DoseRider

DoseRider models dose-response relationships at the pathway level using generalized mixed effect models to calculate trend change doses and benchmark doses for understanding compound effects on biological systems across various doses.

Key Features:

• Pathway-level dose-response modeling: Performs linear and non-linear dose-response modeling at the level of biological pathways or signatures using generalized mixed effect models.
• Trend change dose calculation: Introduces and calculates trend change doses as numerical descriptors of effects derived from complex dose-response curves.
• Benchmark dose assessment: Calculates benchmark doses as quantitative representations of substance effects at the pathway level.
• Multi-omics data support: Analyzes custom or provided multi-omics data including RNA sequencing and metabolomics data.
• Pathway and gene set annotation: Annotates data with a collection of pathways and gene sets from various species.
• MSigDB gene set compatibility: Supports gene sets for chemical and genetic perturbations from MSigDB.

Scientific Applications:

• Pharmacogenomics: Enables dose-response analysis for pharmacological research to understand compound effects on biological systems.
• Toxicogenomics: Supports toxicological research by modeling dose-response relationships and deriving exposure limits.
• Estrogen response analysis: Demonstrated capability to analyze estrogen-regulated gene sets in breast cancer cell lines.
• Environmental chemical assessment: Applicable to analysis of environmental compounds such as bisphenol AF treatment effects.

Methodology:

DoseRider uses generalized mixed effect models for linear and non-linear dose-response modeling at the pathway or signature level. The method calculates trend change doses and benchmark doses from multi-omics data annotated with pathways and gene sets.