as follows:
f1 = StudyFactor(name='light', factor_type=OntologyAnnotation(term="electromagnetic energy"))
f1v = FactorValue(factor_name=f1, value="visible light at 3000K produced by LED array")
f2 = StudyFactor(name='dose', factor_type=OntologyAnnotation(term="quantity"))
# IMPORTANT: note how *FactorValue value* is supplied as an *numeral*
f2v = FactorValue(factor_name=f2, value=250, unit=OntologyAnnotation(term='lux'))
f3 = StudyFactor(name='duration', factor_type=OntologyAnnotation(term="time"))
f3v = FactorValue(factor_name=f3, value=1, unit=OntologyAnnotation(term='hr'))
print(f1v,f2v)
FactorValue( factor_name=light value='visible light at 3000K produced by LED array' unit= ) FactorValue( factor_name=dose value=250 unit=lux )
#assigning the factor values declared above to the ISA treatment element
te1.factor_values = [f1v,f2v,f3v]
print(te1)
"Treatment (type=radiological intervention, factor_values=[isatools.model.FactorValue(factor_name=isatools.model.StudyFactor(name='dose', factor_type=isatools.model.OntologyAnnotation(term='quantity', term_source=None, term_accession='', comments=[]), comments=[]), value=250, unit=isatools.model.OntologyAnnotation(term='lux', term_source=None, term_accession='', comments=[])), isatools.model.FactorValue(factor_name=isatools.model.StudyFactor(name='duration', factor_type=isatools.model.OntologyAnnotation(term='time', term_source=None, term_accession='', comments=[]), comments=[]), value=1, unit=isatools.model.OntologyAnnotation(term='hr', term_source=None, term_accession='', comments=[])), isatools.model.FactorValue(factor_name=isatools.model.StudyFactor(name='light', factor_type=isatools.model.OntologyAnnotation(term='electromagnetic energy', term_source=None, term_accession='', comments=[]), comments=[]), value='visible light at 3000K produced by LED array', unit=None)])
ISA Study Design Element
, of type Treatment
, following the same pattern.¶te3 = Treatment()
te3.type = 'radiological intervention'
rays = StudyFactor(name='light', factor_type=OntologyAnnotation(term="electromagnetic energy"))
raysv = FactorValue(factor_name=rays, value='visible light at 3000K produced by LED array')
rays_intensity = StudyFactor(name='dose', factor_type=OntologyAnnotation(term="quantity"))
rays_intensityv= FactorValue(factor_name=rays_intensity, value = 250, unit=OntologyAnnotation(term='lux'))
rays_duration = StudyFactor(name = 'duration', factor_type=OntologyAnnotation(term="time"))
rays_durationv = FactorValue(factor_name=rays_duration, value=1, unit=OntologyAnnotation(term='hour'))
te3.factor_values = [raysv,rays_intensityv,rays_durationv]
print(te3)
"Treatment (type=radiological intervention, factor_values=[isatools.model.FactorValue(factor_name=isatools.model.StudyFactor(name='dose', factor_type=isatools.model.OntologyAnnotation(term='quantity', term_source=None, term_accession='', comments=[]), comments=[]), value=250, unit=isatools.model.OntologyAnnotation(term='lux', term_source=None, term_accession='', comments=[])), isatools.model.FactorValue(factor_name=isatools.model.StudyFactor(name='duration', factor_type=isatools.model.OntologyAnnotation(term='time', term_source=None, term_accession='', comments=[]), comments=[]), value=1, unit=isatools.model.OntologyAnnotation(term='hour', term_source=None, term_accession='', comments=[])), isatools.model.FactorValue(factor_name=isatools.model.StudyFactor(name='light', factor_type=isatools.model.OntologyAnnotation(term='electromagnetic energy', term_source=None, term_accession='', comments=[]), comments=[]), value='visible light at 3000K produced by LED array', unit=None)])
ISA Study Design Element
.¶# Creation of another ISA element, which is not a Treatment element, which is of type `screen` by default
# nte2 = NonTreatment()
# nte2.type = 'washout'
# net2.duration_unit=OntologyAnnotation(term="days")
nte2 = NonTreatment(element_type='washout', duration_unit=OntologyAnnotation(term="days"))
print(nte2)
NonTreatment( type='washout', duration=isatools.model.FactorValue(factor_name=isatools.model.StudyFactor(name='DURATION', factor_type=isatools.model.OntologyAnnotation(term='time', term_source=None, term_accession='', comments=[]), comments=[]), value=0.0, unit=isatools.model.OntologyAnnotation(term='days', term_source=None, term_accession='', comments=[])) )
# setting the factor values associated with 'default' DURATION Factor associated with such elements
nte2.duration.value=2
nte2.duration.unit=OntologyAnnotation(term="weeks")
ISA Study Design Element
.¶nte3 = NonTreatment(element_type='follow-up', duration_value=1, duration_unit=OntologyAnnotation(term="month"))
#print(nte3)
Cell
for each ISA Element
.¶In this example, a single Element
is hosted by a Cell
, which must be named. In more complex designs (e.g. study designs with assymetric arms), a Cell
may contain more than one Element
, hence the list attribute.
st_cl1= StudyCell(name="st_cl1", elements=[nte1])
st_cl2= StudyCell(name="st_cl2", elements=[te1])
st_cl3= StudyCell(name="st_cl3", elements=[nte2])
st_cl4= StudyCell(name="st_cl4", elements=[te3])
st_cl5= StudyCell(name="st_cl5", elements=[nte3])
Study Arm
and setting the number of subjects associated to that unique sequence of ISA Cell
s.¶genotype_cat = OntologyAnnotation(term="genotype")
genotype_value1 = OntologyAnnotation(term="control - normal")
genotype_value2 = OntologyAnnotation(term="mutant")
arm1 = StudyArm(
name='Arm 1',
group_size=2
)
arm1.source_type=Characteristic(
category=genotype_cat,
value=genotype_value1
)
print(arm1)
"StudyArm( name=Arm 1, source_type=Characteristic( category=genotype value=control - normal unit= comments=0 Comment objects ), group_size=2, no. cells=0, no. sample_assay_plans=0 )
Sample Assay Plan
, defining which Sample
are to be collected and which Assay
s to be used¶whole_patient=ProductNode(
id_="MAT1",
name="subject",
node_type=SAMPLE,
size=1,
characteristics=[
Characteristic(
category=OntologyAnnotation(term='organism part'),
value=OntologyAnnotation(term='whole organism')
)
]
)
saliva=ProductNode(
id_="MAT2",
name="saliva",
node_type=SAMPLE,
size=1,
characteristics=[
Characteristic(
category=OntologyAnnotation(term='organism part'),
value=OntologyAnnotation(term='saliva')
)
]
)
Here we load an isa assay definition in the form of an ordered dictionary. It corresponds to an ISA configuration assay table but expressed in JSON.
We now show how to create an new AssayGraph structure from scratch, as if we were defining a completely new assay type.
light_sensitivity_phenotyping_1 = OrderedDict([
('measurement_type', OntologyAnnotation(term='melatonine concentration')),
('technology_type', OntologyAnnotation(term='radioimmunoprecipitation assay')),
('extraction', {}),
('extract', [
{
'node_type': EXTRACT,
'characteristics_category': OntologyAnnotation(term='extract type'),
'characteristics_value': OntologyAnnotation(term='extract'),
'size': 1,
'technical_replicates': None,
'is_input_to_next_protocols': True
}]),
('radioimmunoprecipitation', {
OntologyAnnotation(term='instrument'): [OntologyAnnotation(term='Beckon Dickison XYZ')],
OntologyAnnotation(term='antibody'): [OntologyAnnotation(term='AbCam antiMelatonine ')],
OntologyAnnotation(term='time point'): [OntologyAnnotation(term='1 hr'),
OntologyAnnotation(term='2 hr')]
}),
('raw_data_file', [
{
'node_type': DATA_FILE,
'size': 1,
'technical_replicates': 1,
'is_input_to_next_protocols': False
}
])
])
light_sensitivity_phenotyping_2 = OrderedDict([
('measurement_type', OntologyAnnotation(term='light sensitivity')),
('technology_type', OntologyAnnotation(term='electroencephalography')),
('data_collection', {
OntologyAnnotation(term='instrument'): [OntologyAnnotation(term='Somnotouch')],
OntologyAnnotation(term='sampling_rate'): [OntologyAnnotation(term='200 Hz')],
OntologyAnnotation(term='time point'): [OntologyAnnotation(term='1 hr'),
OntologyAnnotation(term='2 hr')]
}),
('raw_data_file', [
{
'node_type': DATA_FILE,
'size': 1,
'technical_replicates': 1,
'is_input_to_next_protocols': False
}
])
])
light_sensitivity_phenotyping_3 = OrderedDict([
('measurement_type', OntologyAnnotation(term='light sensitivity phenotyping')),
('technology_type', OntologyAnnotation(term='direct measurement')),
('data_collection', {
OntologyAnnotation(term='variables'): [OntologyAnnotation(term='sleepiness'),
OntologyAnnotation(term='heart rate'),
OntologyAnnotation(term='pupilla size')],
OntologyAnnotation(term='time point'): [OntologyAnnotation(term='1 hr'),
OntologyAnnotation(term='2 hr')]
}),
('raw_data_file', [
{
'node_type': DATA_FILE,
'size': 1,
'technical_replicates': 1,
'is_input_to_next_protocols': False
}
])
])
alterness_assay_graph = AssayGraph.generate_assay_plan_from_dict(light_sensitivity_phenotyping_1)
melatonine_assay_graph = AssayGraph.generate_assay_plan_from_dict(light_sensitivity_phenotyping_2)
general_phenotyping_assay_graph = AssayGraph.generate_assay_plan_from_dict(light_sensitivity_phenotyping_3)
sap1 = SampleAndAssayPlan(name='sap1', sample_plan=[whole_patient,saliva],assay_plan=[alterness_assay_graph,melatonine_assay_graph,general_phenotyping_assay_graph])
sap1.add_element_to_map(sample_node=saliva, assay_graph=melatonine_assay_graph)
sap1.add_element_to_map(sample_node=whole_patient, assay_graph=alterness_assay_graph)
sap1.add_element_to_map(sample_node=whole_patient,assay_graph=general_phenotyping_assay_graph)
sap1.sample_to_assay_map
{isatools.create.model.ProductNode(id=MAT2, type=sample, name=saliva, characteristics=[isatools.model.Characteristic(category=isatools.model.OntologyAnnotation(term='organism part', term_source=None, term_accession='', comments=[]), value=isatools.model.OntologyAnnotation(term='saliva', term_source=None, term_accession='', comments=[]), unit=None, comments=[])], size=1, extension=None): {isatools.create.model.AssayGraph(id=cce3713d-dfd3-4942-8d87-cb391156d756, measurement_type=OntologyAnnotation( term=light sensitivity term_source= term_accession= comments=0 Comment objects ), technology_type=OntologyAnnotation( term=electroencephalography term_source= term_accession= comments=0 Comment objects ), nodes={isatools.create.model.ProductNode(id=raw_data_file_000_001, type=data file, name=raw_data_file, characteristics=[], size=1, extension=None), isatools.create.model.ProtocolNode(id=data_collection_001, name=assay0 - data_collection, protocol_type=OntologyAnnotation( term=assay0 - data_collection term_source= term_accession= comments=0 Comment objects ), uri=, description=, version=, parameter_values=[isatools.model.ParameterValue(category=isatools.model.ProtocolParameter(parameter_name=isatools.model.OntologyAnnotation(term='instrument', term_source=None, term_accession='', comments=[]), comments=[]), value=isatools.model.OntologyAnnotation(term='Somnotouch', term_source=None, term_accession='', comments=[]), unit=None, comments=[]), isatools.model.ParameterValue(category=isatools.model.ProtocolParameter(parameter_name=isatools.model.OntologyAnnotation(term='sampling_rate', term_source=None, term_accession='', comments=[]), comments=[]), value=isatools.model.OntologyAnnotation(term='200 Hz', term_source=None, term_accession='', comments=[]), unit=None, comments=[]), isatools.model.ParameterValue(category=isatools.model.ProtocolParameter(parameter_name=isatools.model.OntologyAnnotation(term='time point', term_source=None, term_accession='', comments=[]), comments=[]), value=isatools.model.OntologyAnnotation(term='2 hr', term_source=None, term_accession='', comments=[]), unit=None, comments=[])]), isatools.create.model.ProtocolNode(id=data_collection_000, name=assay0 - data_collection, protocol_type=OntologyAnnotation( term=assay0 - data_collection term_source= term_accession= comments=0 Comment objects ), uri=, description=, version=, parameter_values=[isatools.model.ParameterValue(category=isatools.model.ProtocolParameter(parameter_name=isatools.model.OntologyAnnotation(term='instrument', term_source=None, term_accession='', comments=[]), comments=[]), value=isatools.model.OntologyAnnotation(term='Somnotouch', term_source=None, term_accession='', comments=[]), unit=None, comments=[]), isatools.model.ParameterValue(category=isatools.model.ProtocolParameter(parameter_name=isatools.model.OntologyAnnotation(term='sampling_rate', term_source=None, term_accession='', comments=[]), comments=[]), value=isatools.model.OntologyAnnotation(term='200 Hz', term_source=None, term_accession='', comments=[]), unit=None, comments=[]), isatools.model.ParameterValue(category=isatools.model.ProtocolParameter(parameter_name=isatools.model.OntologyAnnotation(term='time point', term_source=None, term_accession='', comments=[]), comments=[]), value=isatools.model.OntologyAnnotation(term='1 hr', term_source=None, term_accession='', comments=[]), unit=None, comments=[])]), isatools.create.model.ProductNode(id=raw_data_file_000_000, type=data file, name=raw_data_file, characteristics=[], size=1, extension=None)}, links=[('data_collection_000', 'raw_data_file_000_000'), ('data_collection_001', 'raw_data_file_000_001')], quality_control=None)}, isatools.create.model.ProductNode(id=MAT1, type=sample, name=subject, characteristics=[isatools.model.Characteristic(category=isatools.model.OntologyAnnotation(term='organism part', term_source=None, term_accession='', comments=[]), value=isatools.model.OntologyAnnotation(term='whole organism', term_source=None, term_accession='', comments=[]), unit=None, comments=[])], size=1, extension=None): {isatools.create.model.AssayGraph(id=05599bfb-d02b-471e-b9b1-6ce6758791db, measurement_type=OntologyAnnotation( term=melatonine concentration term_source= term_accession= comments=0 Comment objects ), technology_type=OntologyAnnotation( term=radioimmunoprecipitation assay term_source= term_accession= comments=0 Comment objects ), nodes={isatools.create.model.ProductNode(id=extract_000_000, type=extract, name=extract, characteristics=[isatools.model.Characteristic(category=isatools.model.OntologyAnnotation(term='extract type', term_source=None, term_accession='', comments=[]), value=isatools.model.OntologyAnnotation(term='extract', term_source=None, term_accession='', comments=[]), unit=None, comments=[])], size=1, extension=None), isatools.create.model.ProtocolNode(id=extraction_000, name=assay0 - extraction, protocol_type=OntologyAnnotation( term=assay0 - extraction term_source= term_accession= comments=0 Comment objects ), uri=, description=, version=, parameter_values=[]), isatools.create.model.ProtocolNode(id=radioimmunoprecipitation_001_000, name=assay0 - radioimmunoprecipitation, protocol_type=OntologyAnnotation( term=assay0 - radioimmunoprecipitation term_source= term_accession= comments=0 Comment objects ), uri=, description=, version=, parameter_values=[isatools.model.ParameterValue(category=isatools.model.ProtocolParameter(parameter_name=isatools.model.OntologyAnnotation(term='instrument', term_source=None, term_accession='', comments=[]), comments=[]), value=isatools.model.OntologyAnnotation(term='Beckon Dickison XYZ', term_source=None, term_accession='', comments=[]), unit=None, comments=[]), isatools.model.ParameterValue(category=isatools.model.ProtocolParameter(parameter_name=isatools.model.OntologyAnnotation(term='antibody', term_source=None, term_accession='', comments=[]), comments=[]), value=isatools.model.OntologyAnnotation(term='AbCam antiMelatonine ', term_source=None, term_accession='', comments=[]), unit=None, comments=[]), isatools.model.ParameterValue(category=isatools.model.ProtocolParameter(parameter_name=isatools.model.OntologyAnnotation(term='time point', term_source=None, term_accession='', comments=[]), comments=[]), value=isatools.model.OntologyAnnotation(term='2 hr', term_source=None, term_accession='', comments=[]), unit=None, comments=[])]), isatools.create.model.ProductNode(id=raw_data_file_000_001, type=data file, name=raw_data_file, characteristics=[], size=1, extension=None), isatools.create.model.ProtocolNode(id=radioimmunoprecipitation_000_000, name=assay0 - radioimmunoprecipitation, protocol_type=OntologyAnnotation( term=assay0 - radioimmunoprecipitation term_source= term_accession= comments=0 Comment objects ), uri=, description=, version=, parameter_values=[isatools.model.ParameterValue(category=isatools.model.ProtocolParameter(parameter_name=isatools.model.OntologyAnnotation(term='instrument', term_source=None, term_accession='', comments=[]), comments=[]), value=isatools.model.OntologyAnnotation(term='Beckon Dickison XYZ', term_source=None, term_accession='', comments=[]), unit=None, comments=[]), isatools.model.ParameterValue(category=isatools.model.ProtocolParameter(parameter_name=isatools.model.OntologyAnnotation(term='antibody', term_source=None, term_accession='', comments=[]), comments=[]), value=isatools.model.OntologyAnnotation(term='AbCam antiMelatonine ', term_source=None, term_accession='', comments=[]), unit=None, comments=[]), isatools.model.ParameterValue(category=isatools.model.ProtocolParameter(parameter_name=isatools.model.OntologyAnnotation(term='time point', term_source=None, term_accession='', comments=[]), comments=[]), value=isatools.model.OntologyAnnotation(term='1 hr', term_source=None, term_accession='', comments=[]), unit=None, comments=[])]), isatools.create.model.ProductNode(id=raw_data_file_000_000, type=data file, name=raw_data_file, characteristics=[], size=1, extension=None)}, links=[('extract_000_000', 'radioimmunoprecipitation_000_000'), ('extract_000_000', 'radioimmunoprecipitation_001_000'), ('extraction_000', 'extract_000_000'), ('radioimmunoprecipitation_000_000', 'raw_data_file_000_000'), ('radioimmunoprecipitation_001_000', 'raw_data_file_000_001')], quality_control=None), isatools.create.model.AssayGraph(id=d7726069-1823-4f49-b10c-8856036ad082, measurement_type=OntologyAnnotation( term=light sensitivity phenotyping term_source= term_accession= comments=0 Comment objects ), technology_type=OntologyAnnotation( term=direct measurement term_source= term_accession= comments=0 Comment objects ), nodes={isatools.create.model.ProductNode(id=raw_data_file_000_002, type=data file, name=raw_data_file, characteristics=[], size=1, extension=None), isatools.create.model.ProductNode(id=raw_data_file_000_003, type=data file, name=raw_data_file, characteristics=[], size=1, extension=None), isatools.create.model.ProtocolNode(id=data_collection_004, name=assay0 - data_collection, protocol_type=OntologyAnnotation( term=assay0 - data_collection term_source= term_accession= comments=0 Comment objects ), uri=, description=, version=, parameter_values=[isatools.model.ParameterValue(category=isatools.model.ProtocolParameter(parameter_name=isatools.model.OntologyAnnotation(term='variables', term_source=None, term_accession='', comments=[]), comments=[]), value=isatools.model.OntologyAnnotation(term='pupilla size', term_source=None, term_accession='', comments=[]), unit=None, comments=[]), isatools.model.ParameterValue(category=isatools.model.ProtocolParameter(parameter_name=isatools.model.OntologyAnnotation(term='time point', term_source=None, term_accession='', comments=[]), comments=[]), value=isatools.model.OntologyAnnotation(term='1 hr', term_source=None, term_accession='', comments=[]), unit=None, comments=[])]), isatools.create.model.ProductNode(id=raw_data_file_000_004, type=data file, name=raw_data_file, characteristics=[], size=1, extension=None), isatools.create.model.ProtocolNode(id=data_collection_000, name=assay0 - data_collection, protocol_type=OntologyAnnotation( term=assay0 - data_collection term_source= term_accession= comments=0 Comment objects ), uri=, description=, version=, parameter_values=[isatools.model.ParameterValue(category=isatools.model.ProtocolParameter(parameter_name=isatools.model.OntologyAnnotation(term='variables', term_source=None, term_accession='', comments=[]), comments=[]), value=isatools.model.OntologyAnnotation(term='sleepiness', term_source=None, term_accession='', comments=[]), unit=None, comments=[]), isatools.model.ParameterValue(category=isatools.model.ProtocolParameter(parameter_name=isatools.model.OntologyAnnotation(term='time point', term_source=None, term_accession='', comments=[]), comments=[]), value=isatools.model.OntologyAnnotation(term='1 hr', term_source=None, term_accession='', comments=[]), unit=None, comments=[])]), isatools.create.model.ProductNode(id=raw_data_file_000_001, type=data file, name=raw_data_file, characteristics=[], size=1, extension=None), isatools.create.model.ProtocolNode(id=data_collection_002, name=assay0 - data_collection, protocol_type=OntologyAnnotation( term=assay0 - data_collection term_source= term_accession= comments=0 Comment objects ), uri=, description=, version=, parameter_values=[isatools.model.ParameterValue(category=isatools.model.ProtocolParameter(parameter_name=isatools.model.OntologyAnnotation(term='variables', term_source=None, term_accession='', comments=[]), comments=[]), value=isatools.model.OntologyAnnotation(term='heart rate', term_source=None, term_accession='', comments=[]), unit=None, comments=[]), isatools.model.ParameterValue(category=isatools.model.ProtocolParameter(parameter_name=isatools.model.OntologyAnnotation(term='time point', term_source=None, term_accession='', comments=[]), comments=[]), value=isatools.model.OntologyAnnotation(term='1 hr', term_source=None, term_accession='', comments=[]), unit=None, comments=[])]), isatools.create.model.ProtocolNode(id=data_collection_005, name=assay0 - data_collection, protocol_type=OntologyAnnotation( term=assay0 - data_collection term_source= term_accession= comments=0 Comment objects ), uri=, description=, version=, parameter_values=[isatools.model.ParameterValue(category=isatools.model.ProtocolParameter(parameter_name=isatools.model.OntologyAnnotation(term='variables', term_source=None, term_accession='', comments=[]), comments=[]), value=isatools.model.OntologyAnnotation(term='pupilla size', term_source=None, term_accession='', comments=[]), unit=None, comments=[]), isatools.model.ParameterValue(category=isatools.model.ProtocolParameter(parameter_name=isatools.model.OntologyAnnotation(term='time point', term_source=None, term_accession='', comments=[]), comments=[]), value=isatools.model.OntologyAnnotation(term='2 hr', term_source=None, term_accession='', comments=[]), unit=None, comments=[])]), isatools.create.model.ProtocolNode(id=data_collection_003, name=assay0 - data_collection, protocol_type=OntologyAnnotation( term=assay0 - data_collection term_source= term_accession= comments=0 Comment objects ), uri=, description=, version=, parameter_values=[isatools.model.ParameterValue(category=isatools.model.ProtocolParameter(parameter_name=isatools.model.OntologyAnnotation(term='variables', term_source=None, term_accession='', comments=[]), comments=[]), value=isatools.model.OntologyAnnotation(term='heart rate', term_source=None, term_accession='', comments=[]), unit=None, comments=[]), isatools.model.ParameterValue(category=isatools.model.ProtocolParameter(parameter_name=isatools.model.OntologyAnnotation(term='time point', term_source=None, term_accession='', comments=[]), comments=[]), value=isatools.model.OntologyAnnotation(term='2 hr', term_source=None, term_accession='', comments=[]), unit=None, comments=[])]), isatools.create.model.ProductNode(id=raw_data_file_000_005, type=data file, name=raw_data_file, characteristics=[], size=1, extension=None), isatools.create.model.ProtocolNode(id=data_collection_001, name=assay0 - data_collection, protocol_type=OntologyAnnotation( term=assay0 - data_collection term_source= term_accession= comments=0 Comment objects ), uri=, description=, version=, parameter_values=[isatools.model.ParameterValue(category=isatools.model.ProtocolParameter(parameter_name=isatools.model.OntologyAnnotation(term='variables', term_source=None, term_accession='', comments=[]), comments=[]), value=isatools.model.OntologyAnnotation(term='sleepiness', term_source=None, term_accession='', comments=[]), unit=None, comments=[]), isatools.model.ParameterValue(category=isatools.model.ProtocolParameter(parameter_name=isatools.model.OntologyAnnotation(term='time point', term_source=None, term_accession='', comments=[]), comments=[]), value=isatools.model.OntologyAnnotation(term='2 hr', term_source=None, term_accession='', comments=[]), unit=None, comments=[])]), isatools.create.model.ProductNode(id=raw_data_file_000_000, type=data file, name=raw_data_file, characteristics=[], size=1, extension=None)}, links=[('data_collection_000', 'raw_data_file_000_000'), ('data_collection_001', 'raw_data_file_000_001'), ('data_collection_002', 'raw_data_file_000_002'), ('data_collection_003', 'raw_data_file_000_003'), ('data_collection_004', 'raw_data_file_000_004'), ('data_collection_005', 'raw_data_file_000_005')], quality_control=None)}}
Study Design Arm
by adding the first set of ISA Cells
and setting the Sample Assay Plan
¶arm1.add_item_to_arm_map(st_cl1, sap1)
# print(arm1)
Arm
by adding a new Cell
, which uses the same Sample Assay Plan
as the one used in Cell #1.¶Of course, the Sample Assay Plan
for this new Cell
could be different. It would have to be to built as shown before.
arm1.add_item_to_arm_map(st_cl2, sap1)
# Adding the last section of the Arm, with a cell which also uses the same sample assay plan.
arm1.add_item_to_arm_map(st_cl3, sap1)
arm1.add_item_to_arm_map(st_cl4, sap1)
arm1.add_item_to_arm_map(st_cl5, sap1)
arm2 = StudyArm(name='Arm 2')
arm2.group_size=2
arm2.source_type=Characteristic(category=genotype_cat,
value=genotype_value2)
# st_cl6= StudyCell(name="st_cl6", elements=[nte1])
# st_cl7= StudyCell(name="st_cl7", elements=[te1])
# st_cl8= StudyCell(name="st_cl8", elements=[nte2])
# st_cl9= StudyCell(name="st_cl9", elements=[te3])
# st_cl10= StudyCell(name="st_cl10", elements=[nte3])
arm2.source_type.category
arm2.add_item_to_arm_map(st_cl1,sap1)
arm2.add_item_to_arm_map(st_cl4,sap1)
arm2.add_item_to_arm_map(st_cl3,sap1)
arm2.add_item_to_arm_map(st_cl2,sap1)
arm2.add_item_to_arm_map(st_cl5,sap1)
arm3 = StudyArm(name='Arm 3')
arm3.group_size=2
arm3.source_type=Characteristic(category=genotype_cat,
value=genotype_value1
)
arm3.add_item_to_arm_map(st_cl1,sap1)
arm3.add_item_to_arm_map(st_cl2,sap1)
arm3.add_item_to_arm_map(st_cl3,sap1)
arm3.add_item_to_arm_map(st_cl4,sap1)
arm3.add_item_to_arm_map(st_cl5,sap1)
arm4 = StudyArm(name='Arm 4')
arm4.group_size=2
arm4.source_type=Characteristic(category=genotype_cat,
value=genotype_value2)
arm4.add_item_to_arm_map(st_cl1,sap1)
arm4.add_item_to_arm_map(st_cl4,None)
arm4.add_item_to_arm_map(st_cl3,sap1)
arm4.add_item_to_arm_map(st_cl2,None)
arm4.add_item_to_arm_map(st_cl5,sap1)
Study Design
object, which will receive the Arms
defined by the user.¶study_design_final= StudyDesign(name='trial design #1')
# print(sd)
# Adding a study arm to the study design object.
study_design_final.add_study_arm(arm1)
study_design_final.add_study_arm(arm2)
study_design_final.add_study_arm(arm3)
study_design_final.add_study_arm(arm4)
study_finale = study_design_final.generate_isa_study()
investigation1.studies.append(study_finale)
# print(investigation1.studies[0].name)
# Let's now serialize the ISA study design to JSON
from isatools.create.model import StudyDesignEncoder
f=json.dumps(study_design_final, cls=StudyDesignEncoder, sort_keys=True, indent=4, separators=(',', ': '))
final_dir = os.path.abspath(os.path.join('notebook-output', 'isa-study-custom-assay-light-sensitivity'))
with open(os.path.join(final_dir,'./light-sensitivity-study_design_final.json'), 'w') as isa_sdf_jf:
json.dump(json.loads(f), isa_sdf_jf)
# print(json.dumps(investigation, cls=ISAJSONEncoder, sort_keys=True, indent=4, separators=(',', ': ')))
from isatools import isatab
isatab.dump(investigation1, final_dir)
from isatools.isatab import dump_tables_to_dataframes as dumpdf
dataframes = dumpdf(investigation)
2021-07-21 17:43:54,822 [INFO]: isatab.py(_all_end_to_end_paths:1131) >> [3, 4, 5, 6] 2021-07-21 17:43:54,828 [WARNING]: isatab.py(write_study_table_files:1194) >> [8, 7, 3, 10, 9, 12, 11, 14, 13, 16, 15, 18, 17, 20, 19, 22, 21, 24, 23, 26, 25, 28, 27, 30, 29, 32, 31, 34, 33, 36, 35, 38, 37, 40, 39, 42, 41, 44, 43, 46, 45, 48, 47, 4, 50, 49, 52, 51, 54, 53, 56, 55, 58, 57, 60, 59, 62, 61, 64, 63, 66, 65, 68, 67, 70, 69, 72, 71, 74, 73, 76, 75, 78, 77, 80, 79, 82, 81, 84, 83, 86, 85, 88, 87, 5, 90, 89, 92, 91, 94, 93, 96, 95, 98, 97, 100, 99, 102, 101, 104, 103, 106, 105, 108, 107, 110, 109, 112, 111, 114, 113, 116, 115, 118, 117, 120, 119, 122, 121, 124, 123, 126, 125, 128, 127, 6, 130, 129, 132, 131, 134, 133, 136, 135, 138, 137, 140, 139, 142, 141, 144, 143, 146, 145, 148, 147, 150, 149] 2021-07-21 17:43:54,829 [INFO]: isatab.py(_longest_path_and_attrs:1091) >> [[3, 8, 7], [3, 10, 9], [3, 12, 11], [3, 14, 13], [3, 16, 15], [3, 18, 17], [3, 20, 19], [3, 22, 21], [3, 24, 23], [3, 26, 25], [3, 28, 27], [3, 30, 29], [3, 32, 31], [3, 34, 33], [3, 36, 35], [3, 38, 37], [3, 40, 39], [3, 42, 41], [3, 44, 43], [3, 46, 45], [4, 48, 47], [4, 50, 49], [4, 52, 51], [4, 54, 53], [4, 56, 55], [4, 58, 57], [4, 60, 59], [4, 62, 61], [4, 64, 63], [4, 66, 65], [4, 68, 67], [4, 70, 69], [4, 72, 71], [4, 74, 73], [4, 76, 75], [4, 78, 77], [4, 80, 79], [4, 82, 81], [4, 84, 83], [4, 86, 85], [5, 88, 87], [5, 90, 89], [5, 92, 91], [5, 94, 93], [5, 96, 95], [5, 98, 97], [5, 100, 99], [5, 102, 101], [5, 104, 103], [5, 106, 105], [5, 108, 107], [5, 110, 109], [5, 112, 111], [5, 114, 113], [5, 116, 115], [5, 118, 117], [5, 120, 119], [5, 122, 121], [5, 124, 123], [5, 126, 125], [6, 130, 129], [6, 132, 131], [6, 134, 133], [6, 136, 135], [6, 138, 137], [6, 140, 139], [6, 142, 141], [6, 144, 143], [6, 146, 145], [6, 148, 147], [6, 150, 149], [6, 128, 127]]
--------------------------------------------------------------------------- KeyError Traceback (most recent call last) /var/folders/5n/rl6lqnks4rqb59pbtpvvntqw0000gr/T/ipykernel_17990/2479725034.py in <module> 1 # print(json.dumps(investigation, cls=ISAJSONEncoder, sort_keys=True, indent=4, separators=(',', ': '))) 2 from isatools import isatab ----> 3 isatab.dump(investigation1, final_dir) 4 5 from isatools.isatab import dump_tables_to_dataframes as dumpdf ~/.pyenv/versions/3.9.0/envs/isa-api-py39/lib/python3.9/site-packages/isatools/isatab.py in dump(isa_obj, output_path, i_file_name, skip_dump_tables, write_factor_values_in_assay_table) 1047 pass 1048 else: -> 1049 write_study_table_files(investigation, output_path) 1050 write_assay_table_files( 1051 investigation, output_path, write_factor_values_in_assay_table) ~/.pyenv/versions/3.9.0/envs/isa-api-py39/lib/python3.9/site-packages/isatools/isatab.py in write_study_table_files(inv_obj, output_dir) 1295 fvlabel = "{0}.Factor Value[{1}]".format( 1296 olabel, fv.factor_name.name) -> 1297 write_value_columns(df_dict, fvlabel, fv) 1298 """if isinstance(pbar, ProgressBar): 1299 pbar.finish()""" ~/.pyenv/versions/3.9.0/envs/isa-api-py39/lib/python3.9/site-packages/isatools/isatab.py in write_value_columns(df_dict, label, x) 1715 if isinstance(x.value, (int, float)) and x.unit: 1716 if isinstance(x.unit, OntologyAnnotation): -> 1717 df_dict[label][-1] = x.value 1718 df_dict[label + ".Unit"][-1] = x.unit.term 1719 df_dict[label + ".Unit.Term Source REF"][-1] = \ KeyError: 'Sample Name.0.Factor Value[DURATION]'