dexom_python package top-level modules

Submodules

model_functions module

dexom_python.model_functions.check_model_options(model, timelimit=600, feasibility=1e-07, mipgaptol=0.001, verbosity=1)

dexom_python.model_functions.check_threshold_tolerance(model, epsilon, threshold)

dexom_python.model_functions.get_all_reactions_from_model(model, save=True, shuffle=True, out_path='')

Outputs a list of all reactions in the model. If possible, all blocked reactions are removed. Optionally, the reaction-list can be shuffled.

Parameters

• model (cobra.Model) –

• save (bool) – by default, exports the reactions in a csv format

• shuffle (bool) – set to True to shuffle the order of the reactions

• out_path (str) – output path

Returns

rxn_list

Return type

A list of all reactions in the model

dexom_python.model_functions.get_subsystems_from_model(model, save=True, out_path='')

Creates a list of all subsystems of a model and their associated reactions

Parameters

• model (cobra.Model) –

• save (bool) –

• out_path (str) –

Returns

• rxn_sub (pandas.DataFrame) – a DataFrame with reaction names as index and subsystem name as column

• sub_list (list) – a list of subsystems

dexom_python.model_functions.load_reaction_weights(filename, rxn_names='reactions', weight_names='weights')

loads reaction weights from a .csv file

Parameters

• filename (str) – the path + name of a .csv file containing reaction weights

• rxn_names (str) – the name of the column containing the reaction names

• weight_names (str) – the name of the column containing the weights

Returns

reaction_weights

Return type

dict

dexom_python.model_functions.read_model(modelfile, solver='cplex')

dexom_python.model_functions.save_reaction_weights(reaction_weights, filename='reaction_weights.csv')

Parameters

• reaction_weights (dict) – a dictionary where keys = reaction IDs and values = weights

• filename (str) –

Returns

reaction_weights

Return type

pandas.DataFrame

gpr_rules module

dexom_python.gpr_rules.apply_gpr(model, gene_weights, save=True, filename='reaction_weights', duplicates='remove', null=0.0)

Applies the GPR rules from a given metabolic model for creating reaction weights

Parameters

• model (cobra.Model) – a cobrapy model

• gene_weights (dict or pandas.Series or pandas.DataFrame) – a dictionary of pandas Series containing gene IDs & weights

• save (bool) – if True, saves the reaction weights as a csv file

• filename (str) – path where the file will be saved

• duplicates (str, any of "remove", "max", "min", "mean", "median") – determines how to deal with genes presenting several expression values

• null (float) – value to return for reactions/genes with no information

Returns

reaction_weights

Return type

dict where keys = reaction IDs and values = weights

dexom_python.gpr_rules.expression2qualitative(genes, column_list=None, proportion=0.25, method='keep', significant_genes='both', save=True, outpath='geneweights')

Transforms gene expression values/ gene scores into qualitative gene weights

Parameters

• genes (pandas.DataFrame) – dataframe with gene IDs in the index and gene expression values in a later column

• column_list (list) – column indexes containing gene expression values to be transformed. If empty, all columns will be transformed

• proportion (float) – proportion of genes to be used for determining high/low gene expression

• method (str) – one of “max”, “mean” or “keep”. chooses how to deal with genes containing multiple conflicting expression values

• significant_genes (str) – one of “high”, “low” or “both”. chooses whether the conversion is applied only for the genes with highest expression, lowest epxression, or both

• save (bool) – if True, saves the resulting gene weights

• outpath (str) – if save=True, the .csv file will be saved to this path

Returns

gene_weights – (-1 for low expression, 1 for high expression, 0 for in-between or no information)

Return type

a pandas DataFrame containing qualitative gene weights

dexom_python.gpr_rules.main()

This function is called when you run this script from the commandline. It applies GPR rules to transform gene weights into reaction weights Use –help to see commandline parameters

dexom_python.gpr_rules.replace_MulMax_AddMin(expression)

Function used for parsing gpr expressions

Parameters

expression (symengine expression) – a symengine/sympy expression of a gpr rule

imat_functions module

exception dexom_python.imat_functions.ImatException

Bases: Exception

dexom_python.imat_functions.create_full_variable_single(model, rid, reaction_weights, epsilon, threshold)

dexom_python.imat_functions.create_full_variables(model, reaction_weights, epsilon, threshold)

Creates binary indicator variables in the model for every reaction.

dexom_python.imat_functions.create_new_partial_variable_single(model, rid, epsilon, threshold, pos)

dexom_python.imat_functions.create_new_partial_variables(model, reaction_weights, epsilon, threshold)

Creates binary indicator variables in the model for reactions with nonzero weight.

dexom_python.imat_functions.imat(model, reaction_weights=None, epsilon=0.0003, threshold=0.0002, full=False)

Modified version of the integrative Metabolic Analysis Tool with reaction weights

Parameters

• model (cobra.Model) – a cobrapy model

• reaction_weights (dict) – keys are reaction IDs, values are weights

• epsilon (float) – activation threshold for highly expressed reactions

• threshold (float) – activation threshold for all reactions

• full (bool) – if True, create variables for all reactions. if False, only for reactions with non-zero weights

Returns

solution

Return type

cobra.Solution

dexom_python.imat_functions.main()

This function is called when you run this script from the commandline. It performs the modified iMAT algorithm with reaction weights. Use –help to see commandline parameters

result_functions module

dexom_python.result_functions.compile_solutions(solutions, out_path='compiled_solutions', solution_pattern='*.csv', model=None, threshold=None)

Compiles individual solution files into one binary solution DataFrame

Parameters

• solutions (list or str) – If list, must contain either solution files in .csv format, or Solution objects, or binary solution arrays. - If str, must be a folder in which the solution files math the sollution_pattern parameter

• out_path (str) – path to which the combined solutions will be saved

• solution_pattern (str) – If reading solutions from a folder, this is the pattern which will be used to recognize solution files

• model (cobrapy Model) – required if the solutions parameter is a list of Solution objects

• threshold (float) – required if the solutions parameter is a list of Solution objects

Returns

sol_frame

Return type

pandas DataFrame containg binary solutions

dexom_python.result_functions.main()

This function is called when you run this script from the commandline. It plots a 2-dimensional PCA of enumeration solutions and saves as png Use –help to see commandline parameters

dexom_python.result_functions.plot_pca(solution_path, rxn_enum_solutions=None, save=True, save_name='')

Plots a 2-dimensional PCA of enumeration solutions

Parameters

• solution_path (str) – csv file of enumeration solutions

• rxn_enum_solutions (str) – csv file of enumeration solutions. If specified, will plot these solutions in a different color

• save (bool) – if True, the pca-plot will be saved

• save_name (str) – name of the file to save

Returns

pca

Return type

sklearn.decomposition.PCA

dexom_python.result_functions.read_solution(filename, model=None, solution_index=0)

Reads a solution from a .csv file. If the provided file is a binary solutions file (as output by enumeration methods), the solution number solution_index will be read

Parameters

• filename (str) – name of the file containing the solution

• model (cobra.Model) – optional unless the filename points to a binary solution file without reaction IDs

• solution_index (int) – defines which solution will be read from the binary solution file

Returns

• solution (cobra.Solution)

• sol_bin (numpy.array)

dexom_python.result_functions.write_solution(model, solution, threshold, filename='imat_sol.csv')

Writes an optimize solution as a txt file. The solution is written in a column format :param solution: :type solution: cobra.Solution :param threshold: :type threshold: float :param filename: :type filename: str

toy_models module

dexom_python.toy_models.create_reaction(model, rname, formula, gene_rule=None, fullname=None, lower_bound=0.0, upper_bound=1000.0, subsystem='')

dexom_python.toy_models.dagNet(num_layers, num_metabolites_per_layer, export=False, solver='cplex')

Creates a dagNet model where the metabolites of successive layers are all interconnected

Parameters

• num_layers (int) – number of layers

• num_metabolites_per_layer (int) – number of metabolites per layer

• export (bool) – if True, exports the model as .json and the reaction weights as .csv

• solver (str) – a valid cobrapy solver

Returns

• model (cobra.Model)

• reaction_weights (dict)

dexom_python.toy_models.r13m10(export=False, solver='cplex')

This is the model that is used for the unit tests in the tests/model folder

Parameters

• export (bool) – if True, exports the model as .json, .xml and .mat, and the reaction weights as .csv

• solver (str) – a valid cobrapy solver

Returns

• model (cobra.Model)

• reaction_weights (dict)

dexom_python.toy_models.small4M(export=False, solver='cplex')

creates the small4M model

Parameters

• export (bool) – if True, exports the model as .json and the reaction weights as .csv

• solver (str) – a valid cobrapy solver

Returns

• model (cobra.Model)

• reaction_weights (dict)

dexom_python.toy_models.small4S(export=False, solver='cplex')

creates the small4S model

Parameters

• export (bool) – if True, exports the model as .json and the reaction weights as .csv

• solver (str) – a valid cobrapy solver

Returns

• model (cobra.Model)

• reaction_weights (dict)

pathway_enrichment module

dexom_python.pathway_enrichment.Fisher_groups(model, solpath, outpath='Fisher_groups')

!!! This only works if the pathway name is stored in the model.groups property !!! For models where the pathways are stored in the model.reactions.subsystem property, use the Fischer_subsystems function

Performs pathway over- and underrepresentation analysis

Parameters

• model (cobra.Model) –

• solpath (str) – file containing DEXOM solutions

• outpath (str) – path to which results are saved

Returns

over, under

Return type

pandas.DataFrames (saved as .csv files) containing -log10 BH-adjusted p-values

dexom_python.pathway_enrichment.Fisher_subsystems(solpath, subframe, sublist, outpath='Fisher_subsystems')

!!! This only works if the pathway name is stored in the model.reaction.subsystem property !!! For models where the pathways are stored in the model.groups property, use the Fischer_groups function

Performs pathway over- and underrepresentation analysis

Parameters

• solpath (str) – file containing DEXOM solutions

• subframe (pandas.DataFrame) – dataframe with ‘ID’ column containing reaction IDs and one ‘subsystem’ column containing pathways

• sublist (list) – list of subsystems

• outpath (str) – path to which results are saved

Returns

over, under

Return type

pandas.DataFrames (saved as .csv files) containing -log10 BH-adjusted p-values

dexom_python.pathway_enrichment.main()

This function is called when you run this script from the commandline. It performs pathway enrichment analysis using a hypergeometric test (Fischer exact test) Use –help to see commandline parameters

dexom_python.pathway_enrichment.plot_Fisher_pathways(filename_over, filename_under, sublist, outpath='pathway_enrichment')

Module contents