Calculate Scores

calculate_scores()

A high-level function that simulates all of the scores for a range of models and environments, and returns a DataFrame and list of metabolites for concise interpretation:

CommScores.calculate_scores(pairs, models_media=None, environments=None, annotated_genomes=True, lazy_load=False,
                            kbase_obj=None, cip_score=True, costless=True, skip_bad_media=False, anme_comm=False,
                            print_progress=False)

• pairs list|dict: A provided list denotes all of the argument inputs from parallelization – pairs, models_media, environments, annotated_genomes, lazy_load, kbase_obj – and is accordingly unpacked. A provided dictionary specifies all of the models that are paired with each given model, as a concise means of simulating only specified pairs.

• models_media dict: The minimal media of each member, which follows the structure: <member ID>: {“media”: {< exchange ID> : < flux >}}.

• environments dict: The media environments in which the member models will be simulated.

• annotated_genomes dict: The collection of annotated genomes that will be compared, as an alternative to acquiring the model genomes via kbase_object.

• lazy_load bool: specifies whether only models that are necessary for the current comparison are loaded, to save RAM for analyses of many models.

• kbase_object cobrakbase.kbaseapi.KBaseAPI: The KBase API object that allows the corresponding genomes for each model to acquired.

• cip_score bool: specifies whether the CIP score will be computed and reported with the other scores.

• costless bool: specifies whether the costless MIP subscore is computed and reported, which is the number of cross-fed compounds that are also costlessly excreted.

• skip_bad_media bool: specifies whether media in which the members do not grow are skipped without error or throw an error.

• anme_comm bool: specifies whether an environment is parameterized to the models, which may be undesirable for some communities that fail to growth in isolation, such as syntrophic ANME members.

• print_progress bool: specifies whether progress and auxillary information is printed with each loop over all pair and environment combinations.

Returns tuple: The first tuple element is a list of Pandas Series objects that represent the results of all scores for a single pair in a single environment, which can be seamlessly converted into a Pandas Dataframe via pandas.concat(series_list, axis=1).T. The second tuple element is the list of dictionaries that detail the metabolites that are involved with each respective score.