dexom_python.enum_functions enumeration modules

Submodules

enumeration module

class dexom_python.enum_functions.enumeration.EnumSolution(solutions, binary, objective_value)

Bases: object

class for solutions of enumeration methods

Parameters:

• solutions (list) – A list of pandas dataframes containing flux values with reaction ids as index

• binary (list) – A list containing binary arrays of reaction activity (0 for inactive, 1 for active)

• objective_value (float) – objective value returned by the solver at the end of the optimization

dexom_python.enum_functions.enumeration.analyze_div_enum_results(result_path, solution_path, out_path)

This function calculates the average pairwise hamming distance and average next neighbour distance for each iteration - it’s very slow

Parameters:

• result_path (csv results file from diversity-enum) –

• solution_path (csv solution file from diversity-enum) –

• out_path (path for saving) –

dexom_python.enum_functions.enumeration.check_reaction_weights(rw)

This function raises a TypeError if the reaction-weight object is not a dictionary or a ValueError if the dictionary contains no nonzero values.

Parameters:

rw (dict) –

dexom_python.enum_functions.enumeration.combine_binary_solutions_and_fluxes(sol_path, solution_pattern='*solutions*.csv', out_path='')

Combines several binary solution files into one

Parameters:

• sol_path (str) – folder in which binary solutions are saved

• solution_pattern (str) – pattern which is used to find binary solution files

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

Returns:

uniquesol

Return type:

pandas.DataFrame

dexom_python.enum_functions.enumeration.create_enum_variables(model, reaction_weights, eps=0.01, thr=0.001, full=False)

dexom_python.enum_functions.enumeration.get_recent_solution_and_iteration(dirpath, startsol_num=1, solution_pattern='*solution*.csv')

This functions fetches a solution from a given directory. The solutions are ordered by creation time, and one solution is picked using an exponential distribution (meaning that the most recent solution has the highest probability of being chosen)

Parameters:

• dirpath (str) – a directory containing imat solutions

• startsol_num (int) – the number of starting solutions present in the directory

• solution_pattern (str) – pattern which is used to find the solution files

Returns:

• solution (a Solution object)

• iteration (int) – calculates the current iteration, based on how many solutions are already present in the folder

dexom_python.enum_functions.enumeration.read_prev_sol(prev_sol_arg, model, rw, eps=0.01, thr=0.001, a=0.95, startsol=1, pattern='*solution_*.csv', allowerrors=False)

rxn_enum_functions module

class dexom_python.enum_functions.rxn_enum_functions.RxnEnumSolution(all_solutions, unique_solutions, all_binary, unique_binary, all_reactions=None, unique_reactions=None, objective_value=-1.0)

Bases: object

dexom_python.enum_functions.rxn_enum_functions.rxn_enum(model, reaction_weights, prev_sol=None, rxn_list=None, eps=0.01, thr=0.001, obj_tol=0.001, out_path='enum_rxn', save=False)

Reaction enumeration method

Parameters:

• model (cobrapy Model) –

• reaction_weights (dict) – keys = reactions and values = weights

• prev_sol (imat Solution object) – an imat solution used as a starting point

• rxn_list (list) – a list of reactions on which reaction-enumeration will be performed. By default, all reactions are used

• eps (float) – activation threshold in imat

• thr (float) – detection threshold of activated reactions

• obj_tol (float) – variance allowed in the objective_values of the solutions

• out_path (str) – path to which the solutions are saved if save==True

• save (bool) – if True, every individual solution is saved in the iMAT solution format

Returns:

solution

Return type:

RxnEnumSolution object

icut_functions module

dexom_python.enum_functions.icut_functions.create_icut_constraint(model, reaction_weights, epsilon, threshold, prev_sol, name, full=False)

Creates an icut constraint on the previously found solution. This solution is excluded from the solution space.

dexom_python.enum_functions.icut_functions.icut(model, reaction_weights, prev_sol=None, eps=0.01, thr=0.001, obj_tol=0.001, maxiter=10, full=False)

integer-cut method

Parameters:

• model (cobrapy Model) –

• reaction_weights (dict) – keys = reactions and values = weights

• prev_sol (imat Solution object) – an imat solution used as a starting point

• eps (float) – activation threshold in imat

• thr (float) – detection threshold of activated reactions

• obj_tol (float) – variance allowed in the objective_values of the solutions

• maxiter (foat) – maximum number of solutions to check for

• full (bool) – if True, carries out integer-cut on all reactions; if False, only on reactions with non-zero weights

Returns:

solution – In the case of integer-cut, all_solutions and unique_solutions are identical

Return type:

EnumSolution object

maxdist_functions module

dexom_python.enum_functions.maxdist_functions.create_maxdist_objective(model, reaction_weights, prev_sol, prev_sol_bin, only_ones=False, full=False)

Create the new objective for the maxdist algorithm. This objective is the minimization of similarity between the binary solution vectors If only_ones is set to False, the similarity will only be calculated with overlapping ones

dexom_python.enum_functions.maxdist_functions.maxdist(model, reaction_weights, prev_sol=None, eps=0.01, thr=0.001, obj_tol=0.001, maxiter=10, icut=True, full=False, only_ones=False)

Parameters:

• model (cobrapy Model) –

• reaction_weights (dict) – keys = reactions and values = weights

• prev_sol (imat Solution object) – an imat solution used as a starting point

• eps (float) – activation threshold in imat

• thr (float) – detection threshold of activated reactions

• obj_tol (float) – variance allowed in the objective_values of the solutions

• maxiter (foat) – maximum number of solutions to check for

• icut (bool) – if True, icut constraints are applied

• full (bool) – if True, carries out integer-cut on all reactions; if False, only on reactions with non-zero weights

• only_ones (bool) – if True, only the ones in the binary solution are used for distance calculation (as in dexom matlab)

Returns:

solution

Return type:

EnumSolution object

diversity_enum_functions module

dexom_python.enum_functions.diversity_enum_functions.diversity_enum(model, reaction_weights, prev_sol=None, eps=0.01, thr=0.001, obj_tol=0.001, maxiter=10, dist_anneal=0.95, out_path='enum_dexom', icut=True, full=False, save=False)

diversity-based enumeration

Parameters:

• model (cobrapy Model) –

• reaction_weights (dict) – keys = reactions and values = weights

• prev_sol (Solution instance) – a previous imat solution

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

• thr (float) – detection threshold of activated reactions

• obj_tol (float) – variance allowed in the objective_values of the solutions

• maxiter (foat) – maximum number of solutions to search for

• dist_anneal (float) – parameter which influences the probability of selecting reactions

• out_path (str) – path to which the solutions are saved if save==True

• icut (bool) – if True, icut constraints are applied

• full (bool) – if True, the full-DEXOM implementation is used

• save (bool) – if True, every individual solution is saved in the iMAT solution format

Returns:

• solution (an EnumSolution object)

• stats (a pandas.DataFrame containing the number of selected reactions and runtime of each iteration)

permutation_functions module

dexom_python.enum_functions.permutation_functions.permute_genelabels(model, allgenes, geneindex=None, nperms=10, error_tol=10)

This function performs a permutation test in which the labels of gene expression values are randomly shuffled, and then an iMAT solution is computed with the new geneweights.

Parameters:

• model (cobra.Model) – a cobrapy model

• allgenes (pandas.Series) – index = gene IDs and values = expression values

• geneindex (pandas.Series or None) – index = model gene IDs and values = allgenes gene IDs

• nperms (int) – number of permutations to perform

• error_tol (int) – maximum number of consecutive failed iterations before interrupting the program

Returns:

• perm_solutions (list of imat solutions)

• perm_binary (pandas.Dataframe of binary solutions)

• perm_recs (pandas.Dataframe of reaction-weights)

• perm_genes (pandas.Dataframe of permuted gene expression values)

riptide_functions module

dexom_python.enum_functions.riptide_functions.imat_riptide_like(model, reaction_weights=None, objective_direction='max')

Modified version of the integrative Metabolic Analysis Tool with reaction weights. This function is currently still in testing phase.

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

Module contents