EDDA

EDDA optimizes experimental design and analytical choices for differential abundance analysis across high-throughput assays including RNA-seq, Nanostring assays, RIP-seq, and metagenomic sequencing.

Key Features:

• Rational Test Selection: Selects statistical tests tailored to dataset characteristics and experimental conditions to improve detection of differential abundance.
• Performance Prediction: Predicts performance metrics of analytical approaches to anticipate analysis outcomes and compare methods.
• Resource Optimization: Models experiments to reduce sequencing costs (reported up to five-fold in single-cell RNA-seq) and to improve biomarker detection efficiency.
• Mode-based Normalization: Incorporates a novel mode-based normalization technique that increases robustness of differential abundance detection by 10%–20% and boosts precision by up to 140%.
• Experimental Modeling: Simulates experimental setups to evaluate sensitivity and resource requirements for different analytical strategies.

Scientific Applications:

• RNA-seq: Aids design of bulk and single-cell RNA-seq experiments by optimizing test selection and normalization for improved differential abundance detection.
• Nanostring Assays: Supports design and performance prediction for Nanostring-based studies targeting differential expression or abundance.
• RIP-seq and Metagenomic Sequencing: Guides test selection and experimental planning for RIP-seq and metagenomic differential abundance analyses.

Methodology:

Selects statistical tests tailored to datasets and conditions, predicts performance of analytical approaches, models experiments, and applies a novel mode-based normalization technique reported to improve robustness by 10%–20% and precision by up to 140%.