Analyze High-Dimensional High-Throughput Dataset and Quality Control Single-Cell RNA-Seq
Generate event coverage analysis and visualization for alternative spl...
Determine optimal cutoff thresholds based on Screen Strength analysis.
Perform quality control analysis for high-throughput screening data.
Generate SVM decision boundaries for positive and negative control sep...
Calculation of zeta and weighted zeta score.
Launch ZetaSuite Shiny Application
Calculate zeta score for single cell RNA-seq quality control.
Z-score normalization for high-throughput screening data.
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.