ROTS

ROTS computes a Reproducibility-Optimized Test Statistic to identify differentially expressed genes in omics data (microarray).

Key Features:

• Enhanced Reproducibility-Optimization Procedure: Selects a gene-ranking test statistic directly from the dataset using a reproducibility-optimization procedure without requiring predefined assumptions.
• Consistent Performance Across Conditions: Demonstrates robust performance across simulated conditions and real datasets, including the Affymetrix spike-in dataset and an in-house cDNA microarray study of asthma.
• Broad Applicability Beyond Differential Expression: Applies the reproducibility-optimization procedure to other omics analysis tasks beyond differential expression.

Scientific Applications:

• Microarray Studies: Identifies differentially expressed genes in microarray experiments.
• Gene Expression Analysis: Analyzes disease-related expression changes, exemplified by application to asthma-related gene expression in a cDNA microarray study.

Methodology:

Implements a data-driven reproducibility-optimization procedure that dynamically selects the most appropriate gene-ranking/test statistic from the input dataset without relying on simulated or spike-in training data or predefined assumptions.