ZetaSuite

ZetaSuite applies the Zeta statistic to analyze two-dimensional high-throughput functional genomics data, enabling identification of global splicing regulators and other significant biological interactions from experiments such as RNA interference (RNAi) screens, large-scale cancer dependency screens, and single-cell transcriptomics.

Key Features:

• Zeta Statistic: Implements the Zeta statistic to quantify signals across two-dimensional high-throughput datasets and improve detection of significant biological interactions.
• Broad Applicability: Applicable to diverse two-dimensional functional genomics data types, including RNA interference (RNAi) screens, large-scale cancer dependency screens, and single-cell transcriptomics.
• Benchmarked Performance: Performance has been compared with existing methods using multiple benchmarked datasets to evaluate sensitivity and specificity.
• Data Processing Capabilities: Processes public large-scale functional genomics datasets to extract biological insights from extensive and complex data matrices.

Scientific Applications:

• Identification of global splicing regulators: Detects regulators from multi-target screens such as RNAi experiments by analyzing two-dimensional readouts.
• Cancer dependency screens: Analyzes large-scale cancer dependency datasets to prioritize potential therapeutic targets or biomarkers.
• Single-cell transcriptomics: Analyzes cell-resolved two-dimensional data to elucidate cell-specific regulatory networks and interactions.

Methodology:

Computational steps explicitly include application of the Zeta statistic to two-dimensional datasets, benchmarking comparisons with existing methods on multiple benchmarked datasets, and processing public large-scale functional genomics data.