microbiomeDAAcomp

Description

microbiomeDAAcomp is a comprehensive R package designed for comparing and evaluating Differential Abundance Analysis (DAA) methods in microbiome studies. It provides a unified framework to assess various DAA methods' performance, helping researchers select the most appropriate method based on their specific data characteristics and research needs.

Installation

You can install the development version of microbiomeDAAcomp from GitHub: ```r

install.packages("devtools")

devtools::install_github("cafferychen777/microbiomeDAAcomp") ```

Documentation

• Package Documentation: Comprehensive function documentation is available through R's help system
• Vignettes: Detailed tutorials and examples are available: ```r # View the introduction vignette vignette("introduction", package = "microbiomeDAAcomp")

# List all available vignettes vignette(package = "microbiomeDAAcomp") ```

Key Features

1. DAA Method Integration

• Supports multiple popular DAA methods:
  • DESeq2
  • ALDEx2
  • ANCOM-BC
• Unified interface for method execution and comparison

2. Performance Evaluation

• Comprehensive metrics calculation:
  • Sensitivity
  • Specificity
  • Precision
  • F1 score
  • MCC (Matthews Correlation Coefficient)
• Confidence interval estimation
• Performance ranking across methods

3. Statistical Analysis

• Power analysis for experimental design
• Sensitivity analysis for parameter tuning
• Statistical comparison between methods:
  • Friedman test
  • Post-hoc analysis (Nemenyi test)

4. Visualization

• Performance visualization options:
  • Heatmaps
  • Box plots
  • Violin plots
• Method comparison plots
• Interactive plotting support (via plotly)

Quick Start

For detailed examples and tutorials, please refer to our vignettes: ```r

View the introduction vignette with complete examples

vignette("introduction", package = "microbiomeDAAcomp") ```

Basic usage: ```r library(microbiomeDAAcomp)

Run multiple DAA methods

See vignette("introduction") for complete examples with real data

results <- rundaamethods( data = your_data, methods = c("DESeq2", "ALDEx2", "ANCOM-BC"), alpha = 0.05 )

Evaluate performance

performance <- evaluateperformance( results = results, truestatus = truedifferentialstatus, metrics = c("sensitivity", "specificity", "precision") )

Visualize results

plotperformance(performance, plottype = "heatmap") ```

For more examples and detailed usage instructions, please check our comprehensive vignettes: ```r

List all available vignettes

vignette(package = "microbiomeDAAcomp") ```

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Citation

If you use this package in your research, please cite: Yang, C. (2024). microbiomeDAAcomp: A Comprehensive Toolkit for Microbiome Differential Abundance Analysis Method Comparison. R package version 1.0.0.

Support and Resources

• Documentation: Complete package documentation and vignettes
• Issues: GitHub Issues
• Discussions: GitHub Discussions
• Email: support

FAQ

Q: How to handle sparse data?

It's recommended to preprocess the data before running DAA analysis:

```r

Handle sparse data using DESeq2's approach

dds <- DESeqDataSetFromMatrix( countData = counts + 1, # Add pseudocount colData = data.frame(group = groups), design = ~ group ) ```

Q: How to choose the most suitable DAA method?

You can compare the performance of different methods:

```r

Run multiple methods and compare performance

results <- rundaamethods( data = your_data, methods = c("DESeq2", "ALDEx2", "ANCOM-BC") )

Evaluate performance

performance <- evaluateperformance( results = results, truestatus = truedifferentialstatus, metrics = c("sensitivity", "specificity", "precision") )

Compare method performance

comparison <- comparemethods( performanceresults = performance, comparison_type = "comprehensive" ) ```

Troubleshooting

Before reporting issues, please check:

1. R version >= 4.1.0
2. All dependencies are properly installed
3. Input data format meets requirements

Changelog

v1.0.0 (2024-12)

• Initial release
• Implemented core DAA comparison functionality
• Added basic visualization tools

v0.9.0 (2024-11)

• Beta release
• Completed major functionality testing
• Performance optimization

Roadmap

Future plans:

• [ ] Add support for more DAA methods
• [ ] Enhance visualization capabilities
• [ ] Add interactive analysis interface
• [ ] Optimize computational performance
• [ ] Expand documentation and tutorials

Related Projects

• phyloseq
• DESeq2
• ALDEx2