A Unified Framework for Identification and Ecological Interpretation of Microbial Data from Bioenergy Research Centers

A unified framework for identification and ecological interpretation of core microbiomes across time and space, enhancing robustness and reproducibility in microbiome data analysis. 'BRCore' implements the workflow proposed by Shade and Stopnisek (2019) and incorporates additional rarefaction steps. The proposed workflow aims to identify persistent microbiomes using abundance-occupancy distributions and neutral community model fitting. For more details on abundance-occupancy distributions see Shade A, Stopnisek N (2019) , for neutral models, see Sloan et al. (2006) and Burns et al. (2015) .

BRCore

Overview

BRCore provides a unified framework for identification and ecological interpretation of core microbiomes across time and space, enhancing robustness and reproducibility in microbiome data analysis.

BRCore provides tools for:

Rarefaction analysis: Calculate pre-rarefaction metrics and perform rarefactions

• add_rarefaction_metrics()
• multi_rarefy()
• plot_variance_propagation()

Core microbiome identification: Identify core microbial taxa using abundance-occupancy distributions

• identify_core()

Neutral model fitting: Fit and visualize neutral community models

• fit_neutral_model()
• plot_neutral_model()
• sncm.fit()

Visualization: Plot rarefaction diagnostics, abundance-occupancy curves, and core distributions

• plot_rarefaction_metrics()
• plot_abundance_occupancy()
• plot_core_distribution()
• plot_identified_core()

Installation

Stable version from CRAN with:

install.packages("BRCore")

# or if using with `renv`:
renv::init(bioconductor = TRUE)
renv::install("BRCore")

NOTE: If using install.packages() in an renv project, renv will intercept the call. Run renv::init(bioconductor = TRUE) first to ensure Bioconductor dependencies can be resolved and installed.

Development version from GitHub with:

# install.packages("pak")
pak::pak("germs-lab/BRCore")

Note: pak handles dependencies automatically.

Quick Start

library(BRCore)
library(phyloseq)

# Add rarefaction metrics
bcse_metrics <- add_rarefaction_metrics(data = bcse)

# Perform rarefaction
bcse_rarefied_list <- multi_rarefy(
  physeq_obj = bcse,
  depth_level = 1000,
  num_iter = 3,
  set_seed = 7642
)

# Update phyloseq object with rarefied data
bcse_rare_single <- update_otu_table(
  physeq_obj = bcse,
  rarefied_otus = bcse_rarefied_list,
  iteration = 2
) # Your preffered iteration can be used here

# Identify core microbiome

# With a single iteration of rarefaction
bcse_core <- identify_core(
  physeq_obj = bcse_rare_single,
  priority_var = "Crop",
  increase_value = 0.02,
  seed = 2134
)


# With multiple iterations of rarefaction
bcse_core_multi <- identify_core(
  physeq_obj = bcse,
  rarefied_list = bcse_rarefied_list,
  priority_var = "Crop",
  increase_value = 0.02,
  depth_level = 1000,
  seed = 2134
)

# Visualize abundance-occupancy distribution
plot_abundance_occupancy(core_result = bcse_core, core_set = "increase")

# Fit and plot neutral model
bcse_neutral <- fit_neutral_model(
  otu_table = bcse_core$otu_table,
  core_set = bcse_core$increase_core,
  abundance_occupancy = bcse_core$abundance_occupancy
)

plot_neutral_model(bcse_neutral)

Documentation

For detailed examples and use cases, see the package vignette:

vignette("BRCore-vignette", package = "BRCore")

Contributing

Contributions to BRCore are welcome! Please see the CONTRIBUTING.md file for guidelines on how to contribute.

Code of Conduct

Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.