ZinaSuite is a modernized R package for interactively exploring UCSC Xena datasets. It provides a comprehensive framework for downloading, analyzing, and visualizing cancer genomics data with enhanced performance through mirai-based asynchronous computing and modular Shiny application design.
- 🔬 Multi-Dataset Support: TCGA, PCAWG, and CCLE datasets
- ⚡ High Performance: Asynchronous computing with mirai for parallel processing
- đź“Š Comprehensive Analysis: Correlation, survival, mutation, immune, and dimension analysis
- 🎨 Publication-Ready Visualizations: ggplot2-based plots with customizable themes
- 🖥️ Interactive Shiny App: Modular design with 11 functional modules
- đź’ľ Smart Caching: Memory and disk caching to reduce redundant queries
- đź“‘ Report Generation: Export results to HTML, PDF, Word, Excel
- âś… Data Validation: Built-in validation for gene symbols, cancer types, and sample IDs
# Install from GitHub
remotes::install_github("WangLabCSU/ZinaSuite")
# Or install from CRAN (when available)
install.packages("ZinaSuite")library(ZinaSuite)
# Query TP53 expression in TCGA BRCA
data <- query_gene_expression("TP53", cancer = "BRCA")
head(data)
# Query multiple genes
multi_data <- query_molecules(c("TP53", "BRCA1", "EGFR"), cancer = "BRCA")# Analyze correlation between two genes
cor_result <- analyze_correlation("TP53", "BRCA1", cancer = "BRCA")
print(cor_result)
# Visualize correlation
vis_correlation("TP53", "BRCA1", cancer = "BRCA")# Survival analysis by gene expression
surv_result <- analyze_survival_by_expression("TP53", cancer = "BRCA")
vis_survival_by_gene("TP53", cancer = "BRCA")# Start the interactive Shiny application
run_zinasuite()ZinaSuite includes a comprehensive Shiny application with 11 functional modules:
| Module | Description |
|---|---|
| Home | Welcome page and quick start guide |
| Data Query | Query gene expression, mutation, CNV, methylation data |
| Analysis | Correlation and survival analysis |
| Visualization | Distribution plots, correlation plots, heatmaps |
| Pan-Cancer | Pan-cancer analysis across 33 cancer types |
| Mutation | Mutation frequency and co-occurrence analysis |
| Dimension | PCA, t-SNE, UMAP dimensionality reduction |
| Immune | Immune infiltration and correlation analysis |
| PharmacoGenomics | Drug sensitivity and pharmacogenomics analysis |
| Batch | Batch processing for multiple genes |
| About | Package information and documentation |
The Shiny app follows a modular architecture with:
- UI/Server Separation: Clean separation of concerns
- Business Logic Extraction: Testable functions independent of Shiny
- Reactive State Management: Shared app state across modules
- Async Computing: Non-blocking operations with mirai
- Progress Tracking: Real-time progress updates
# Use cached queries for better performance
data <- cached_query(
function() query_gene_expression("TP53", cancer = "BRCA"),
cache_key = "TP53_BRCA",
ttl = 3600 # Cache for 1 hour
)
# Manage cache
cache <- get_zina_cache()
cache$stats() # View cache statistics
clear_zina_cache() # Clear all cache# Create analysis pipeline
pipeline <- create_pipeline(list(
query = function(gene) query_gene_expression(gene, cancer = "BRCA"),
analyze = function(data) analyze_survival_by_expression(data),
visualize = function(result) vis_survival(result)
), name = "Gene Survival Analysis")
# Run pipeline
result <- run_pipeline(pipeline, gene = "TP53")# Generate analysis report
result <- analyze_correlation("TP53", "BRCA1", cancer = "BRCA")
generate_report(
result,
output_file = "analysis_report.html",
output_format = "html",
title = "TP53-BRCA1 Correlation Analysis"
)
# Export data
export_data(result, "results.xlsx", format = "excel")# Validate inputs before analysis
validate_gene_symbol("TP53")
validate_cancer_type("BRCA")
validate_sample_ids(c("TCGA-01-1234-01", "TCGA-02-5678-01"))
# Data quality report
report <- data_quality_report(expression_data)
print(report)| Function | Description |
|---|---|
query_gene_expression() |
Query gene expression data |
query_mutation() |
Query mutation data |
query_cnv() |
Query copy number variation |
analyze_correlation() |
Correlation analysis |
analyze_survival() |
Survival analysis |
vis_correlation() |
Correlation visualization |
vis_survival() |
Survival visualization |
| Function | Description |
|---|---|
run_zinasuite() |
Launch Shiny application |
build_query_params() |
Build query parameters |
validate_query_input() |
Validate query inputs |
execute_data_query() |
Execute data query |
create_batch_job() |
Create batch processing job |
ZinaSuite includes comprehensive test suites:
# Run all tests
devtools::test()
# Run specific test file
devtools::test(filter = "shiny-logic")Test Coverage: 319 tests covering core functions, Shiny logic, data validation, and visualization.
| Feature | UCSCXenaShiny | ZinaSuite |
|---|---|---|
| Architecture | Monolithic | Modular |
| Async Computing | Limited | Full mirai support |
| Test Coverage | Basic | Comprehensive (319 tests) |
| Caching | None | Memory + Disk with TTL |
| Report Generation | Limited | HTML/PDF/Word/Excel |
| Data Validation | Basic | Extensive |
| Code Organization | Mixed | Clean separation |
We welcome contributions! Please see our Contributing Guide for details.
If you use ZinaSuite in your research, please cite:
Wang S, et al. (2025). ZinaSuite: A Modern R Package for UCSC Xena Data Analysis.
Journal of Open Source Software, 10(XXX), XXXX.
This project is licensed under the MIT License - see the LICENSE file for details.
- UCSC Xena Team for providing the data hub
- UCSCXenaTools package for the foundation
- All contributors to this project
- Issues: GitHub Issues
- Email: w_shixiang@163.com
- Website: https://github.com/WangLabCSU/ZinaSuite
Note: This package is under active development. Please report any issues or feature requests on GitHub.