KBase Output

kbase_output()

The highest-level function in CommScores that executes calculate_scores, possibly with parallelization, and returns a Pandas Dataframe and all involved metabolites for succinct analysis:

CommScores.calculate_scores(all_models:iter=None, pairs:dict=None, mem_media:dict=None, pair_limit:int=None, exclude_pairs:list=None,
                            kbase_obj=None, annotated_genomes:dict=True, see_media=True, environments:iter=None, pool_size:int=None,
                            cip_score=True, costless=True, skip_bad_media=False, anme_comm=False, print_progress=False)

  • all_models list: All of the models that will be simulated. This can be a list of models, where all combinations of the models will be simulated. This argument can alternatively be a list of lists of models, where only pairs between the various lists are examined. The set of all models can also be deduced from pairs if that argument is provided instead of all_models.

  • pairs dict: A dictionary of the models that are paired with each given model, as a concise means of simulating only specified pairs.

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

  • pair_limit int: Defines a limit for the number of pairs that are examined for a given system, where a limit that is less than the total number of combinations randomly samples the possible pairwise combinations.

  • excluded_pairs list|set: The collection of model pairs that will not be simulated, even if they can be constructed from the provided model sets.

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

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

  • see_media bool: specifies whether the computed minimal media for the member models will be printed, which can facilitate storing these media and providing them through the mem_media argument in future simulations to save computation time.

  • environments list<dict|cobrakbase.core.kbasebiochem.media.Media>: The media environments in which the member models will be simulated.

  • pool_size int: Defines the number of instances that will be employed to parallelize the computation, where parallelization only occurs when this argument is not None.

  • 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 Pandas DataFrame object where each row presents all of the score values for a single pair in a single environment. The second tuple element is the list of dictionaries that detail the metabolites that are involved with each respective score. These outputs can be directly fed into the commscores_report function to construct an HTML report of the results.