Usage¶
The Calibrator¶
The Calibrator class is the main entry point to create and use calibration models for concentration calculation. It harbors all functionalities to load data, add calibration models, fit calibration models, calculate concentrations, and serialize serialization.
The Calibrator contains a Standard, which harbors the data structure structuring all aspects of a fitted calibration model, measured standards, and measurement conditions.
Initialization¶
The Calibrator can be initialized by providing the concentrations and respective measured signals as lists. Additionally, the molecule_id and pubchem_cid and the respective conc_unit need to be provided. The molecule_id serves as an internal id to reference the molecule in equations (E.g. 's1'). The pubchem_cid serves as a global identifier, to uniquely reference a molecule in the PubChem database. Optionally, a molecule_name and a wavelength can be provided if the signal data originates from a spectrophotometric measurement. Setting the cutoff can be provided to exclude signals above a certain value.
Units are handled as predefined objects which can be imported from the calipytion.units module.
from calipytion import Calibrator
from calipytion.units import mM
calibrator = Calibrator(
molecule_id="s0",
pubchem_cid=439153,
molecule_name="NADH",
conc_unit=mM,
concentrations=[0, 0.5, 1, 1.5, 2, 2.5, 3, 3.5],
signals=[0.02, 0.509, 1.076, 1.534, 2.008, 2.482, 2.898, 3.176],
wavelength=420,
)
)
From .xlsx file¶
Alternatively, the calibrator can be initialized by loading the data from a sheet of an Excel file, using the from_excel method. By default, the first sheet is loaded. Otherwise, the sheet_name can be provided, to specify the sheet to be loaded. Alongside the path of the file, the molecule_id, molecule_name, and conc_unit need to be provided. Optionally, the wavelength can be provided. Furthermore, using skip_rows the number of rows to be skipped can be specified. By default, the first row is skipped, as it is assumed to contain the column names.
from calipytion import Calibrator
from calipytion.units import mM
calibrator = Calibrator.from_excel(
path="data.xlsx",
molecule_id="s0",
molecule_name="NADH",
conc_unit=mM,
wavelength=420,
skip_rows=1
)
From .json standard¶
Natively the Calibrator alongside a fitted calibration model is serialized as a Standard JSON file. This file can be read to reuse a calibrator, using the from_json method.
Adding calibration models¶
By default, the Calibrator is initialized with three polynomial calibration models, a linear, quadratic, and cubic model. These models do not include a parameter for the intercept. Thus blanked signals are assumed to be zero.
Additional models can be added by using the add_model method. The method requires a name and a signal_law, describing the mathematical relationship between the signal and the concentration. Only the right side of the equation needs to be provided. The left side is assumed to be the signal. Additionally, the variable of the concentration must match the molecule_id provided during initialization.
Furthermore, an initial_value, the lower_bound, and the upper_bound can be provided for all parameters.
# delete default models
calibrator.models = []
# add a linear, quadratic, cubic, and rational model with intercept
linear = calibrator.add_model(
name="linear",
signal_law="a * s0 + b",
upper_bound=1e8,
lower_bound=-1e8,
)
quadratic = calibrator.add_model(
name="quadratic",
signal_law="a * s0 ** 2 + b * s0 + c",
upper_bound=1e8,
lower_bound=-1e8,
)
cubic = calibrator.add_model(
name="cubic",
signal_law="a * s0 ** 3 + b * s0 ** 2 + c * s0 + d",
upper_bound=1e8,
lower_bound=-1e8,
)
rational = calibrator.add_model(
name="rational",
signal_law="a * s0 / (b + s0) + c",
upper_bound=1e8,
lower_bound=-1e8,
)
Fitting calibration models¶
After specifying the calibration models, the models can be fitted to the data using the fit_models method. This fits all models that were added to the calibrator. After fitting, a table with fit statistics such as Akaike Information Criterion (AIC), R2, and Root Mean Squared Deviation (RMSD) is printed alongside the equation and relative parameter standard errors of each model.
Model Overview
| Model Name | AIC | R squared | RMSD | Equation | Relative Parameter Standard Errors |
|---|---|---|---|---|---|
| cubic | -54 | 0.9996 | 0.0205 | a * s0 ** 3 + b * s0 ** 2 + c * s0 + d | d: 131.1%, c: 7.4%, a: 42.5%, b: 87.2% |
| quadratic | -49 | 0.9991 | 0.0318 | a * s0 ** 2 + b * s0 + c | c: 378.3%, a: 20.3%, b: 4.0% |
| rational | -48 | 0.9989 | 0.0351 | a * s0 / (b + s0) + c | a: 20.6%, c: 433.9%, b: 25.8% |
| linear | -37 | 0.9946 | 0.0778 | a * s0 + b | a: 3.0%, b: 58.1% |
Visualizing calibration models¶
After fitting, the calibration models can be visualized using the visualize method. The method returns an interactive figure, allowing the user to inspect the calibration models and the residuals and to compare the models.
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Concentration calculation¶
For concentration calculation, one of the fitted models can be chosen and given to the calculate_concentrations method. The data of unknown signals is provided as a list. By default, concentrations are only calculated if the signal is within the calibration range, which is based on the calibration data. Optionally, extrapolation can be enabled by setting extrapolate=True.
concentrations = calibrator.calculate_concentrations(
model=cubic, signals=[0.509, 0.9, 4], extrapolate=False
)
print(concentrations)
# -> [0.48576600549464755, 0.8658856333797263, nan]
Extrapolation
The calculate_concentrations method returns a list of concentrations. If a signal is outside the calibration range, the concentration is set to nan.
Extrapolation can be enabled by setting extrapolate=True.
Serialization¶
Finally, the data of the calibrator can be enriched with additional information on the calibrated molecule and the measurement conditions to form a valid Standard. This is done by using the create_standard method. This method expects a ph, temperature, temp_unit, as well as the chosen model. Furthermore, the retention_time can be provided to further characterize the context of the calibration.
from calipytion.units import C
standard = calibrator.create_standard(
ph=7.4,
temperature=25,
temp_unit=C,
retention_time=7.53
)
# save the standard as a JSON file
with open("abts_standard.json", "w") as f:
f.write(standard.model_dump_json(indent=4))
Applying a calibrator to an EnzymeML Document¶
The Calibrator can be applied to an EnzymeMLDocument to calculate concentrations of signals. This is done by using the apply_to_enzymeml method. The method expects an EnzymeMLDocument as input. Optionally, the extrapolate parameter can be set to enable extrapolation.
Note
In order to apply a calibrator to an EnzymeMLDocument, the measurement data within the EnzymeMLDocument must reference the same molecule. Therefore, the species_id in the EnzymeMLDocument must match the molecule_id of the Calibrator. The EnzymeMLDocument must be an instance of pyenzyme.EnzymeMLDocument.