geneontology

topicpy.geneontology.geneontology

alias of topicpy.geneontology.geneontology

topicpy.geneontology.topic_analysis(directory, l, algorithm='topsbm', background=None, verbose=True, save_Pvalues=True)

Parameters:

• directory – where to find files
• l – level of the analisys
• algorithm – name of folder and files containing topics table (e.g. path/to/topsbm/topsbm_level_3_topics.csv)
• background – List of background genes
• verbose – verbosity
• save_Pvalues – save data for P-values plot

topicpy.geneontology.save_plot_Pvalues(df_topics, l, directory, algorithm)

Parameters:

• df_topics – Topics DataFrame
• l – level of the analysis
• directory –

topicpy.geneontology.get_ontology_df(topic, cutoff=0.05, threshhold=0.5, gene_sets=['GO_Molecular_Function_2018', 'GO_Biological_Process_2018', 'GO_Cellular_Component_2018', 'Human_Phenotype_Ontology', 'GTEx_Tissue_Sample_Gene_Expression_Profiles_up', 'GTEx_Tissue_Sample_Gene_Expression_Profiles_down', 'Tissue_Protein_Expression_from_Human_Proteome_Map', 'KEGG_2019_Human', 'NCI-60_Cancer_Cell_Lines'], background=None) → pandas.core.frame.DataFrame

Parameters:

• topic – list of genes
• background – enrichment test background
• cutoff – Enrichments cutoff
• threshhold – threshold on Adjusted P-value

Returns:

DataFrame with terms and P-vals