Analyze High-Dimensional High-Throughput Dataset and Quality Control Single-Cell RNA-Seq

The advent of genomic technologies has enabled the generation of two-dimensional or even multi-dimensional high-throughput data, e.g., monitoring multiple changes in gene expression in genome-wide siRNA screens across many different cell types (E Robert McDonald 3rd (2017) and Tsherniak A (2017) ) or single cell transcriptomics under different experimental conditions. We found that simple computational methods based on a single statistical criterion is no longer adequate for analyzing such multi-dimensional data. We herein introduce 'ZetaSuite', a statistical package initially designed to score hits from two-dimensional RNAi screens.We also illustrate a unique utility of 'ZetaSuite' in analyzing single cell transcriptomics to differentiate rare cells from damaged ones (Vento-Tormo R (2018) ). In 'ZetaSuite', we have the following steps: QC of input datasets, normalization using Z-transformation, Zeta score calculation and hits selection based on defined Screen Strength.

ZetaSuite

An R package for analyzing multi-dimensional high-throughput screening data, particularly two-dimensional RNAi screens and single-cell RNA sequencing data.

Installation

# Install from CRAN
install.packages("ZetaSuite")

# Or install from GitHub
devtools::install_github("username/ZetaSuite")

# Load the package
library(ZetaSuite)

Quick Start

# 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)

Interactive Shiny Application

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

Features

• 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

Documentation

For detailed documentation and examples, see the package vignette:

vignette("ZetaSuite")

Bug Reports

If you encounter any bugs or have feature requests, please report them on our GitHub issues page:

Report a Bug

Citation

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

License

This package is licensed under the MIT License - see the LICENSE file for details.