scDeepFusion: Auditable Gene-Module Representation Learning with Typed-Expert Routing for Single-Cell RNA-seq Integration

Single-cell RNA sequencing presents a challenging representation learning problem: extreme sparsity, batch effects, and limited interpretability hinder accurate characterization of cellular states, particularly for rare or transcriptionally similar subtypes. We present scDeepFusion, a gene-set-level representation learning framework that represents cells by pathway-informed features rather than isolated gene measurements. Three components define the framework: (1) a module-activity representation that aggregates gene fluctuations over curated gene sets, suppressing dropout noise while preserving pathway-level biology; (2) a typed-expert routing architecture in which a per-cell sparse router assigns cells to knowledge-source experts (curated pathways, perturbation signatures, and a gene-line expert), yielding embeddings whose knowledge provenance is directly auditable from the exported routing weights; and (3) a teacher-aligned training scheme in which a coarse alignment loss anchors the representation to a reference embedding while auxiliary reconstruction and denoising objectives act as regularizers. Across five datasets evaluated under the standard scIB protocol with multi-seed controls on the flagship dataset and pathway-scoring baselines, scDeepFusion matches or exceeds strong integration baselines on aggregate metrics, attaining the highest Overall score among integration baselines on all four established benchmarks, with particular strengths on rare and subtype-level populations in label-weak settings. The framework further scales to 400k-cell training runs on the >1M-cell Tabula Sapiens atlas. This work establishes a principled and auditable framework for using curated biological knowledge in single-cell representation learning.