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: 1.0.1 Depends: R (≥ 2.10) Imports: RColorBrewer, Rtsne, dplyr, e1071, ggplot2, reshape2, gridExtra, mixtools Suggests: knitr, rmarkdown Published: 2022-05-24 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> License: GPL-2 | GPL-3 NeedsCompilation: no CRAN checks: ZetaSuite results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=ZetaSuite to link to this page.
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