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: 1.0.0
Depends: R (≥ 2.10)
Imports: RColorBrewer, Rtsne, dplyr, e1071, ggplot2, reshape2, scater, gridExtra, mixtools
Suggests: knitr, rmarkdown
Published: 2022-03-31
Author: Yajing Hao ORCID iD [aut], Shuyang Zhang ORCID iD [ctb], Junhui Li [cre], Guofeng Zhao [ctb], Xiang-Dong Fu ORCID iD [cph, fnd]
Maintainer: Junhui Li <ljh.biostat at gmail.com>
License: GPL-2 | GPL-3
NeedsCompilation: no
CRAN checks: ZetaSuite results

Documentation:

Reference manual: ZetaSuite.pdf
Vignettes: ZetaSuite

Downloads:

Package source: ZetaSuite_1.0.0.tar.gz
Windows binaries: r-devel: ZetaSuite_1.0.0.zip, r-release: ZetaSuite_1.0.0.zip, r-oldrel: ZetaSuite_1.0.0.zip
macOS binaries: r-release (arm64): ZetaSuite_1.0.0.tgz, r-release (x86_64): ZetaSuite_1.0.0.tgz, r-oldrel: ZetaSuite_1.0.0.tgz

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