ZetaSuite: 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) <doi:10.1016/j.cell.2017.07.005> and Tsherniak A (2017) <doi:10.1016/j.cell.2017.06.010>) 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) <doi:10.1038/s41586-018-0698-6>). 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.

Version:	1.0.2
Depends:	R (≥ 2.10)
Imports:	RColorBrewer, Rtsne, e1071, ggplot2, reshape2, gridExtra, mixtools, shinyjs, shinydashboard, shiny, plotly, DT
Suggests:	knitr, rmarkdown
Published:	2025-09-24
DOI:	10.32614/CRAN.package.ZetaSuite
Author:	Yajing Hao [aut], Shuyang Zhang [ctb], Junhui Li [cre], Guofeng Zhao [ctb], Xiang-Dong Fu [cph, fnd]
Maintainer:	Junhui Li <ljh.biostat at gmail.com>
BugReports:	https://github.com/JunhuiLi1017/ZetaSuite/issues
License:	MIT + file LICENSE
NeedsCompilation:	no
Materials:	README
CRAN checks:	ZetaSuite results

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