Create an ISA Study representing a 2x2x2 factorial design with single measurements¶
Abstract:¶
In this notebook, we’ll show how a study design defines a series of interventions with two distinct radiological agents, at two distinct dose levels and 2 distinct timepoints of observation post-exposure.
Factor Name |
Factor Type |
Factor Value |
Unit |
Factor Value |
Unit |
Number of factor levels |
|---|---|---|---|---|---|---|
radionucleide |
radiological agent |
Cs 137 |
Fe 56 |
2 |
||
dose |
intensity |
2.5 |
cGy |
10 |
cGy |
2 |
time post exposure |
time |
1 |
hr |
72 |
hr |
2 |
In this experiment, muscle samples are collected once from each study subject (n=10 per group) and metabolite profiling using 1D 13C NMR is performed on the supernatant and pellet of the extracted fraction.
Subjects were also phenotyped using a custom hyperspectral imaging.
Let’s get the toolkit¶
# 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 pandas as pd
import datetime as dt
import json
from collections import OrderedDict
from isatools.model import (
Investigation,
Study,
StudyFactor,
FactorValue,
OntologyAnnotation,
Sample,
Characteristic,
Protocol
)
from isatools.create.model import (
Treatment,
StudyCell,
StudyArm,
ProductNode,
AssayGraph,
SampleAndAssayPlan,
StudyDesign,
QualityControl
)
from isatools.create.constants import (
SAMPLE,
EXTRACT,
DATA_FILE
)
from isatools.isajson import ISAJSONEncoder
Creating the ISA Study core metadata¶
investigation = Investigation()
study = Study(filename="s_study_2by2by2.txt")
study.identifier = "2x2x2"
study.title = "2x2x2 factorial design study"
study.description = "a simple full factorial design study 2x2x2"
study.submission_date = "2021-04-21" # Note the ISO8601 format for dates
study.public_release_date = "2021-05-30" # Note the ISO8601 format for dates
study.protocols = [Protocol(name="sample collection")]
investigation.studies = [study]
print(investigation)
print(json.dumps(investigation, cls=ISAJSONEncoder, sort_keys=True, indent=4, separators=(',', ': ')))
Let’s build the ISA Study Design Object¶
Declaring the 3 independent variables (ISA Factors) of the Study¶
f1 = StudyFactor(name='radionucleide', factor_type=OntologyAnnotation(term="radiological agent"))
f2 = StudyFactor(name='dose', factor_type=OntologyAnnotation(term="intensity"))
f3 = StudyFactor(name='time post exposure', factor_type=OntologyAnnotation(term="time"))
Declaring the treatment groups¶
te1 = Treatment()
te1.type='radiological intervention'
f1v1 = FactorValue(factor_name=f1, value=OntologyAnnotation(term="Cs 137"))
f2v1 = FactorValue(factor_name=f2, value=2.5, unit=OntologyAnnotation(term='cGy'))
f3v1 = FactorValue(factor_name=f3, value=1, unit=OntologyAnnotation(term='hr'))
te1.factor_values = (f1v1,f2v1,f3v1)
# te1.factor_values.add(f1v1)
te6 = Treatment()
te6.type='radiological intervention'
f1v1 = FactorValue(factor_name=f1, value=OntologyAnnotation(term="Cs 137"))
f2v1 = FactorValue(factor_name=f2, value=2.5,unit=OntologyAnnotation(term='cGy'))
f3v2 = FactorValue(factor_name=f3, value=72, unit=OntologyAnnotation(term='hr'))
te6.factor_values = (f1v1,f2v1,f3v2)
te2 = Treatment()
te2.type='radiological intervention'
f1v1 = FactorValue(factor_name=f1, value=OntologyAnnotation(term="Cs 137"))
f2v2 = FactorValue(factor_name=f2, value=10,unit=OntologyAnnotation(term='cGy'))
f3v2 = FactorValue(factor_name=f3, value=72, unit=OntologyAnnotation(term='hr'))
te2.factor_values = (f1v1,f2v2,f3v2)
te7 = Treatment()
te7.type='radiological intervention'
f1v2 = FactorValue(factor_name=f1, value=OntologyAnnotation(term="Cs 137"))
f2v1 = FactorValue(factor_name=f2, value=10,unit=OntologyAnnotation(term='cGy'))
f3v2 = FactorValue(factor_name=f3, value=72, unit=OntologyAnnotation(term='hr'))
te7.factor_values = (f1v2,f2v1,f3v2)
te3 = Treatment()
te3.type='radiological intervention'
f1v2 = FactorValue(factor_name=f1, value=OntologyAnnotation(term="Fe 56"))
f2v1 = FactorValue(factor_name=f2, value=2.5,unit=OntologyAnnotation(term='cGy'))
f3v1 = FactorValue(factor_name=f3, value=1, unit=OntologyAnnotation(term='hr'))
te3.factor_values = (f1v2,f2v1,f3v1)
te5 = Treatment()
te5.type='radiological intervention'
f1v2 = FactorValue(factor_name=f1, value=OntologyAnnotation(term="Fe 56"))
f2v1 = FactorValue(factor_name=f2, value=2.5,unit=OntologyAnnotation(term='cGy'))
f3v2 = FactorValue(factor_name=f3, value=72, unit=OntologyAnnotation(term='hr'))
te5.factor_values = (f1v2,f2v1,f3v2)
te8 = Treatment()
te8.type='radiological intervention'
f1v2 = FactorValue(factor_name=f1, value=OntologyAnnotation(term="Fe 56"))
f2v2 = FactorValue(factor_name=f2, value=10,unit=OntologyAnnotation(term='cGy'))
f3v1 = FactorValue(factor_name=f3, value=1, unit=OntologyAnnotation(term='hr'))
te8.factor_values = (f1v2,f2v2,f3v1)
te4 = Treatment()
te4.type='radiological intervention'
f1v2 = FactorValue(factor_name=f1, value=OntologyAnnotation(term="Fe 56"))
f2v2 = FactorValue(factor_name=f2, value=10,unit=OntologyAnnotation(term='cGy'))
f3v2 = FactorValue(factor_name=f3, value=72, unit=OntologyAnnotation(term='hr'))
te4.factor_values = (f1v2,f2v2,f3v2)
Now building the Study Arms¶
st_cl1= StudyCell(name="st_cl1", elements=[te1])
st_cl2= StudyCell(name="st_cl2", elements=[te2])
st_cl3= StudyCell(name="st_cl3", elements=[te3])
st_cl4= StudyCell(name="st_cl4", elements=[te4])
st_cl5= StudyCell(name="st_cl5", elements=[te5])
st_cl6= StudyCell(name="st_cl6", elements=[te6])
st_cl7= StudyCell(name="st_cl7", elements=[te7])
st_cl8= StudyCell(name="st_cl8", elements=[te8])
arm1 = StudyArm(name='Group 1', group_size=10, source_type=Characteristic(category=OntologyAnnotation(term="Study Subject"),value=OntologyAnnotation(term="Mus musculus")))
arm2 = StudyArm(name='Group 2', group_size=10)
arm3 = StudyArm(name='Group 3', group_size=10)
arm4 = StudyArm(name='Group 4', group_size=10)
arm5 = StudyArm(name='Group 5', group_size=10)
arm6 = StudyArm(name='Group 6', group_size=10)
arm7 = StudyArm(name='Group 7', group_size=10)
arm8 = StudyArm(name='Group 8', group_size=10)
input_material1=ProductNode(id_="MAT1", name="muscle tissue", node_type=SAMPLE,size=1,characteristics=[Characteristic(category=OntologyAnnotation(term='organism part'), value=OntologyAnnotation(term='muscle'))])
#input_material2=ProductNode(id_="MAT2", name="blood", node_type=SAMPLE,size=1,characteristics=[Characteristic(category='organism part', value='blood')])
A new data structure for defining an assay workflow¶
The following cells show 2 distinct assay workflows, which are consumed by the ISAcreate module of the ISA-API to drive to creation of ISA objects. In this notebook, we show how the data structure can be used to pass workflow settings. Each Protocol Node can be used to specify the number of technical replicates and parameter settings for each data acquisition to be executed. Note how these values are OntologyAnnotation and therefore can be marked up with URI.
nmr_assay_dict = OrderedDict([
('measurement_type', OntologyAnnotation(term='metabolite profiling')),
('technology_type', OntologyAnnotation(term='nmr spectroscopy')),
('extraction', {}),
('extract', [
{
'node_type': EXTRACT,
'characteristics_category': OntologyAnnotation(term='extract type'),
'characteristics_value': OntologyAnnotation(term='supernatant'),
'size': 1,
'technical_replicates': None,
'is_input_to_next_protocols': True
},
{
'node_type': EXTRACT,
'characteristics_category': OntologyAnnotation(term='extract type'),
'characteristics_value': OntologyAnnotation(term='pellet'),
'size': 1,
'technical_replicates': None,
'is_input_to_next_protocols': True
}
]),
('nmr_spectroscopy', {
OntologyAnnotation(term='instrument'): [OntologyAnnotation(term='Bruker AvanceII 1 GHz')],
OntologyAnnotation(term='acquisition_mode'): [OntologyAnnotation(term='1D 13C NMR')],
OntologyAnnotation(term='pulse_sequence'): [OntologyAnnotation(term='CPMG')]
}),
('raw_spectral_data_file', [
{
'node_type': DATA_FILE,
'size': 1,
'technical_replicates': 2,
'is_input_to_next_protocols': False
}
])
])
custom_nasa_lab_dict = OrderedDict([
('measurement_type', OntologyAnnotation(term='phenotyping')),
('technology_type', OntologyAnnotation(term='hyperspectral imaging')),
('hyperspectral imaging', {
OntologyAnnotation(term='instrument'): [OntologyAnnotation(term='Hitachi ZBR-II')],
OntologyAnnotation(term='acquisition_mode'): [OntologyAnnotation(term='multimodal')],
OntologyAnnotation(term='wavelength'): [OntologyAnnotation(term='near-IR'),OntologyAnnotation(term='far-IR'),OntologyAnnotation(term='UV')]
}),
('raw_spectral_data_file', [
{
'node_type': DATA_FILE,
'size': 1,
'technical_replicates': 2,
'is_input_to_next_protocols': False
}
])
])
nmr_assay_graph = AssayGraph.generate_assay_plan_from_dict(nmr_assay_dict)
interspersed_sample_node = ProductNode(node_type=SAMPLE, name="sample qc",
size=2,characteristics=(
Characteristic(category='dilution', value=10, unit='ml'),
))
pre_run_sample_node = ProductNode(node_type=SAMPLE,
name="pre run qc",
size=2,
characteristics=(
Characteristic(category='dilution', value=10, unit='ml'),
))
qc_object = QualityControl(interspersed_sample_type=[(interspersed_sample_node, 10)],
pre_run_sample_type=pre_run_sample_node)
nmr_assay_graph.quality_control=qc_object
sap1 = SampleAndAssayPlan(name="sap1" , sample_plan=[input_material1],assay_plan=[nmr_assay_graph])
sample2assay_plan={input_material1: [nmr_assay_graph]}
sap1.sample_to_assay_map=sample2assay_plan
sorted(sap1.assay_plan)[0].quality_control
Associating each study arm with the study cell and its sample to assay plan¶
arm1.add_item_to_arm_map(st_cl1,sap1)
arm2.add_item_to_arm_map(st_cl2,sap1)
arm3.add_item_to_arm_map(st_cl3,sap1)
arm4.add_item_to_arm_map(st_cl4,sap1)
arm5.add_item_to_arm_map(st_cl5,sap1)
arm6.add_item_to_arm_map(st_cl6,sap1)
arm7.add_item_to_arm_map(st_cl7,sap1)
arm8.add_item_to_arm_map(st_cl8,sap1)
Adding all the study arm to the study design object¶
study_design= StudyDesign(name='parallel group design 2x2x2 #1')
study_design.add_study_arm(arm1)
study_design.add_study_arm(arm2)
study_design.add_study_arm(arm3)
study_design.add_study_arm(arm4)
study_design.add_study_arm(arm5)
study_design.add_study_arm(arm6)
study_design.add_study_arm(arm7)
study_design.add_study_arm(arm8)
Let’s now serialize the ISA Study Design Object as a JSON document.¶
import json
from isatools.isajson import ISAJSONEncoder
from isatools.create.model import StudyDesignEncoder
f=json.dumps(study_design, cls=StudyDesignEncoder, sort_keys=True, indent=4, separators=(',', ': '))
Let’s produce a graphical representation of the study groups and their size using python bokeh library¶
from bokeh.io import output_file, show
from bokeh.plotting import figure
from bokeh.models import ColumnDataSource, Range1d, BoxAnnotation, Label, Legend, LegendItem, LabelSet
from bokeh.models.tools import HoverTool
import holoviews as hv
from holoviews import opts, dim
# hv.extension('bokeh')
def get_treatment_factors(some_element):
treat = ""
for j in range(len(some_element['factorValues'])):
if j < len(some_element['factorValues']) - 1:
if 'unit' in some_element['factorValues'][j].keys():
treat = treat + some_element['factorValues'][j]['factor']['name'].lower() + ": " \
+ str(some_element['factorValues'][j]['value']) + " " \
+ str(some_element['factorValues'][j]['unit']['term'].lower()) + ", "
else:
treat = treat + some_element['factorValues'][j]['factor']['name'].lower() + ": " \
+ str(some_element['factorValues'][j]['value']) + ","
else:
if 'unit' in some_element['factorValues'][j].keys():
treat = treat + some_element['factorValues'][j]['factor']['name'].lower() + ": " \
+ str(some_element['factorValues'][j]['value']) + " " \
+ str(some_element['factorValues'][j]['unit']['term'].lower())
else:
treat = treat + some_element['factorValues'][j]['factor']['name'].lower() + ": " \
+ str(some_element['factorValues'][j]['value'])
return treat
design = json.loads(json.dumps(study_design, cls=StudyDesignEncoder, sort_keys=True, indent=4, separators=(',', ': ')))
frames = []
Items = []
# defining a color pallet for the different element types:
element_colors = {"biological intervention": "rgb(253,232,37)",
"radiological intervention": "rgb(53, 155, 8)",
"dietary intervention": "rgb(53, 155, 8)",
"chemical intervention": "rgb(69, 13, 83)",
"washout": "rgb(45, 62, 120)",
"screen": "rgb(33, 144, 140)",
"run in": "rgb(43, 144, 180)",
"follow-up": "rgb(88, 189, 94)",
"concomitant treatment": "rgb(255, 255, 0)"}
# processing the study design arms and treatment plans:
for key in design["studyArms"].keys():
DF = pd.DataFrame(columns=['Arm', 'Cell', 'Type', 'Start_date', 'End_date', 'Treatment', 'Color'])
arm_name = key
# print("arm: ", arm_name)
size = design["studyArms"][key]["groupSize"]
size_annotation = "n=" + str(size)
cells_per_arm = design["studyArms"][key]["cells"]
cell_counter = 0
for cell in cells_per_arm:
cell_name = cell['name']
elements_per_cell = cell['elements']
for element in elements_per_cell:
treat = ""
element_counter = 0
if 'concomitantTreatments' in element.keys():
element_counter = element_counter + 1
treatments = []
for item in element['concomitantTreatments']:
treatment = get_treatment_factors(item)
treatments.append(treatment)
concomitant = ','.join(treatments[0:-1])
concomitant = concomitant + ' and ' + treatments[-1]
array = [arm_name, cell_name, arm_name + ": [" + concomitant + "]_concomitant_" + str(cell_counter),
dt.datetime(cell_counter + 2000, 1, 1), dt.datetime(cell_counter + 2000 + 1, 1, 1),
str(element['factorValues']),
concomitant,
element_colors["concomitant treatment"]]
Items.append(array)
elif 'type' in element.keys():
treatment = get_treatment_factors(element)
element_counter = element_counter + 1
array = [arm_name, cell_name, arm_name + ": [" + str(element['type']) + "]_" + str(cell_counter),
dt.datetime((cell_counter + 2000), 1, 1), dt.datetime((cell_counter + 2000 + 1), 1, 1),
# str(element['factorValues']),
str(treatment),
element_colors[element['type']]]
Items.append(array)
cell_counter = cell_counter + 1
for i, Dat in enumerate(Items):
DF.loc[i] = Dat
# print("setting:", DF.loc[i])
# providing the canvas for the figure
# print("THESE ARE THE TYPES_: ", DF.Type.tolist())
fig = figure(title='Study Design Treatment Plan',
width=800,
height=400,
y_range=DF.Type.tolist(),
x_range=Range1d(DF.Start_date.min(), DF.End_date.max()),
tools='save')
# adding a tool tip
hover = HoverTool(tooltips="Task: @Type<br>\
Start: @Start_date<br>\
Cell_Name: @Cell<br>\
Treatment: @Treatment")
fig.add_tools(hover)
DF['ID'] = DF.index+0.8
# print("ID: ", DF['ID'])
DF['ID1'] = DF.index+1.2
# print("ID1: ", DF['ID1'])
CDS = ColumnDataSource(DF)
# , legend=str(size_annotation)
r = fig.quad(left='Start_date', right='End_date', bottom='ID', top='ID1', source=CDS, color="Color")
fig.xaxis.axis_label = 'Time'
fig.yaxis.axis_label = 'study arms'
# working at providing a background color change for every arm in the study design
counts = DF['Arm'].value_counts().tolist()
# print("total number of study arms:", len(counts), "| number of phases per arm:", counts)
# box = []
# for i, this_element in enumerate(DF['Arm']):
# if i==0:
# box[i] = BoxAnnotation(bottom=0,
# top=DF['Arm'].value_counts().tolist()[0],
# fill_color="blue")
# elif i % 2 == 0:
# box[i] = BoxAnnotation(bottom=DF['Arm'].value_counts().tolist()[0],
# top=DF['Arm'].value_counts().tolist()[0],
# fill_color="silver")
# else:
# box[i] = BoxAnnotation(bottom=DF['Arm'].value_counts().tolist()[0],
# top=DF['Arm'].value_counts().tolist()[0] + DF['Arm'].value_counts().tolist()[1],
# fill_color="grey",
# fill_alpha=0.1)
# # adding the background color for each arm:
# for element in box:
# fig.add_layout(element)
# # fig.add_layout(box2)
# # fig.add_layout(legend,'right')
caption1 = Legend(items=[(str(size_annotation), [r])])
fig.add_layout(caption1, 'right')
citation = Label(x=10, y=-80, x_units='screen', y_units='screen',
text='parallel group design layout - isa-api 0.12', render_mode='css',
border_line_color='gray', border_line_alpha=0.4,
background_fill_color='white', background_fill_alpha=1.0)
fig.add_layout(citation)
show(fig)
Let’s now generate the full ISA Study object from the ISA Study Design object¶
This is done by invoking the new generate_isa_study method on the ISA study design object
study = study_design.generate_isa_study()
We can now check the objects which have been generated¶
investigation.studies=[study]
print(investigation.studies[0].assays[0])
We can also simply write to file either as ISA-Tab or as ISA-JSON¶
# WRITING ISA-JSON document
print(json.dumps(investigation, cls=ISAJSONEncoder, sort_keys=True, indent=4, separators=(',', ': ')))
#%%capture # remove this line if you want to see printout
from isatools import isatab
isatab.dump(investigation, './notebook-output/isa-2x2x2-single-measure-design')
One can also check out the tables as dataframes¶
from isatools.isatab import dump_tables_to_dataframes as dumpdf
dataframes = dumpdf(investigation)
dataframes.keys()
len(dataframes.keys())
dataframes[list(dataframes.keys())[1]]
or use the graph structure to count objects¶
[x for x in study.assays[0].graph.nodes() if isinstance(study.assays[0].graph.indexes[x], Sample)]
len([x for x in study.assays[0].graph.nodes() if isinstance(study.assays[0].graph.indexes[x], Sample)])
[getattr(study.assays[0].graph.indexes[x], 'name', None) for x in study.assays[0].graph.nodes()]
About this notebook¶
authors: philippe.rocca-serra@oerc.ox.ac.uk, massimiliano.izzo@oerc.ox.ac.uk
license: CC-BY 4.0
support: isatools@googlegroups.com
issue tracker: https://github.com/ISA-tools/isa-api/issues