Skip to contents

Getting Started with MicrobiomeStat

Source: vignettes/MicrobiomeStat.Rmd
MicrobiomeStat.Rmd

Introduction

MicrobiomeStat is a comprehensive R package designed for advanced longitudinal microbiome and multi-omics data analysis. It provides a complete toolkit for:

  • Cross-sectional studies: Single time point analysis
  • Paired samples: Before-after comparisons
  • Longitudinal studies: Multiple time points tracking

Installation

You can install MicrobiomeStat from CRAN:

install.packages("MicrobiomeStat")

Or get the development version from GitHub:

# install.packages("devtools")
devtools::install_github("cafferychen777/MicrobiomeStat")

Quick Start

Loading Data

MicrobiomeStat uses a standardized data structure called data.obj:

# Load example dataset
data(peerj32.obj)

# View data structure
names(peerj32.obj)
# [1] "feature.tab" "feature.ann" "meta.dat" "tree"

Basic Analysis Workflow

1. Alpha Diversity Analysis 

# Calculate alpha diversity
alpha.obj <- mStat_calculate_alpha_diversity(
  peerj32.obj$feature.tab,
  alpha.name = c("shannon", "observed_species")
)

# Visualize alpha diversity
generate_alpha_boxplot_single(
  data.obj = peerj32.obj,
  alpha.obj = alpha.obj,
  alpha.name = "shannon",
  subject.var = "subject",
  time.var = "time",
  t.level = "2",
  group.var = "group",
  strata.var = "sex"
)

2. Beta Diversity Analysis 

# Calculate beta diversity
beta.obj <- mStat_calculate_beta_diversity(
  data.obj = peerj32.obj,
  dist.name = c("BC", "Jaccard")
)

# Visualize beta diversity using PCoA
generate_beta_ordination_single(
  data.obj = peerj32.obj,
  dist.obj = beta.obj,
  pc.obj = NULL,
  subject.var = "subject",
  time.var = "time",
  t.level = "2",
  group.var = "group",
  strata.var = "sex",
  dist.name = "BC"
)

Data Import from Other Formats

MicrobiomeStat can import data from various microbiome analysis platforms:

# From phyloseq
# data.obj <- mStat_convert_phyloseq_to_data_obj(phyloseq.obj)

# From QIIME2
# data.obj <- mStat_import_qiime2_as_data_obj(
#   otu.qza = "table.qza",
#   taxa.qza = "taxonomy.qza",
#   sam.tab = "metadata.txt"
# )

# From DADA2
# data.obj <- mStat_import_dada2_as_data_obj(
#   seq.tab = seqtab,
#   tax.tab = taxtab,
#   sam.tab = metadata
# )

Key Features

1. Comprehensive Statistical Methods

  • LinDA: Linear models for differential abundance analysis
  • Mixed-effects models: For longitudinal data
  • Trend analysis: Temporal patterns identification
  • Volatility analysis: Stability measurements

2. Beautiful Visualizations

All plotting functions in MicrobiomeStat are designed to produce publication-ready figures:

# Taxa barplot
generate_taxa_barplot_single(
  data.obj = peerj32.obj,
  subject.var = "subject",
  time.var = "time",
  group.var = "group",
  strata.var = "sex",
  feature.level = "Phylum",
  feature.dat.type = "count",
  feature.number = 8,
  t.level = "2"
)

3. Automated Report Generation

Generate comprehensive analysis reports with a single function:

# For cross-sectional studies
mStat_generate_report_single(
  data.obj = your_data,
  subject.var = "subject",
  group.var = "treatment",
  t.level = "baseline",
  feature.dat.type = "count",
  output.file = "analysis_report"
)

# For longitudinal studies  
mStat_generate_report_long(
  data.obj = your_data,
  subject.var = "subject",
  time.var = "time",
  t0.level = "0",
  ts.levels = c("1", "2", "3"),
  group.var = "treatment",
  feature.dat.type = "count",
  output.file = "longitudinal_report"
)

Learning Resources

Next Steps

For more detailed tutorials, please check:

  • Cross-sectional analysis workflows
  • Longitudinal data analysis
  • Multi-omics integration
  • Advanced visualization customization

Citation

If you use MicrobiomeStat in your research, please cite:

citation("MicrobiomeStat")