Analyze High-Dimensional High-Throughput Dataset and Quality Control Single-Cell RNA-Seq R package ZetaSuite version 1.0.3 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. Software Depends: R (>= 2.10) Imports: RColorBrewer, Rtsne, e1071, ggplot2, reshape2, gridExtra, mixtools, shinyjs, shinydashboard, shiny, plotly, DT Suggests: knitr, rmarkdown Junhui Li <ljh.biostat@gmail.com> Comprehensive R Archive Network (CRAN) Junhui Li [aut, cre] (ORCID: <https://orcid.org/0000-0003-3973-1700>), Yajing Hao [aut] (ORCID: <https://orcid.org/0000-0003-1384-4176>), Shuyang Zhang [ctb] (ORCID: <https://orcid.org/0000-0002-8428-1828>), Guofeng Zhao [ctb], Xiang-Dong Fu [cph, fnd] (ORCID: <https://orcid.org/0000-0001-5499-8732>) MIT + file LICENSE (https://CRAN.R-project.org/package=ZetaSuite/LICENSE) 2026-04-28 application/tgz https://CRAN.R-project.org/package=ZetaSuite doi:10.32614/CRAN.package.ZetaSuite