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()scnm.fit()Visualization: Plot rarefaction diagnostics, abundance-occupancy curves, and core distributions
plot_rarefaction_metrics()plot_abundance_occupancy()plot_core_distribution()plot_identified_core()Install the latest stable version of BRCore from CRAN with:
install.packages("BRCore")Install the development version of BRCore from GitHub with:
Install the development version of BRCore from GitHub with:
# install.packages("pak")
pak::pak("germs-lab/BRCore")Note: pak handles
dependencies automatically.
library(BRCore)
library(phyloseq)
# Load example data
data("bcse", package = "BRCore")
# 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,
num_iter = 10,
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)For detailed examples and use cases, see the package vignette:
vignette("BRCore-vignette", package = "BRCore")Contributions to BRCore are welcome! Please see the CONTRIBUTING.md file for guidelines on how to contribute.
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.