Welcome to moiety_modeling’s documentation!

User Guide

Description

The moiety_modeling package provides a simple Python interface for moiety model representation, model optimization and model selection. Both moiety models and isotopologue datasets are stored in`JSON` files, which can be used for further model optimization, selection, analysis and visualization.

Installation

moiety_modeling runs under Python 3.6+ and is available through python3-pip. Install via pip or clone the git repo and install the following dependencies and you are ready to go!

Install on Linux

Pip installation (method 1)

python3 -m pip install moiety-modeling

GitHub Package installation (method 2)

Make sure you have git installed:

git clone https://github.com/MoseleyBioinformaticsLab/moiety_modeling.git

Dependecies

moiety_modeling requires the following Python libraries:

• docopt for creating the command-line interface.
• jsonpickle for saving Python objects in a JSON serializable form and outputting to a file.
• numpy and matplotlib for visualization of optimized results.
• scipy for application of optimization methods.
• SAGA-optimize for parameters optimization.

Basic usage

The moiety_modeling package can be used in several ways:

• As a library for accessing and manipulating moiety models and isotopologue datasets stored in the JSON files.

• As a command-line tool:

  • Optimize the moiety model parameters.
  • Analyze the optimization results of moiety model, and select the optimal model.
  • Visuslize the optimized results.

Note

Read The moiety_modeling Tutorial to learn more and see code examples on using the moiety_modeling as a library and as a command-line tool.

The moiety_modeling Tutorial

The moiety_modeling can be used to:

• Construct a moiety model.
• Optimize parameters of a moiety model.
• Analyze optimized results and select the optimal model.
• Visualize the optimized results.

In this document, each use will be explained in details.

The moiety_modeling API tutorial

Using moiety_modeling to construct a moiety model

In moiety modeling, we dissemble molelcules into molecular parts called moieties (called functional groups in chemistry), and then deconvolute molecular isotopic incorporation into moiety isotopic. A constructed moiety model is then stored in a JSON file.

Here is an example of moiety model construction.

>>> import moiety_modeling
>>> import jsonpickle
>>> ribose = moiety_modeling.Moiety("ribose", {'13C': 5}, isotopeStates={'13C': [0, 2, 3, 5]}, nickname='r')   # moiety creation
>>> glucose = moiety_modeling.Moiety("glucose", {'13C': 6}, isotopeStates={'13C': [0, 3, 6]}, nickname='g')
>>> acetyl = moiety_modeling.Moiety("acetyl", {'13C': 2}, isotopeStates={'13C': [0, 2]}, nickname='a')
>>> uracil = moiety_modeling.Moiety("uracil", {'13C': 4}, isotopeStates={'13C': [0, 1, 2, 3]}, nickname='u')
>>> relationship1 = moiety_modeling.Relationship(glucose1, '13C3', ribose1, '13C2', '*', 2)   # relationship creation
>>> relationship2 = moiety_modeling.Relationship(ribose1, '13C3', ribose1, '13C2')
>>> relationship3 = moiety_modeling.Relationship(glucose1, '13C0', ribose1, '13C0')
>>> relationship4 = moiety_modeling.Relationship(glucose1, '13C6', ribose1, '13C5')
>>> UDP_GlcNAC = moiety_modeling.Molecule('UDP_GlcNAC', [ribose, glucose, acetyl, uracil])   # molecule creation
>>> model = moiety_modeling.Model('6_G0R2A1U3_g3r2r3_g6r5', [ribose, glucose, acetyl, uracil], [UDP_GlcNAC], [relationship1, relationship2, relationship3, relationship4])
>>> with open('model.json', 'w') as outFile:   # store the model into JSONPickle file.
        outFile.write(jsonpickle.encode({'models': [model]}))

The moiety states can also be constructed using all the possible states.

>>> glucose = moiety_modeling.Moiety("glucose", {'13C': 6, '18O': 5}, states=['13C_0.18O_0', '13C_6.18O_5'], nickname='g')
>>> ribose = moiety_modeling.Moiety("ribose", {'13C': 5, '18O': 4}, states=['13C_0.18O_0', '13C_5.18O_4'], nickname='r')
>>> acetyl = moiety_modeling.Moiety("acetyl", {'13C': 2, '18O': 1}, states=['13C_0.18O_0', '13C_2.18O_1'], nickname='a')
>>> uracil = moiety_modeling.Moiety("uracil", {'13C': 4, '18O': 2}, states=['13C_0.18O_0', '13C_1.18O_0', '13C_2.18O_1', '13C_2.18O_0', '13C_3.18O_0', '13C_3.18O_1'], nickname='u')

Using moiety_modeling to store isotopologue dataset

The Dataset class in the ‘moiety_modeling’ package organizes a single mass spectroscopy isotopologue profile dataset into a dictionary-based data structure.

Here is an example of dataset construction.

>>> import moiety_modeling
>>> import jsonpickle
>>> dataset1 = moiety_modeling.Dataset("12h", {'UDP_GlcNAC':
              [{'labelingIsotopes': '13C_0', 'height': 0.0175442549, 'heightSE': 0},
               {'labelingIsotopes': '13C_1', 'height': 0, 'heightSE': 0},
               {'labelingIsotopes': '13C_2', 'height': 0.0007113347, 'heightSE': 0},
               {'labelingIsotopes': '13C_3', 'height': 0.0002990498, 'heightSE': 0},
               {'labelingIsotopes': '13C_4', 'height': 0.0012322448, 'heightSE': 0},
               {'labelingIsotopes': '13C_5', 'height': 0.0962990868, 'heightSE': 0},
               {'labelingIsotopes': '13C_6', 'height': 0.0737941503, 'heightSE': 0},
               {'labelingIsotopes': '13C_7', 'height': 0.0194440036, 'heightSE': 0},
               {'labelingIsotopes': '13C_8', 'height': 0.063026207, 'heightSE': 0},
               {'labelingIsotopes': '13C_9', 'height': 0.0058731399, 'heightSE': 0},
               {'labelingIsotopes': '13C_10', 'height': 0.0312896069, 'heightSE': 0},
               {'labelingIsotopes': '13C_11', 'height': 0.3124695022, 'heightSE': 0},
               {'labelingIsotopes': '13C_12', 'height': 0.0573898846, 'heightSE': 0},
               {'labelingIsotopes': '13C_13', 'height': 0.277122791, 'heightSE': 0},
               {'labelingIsotopes': '13C_14', 'height': 0.0234859781, 'heightSE': 0},
               {'labelingIsotopes': '13C_15', 'height': 0.0200187655, 'heightSE': 0},
               {'labelingIsotopes': '13C_16', 'height': 0, 'heightSE': 0},
               {'labelingIsotopes': '13C_17', 'height': 0, 'heightSE': 0}]})
>>> dataset2 = moiety_modeling.Dataset("24h", {'UDP_GlcNAC':
              [{'labelingIsotopes': '13C_0', 'height': 0.00697626, 'heightSE': 0},
               {'labelingIsotopes': '13C_1', 'height': 0, 'heightSE': 0},
               {'labelingIsotopes': '13C_2', 'height': 0.0008426934, 'heightSE': 0},
               {'labelingIsotopes': '13C_3', 'height': 0.0007070956, 'heightSE': 0},
               {'labelingIsotopes': '13C_4', 'height': 0.0006206594, 'heightSE': 0},
               {'labelingIsotopes': '13C_5', 'height': 0.068147345, 'heightSE': 0},
               {'labelingIsotopes': '13C_6', 'height': 0.0499393097, 'heightSE': 0},
               {'labelingIsotopes': '13C_7', 'height': 0.023993641, 'heightSE': 0},
               {'labelingIsotopes': '13C_8', 'height': 0.062901247, 'heightSE': 0},
               {'labelingIsotopes': '13C_9', 'height': 0.0056603032, 'heightSE': 0},
               {'labelingIsotopes': '13C_10', 'height': 0.0281210238, 'heightSE': 0},
               {'labelingIsotopes': '13C_11', 'height': 0.2482899264, 'heightSE': 0},
               {'labelingIsotopes': '13C_12', 'height': 0.0613088541, 'heightSE': 0},
               {'labelingIsotopes': '13C_13', 'height': 0.3325253653, 'heightSE': 0},
               {'labelingIsotopes': '13C_14', 'height': 0.0499904271, 'heightSE': 0},
               {'labelingIsotopes': '13C_15', 'height': 0.0537153908, 'heightSE': 0},
               {'labelingIsotopes': '13C_16', 'height': 0.0062604583, 'heightSE': 0},
               {'labelingIsotopes': '13C_17', 'height': 0, 'heightSE': 0}]})   # dataset creation
>>> with open('dataset.json', 'w') as outFile: # store dataset into JSONPickle file.
        outFile.write(jsonpickle.encode({'datasets': [dataset1, dataset2]}))

Setting optimization parameters

The optimization parameters are stored in a JSON file. Several optimization methods, including SAGA and three scipy optimization methods (‘TNC’, ‘SLSQP’, ‘L_BFGS_B’), are available in the package. When using the SAGA optimization method, optimization parameters for SAGA should be specified. The parameters for scipy optimization methods can also be modified. Please refer the correponding API for detailed information.

Here is the example of optimization parameters construction.

>>> import jsonpickle
>>> setting = {'SAGA': {'methodParameters': {'alpha': 1, 'crossoverRate': 0.05, 'mutationRate': 3, 'populationSize': 20,
               'startTemperature': 0.5, 'stepNumber': 500, 'temperatureStepSize': 100},
               'noPrintAllResults': 1, 'noPrintBestResults': 0, 'optimizationSetting': 'SAGA_500'},
               'TNC': {'methodParameters': None, 'optimizationSetting': 'TNC'} }
>>> with open('optimizationSetting.json', 'w') as outFile:
        outFile.write(jsonpickle.encode({'optimizations': setting}))

The moiety_modeling CLI tutorial

Using moiety_modeling to optimize parameters of moiety model

To conduct the optimization, moiety model, datasets, and optimization settings should be provided. Please use the -h for more information of the option parameters.

python3 -m moiety_modeling modeling --models=<model_jsonfile> --datasets=<dataset_jsonfile> --optimizations=<optimizationSetting_json> --repetition=100 --split --multiprocess --energyFunction=logDifference

Using moiety_modeling to analyze optimized results and select the optimal model

The moiety_modeling package provides facilities to analyze the optimization results, select the optimal model, and compare the selection results under different optimization settings. Please refer to API for detailed information of option parameters.

python3 -m moiety_modeling analyze optimizations --a <optimizationPaths_txtfile>   # To analyze the optimization results of multiple moiety models together.
python3 -m moiety_modeling analyze optimizations --s <optimzationResults_jsonfile>   # To analyze the optimization results of a single moiety model.
python3 -m moiety_modeling analyze rank <analysisPaths_txtfile> --rankCriteria=AICc   # To rank models according to the selection criteria.
python3 -m moiety_modeling analyze table <rankPaths_txtfile>   # To compare the selection results under different optimizaton settings.

Using moiety_modeling to visualize the optimized results

The ‘moiety_modeling’ package provides facilities to visualize the optimization results.

python3 -m moiety_modeling plot moiety <analysisResults_jsonfile>   # To plot the distribution of calculated moiety modeling parameters.
python3 -m moiety_modeling plot isotopologue <analysisResults_jsonfile>   # To plot the comparison of calculated and observed isotopologue intensities.

The moiety_modeling API Reference

Routines for working with moiety modeling.

This package includes the following modules:

modeling

This module provides the Dataset class to organize a single mass spectroscopy profile dataset into a dictionary-based data structure, the ModelOptimization and derived classes for performing a single model optimization, and the OptimizationManager class to manage the optimization process of moiety modeling.

model

This module provides the Moiety, Molecule, Relationship, and Model classes for representing a moiety model.

analysis

This module provides several classes to analyze the optimization results, select the optimal model, and visualize the results. The ResultAnalysis class is responsible for generating general statistics from the optimization results. The ModelRank class selects the model that best reflects the observed isotopologue profile. The ComparisonTable class compares the optimal model selected under different optimization settings. The PlotMoietyDistribution class plots the distribution of moiety value of the moiety model. The PlotIsotopologueIntensity class plots comparison of the observed and the calculated isotopologue intensity.

cli

This module provides a command-line interface for the moiety_modeling package.

moiety_modeling.modeling

This module provides the Dataset class to organize a single mass spectroscopy isotopologue profile dataset into a dictionary-based data structure, the ModelOptimization and derived classes for performing a single model optimization, and the OptimizationManager class to manage the optimization process of moiety modeling.

The optimization results are stored in the JSON file. A txt file containing all the paths to the generated JSON files is created to facilitate further model analysis.

class moiety_modeling.modeling.Dataset(datasetName, *args, **kwargs)

Dataset class that stores a single mass spectroscopy isotopologue profile in the form of UserDict.

__init__(datasetName, *args, **kwargs)

Dataset initializer.

Parameters:datasetName (str) – the name of the dataset.

class moiety_modeling.modeling.ModelOptimization(model, datasets, path, methodParameters, optimizationSetting, energyFunction)

The abstract ModelOptimization class.

__init__(model, datasets, path, methodParameters, optimizationSetting, energyFunction)

ModelOptimization initializer.

Parameters:

• model (Model) – a Model instance.
• datasets (list) – a list of Dataset instances.
• path (str) – the subdirectory path to save the optimization results.
• methodParameters (dict) – the parameters for optimization method.
• optimizationSetting (str) – abbreviated name for the optimization.
• energyFunction (str) – the energy function used in the optimization.

energyCalculation(vector)

Calculate the energy of the model.

Parameters:vector (list) – a list of model parameter values. Return double:the energy.

absDifferenceEnergyFunction(moietyStateValue, dataset)

The absolute difference energy function. The absolute value between the observed and the calculated isotopologues: energy = sum(|I<cal> - I<obs>|).

Parameters:

• moietyStateValue (list) – a list of values for the moietyStates.
• dataset (Dataset) – a Dataset instance.

Returns:

the energy of the model.

Return type:

double

logDifferenceEnergyFunction(moietyStateValue, dataset)

The log difference energy function. The difference between the log of observed and the calculated isotopologues: energy = sum(|log(I<cal>) - log(I<obs>)|)

Parameters:

• moietyStateValue (list) – a list of values for the moietyStates.
• dataset (Dataset) – a Dataset instance.

Returns:

the energy of the model.

Return type:

double

squareDifferenceEnergyFunction(moietyStateValue, dataset)

The absolute difference energy function. The absolute value between the observed and the calculated isotopologues: energy = sum(|I<cal> - I<obs>|).

Parameters:

• moietyStateValue (list) – a list of values for the moietyStates.
• dataset (Dataset) – a Dataset instance.

Returns:

the energy of the model.

Return type:

double

AICDifferenceEnergyFunction(moietyStateValue, dataset)

The absolute difference energy function. The absolute value between the observed and the calculated isotopologues: energy = sum(|I<cal> - I<obs>|).

Parameters:

• moietyStateValue (list) – a list of values for the moietyStates.
• dataset (Dataset) – a Dataset instance.

Returns:

the energy of the model.

Return type:

double

optimizationScripts()

To save the optimization scripts of the optimization for check. :return: None :rtype: None

class moiety_modeling.modeling.SAGAoptimization(model, datasets, path, methodParameters, optimizationSetting, energyFunction, noPrintBestResults, noPrintAllResults)

The SAGAoptimization class (for combined datasets) inherited from ModelOptimization.

__init__(model, datasets, path, methodParameters, optimizationSetting, energyFunction, noPrintBestResults, noPrintAllResults)

SAGAoptimization initializer.

Parameters:

• model (Model) – a Model instance.
• datasets (list) – a list of Dataset instances.
• path (str) – the path to save the optimization results.
• methodParameters (dict) – the parameters for optimization method.
• optimizationSetting (str) – abbreviated name for the optimization.
• energyFunction (str) – the energy function used in the optimization.
• noPrintBestResults (int) – not to save the all the best results of the optimization process.
• noPrintAllResults (int) – not to save the all the results of the optimization process.

bestResultsFile(i)

Open the file to record the best results during the optimization process.

Parameters:i (int) – the ith optimization. Returns:the file handler. Return type:TextIOWrapper

allResultsFile(i)

Open the file to record the all optimization results.

Parameters:i (int) – the ith optimization. Returns:the file handler. Return type:TextIOWrapper.

optimizeSingle(i)

To perform one optimization.

Parameters:i (int) – the ith optimization. Returns:the best Guess from the optimization process. Return type:Guess.

creatSubdir()

To create subdirectories to store the optimization process.

Returns:None. Return type:None.

class moiety_modeling.modeling.SAGAseparateOptimization(model, datasets, path, methodParameters, optimizationSetting, energyFunction, noPrintBestResults, noPrintAllResults)

The SAGAseparateOptimization class (for split dataset) inherited from SAGAoptimization.

__init__(model, datasets, path, methodParameters, optimizationSetting, energyFunction, noPrintBestResults, noPrintAllResults)

SAGAseparateOptimization initializer.

Parameters:

• model (Model) – a Model instance.
• datasets (list) – a list of Dataset instances.
• path (str) – the path to save the optimization results.
• methodParameters (dict) – the parameters for optimization method.
• optimizationSetting (str) – abbreviated name for the optimization.
• energyFunction (str) – the energy function used in the optimization.
• noPrintBestResults (int) – not to save the all the best results of the optimization process.
• noPrintAllResults (int) – not to save the all the results of the optimization process.

optimizeSingle(i)

Perform one optimization.

Parameters:i (int) – the ith optimization. Returns:the best Guess from the optimization process. Return type:Guess.

class moiety_modeling.modeling.ScipyGuess(elements, energy)

To convert optimization results to Guess instance.

__init__(elements, energy)

ScipyGuess initializer.

Parameters:

• elements (list) – a list of values for model parameters.
• energy (double) – the energy of the Guess calculated from an energy function.

class moiety_modeling.modeling.ScipyOptimization(model, datasets, path, methodParameters, optimizationSetting, energyFunction, method)

The ScipyOptimization class (for combined datasets) inherited from ModelOptimization.

__init__(model, datasets, path, methodParameters, optimizationSetting, energyFunction, method)

ScipyOptimization initializer.

Parameters:

• model (Model) – the Model instance.
• datasets (list) – a list of Dataset instances.
• path (str) – the path to save the optimization results.
• methodParameters (dict) – the parameters for optimization method.
• optimizationSetting (str) – abbreviated name for the optimization.
• energyFunction (str) – the energy function used in the optimization.
• method (str) – the scipy optimization method.

optimizeSingle(i)

To perform one optimization.

Parameters:i – the ith optimization. Returns:the best ScipyGuess from the optimization process. Return type:ScipyGuess.

class moiety_modeling.modeling.ScipySeparateOptimization(model, datasets, path, methodParameters, energyFunction, optimizationSetting, method)

The ScipySeparateOptimization class (for split dataset) inherited from ScipyOptimization.

__init__(model, datasets, path, methodParameters, energyFunction, optimizationSetting, method)

ScipySeparateOptimization initializer.

Parameters:

• model (Model) – the Model instance.
• datasets (list) – a list of Dataset instances.
• path (str) – the path to save the optimization results.
• methodParameters (dict) – the parameters for optimization method.
• energyFunction (str) – the energy function used in the optimization.
• optimizationSetting (str) – abbreviated name for the optimization.
• method (str) – the scipy optimization method.

optimizeSingle(i)

To perform single optimization.

Parameters:i (int) – the ith optimization. Returns:the best ScipyGuess from the optimization process. Return type:ScipyGuess.

class moiety_modeling.modeling.OptimizationManager(models, datasets, optimizations, path, split=True, multiprocess=True, force=True, times=100, energyFunction='logDifference', printOptimizationScript=True)

OptimizationManager class manage the optimization process based on the optimization settings.

__init__(models, datasets, optimizations, path, split=True, multiprocess=True, force=True, times=100, energyFunction='logDifference', printOptimizationScript=True)

OptimizationManager initializer.

Parameters:

• models (list) – a list of Moiety instances.
• datasets (list) – a list of Dataset instances.
• optimizations (dict) – a list of optimization settings.
• path (str) – the path to store the optimization results.
• split (True) – to split the dataset or not.
• multiprocess (True) – to apply multiprocess or not.
• force (True) – to force optimization process or not.
• times (int) – the times of optimization for each moiety model.
• energyFunction (str) – the energy function used for optimization.
• printOptimizationScript (True) – to print out the optimization script or not.

optimizeModels()

To optimize moiety models based on the optimization settings.

Returns:None Return type:None

moiety_modeling.model

This module provides the Moiety class, the Molecule class, the Relationship class, and the Model class to construct moiety model.

class moiety_modeling.model.Moiety(name, maxIsotopeNum, states=None, isotopeStates=None, nickname=' ', ranking=0)

Moiety class describes the Moiety entity in the moiety model.

__init__(name, maxIsotopeNum, states=None, isotopeStates=None, nickname=' ', ranking=0)

Moiety initializer.

Parameters:

• name (str) – the name of the moiety.
• maxIsotopeNum (dict) – the dictionary of the labeling isotopes and the corresponding number in the moiety. eg: {‘13C’: 5, ‘15N’: 3}.
• states (list) – the list of states of the moiety.
• isotopeStates (dict) – the dictionary of the labeling isotopes and the corresponding states in the moiety. eg: {‘13C’: [0, 2, 5], ‘15N’: [2, 3]}.
• nickname (str) – the nickname of the moiety.
• ranking (int) – the ranking of the moiety.

class moiety_modeling.model.Relationship(moiety, moietyState, equivalentMoiety, equivalentMoietyState, operator=None, coefficient=None)

Relationship class describes the relationship between moiety states in the moiety model.

__init__(moiety, moietyState, equivalentMoiety, equivalentMoietyState, operator=None, coefficient=None)

Relationship initializer.

Parameters:

• moiety (Moiety) – the Moiety in the relationship.
• moietyState (str) – the state of the moiety (eg: ‘13C_0.15N_1’).
• equivalentMoiety (Moiety) – Moiety in the relationship.
• equivalentMoietyState (str) – the state of the equivalentMoiety (eg: ‘13C_0.15N_1’).
• operator (str) – the operator of the relationship (‘*’, ‘/’).
• coefficient (double) – the coefficient of the relationship.

class moiety_modeling.model.Molecule(name, moieties)

Molecule class describes the Molecule entity in the moiety model

__init__(name, moieties)

Molecule initializer.

Parameters:

• name (str) – the name of the molecule.
• moieties (list) – the list of Moiety instances that make up for the Molecule.

__eq__(other)

Return self==value.

class moiety_modeling.model.Model(name, moieties, molecules, relationships=None)

Model class describes the moiety Model entity.

__init__(name, moieties, molecules, relationships=None)

Model initializer.

Parameters:

• name (str) – the name of the model.
• moieties (list) – a list of Moiety instances.
• molecules (list) – a list of Molecule instances.
• relationships (list) – a list of Relationship instances.

moiety_modeling.analysis

This module provides several classes to analyze the optimization results, select the optimal model, and visualize the results. The ResultAnalysis class is responsible for generating general statistics from the optimization results. The ModelRank class selects the model that best reflects the observed isotopologue profile. The ComparisonTable class compares the optimal model selected under different optimization settings. The PlotMoietyDistribution class plots the distribution of moiety value of the moiety model. The PlotIsotopologueIntensity class plots comparison of the observed and the calculated isotopologue intensity.

class moiety_modeling.analysis.ResultsAnalysis(filename, path=None)

ResultsAnalysis class performs the analysis of moiety model optimization results.

__init__(filename, path=None)

ResultsAnalysis initializer.

Parameters:

• filename (str) – the filenames of optimization results file.
• path (str) – the path to store the analysis results.

analyze()

Analyze the optimization results for each model.

Return dict:the analysis results.

class moiety_modeling.analysis.ModelRank(pathFile, path, selectionCriterion)

ModelRank class ranks the models according to the selection criteria.

__init__(pathFile, path, selectionCriterion)

ModelRank initializer.

Parameters:

• pathFile (str) – the txt file containing paths to the model analysis results.
• path (str) – the path to store the model rank results.
• selectionCriterion (str) – the selection criteria (eg: AIC, BIC, BICc).

rank()

To rank the models according to the selection criteria.

Return list:the rank results.

class moiety_modeling.analysis.ComparisonTable(pathFile, path)

ComparisonTable class collects the best and second best models under different optimization settings.

__init__(pathFile, path)

ComparisonTable initializer.

Parameters:

• pathFile (str) – the filenames of model rank at different situations.
• path (str) – the path to directory that stores the result.

makeTable()

To make the comparison table under different optimization settings.

Returns:None Return type:None

class moiety_modeling.analysis.PlotMoietyDistribution(filename, path)

PlotMoietyDistribution class plots the moiety state distribution of the optimization results.

__init__(filename, path)

PlotMoietyDistribution initializer.

Parameters:

• filename (str) – the json file of model analysis results.
• path (str) – the path to store the plot results.

plotMoiety()

To plot the distribution of moiety states.

Returns:None Return type:None

class moiety_modeling.analysis.PlotIsotopologueIntensity(filename, path)

PlotIsotopologueIntensity class plots the comparison of calculated and observed isotoplogue intensity.

__init__(filename, path)

PlotIsotopologueIntensity initializer.

Parameters:

• filename (str) – The json file of model analysis results.
• path (str) – the path to store the plot results.

plotIsotopologue()

To plot the comparison of observed and calculated isotoplogue intensities.

Returns:None Return type:None

The moiety_modeling command-line interface option

Usage:

moiety_modeling -h | –help moiety_modeling –version moiety_modeling modeling [–combinedData=<combined_jsonfile>] [–models=<models_jsonfile] [–datasets=<datasets_jsonfile>] [–optimizations=<optimizations_jsonfile>] [–working=<working_dir>] [–repetition=<optim_count>] [–split] [–force] [–multiprocess] [–energyFunction=<function>] [–printOptimizationScripts] moiety_modeling analyze optimizations –a <optimizationPaths_txtfile> [–working=<working_dir>] moiety_modeling analyze optimizations –s <optimizationResults_jsonfile> [–working=<working_dir>] moiety_modeling analyze rank <analysisPaths_txtfile> [–working=<working_dir>] [–rankCriteria=<rankCriteria>] moiety_modeling analyze table <rankPaths_txtfile> [–working=<working_dir>] moiety_modeling plot moiety <analysisResults_jsonfile> [–working=<working_dir>] moiety_modeling plot isotopologue <analysisResults_jsonfile> [–working=<working_dir>]

Options:

-h, --help	Show this screen.
--version	Show version.
--combinedData=<combined_jsonfile>
	JSON description file of the combined data (eg: models, datasets, optimization settings)
--models=<models_jsonfile>
	JSON description file of the moiety models.
--datasets=<datasets_jsonfile>
	JSON description file of the datasets.
--optimizations=<optimizations_jsonfile>
	JSON description file of the optimization setting.
--working=<working_dir>
	Alternative path to save the results.
--repetition=<optim_count>
	The number of optimization repetitions to perform [default: 100].
--split	To split the datasets or not.
--force	To force optimization process if error occurs.
--multiprocess	To perform with multiprocessing or not.
--printOptimizationScripts
	To print the optimization script or not.
--a	To analyze a bunch of optimization results together with the path file.
--s	To analyze a single moiety model optimization results.
--energyFunction=<enegyFunction>
	The energyFunction of the moiety modeling optimization.
--optimizationSetting=<optimizationSetting>
	The optimization setting of the moiety modeling optimization.
--rankCriteria=<rankCriteria>
	The criteria for model ranking [default: AICc].

moiety_modeling.cli.cli(args)

Indices and tables

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