posGene

<p>A data frame with 299 different well IDs in which the cells treated with siRNAs targeting to PTB.If users didn't have the build-in positive controls, choose the parameters -withoutsvm and the filename can use any name such as 'NA'.</p>

datasets

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.

Junhui Li

ZetaSuite

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

Yajing Hao

Shuyang Zhang

Guofeng Zhao

Xiang-Dong Fu

posGene function

<p>A data frame with 299 different well IDs in which the cells treated with siRNAs targeting to PTB.</p><dl>
    A data frame with 299 different well IDs in which the cells treated with siRNAs targeting to PTB. These wells were served as positive control.
  
</dl>


Format

Input positive file. — posGene

Input positive file.

posGene: Input positive file.

Description

Usage

Arguments

Format

Details

Examples