Controlled Diagnostic Study Reveals When Causal GRN Inference Fails
A recent study published on arXiv, identified by ID 2605.04930, presents a controlled diagnostic framework for Gene Regulatory Network (GRN) inference. It isolates seven key pathologies affecting inference accuracy, including dropout and cell-type mixing. The research evaluates six representative methods across three inference paradigms, revealing that causal methods frequently do not surpass correlation-based baselines in realistic scenarios. Moreover, it highlights that existing benchmarks lack adequate control due to the presence of co-occurring pathologies. The study aims to clarify the specific conditions under which these methods succeed or fail, focusing on single-cell RNA-seq data.
Key facts
- Study introduces a controlled diagnostic framework for GRN inference.
- Seven biologically motivated pathologies are isolated: dropout, latent confounders, cell-type mixing, feedback loops, network density, sample size, pseudotime drift.
- Six representative methods spanning three inference paradigms are evaluated.
- Causal methods often fail to outperform correlation-based baselines in realistic benchmarks.
- Existing benchmarks are insufficiently controlled due to co-occurring pathologies.
- The study aims to identify specific conditions for method success or failure.
- Published on arXiv with ID 2605.04930.
- Focuses on single-cell RNA-seq data.
Entities
Institutions
- arXiv