BRACoD 0.3.6

BRACoD is a method to identify associations between bacteria and physiological variables in Microbiome data

BRACoD: Bayesian Regression Analysis of Compositional Data

Installation

Installation in python:

pip install BRACoD

There is also an R interface, which depends on the python version being installed. There is a helper function that will do it for you, but it might be easier to do it with pip. You can install via CRAN.

install.packages("BRACoD.R")

Or you can install via github.

devtools::install_github("ajverster/BRACoD/BRACoD.R")

Python Walkthrough

1. Simulate some data and normalize it

   import BRACoD
   import numpy as np
   sim_counts, sim_y, contributions = BRACoD.simulate_microbiome_counts(BRACoD.df_counts_obesity)
   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

   taxon_identified = df_results["taxon_num"].values
   taxon_actual = np.where(contributions != 0)[0]
   
   precision, recall, f1 = BRACoD.score(taxon_identified, taxon_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(BRACoD.df_counts_obesity)
   df_rel = BRACoD.scale_counts(df_counts)
   df_rel, Y = BRACoD.remove_null(df_rel, BRACoD.df_scfa_obesity["butyric"].values)
   trace = BRACoD.run_bracod(df_rel, Y, n_sample = 1000, n_burn=1000, njobs=4)
   df_results = BRACoD.summarize_trace(trace, df_rel.columns, 0.3)
   

The taxonomy information for these OTUs is available at BRACoD.df_taxonomy

R Walkthrough

1. Simulate some data and normalize it

   library('BRACoD.R')
   data(obesity)
   r <- simulate_microbiome_counts(df_counts_obesity)
   
   sim_counts <- r$sim_counts
   sim_y <- r$sim_y
   contributions <- r$contributions
   sim_relab <- scale_counts(sim_counts)
   

2. Run BRACoD

   trace <- run_bracod(sim_relab, sim_y, n_sample = 1000, n_burn=1000, njobs=4)
   

3. Examine the diagnostics

   convergence_tests(trace, sim_relab)
   

4. Examine the results

   df_results <- summarize_trace(trace, colnames(sim_counts))
   

5. Compare the results to the simulated truth

   taxon_identified <- df_results$taxon_num
   taxon_actual <- which(contributions != 0)
   
   r <- score(taxon_identified, taxon_actual)
   
   print(sprintf("Precision: %.2f, Recall: %.2f, F1: %.2f", r$precision, r$recall, r$f1))
   

6. Try with your real data. We have included some functions to help you threshold and process your data

   df_counts_obesity_sub <- threshold_count_data(df_counts_obesity)
   df_rel <- scale_counts(df_counts_obesity_sub)
   r <- remove_null(df_rel, df_scfa$butyric)
   df_rel <- r$df_rel
   Y <- r$Y
   
   trace <- run_bracod(df_rel, Y, n_sample = 1000, n_burn=1000, njobs=4)
   df_results <- summarize_trace(trace, colnames(df_counts_obesity_sub), 0.3)