Metadata-Version: 2.1
Name: BRACoD
Version: 0.2.0
Summary: BRACoD is a method to identify associations between bacteria and physiological variables in Microbiome data
Home-page: https://github.com/ajverster/BRACoD/tree/main
Author: ['Adrian Verster']
Author-email: adrian.verster@canada.ca
License: UNKNOWN
Description: # BRACoD
        
        Installation: 
        
            pip install BRACoD
        
        If you want to use the R interface, install reticulate in R
        
        
        Walkthrough
        
        1. Simulate some data and normalize it
        
            sim_counts, sim_y, contributions = BRACoD.simulate_microbiome_counts(BRACoD.example_otu_data)
            sim_relab = BRACoD.scale_counts(sim_counts)
        
        
        2. Run BRACoD
        
            trace = BRACoD.run_bracod(sim_relab, sim_y, n_sample = 1000, n_burn=1000, njobs=4)
        
        3. Examine the diagnostics
        
            BRACoD.convergence_tests(trace, sim_relab)
        
        4. Examine the results
        
            df_results = BRACoD.summarize_trace(trace, sim_counts.columns, 0.3)
        
        5. Compare the results to the simulated truth
        
            bugs_identified = df_results["bugs"].values
            bugs_actual = np.where(contributions != 0)[0]
        
            precision, recall, f1 = BRACoD.score(bugs_identified, bugs_actual)
            print("Precision: {}, Recall: {}, F1: {}".format(precision, recall, f1))
        
        6. Try with your real data. We have included some functions to help you threshold and process your data
            df_counts = BRACoD.threshold_count_data(df_counts)
            df_rel = BRACoD.scale_counts(df_counts)
            df_rel, Y = remove_null(df_rel, Y)
            trace = BRACoD.run_bracod(df_rel, Y, n_sample = 1000, n_burn=1000, njobs=4)
            df_results = BRACoD.summarize_trace(trace, sim_counts.columns, 0.3)
        
Platform: UNKNOWN
Requires-Python: >3.7
Description-Content-Type: text/markdown
