An R package for analyzing multi-dimensional high-throughput screening data, particularly two-dimensional RNAi screens and single-cell RNA sequencing data.
# Install from CRAN
install.packages("ZetaSuite")
# Or install from GitHub
devtools::install_github("username/ZetaSuite")
# Load the package
library(ZetaSuite)# Load example data
data(countMat)
data(negGene)
data(posGene)
data(nonExpGene)
# Quality Control
qc_results <- QC(countMat, negGene, posGene)
# Z-score normalization
zscore_matrix <- Zscore(countMat, negGene)
# Event coverage analysis
ec_results <- EventCoverage(zscore_matrix, negGene, posGene)
# Zeta score calculation
zeta_scores <- Zeta(zscore_matrix, ec_results[[1]]$ZseqList)
# FDR cutoff analysis
fdr_results <- FDRcutoff(zeta_scores, negGene, posGene, nonExpGene)Launch the interactive web interface for ZetaSuite:
# Launch the Shiny app
ZetaSuiteApp()
# Launch without opening browser automatically
ZetaSuiteApp(launch.browser = FALSE)
# Launch on a specific port
ZetaSuiteApp(port = 3838)The Shiny app provides:
- Interactive data upload and visualization
- Step-by-step analysis workflow
- Real-time results and plots
- Data export capabilities
- Built-in example dataset
- Quality Control Analysis: Comprehensive evaluation of experimental design and data quality
- Z-score Normalization: Standardization using negative controls as reference
- Event Coverage Analysis: Quantification of regulatory effects across thresholds
- Zeta Score Calculation: Area-under-curve based scoring for regulatory effects
- SVM-based Background Correction: Machine learning approach to filter noise
- Screen Strength Analysis: Optimal threshold selection for hit identification
- Single Cell Quality Control: Cell quality assessment for scRNA-seq data
For detailed documentation and examples, see the package vignette:
vignette("ZetaSuite")If you encounter any bugs or have feature requests, please report them on our GitHub issues page:
If you use ZetaSuite in your research, please cite:
Hao, Y., Zhang, S., Shao, C. et al. ZetaSuite: computational analysis of two-dimensional high-throughput data from multi-target screens and single-cell transcriptomics. Genome Biol 23, 162 (2022). https://doi.org/10.1186/s13059-022-02729-4
This package is licensed under the MIT License - see the LICENSE file for details.