diff --git a/.gitignore b/.gitignore index 23c482eb..b103f2ee 100644 --- a/.gitignore +++ b/.gitignore @@ -12,4 +12,4 @@ volestipy.egg-info venv lp_solve_5.5/ .devcontainer/ -.github/dependabot.yml \ No newline at end of file +.github/dependabot.yml diff --git a/README.md b/README.md index 9e122771..c0d9113b 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,4 @@ -

+

**dingo** is a Python package that analyzes metabolic networks. It relies on high dimensional sampling with Markov Chain Monte Carlo (MCMC) @@ -290,7 +290,7 @@ plot_histogram( The default number of bins is 60. dingo uses the package `matplotlib` for plotting. -![histogram](./doc/e_coli_aconta.png) +![histogram](https://raw.githubusercontent.com/GeomScale/dingo/refs/heads/develop/doc/e_coli_aconta.png) ### Plot a copula between two fluxes @@ -314,6 +314,6 @@ plot_copula(data_flux1, data_flux2, n=10) The default number of cells is 5x5=25. dingo uses the package `plotly` for plotting. -![histogram](./doc/aconta_ppc_copula.png) +![histogram](https://raw.githubusercontent.com/GeomScale/dingo/refs/heads/develop/doc/aconta_ppc_copula.png) diff --git a/dingo/MetabolicNetwork.py b/dingo/MetabolicNetwork.py index 7be66c0a..ec8df538 100644 --- a/dingo/MetabolicNetwork.py +++ b/dingo/MetabolicNetwork.py @@ -250,18 +250,18 @@ def set_active_bound(reaction: str, reac_index: int, bound: float) -> None: # Turn off reactions not present in media for rxn_id in exchange_rxns - frozen_media_rxns: """ - is_export for us, needs to check on the S - order reactions to their lb and ub + is_export for us, needs to check on the S + order reactions to their lb and ub """ # is_export = rxn.reactants and not rxn.products reac_index = self._reactions.index(rxn_id) - products = np.any(self._S[:,reac_index] > 0) + products = np.any(self._S[:,reac_index] > 0) reactants_exist = np.any(self._S[:,reac_index] < 0) is_export = True if not products and reactants_exist else False set_active_bound( rxn_id, reac_index, min(0.0, -self._lb[reac_index] if is_export else self._ub[reac_index]) ) - + def set_solver(self, solver: str): self._parameters["solver"] = solver diff --git a/dingo/__init__.py b/dingo/__init__.py index 067c8b98..cc5d804e 100644 --- a/dingo/__init__.py +++ b/dingo/__init__.py @@ -75,7 +75,7 @@ def dingo_main(): # Move to the output directory os.chdir(output_path_dir) - + set_default_solver(args.solver) if args.model_name is None: @@ -133,7 +133,7 @@ def dingo_main(): raise Exception("An unknown format file given.") model.set_solver(args.solver) - + result_obj = model.fva() with open("dingo_fva_" + name + ".pckl", "wb") as dingo_fva_file: @@ -147,7 +147,7 @@ def dingo_main(): model = MetabolicNetwork.fom_mat(args.metabolic_network) else: raise Exception("An unknown format file given.") - + model.set_solver(args.solver) result_obj = model.fba() diff --git a/dingo/bindings/bindings.cpp b/dingo/bindings/bindings.cpp index 0e3a62f4..3bbe9d63 100644 --- a/dingo/bindings/bindings.cpp +++ b/dingo/bindings/bindings.cpp @@ -461,7 +461,7 @@ void HPolytopeCPP::apply_rounding(int rounding_method, double* new_A, double* ne // run the rounding method if (rounding_method == 1) { // max ellipsoid - round_res = inscribed_ellipsoid_rounding(P, CheBall.first); + round_res = inscribed_ellipsoid_rounding(P, CheBall.first); } else if (rounding_method == 2) { // isotropization round_res = svd_rounding(P, CheBall, 1, rng); diff --git a/dingo/illustrations.py b/dingo/illustrations.py index fd37b651..fbdbb1f5 100644 --- a/dingo/illustrations.py +++ b/dingo/illustrations.py @@ -32,7 +32,7 @@ def plot_copula(data_flux1, data_flux2, n = 5, width = 900 , height = 600, expor fig = go.Figure( data = [go.Surface(z=copula)], layout = go.Layout( - height = height, + height = height, width = width, ) ) @@ -48,7 +48,7 @@ def plot_copula(data_flux1, data_flux2, n = 5, width = 900 , height = 600, expor margin=dict(r=30, b=30, l=30, t=50)) fig.layout.template = None - + fig.show() fig_name = data_flux1[1] + "_" + data_flux2[1] + "_copula." + export_format @@ -97,7 +97,7 @@ def plot_corr_matrix(corr_matrix, reactions, removed_reactions=[], format="svg") removed_reactions -- A list with the removed reactions in case of a preprocess. If provided removed reactions are not plotted. """ - + sns_colormap = [[0.0, '#3f7f93'], [0.1, '#6397a7'], [0.2, '#88b1bd'], @@ -109,26 +109,26 @@ def plot_corr_matrix(corr_matrix, reactions, removed_reactions=[], format="svg") [0.8, '#e8848b'], [0.9, '#e15e68'], [1.0, '#da3b46']] - + if removed_reactions != 0: for reaction in reactions: index = reactions.index(reaction) if reaction in removed_reactions: reactions[index] = None - - fig = px.imshow(corr_matrix, + + fig = px.imshow(corr_matrix, color_continuous_scale = sns_colormap, x = reactions, y = reactions, origin="upper") - + fig.update_layout( xaxis=dict(tickfont=dict(size=5)), yaxis=dict(tickfont=dict(size=5)), width=900, height=900, plot_bgcolor="rgba(0,0,0,0)") - + fig.update_traces(xgap=1, ygap=1, hoverongaps=False) - + fig.show() - + fig_name = "CorrelationMatrix." + format pio.write_image(fig, fig_name, scale=2) @@ -141,18 +141,18 @@ def plot_dendrogram(dissimilarity_matrix, reactions , plot_labels=False, t=2.0, dissimilarity_matrix -- A matrix produced from the "cluster_corr_reactions" function reactions -- A list with the model's reactions plot_labels -- A boolean variable that if True plots the reactions labels in the dendrogram - t -- A threshold that defines a threshold that cuts the dendrogram + t -- A threshold that defines a threshold that cuts the dendrogram at a specific height and colors the occuring clusters accordingly - linkage -- linkage defines the type of linkage. + linkage -- linkage defines the type of linkage. Available linkage types are: single, average, complete, ward. """ fig = ff.create_dendrogram(dissimilarity_matrix, labels=reactions, - linkagefun=lambda x: hierarchy.linkage(x, linkage), + linkagefun=lambda x: hierarchy.linkage(x, linkage), color_threshold=t) fig.update_layout(width=800, height=800) - + if plot_labels == False: fig.update_layout( xaxis=dict( @@ -166,9 +166,9 @@ def plot_dendrogram(dissimilarity_matrix, reactions , plot_labels=False, t=2.0, yaxis=dict( title_font=dict(size=10), tickfont=dict(size=8) ) ) - + fig.show() - + def plot_graph(G, pos): @@ -184,24 +184,24 @@ def plot_graph(G, pos): for u, v, data in G.edges(data=True): x0, y0 = pos[u] x1, y1 = pos[v] - + edge_color = 'blue' if data['weight'] > 0 else 'red' - - fig.add_trace(go.Scatter(x=[x0, x1], y=[y0, y1], mode='lines', - line=dict(width=abs(data['weight']) * 1, + + fig.add_trace(go.Scatter(x=[x0, x1], y=[y0, y1], mode='lines', + line=dict(width=abs(data['weight']) * 1, color=edge_color), hoverinfo='none', showlegend=False)) for node in G.nodes(): x, y = pos[node] node_name = G.nodes[node].get('name', f'Node {node}') - - fig.add_trace(go.Scatter(x=[x], y=[y], mode='markers', + + fig.add_trace(go.Scatter(x=[x], y=[y], mode='markers', marker=dict(size=10), text=[node_name], textposition='top center', name = node_name, showlegend=False)) - + fig.update_layout(width=800, height=800) - fig.show() \ No newline at end of file + fig.show() diff --git a/dingo/loading_models.py b/dingo/loading_models.py index 84efceb5..6c823723 100644 --- a/dingo/loading_models.py +++ b/dingo/loading_models.py @@ -23,7 +23,7 @@ def read_json_file(input_file): input_file -- a json file that contains the information about a mettabolic network, for example see http://bigg.ucsd.edu/models """ - try: + try: cobra.io.load_matlab_model( input_file ) except: cobra_config = cobra.Configuration() @@ -45,7 +45,7 @@ def read_mat_file(input_file): Keyword arguments: input_file -- a mat file that contains a MATLAB structure with the information about a mettabolic network, for example see http://bigg.ucsd.edu/models """ - try: + try: cobra.io.load_matlab_model( input_file ) except: cobra_config = cobra.Configuration() @@ -56,8 +56,8 @@ def read_mat_file(input_file): return (parse_cobra_model( model )) def read_sbml_file(input_file): - """A Python function, based on the cobra.io.read_sbml_model() function of cabrapy - and the extract_polytope() function of PolyRound + """A Python function, based on the cobra.io.read_sbml_model() function of cabrapy + and the extract_polytope() function of PolyRound (https://gitlab.com/csb.ethz/PolyRound/-/blob/master/PolyRound/static_classes/parse_sbml_stoichiometry.py) to read an SBML file (.xml) and return: (a) lower/upper flux bounds @@ -68,10 +68,10 @@ def read_sbml_file(input_file): (f) the objective function to maximize the biomass pseudoreaction Keyword arguments: - input_file -- a xml file that contains an SBML model with the information about a mettabolic network, for example see: + input_file -- a xml file that contains an SBML model with the information about a mettabolic network, for example see: https://github.com/VirtualMetabolicHuman/AGORA/blob/master/CurrentVersion/AGORA_1_03/AGORA_1_03_sbml/Abiotrophia_defectiva_ATCC_49176.xml """ - try: + try: cobra.io.read_sbml_model( input_file ) except: cobra_config = cobra.Configuration() diff --git a/dingo/preprocess.py b/dingo/preprocess.py index 4f711ff2..4713bc24 100644 --- a/dingo/preprocess.py +++ b/dingo/preprocess.py @@ -8,30 +8,30 @@ class PreProcess: - + def __init__(self, model, tol = 1e-6, open_exchanges = False, verbose = False): """ model -- parameter gets a cobra model as input - - tol -- parameter gets a cutoff value used to classify + + tol -- parameter gets a cutoff value used to classify zero-flux and mle reactions and compare FBA solutions before and after reactions removal - + open_exchanges -- parameter is used in the function that identifies blocked reactions - It controls whether or not to open all exchange reactions + It controls whether or not to open all exchange reactions to very high flux ranges. - - verbose -- A boolean type variable that if True + + verbose -- A boolean type variable that if True additional information for preprocess is printed. """ - + self._model = model self._tol = tol - + self._open_exchanges = open_exchanges self._verbose = verbose - + if self._tol > 1e-6 and verbose == True: print("Tolerance value set to",self._tol,"while default value is 1e-6. A looser check will be performed") @@ -49,10 +49,10 @@ def _objective_function(self): """ A function used to find the objective function of a model """ - + objective = str(self._model.summary()._objective) self._objective = objective.split(" ")[1] - + return self._objective @@ -60,26 +60,26 @@ def _initial(self): """ A function used to find reaction ids of a model """ - + self._initial_reactions = [ reaction.id for reaction in \ self._model.reactions ] - + return self._initial_reactions - - + + def _reaction_bounds_dictionary(self): """ A function used to create a dictionary that maps reactions with their corresponding bounds. It is used to later restore bounds to their wild-type values """ - + self._reaction_bounds_dict = { } - + for reaction_id in self._initial_reactions: bounds = self._model.reactions.get_by_id(reaction_id).bounds self._reaction_bounds_dict[reaction_id] = bounds - + return self._reaction_bounds_dict @@ -87,41 +87,41 @@ def _essentials(self): """ A function used to find all the essential reactions and append them into a list. Essential reactions are - the ones that are required for growth. If removed the + the ones that are required for growth. If removed the objective function gets zeroed. """ - + self._essential_reactions = [ reaction.id for reaction in \ cobra.flux_analysis.find_essential_reactions(self._model) ] - + return self._essential_reactions - + def _zero_flux(self): """ A function used to find zero-flux reactions. - “Zero-flux” reactions cannot carry a flux while maintaining + “Zero-flux” reactions cannot carry a flux while maintaining at least 90% of the maximum growth rate. These reactions have both a min and a max flux equal to 0, when running a FVA analysis with the fraction of optimum set to 90% """ - + tol = self._tol - + fva = cobra.flux_analysis.flux_variability_analysis(self._model, fraction_of_optimum=0.9) zero_flux = fva.loc[ (abs(fva['minimum']) < tol ) & (abs(fva['maximum']) < tol)] self._zero_flux_reactions = zero_flux.index.tolist() - + return self._zero_flux_reactions - - + + def _blocked(self): """ A function used to find blocked reactions. "Blocked" reactions that cannot carry a flux in any condition. These reactions can not have any flux other than 0 """ - + self._blocked_reactions = cobra.flux_analysis.find_blocked_reactions(self._model, open_exchanges=self._open_exchanges) return self._blocked_reactions @@ -130,12 +130,12 @@ def _metabolically_less_efficient(self): """ A function used to find metabolically less efficient reactions. "Metabolically less efficient" require a reduction in growth rate if used - These reactions are found when running an FBA and setting the - optimal growth rate as the lower bound of the objective function (in + These reactions are found when running an FBA and setting the + optimal growth rate as the lower bound of the objective function (in this case biomass production. After running an FVA with the fraction of optimum set to 0.95, the reactions that have no flux are the metabolically less efficient. """ - + tol = self._tol fba_solution = self._model.optimize() @@ -146,124 +146,124 @@ def _metabolically_less_efficient(self): fva = cobra.flux_analysis.flux_variability_analysis(self._model, fraction_of_optimum=0.95) mle = fva.loc[ (abs(fva['minimum']) < tol ) & (abs(fva['maximum']) < tol)] self._mle_reactions = mle.index.tolist() - + self._model.reactions.get_by_id(self._objective).lower_bound = wt_lower_bound - + return self._mle_reactions - - + + def _remove_model_reactions(self): """ A function used to set the lower and upper bounds of certain reactions to 0 (it turns off reactions) """ - + for reaction in self._removed_reactions: self._model.reactions.get_by_id(reaction).lower_bound = 0 self._model.reactions.get_by_id(reaction).upper_bound = 0 - + return self._model - - + + def reduce(self, extend=False): """ A function that calls the "remove_model_reactions" function - and removes blocked, zero-flux and metabolically less efficient + and removes blocked, zero-flux and metabolically less efficient reactions from the model. - - Then it finds the remaining reactions in the model after + + Then it finds the remaining reactions in the model after exclusion of the essential reactions. - + When the "extend" parameter is set to True, the function performes - an additional check to remove further reactions. These reactions - are the ones that if knocked-down, they do not affect the value - of the objective function. Reactions are removed in an ordered way. - The ones with the least overall correlation in a correlation matrix - are removed first. These reactions are removed one by one from the model. - If this removal produces an infeasible solution (or a solution of 0) + an additional check to remove further reactions. These reactions + are the ones that if knocked-down, they do not affect the value + of the objective function. Reactions are removed in an ordered way. + The ones with the least overall correlation in a correlation matrix + are removed first. These reactions are removed one by one from the model. + If this removal produces an infeasible solution (or a solution of 0) to the objective function, these reactions are restored to their initial bounds. - - A dingo-type tuple is then created from the cobra model + + A dingo-type tuple is then created from the cobra model using the "cobra_dingo_tuple" function. - + The outputs are (a) A list of the removed reactions ids (b) A reduced dingo model - """ - + """ + # create a list from the combined blocked, zero-flux, mle reactions blocked_mle_zero = self._blocked_reactions + self._mle_reactions + self._zero_flux_reactions - list_removed_reactions = list(set(blocked_mle_zero)) + list_removed_reactions = list(set(blocked_mle_zero)) self._removed_reactions = list_removed_reactions - + # remove these reactions from the model self._remove_model_reactions() - + remained_reactions = list((Counter(self._initial_reactions)-Counter(self._removed_reactions)).elements()) remained_reactions = list((Counter(remained_reactions)-Counter(self._essential_reactions)).elements()) - + tol = self._tol - + if extend != False and extend != True: raise Exception("Wrong Input to extend parameter") - + elif extend == False: - + if self._verbose == True: print(len(self._removed_reactions), "of the", len(self._initial_reactions), \ - "reactions were removed from the model with extend set to", extend) - + "reactions were removed from the model with extend set to", extend) + # call this functon to convert cobra to dingo model self._dingo_model = MetabolicNetwork.from_cobra_model(self._model) - return self._removed_reactions, self._dingo_model + return self._removed_reactions, self._dingo_model elif extend == True: - + reduced_dingo_model = MetabolicNetwork.from_cobra_model(self._model) - reactions = reduced_dingo_model.reactions + reactions = reduced_dingo_model.reactions sampler = PolytopeSampler(reduced_dingo_model) steady_states = sampler.generate_steady_states() - # calculate correlation matrix with additional filtering from copula indicator + # calculate correlation matrix with additional filtering from copula indicator corr_matrix = correlated_reactions( - steady_states, + steady_states, pearson_cutoff = 0, - indicator_cutoff = 0, + indicator_cutoff = 0, cells = 10, cop_coeff = 0.3, lower_triangle = False) - + # convert pearson values to absolute values abs_array = abs(corr_matrix) - # sum absolute pearson values per row + # sum absolute pearson values per row sum_array = np.sum((abs_array), axis=1) # get indices of ordered sum values order_sum_indices = np.argsort(sum_array) - + fba_solution_before = self._model.optimize().objective_value - + # count additional reactions with a possibility of removal - additional_removed_reactions_count = 0 - + additional_removed_reactions_count = 0 + # find additional reactions with a possibility of removal for index in order_sum_indices: if reactions[index] in remained_reactions: reaction = reactions[index] - + # perform a knock-out and check the output self._model.reactions.get_by_id(reaction).lower_bound = 0 self._model.reactions.get_by_id(reaction).upper_bound = 0 - + try: fba_solution_after = self._model.optimize().objective_value if (abs(fba_solution_after - fba_solution_before) > tol): # restore bounds self._model.reactions.get_by_id(reaction).bounds = self._reaction_bounds_dict[reaction] - + # if system has no solution except: self._model.reactions.get_by_id(reaction).bounds = self._reaction_bounds_dict[reaction] - + finally: if (fba_solution_after != None) and (abs(fba_solution_after - fba_solution_before) < tol): self._removed_reactions.append(reaction) @@ -272,12 +272,12 @@ def reduce(self, extend=False): # restore bounds self._model.reactions.get_by_id(reaction).bounds = self._reaction_bounds_dict[reaction] - - if self._verbose == True: + + if self._verbose == True: print(len(self._removed_reactions), "of the", len(self._initial_reactions), \ "reactions were removed from the model with extend set to", extend) print(additional_removed_reactions_count, "additional reaction(s) removed") - + # call this functon to convert cobra to dingo model self._dingo_model = MetabolicNetwork.from_cobra_model(self._model) - return self._removed_reactions, self._dingo_model + return self._removed_reactions, self._dingo_model diff --git a/dingo/utils.py b/dingo/utils.py index 303840cb..ed560cf2 100644 --- a/dingo/utils.py +++ b/dingo/utils.py @@ -37,14 +37,14 @@ def compute_copula(flux1, flux2, n): rng = range((j*math.floor(N/n)),((j+1)*math.floor(N/n))) grouped_flux1[I1[rng]] = j grouped_flux2[I2[rng]] = j - + for i in range(n): for j in range(n): copula[i,j] = sum((grouped_flux1==i) *( grouped_flux2==j)) - + copula = copula / N return copula - + def apply_scaling(A, b, cs, rs): """A Python function to apply the scaling computed by the function `gmscale` to a convex polytope @@ -207,7 +207,7 @@ def get_matrices_of_full_dim_polytope(A, b, Aeq, beq): -def correlated_reactions(steady_states, reactions=[], pearson_cutoff = 0.90, indicator_cutoff = 10, +def correlated_reactions(steady_states, reactions=[], pearson_cutoff = 0.90, indicator_cutoff = 10, cells = 10, cop_coeff = 0.3, lower_triangle = True, verbose = False): """A Python function to calculate the pearson correlation matrix of a model and filter values based on the copula's indicator @@ -222,36 +222,36 @@ def correlated_reactions(steady_states, reactions=[], pearson_cutoff = 0.90, ind lower_triangle -- A boolean variable that if True plots only the lower triangular matrix verbose -- A boolean variable that if True additional information is printed as an output. """ - + if cop_coeff > 0.4 or cop_coeff < 0.2: raise Exception("Input value to cop_coeff parameter must be between 0.2 and 0.4") - + # calculate coefficients to access red and blue copula mass cop_coeff_1 = cop_coeff cop_coeff_2 = 1 - cop_coeff cop_coeff_3 = 1 + cop_coeff - + # compute correlation matrix corr_matrix = np.corrcoef(steady_states, rowvar=True) - + # replace not assigned values with 0 corr_matrix[np.isnan(corr_matrix)] = 0 # create a copy of correlation matrix to replace/filter values - filtered_corr_matrix = corr_matrix.copy() - + filtered_corr_matrix = corr_matrix.copy() + # find indices of correlation matrix where correlation does not occur no_corr_indices = np.argwhere((filtered_corr_matrix < pearson_cutoff) & (filtered_corr_matrix > -pearson_cutoff)) - + # replace values from the correlation matrix that do not overcome # the pearson cutoff with 0 - for i in range(0, no_corr_indices.shape[0]): + for i in range(0, no_corr_indices.shape[0]): index1 = no_corr_indices[i][0] index2 = no_corr_indices[i][1] - - filtered_corr_matrix[index1, index2] = 0 - # if user does not provide an indicator cutoff then do not proceed + filtered_corr_matrix[index1, index2] = 0 + + # if user does not provide an indicator cutoff then do not proceed # with the filtering of the correlation matrix if indicator_cutoff == 0: if lower_triangle == True: @@ -262,42 +262,42 @@ def correlated_reactions(steady_states, reactions=[], pearson_cutoff = 0.90, ind np.fill_diagonal(filtered_corr_matrix, 1) return filtered_corr_matrix else: - # a dictionary that will store for each filtered reaction combination, - # the pearson correlation value, the copula's indicator value + # a dictionary that will store for each filtered reaction combination, + # the pearson correlation value, the copula's indicator value # and the correlation classification indicator_dict = {} - + # keep only the lower triangle corr_matrix = np.tril(corr_matrix) # replace diagonal values with 0 np.fill_diagonal(corr_matrix, 0) - + # find indices of correlation matrix where correlation occurs corr_indices = np.argwhere((corr_matrix > pearson_cutoff) | (corr_matrix < -pearson_cutoff)) - + # compute copula for each set of correlated reactions for i in range(0, corr_indices.shape[0]): - + index1 = corr_indices[i][0] index2 = corr_indices[i][1] - + reaction1 = reactions[index1] reaction2 = reactions[index2] - + flux1 = steady_states[index1] flux2 = steady_states[index2] - + copula = compute_copula(flux1, flux2, cells) rows, cols = copula.shape - + red_mass = 0 blue_mass = 0 indicator = 0 - + for row in range(rows): for col in range(cols): # values in the diagonal - if ((row-col >= -cop_coeff_1*rows) & (row-col <= cop_coeff_1*rows)): + if ((row-col >= -cop_coeff_1*rows) & (row-col <= cop_coeff_1*rows)): # values near the top left and bottom right corner if ((row+col < cop_coeff_2*rows) | (row+col > cop_coeff_3*rows)): red_mass = red_mass + copula[row][col] @@ -307,19 +307,19 @@ def correlated_reactions(steady_states, reactions=[], pearson_cutoff = 0.90, ind blue_mass = blue_mass + copula[row][col] indicator = (red_mass+1e-9) / (blue_mass+1e-9) - + # classify specific pair of reactions as positive or negative - # correlated based on indicator cutoff + # correlated based on indicator cutoff if indicator > indicator_cutoff: pearson = filtered_corr_matrix[index1, index2] indicator_dict[reaction1 + "~" + reaction2] = {'pearson': pearson, - 'indicator': indicator, + 'indicator': indicator, 'classification': "positive"} elif indicator < 1/indicator_cutoff: pearson = filtered_corr_matrix[index1, index2] indicator_dict[reaction1 + "~" + reaction2] = {'pearson': pearson, - 'indicator': indicator, + 'indicator': indicator, 'classification': "negative"} # if they do not overcome the cutoff replace their corresponding @@ -329,12 +329,12 @@ def correlated_reactions(steady_states, reactions=[], pearson_cutoff = 0.90, ind filtered_corr_matrix[index2, index1] = 0 pearson = filtered_corr_matrix[index1, index2] indicator_dict[reaction1 + "~" + reaction2] = {'pearson': pearson, - 'indicator': indicator, + 'indicator': indicator, 'classification': "no correlation"} - + if verbose == True: print("Completed process of",i+1,"from",corr_indices.shape[0],"copulas") - + if lower_triangle == True: filtered_corr_matrix[np.triu_indices(filtered_corr_matrix.shape[0], 1)] = np.nan np.fill_diagonal(filtered_corr_matrix, 1) @@ -346,21 +346,21 @@ def correlated_reactions(steady_states, reactions=[], pearson_cutoff = 0.90, ind -def cluster_corr_reactions(correlation_matrix, reactions, linkage="ward", +def cluster_corr_reactions(correlation_matrix, reactions, linkage="ward", t = 4.0, correction=True): """A Python function for hierarchical clustering of the correlation matrix Keyword arguments: correlation_matrix -- A numpy 2D array of a correlation matrix reactions -- A list with the model's reactions - linkage -- linkage defines the type of linkage. + linkage -- linkage defines the type of linkage. Available linkage types are: single, average, complete, ward. - t -- A threshold that defines a threshold that cuts the dendrogram + t -- A threshold that defines a threshold that cuts the dendrogram at a specific height and produces clusters correction -- A boolean variable that if True converts the values of the the correlation matrix to absolute values. """ - + # function to return a nested list with grouped reactions based on clustering def clusters_list(reactions, labels): clusters = [] @@ -372,21 +372,21 @@ def clusters_list(reactions, labels): cluster.append(reactions[where]) clusters.append(cluster) return clusters - - if correction == True: + + if correction == True: dissimilarity_matrix = 1 - abs(correlation_matrix) else: dissimilarity_matrix = 1 - correlation_matrix - + Z = hierarchy.linkage(dissimilarity_matrix, linkage) labels = hierarchy.fcluster(Z, t, criterion='distance') - + clusters = clusters_list(reactions, labels) return dissimilarity_matrix, labels, clusters - - -def graph_corr_matrix(correlation_matrix, reactions, correction=True, + + +def graph_corr_matrix(correlation_matrix, reactions, correction=True, clusters=[], subgraph_nodes = 5): """A Python function that creates the main graph and its subgraphs from a correlation matrix. @@ -397,27 +397,27 @@ def graph_corr_matrix(correlation_matrix, reactions, correction=True, correction -- A boolean variable that if True converts the values of the the correlation matrix to absolute values. clusters -- A nested list with clustered reactions created from the "" function. - subgraph_nodes -- A variable that specifies a cutoff for a graph's nodes. + subgraph_nodes -- A variable that specifies a cutoff for a graph's nodes. It filters subgraphs with low number of nodes.. """ - + graph_matrix = correlation_matrix.copy() np.fill_diagonal(graph_matrix, 0) - + if correction == True: graph_matrix = abs(graph_matrix) - + G = nx.from_numpy_array(graph_matrix) G = nx.relabel_nodes(G, lambda x: reactions[x]) - + pos = nx.spring_layout(G) unconnected_nodes = list(nx.isolates(G)) G.remove_nodes_from(unconnected_nodes) G_nodes = G.nodes() - + graph_list = [] layout_list = [] - + graph_list.append(G) layout_list.append(pos) @@ -425,14 +425,14 @@ def graph_corr_matrix(correlation_matrix, reactions, correction=True, H_nodes_list = [] for i in range(len(subgraphs)): - if len(subgraphs[i].nodes()) > subgraph_nodes and len(subgraphs[i].nodes()) != len(G_nodes): + if len(subgraphs[i].nodes()) > subgraph_nodes and len(subgraphs[i].nodes()) != len(G_nodes): H = G.subgraph(subgraphs[i].nodes()) for cluster in clusters: if H.has_node(cluster[0]) and H.nodes() not in H_nodes_list: H_nodes_list.append(H.nodes()) - + pos = nx.spring_layout(H) graph_list.append(H) layout_list.append(pos) - - return graph_list, layout_list \ No newline at end of file + + return graph_list, layout_list diff --git a/poetry.lock b/poetry.lock index 635b3761..551f0ecb 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,15 +1,30 @@ # This file is automatically @generated by Poetry and should not be changed by hand. +[[package]] +name = "annotated-types" +version = "0.7.0" +description = "Reusable constraint types to use with typing.Annotated" +category = "main" +optional = false +python-versions = ">=3.8" +files = [ + {file = "annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53"}, + {file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"}, +] + +[package.dependencies] +typing-extensions = {version = ">=4.0.0", markers = "python_version < \"3.9\""} + [[package]] name = "anyio" -version = "4.4.0" +version = "4.5.2" description = "High level 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= "^3.8" -content-hash = "1f3ee3d9ab943dfbe26cc9e3a421396ea1022552386d419e53c55ac614c5f1a5" +content-hash = "aaa0ec3d161f419d69052e658c1b27b3719772bdc6fd0f4c8e4fd5148775da37" diff --git a/pyproject.toml b/pyproject.toml index d0cdf087..21478c36 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,27 +1,29 @@ [tool.poetry] -name = "dingo" -version = "0.1.0" -description = "A python library for metabolic networks sampling and analysis. dingo is part of GeomScale project" -authors = ["Apostolos Chalkis "] +name = "dingo-walk" +version = "0.1.6" +description = "High dimensional polytope sampling in Python with a set of tools for metabolic network sampling and analysis." +authors = ["Apostolos Chalkis ", "Vissarion Fisikopoulos ", "Elias Tsigaridas ", "Haris Zafeiropoulos "] build = 'build.py' +packages = [{ include = "dingo" }] +readme = "README.md" + [tool.poetry.dependencies] python = "^3.8" -sparseqr = {git = "https://github.com/yig/PySPQR.git"} +sparseqr = "^1.2.1" simplejson = "^3.17.2" Cython = "^0.29.22" -numpy = "^1.20.1" scipy = "^1.6.1" argparse = "^1.4.0" matplotlib = "^3.4.1" -cobra = "^0.26.0" plotly = "^5.11.0" kaleido = "0.2.1" pyoptinterface = {version = "^0.2.7", extras = ["highs"]} networkx = "3.1" +cobra = "^0.29.1" [tool.poetry.dev-dependencies] [build-system] -requires = ["poetry-core>=1.0.0", "cython", "numpy==1.20.1"] +requires = ["poetry-core>=1.0.0", "cython", "numpy==1.23.2"] build-backend = "poetry.core.masonry.api" diff --git a/setup.py b/setup.py index 77ec7ec7..9dd89890 100755 --- a/setup.py +++ b/setup.py @@ -15,15 +15,20 @@ import numpy import os +# get ready to read the contents of your README file +from pathlib import Path +this_directory = Path(__file__).parent + # information about the dingo library -version = "0.1.0" +version = "0.1.5" license = ("LGPL3",) packages = ["dingo"] description = "A python library for metabolic networks sampling and analysis" -author = "Apostolos Chalkis" -author_email = "tolis.chal@gmail.com" +author = "Apostolos Chalkis, Vissarion Fisikopoulos, Elias Tsigaridas, Haris Zafeiropoulos" +author_email = "fisikop@gmail.com" name = "dingo" - +long_description = (this_directory / "README.md").read_text() +long_description_content_type='text/markdown' source_directory_list = ["dingo", join("dingo", "bindings")] diff --git a/tests/correlation.py b/tests/correlation.py index fdb21b75..0dbe84a5 100644 --- a/tests/correlation.py +++ b/tests/correlation.py @@ -7,13 +7,13 @@ class TestCorrelation(unittest.TestCase): def test_correlation(self): - + dingo_model = MetabolicNetwork.from_json('ext_data/e_coli_core.json') reactions = dingo_model.reactions sampler = PolytopeSampler(dingo_model) steady_states = sampler.generate_steady_states() - + # calculate correlation matrix with filtering from copula indicator corr_matrix, indicator_dict = correlated_reactions(steady_states, reactions = reactions, @@ -27,16 +27,16 @@ def test_correlation(self): # rows and columns must be equal to model reactions self.assertTrue(corr_matrix.shape[0] == len(reactions)) self.assertTrue(corr_matrix.shape[1] == len(reactions)) - - + + dingo_model = MetabolicNetwork.from_json('ext_data/e_coli_core.json') reactions = dingo_model.reactions sampler = PolytopeSampler(dingo_model) steady_states = sampler.generate_steady_states() - + # calculate correlation matrix without filtering from copula indicator - corr_matrix = correlated_reactions(steady_states, + corr_matrix = correlated_reactions(steady_states, indicator_cutoff = 0, pearson_cutoff = 0, lower_triangle = True, @@ -47,7 +47,7 @@ def test_correlation(self): # rows and columns must be equal to model reactions self.assertTrue(corr_matrix.shape[0] == len(reactions)) self.assertTrue(corr_matrix.shape[1] == len(reactions)) - + if __name__ == "__main__": unittest.main() diff --git a/tests/full_dimensional.py b/tests/full_dimensional.py index 4af2f12a..c1e49d18 100644 --- a/tests/full_dimensional.py +++ b/tests/full_dimensional.py @@ -24,7 +24,7 @@ def test_get_full_dim_json(self): self.assertEqual(sampler.A.shape[0], 175) self.assertEqual(sampler.A.shape[1], 24) - + sampler = self.get_polytope_from_model_with_redundancy_removal(model) self.assertEqual(sampler.A.shape[0], 26) @@ -52,7 +52,7 @@ def test_get_full_dim_mat(self): self.assertEqual(sampler.A.shape[0], 175) self.assertEqual(sampler.A.shape[1], 24) - + sampler = self.get_polytope_from_model_with_redundancy_removal(model) self.assertEqual(sampler.A.shape[0], 26) @@ -66,7 +66,7 @@ def get_polytope_from_model_without_redundancy_removal (met_model): sampler.get_polytope() return sampler - + @staticmethod def get_polytope_from_model_with_redundancy_removal (met_model): diff --git a/tests/max_ball.py b/tests/max_ball.py index 8189f664..ca7f0f45 100644 --- a/tests/max_ball.py +++ b/tests/max_ball.py @@ -18,7 +18,7 @@ class TestMaxBall(unittest.TestCase): - + def test_simple(self): m = 2 n = 5 diff --git a/tests/preprocess.py b/tests/preprocess.py index 64edce9c..01268e0b 100644 --- a/tests/preprocess.py +++ b/tests/preprocess.py @@ -11,59 +11,58 @@ def test_preprocess(self): # load cobra model cobra_model = load_json_model("ext_data/e_coli_core.json") - + # convert cobra to dingo model initial_dingo_model = MetabolicNetwork.from_cobra_model(cobra_model) - + # perform an FBA to find the initial FBA solution initial_fba_solution = initial_dingo_model.fba()[1] - # call the reduce function from the PreProcess class - # with extend=False to remove reactions from the model - obj = PreProcess(cobra_model, tol=1e-5, open_exchanges=False, verbose=False) - removed_reactions, final_dingo_model = obj.reduce(extend=False) - - # calculate the count of removed reactions with extend set to False + # call the reduce function from the PreProcess class + # with extend=False to remove reactions from the model + obj = PreProcess(cobra_model, tol=1e-5, open_exchanges=False, verbose=False) + removed_reactions, final_dingo_model = obj.reduce(extend=False) + + # calculate the count of removed reactions with extend set to False removed_reactions_count = len(removed_reactions) self.assertTrue( 46 - removed_reactions_count == 0 ) - # calculate the count of reactions with bounds equal to 0 + # calculate the count of reactions with bounds equal to 0 # with extend set to False from the dingo model dingo_removed_reactions = np.sum((final_dingo_model.lb == 0) & (final_dingo_model.ub == 0)) self.assertTrue( 46 - dingo_removed_reactions == 0 ) - + # perform an FBA to check the solution after reactions removal final_fba_solution = final_dingo_model.fba()[1] - self.assertTrue(abs(final_fba_solution - initial_fba_solution) < 1e-03) + self.assertTrue(abs(final_fba_solution - initial_fba_solution) < 1e-03) # load models in cobra and dingo format again to restore bounds cobra_model = load_json_model("ext_data/e_coli_core.json") - + # convert cobra to dingo model initial_dingo_model = MetabolicNetwork.from_cobra_model(cobra_model) - - # call the reduce function from the PreProcess class - # with extend=True to remove additional reactions from the model - obj = PreProcess(cobra_model, tol=1e-6, open_exchanges=False, verbose=False) - removed_reactions, final_dingo_model = obj.reduce(extend=True) - - # calculate the count of removed reactions with extend set to True + + # call the reduce function from the PreProcess class + # with extend=True to remove additional reactions from the model + obj = PreProcess(cobra_model, tol=1e-6, open_exchanges=False, verbose=False) + removed_reactions, final_dingo_model = obj.reduce(extend=True) + + # calculate the count of removed reactions with extend set to True removed_reactions_count = len(removed_reactions) self.assertTrue( 46 - removed_reactions_count <= 0 ) - # calculate the count of reactions with bounds equal to 0 + # calculate the count of reactions with bounds equal to 0 # with extend set to True from the dingo model dingo_removed_reactions = np.sum((final_dingo_model.lb == 0) & (final_dingo_model.ub == 0)) self.assertTrue( 46 - dingo_removed_reactions <= 0 ) - + # perform an FBA to check the result after reactions removal final_fba_solution = final_dingo_model.fba()[1] - self.assertTrue(abs(final_fba_solution - initial_fba_solution) < 1e-03) + self.assertTrue(abs(final_fba_solution - initial_fba_solution) < 1e-03) if __name__ == "__main__": unittest.main() - \ No newline at end of file diff --git a/tests/rounding.py b/tests/rounding.py index e3d97c62..e4f356b2 100644 --- a/tests/rounding.py +++ b/tests/rounding.py @@ -68,4 +68,4 @@ def test_rounding_john_position(self): if len(sys.argv) > 1: set_default_solver(sys.argv[1]) sys.argv.pop(1) - unittest.main() \ No newline at end of file + unittest.main() diff --git a/tests/sampling.py b/tests/sampling.py index a0827d68..76e7a9df 100644 --- a/tests/sampling.py +++ b/tests/sampling.py @@ -35,7 +35,7 @@ def test_sample_mat(self): model = MetabolicNetwork.from_mat(input_file_mat) sampler = PolytopeSampler(model) - steady_states = sampler.generate_steady_states(ess = 20000, psrf = True) + steady_states = sampler.generate_steady_states(ess = 20000, psrf = True) self.assertTrue( steady_states.shape[0] == 95 ) self.assertTrue( abs( steady_states[12].mean() - 2.504 ) < 1e-02 ) diff --git a/tests/scaling.py b/tests/scaling.py index 525a9498..835490f0 100644 --- a/tests/scaling.py +++ b/tests/scaling.py @@ -17,7 +17,7 @@ class TestScaling(unittest.TestCase): - + def test_scale_json(self): input_file_json = os.getcwd() + "/ext_data/e_coli_core.json" diff --git a/tutorials/CONTRIBUTING.md b/tutorials/CONTRIBUTING.md index d28a7946..bd0b62d1 100644 --- a/tutorials/CONTRIBUTING.md +++ b/tutorials/CONTRIBUTING.md @@ -2,9 +2,9 @@ :+1::tada: First off, thanks for taking the time to contribute! :tada::+1: -The following is a set of guidelines for contributing to dingo, -which are hosted in the [GeomScale Organization](https://github.com/GeomScale) on GitHub. -These are mostly guidelines, not rules. +The following is a set of guidelines for contributing to dingo, +which are hosted in the [GeomScale Organization](https://github.com/GeomScale) on GitHub. +These are mostly guidelines, not rules. Use your best judgment, and feel free to propose changes to this document in a pull request. ## Table of Contents @@ -22,13 +22,13 @@ Use your best judgment, and feel free to propose changes to this document in a p + [Push](#push) * [Pull request (the joy of sharing)](#pull-request-the-joy-of-sharing) * [Review (ok this is not an exam)](#review-ok-this-is-not-an-exam) - + ## Prerequisites (how to start) * git (see [Getting Started with Git](https://help.github.com/en/github/using-git/getting-started-with-git-and-github)) * a compiler to run tests - gcc, clang, etc. * configured GitHub account - + Other helpful links: * http://git-scm.com/documentation @@ -37,7 +37,7 @@ Other helpful links: ## Testing the development branch of dingo (get the tools ready) -Clone the repository, +Clone the repository, git clone git@github.com:geomscale/dingo.git dingo cd dingo @@ -45,9 +45,9 @@ Clone the repository, the last command should tell you that you are in `develop` branch. -Now you need to get the `volesti` sumbodule that `dingo` makes use of. +Now you need to get the `volesti` sumbodule that `dingo` makes use of. -To do so, you need to run +To do so, you need to run git submodule update --init @@ -59,7 +59,7 @@ Now get the `boost` library: And now you are ready to compile `dingo` - python setup.py install --user + python setup.py install --user Once the last command is completed, you may check if everythings is fine by running some `dingo` tests @@ -72,8 +72,8 @@ If everything is ok, you will see something like this: ## Fork `dingo` repository (this is your repo now!) -You can't work directly in the original `dingo` repository, therefore you should create your fork of this library. -This way you can modify the code and when the job is done send a pull request to merge your changes with the original +You can't work directly in the original `dingo` repository, therefore you should create your fork of this library. +This way you can modify the code and when the job is done send a pull request to merge your changes with the original repository. ![fork](https://raw.githubusercontent.com/hariszaf/dingo/dingo_tutorial/tutorials/figs/fork.png) @@ -101,7 +101,7 @@ clone your repository and checkout develop branch git branch -vv git pull -In this case `hariszaf` was the user's name who had forked `dingo`. Make sure you replace this with your own. +In this case `hariszaf` was the user's name who had forked `dingo`. Make sure you replace this with your own. To see the so far commits, simply run: @@ -114,17 +114,17 @@ For now you should see exactly the same commits as in `dingo` repository. ### GitFlow workflow -`dingo` is using the [GitFlow](http://nvie.com/posts/a-successful-git-branching-model/) workflow. -It's because it is very well suited to collaboration and scaling the development team. +`dingo` is using the [GitFlow](http://nvie.com/posts/a-successful-git-branching-model/) workflow. +It's because it is very well suited to collaboration and scaling the development team. Each repository using this model should contain two main branches: * master - release-ready version of the library * develop - development version of the library - -and could contain various supporting branches for new features and hotfixes. -As a contributor you'll most likely be adding new features or fixing bugs in the development version of the library. -This means that for each contribution you should create a new branch originating from the develop branch, +and could contain various supporting branches for new features and hotfixes. + +As a contributor you'll most likely be adding new features or fixing bugs in the development version of the library. +This means that for each contribution you should create a new branch originating from the develop branch, modify it and send a pull request in order to merge it, again with the develop branch. ### Create new branch for your work @@ -133,18 +133,18 @@ Make sure you're in develop branch running git branch -vv -you should see something like this: +you should see something like this: * develop a76b4be [origin/develop] Update issue templates -Now you should pick **a name for your new branch that doesn't already exist**. +Now you should pick **a name for your new branch that doesn't already exist**. The following returns a list of all the existing remote branches git branch -a Alternatively, you can check them on `GitHub`. -Assume you want to add some new functionality. +Assume you want to add some new functionality. Then you have to create a new branch e.g. `feature/my_cool_new_feature` Create new local branch @@ -156,7 +156,7 @@ push it to your fork git push -u my_fork feature/my_cool_new_feature -Note that the `-u` switch also sets up the tracking of the remote branch. +Note that the `-u` switch also sets up the tracking of the remote branch. Your new branch now is created! ### Verify your new branch (optional) @@ -178,16 +178,16 @@ on your fork repository! ![branch_on_github](https://raw.githubusercontent.com/hariszaf/dingo/dingo_tutorial/tutorials/figs/branches_github.png) -Notice, we are **not** on the `dingo` repository under the `GeomScale` organization, but on the user's personal account. +Notice, we are **not** on the `dingo` repository under the `GeomScale` organization, but on the user's personal account. ## Modify the branch (implement, implement, implement) -Before contributiong to a library by adding a new feature, or a bugfix, or improving documentation, +Before contributiong to a library by adding a new feature, or a bugfix, or improving documentation, it is always wise to interact with the community of developers, for example by opening an issue. ### Tests -Tests are placed in the `test` directory and use the [doctest](https://github.com/onqtam/doctest) library. +Tests are placed in the `test` directory and use the [doctest](https://github.com/onqtam/doctest) library. It is recommended to add new test whenever you contribute a new functionality/feature. Also if your contribution is a bugfix then consider adding this case to the test-suite. @@ -216,16 +216,16 @@ and click the "Create pull request" button. ## Review (ok this is not an exam) -After creating a pull request your code will be reviewed. You can propose one or more reviewers +After creating a pull request your code will be reviewed. You can propose one or more reviewers by clicking on the "Reviewers" button ![reviewer](https://user-images.githubusercontent.com/3660366/72349476-44ecc600-36e5-11ea-81cd-d0938d923529.png) -If there are no objections your changes will be merged. +If there are no objections your changes will be merged. Otherwise you'll see some comments under the pull request and/or under specific lines of your code. -Then you have to make the required changes, commit them and push to your branch. -Those changes will automatically be a part of the same pull request. This procedure will be repeated until the code +Then you have to make the required changes, commit them and push to your branch. +Those changes will automatically be a part of the same pull request. This procedure will be repeated until the code is ready for merging. -If you're curious how it looks like you may see one of the open or closed -[pull requests](https://github.com/GeomScale/dingo/pulls). \ No newline at end of file +If you're curious how it looks like you may see one of the open or closed +[pull requests](https://github.com/GeomScale/dingo/pulls). diff --git a/tutorials/README.md b/tutorials/README.md index bf282714..ac8516f9 100644 --- a/tutorials/README.md +++ b/tutorials/README.md @@ -1,4 +1,4 @@ -# `dingo` tutorials +# `dingo` tutorials -In this directory, you will find material about how to use `dingo` but also on how you could contribute to this open source project. +In this directory, you will find material about how to use `dingo` but also on how you could contribute to this open source project.