FERRET: Framework to Evaluate Robustness in Regulatory Networks Using Heterogeneous Cell Types

Area of Science:

• Computational Biology
• Genomics
• Bioinformatics

Background:

• Current gene regulatory network (GRN) inference evaluation methods rely on simulated data or small networks, failing to capture real biological variability.
• Existing approaches have limitations in assessing the accuracy and robustness of GRN inference algorithms on complex biological datasets.

Purpose of the Study:

• To introduce FERRET, a novel framework for benchmarking single-cell GRN inference methods.
• To provide a robust evaluation strategy that leverages biological data and assesses network similarity across cellular states.

Main Methods:

• FERRET utilizes two metrics: Robustness Area Under the Curve (RAUC) and Monotonicity, to quantify network similarity within and between cell types.
• The framework validates GRN inference methods using experimentally derived ChIP-seq networks and random networks as controls.
• FERRET supports biological validation through pathway enrichment analysis.

Main Results:

• FERRET successfully distinguishes biologically relevant networks from random ones, assigning higher robustness scores to the former.
• The framework demonstrates that biologically related networks exhibit greater similarity than unrelated ones.
• Application of FERRET to real single-cell RNA-sequencing datasets identified GRN inference methods yielding the most robust and biologically informative networks.

Conclusions:

• FERRET offers a robust and biologically grounded approach for evaluating GRN inference methods.
• The framework addresses limitations of existing methods by using real biological data and assessing network consistency.
• FERRET aids in selecting superior GRN inference tools for single-cell data analysis.