Package: biodigest-light
biodigest lightThe light version ofDIGEST(validation ofdisease andgenesets, clusterings or subnetworks) calledbiodigest-light. It greatly facilitates in silico validation of gene and disease sets or clusterings via fully automated validation pipelines comprising disease and gene ID mapping, enrichment
analysis, comparisons of shared genes and variants, and background distribution estimation. Moreover, functionality is provided to automatically update the external databases used by the pipelines.Here the subnetwork function ist excluded. If you wish to use the full DIGEST version, check outbiodigest.Source codeSetup for proper usageimportbiodigestBefore you can run the validation, you need to download precalculated mappings and distance matrices. These can be downloaded in two ways:1. [Recommended] Get data from apiThe API keeps all data up to date and checks for updates at regular intervals. This process takes 1-5 minutes depending on the internet connection.frombiodigestimportsetupsetup.main(setup_type="api")2. Create data from scratchAny mappings are freshly fetched from the databases and the distance matrices are calculated. Be aware that this can take up to 3 hours.frombiodigestimportsetupsetup.main(setup_type="create")Run validationfrombiodigest.single_validationimportsingle_validationresults=single_validation(tar:Union[pd.DataFrame,set],tar_id:str,mode:str,distance:str="jaccard",ref:Union[str,set]=None,ref_id:str=None,enriched:bool=False,mapper:Mapper=FileMapper(),runs:int=config.NUMBER_OF_RANDOM_RUNS,background_model:str="complete",replace=100,verbose:bool=False)All results that can later be saved and visualize are saved inresultsas data typedict().ParametersRequired parameterstar: Target input you want to be validateda cluster should be of typepd.DataFrame()withcolumns=["id","cluster"]a set should be of typeset()tar_id: Is the id type of the target (see possible options below)ref: Reference, to whichtarwill be compared (Only for mode id-set and set-set)a single id should be of typestra set should be of typeset()ref_id: Is the id type of the reference (see possible options below)mode: Desired mode. See possible options below.Optional parametersdistance: Distance measure used for pairwise comparisonenriched: SetTrue, if only enriched attributes of the reference set should be used (Only for set-set)background_model: Model defining how random values should be picked. See possible options below.runs: Number of runs with random target values for p-value calculationreplace: Percentage of how many of the original ids should be replaced with random idsverbose: get additional information during the runSupported typesgene types: entrez, ensembl, symbol, uniprotdisease types: mondo, omim, snomedct, umls, orpha, mesh, doid, ICD-10Modesset: Compare similarity inside the set using the mean of all pairwise comparisonsid-set: Compare target set to reference setset-setCompare target set to reference idclusterCompare cluster quality inside clustering using multiple quality measures (Dunn index, Davied Bouldin Index, Sillhouette Score)Background modelscomplete: Random ids will be picked completely randomlyterm-pres: Random ids will preserve the number of mapped terms for the replaced idsResultThe method call returns the result in a json format of datatype dict which consists of
the following elements:result={'status':'Status text','input_values':{'values':dict(),'mapped_ids':list()},'p_values':{'values':dict()}}status: contains either an error message if a mapping failed or "ok" if IDs could be mappedinput_values:values: table in dict format with the functional or genetic relevance score(s) determined for solely their inputmapped_ids: list containing the IDs with non empty annotations per functional or genetic annotation typep_values: table in dict format with the calculated empirical P-values using the selected background model and other parameters that indicate the significance of the calculated relevance scores derived from the inputSave and visualize resultsfrombiodigest.single_validationimportsave_resultsfrombiodigest.evaluation.d_utils.plotting_utilsimportcreate_plots,create_extended_plots# Save results into json file and 2 .csv table filessave_results(results:dict,prefix:str,out_dir)# Generate and save plots based on resultscreate_plots(results,mode,tar,tar_id,out_dir,prefix,file_type:str="pdf")# Generate and save extended plots based on resultscreate_extended_plots(results,mode,tar,out_dir,prefix,file_type:str="pdf")ParametersRequired parametersresults: Is the output created with methodsingle_validationas data typedict()prefix: Prefix for file namesout_dir: Output directory for the generated filesAdditional required parameters for create_plotstar: Target input you want to be validateda cluster should be of typepd.DataFrame()withcolumns=["id","cluster"]a set should be of typeset()tar_id: Is the id type of the target (see possible options above)Optional parameters for create_plotsfile_type: Type of the plots image files.Example runsCheck out thetutorialto see examples of usage in a script.
Keep in mind, this tutorial supports the fullbiodigestversion and therefor also shows the subnetwork mode.