https://github.com/cafferychen777/microbiomedaacomp
A comprehensive R toolkit for comparing and evaluating Differential Abundance Analysis (DAA) methods in microbiome studies.
https://github.com/cafferychen777/microbiomedaacomp
bioinformatics microbiome
Last synced: 2 months ago
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A comprehensive R toolkit for comparing and evaluating Differential Abundance Analysis (DAA) methods in microbiome studies.
- Host: GitHub
- URL: https://github.com/cafferychen777/microbiomedaacomp
- Owner: cafferychen777
- License: other
- Created: 2024-11-05T20:39:58.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2024-12-09T22:54:46.000Z (5 months ago)
- Last Synced: 2025-01-23T04:23:24.916Z (4 months ago)
- Topics: bioinformatics, microbiome
- Language: R
- Homepage:
- Size: 159 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# 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 <- run_daa_methods(
data = your_data,
methods = c("DESeq2", "ALDEx2", "ANCOM-BC"),
alpha = 0.05
)
# Evaluate performance
performance <- evaluate_performance(
results = results,
true_status = true_differential_status,
metrics = c("sensitivity", "specificity", "precision")
)
# Visualize results
plot_performance(performance, plot_type = "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](https://github.com/cafferychen777/microbiomeDAAcomp/issues)
- **Discussions**: [GitHub Discussions](https://github.com/cafferychen777/microbiomeDAAcomp/discussions)
- **Email**: [support](mailto:[email protected])
## 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 <- run_daa_methods(
data = your_data,
methods = c("DESeq2", "ALDEx2", "ANCOM-BC")
)
# Evaluate performance
performance <- evaluate_performance(
results = results,
true_status = true_differential_status,
metrics = c("sensitivity", "specificity", "precision")
)
# Compare method performance
comparison <- compare_methods(
performance_results = 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](https://github.com/joey711/phyloseq)
- [DESeq2](https://github.com/mikelove/DESeq2)
- [ALDEx2](https://github.com/ggloor/ALDEx2)