DOGMA: Weaving Structural Information into Data-centric Single-cell Transcriptomics Analysis

2026-02-02

AI Summary

This work addresses a critical limitation in current single-cell transcriptomic analysis methods, which often neglect biologically grounded intercellular relationships and data quality issues while struggling to incorporate biological prior knowledge effectively. To overcome these challenges, the authors propose DOGMA, a novel framework that integratively leverages statistical anchors, cell ontologies, phylogenetic trees, and gene ontologies to construct a deterministic graph structure. This architecture enables data reshaping and bridges the semantic gap between features by embedding biological priors directly into the analytical pipeline. DOGMA transcends the constraints of conventional heuristic strategies, achieving state-of-the-art performance across multi-species, multi-organ benchmarks. It substantially enhances zero-shot robustness and sample efficiency while significantly reducing computational overhead.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Reasoning under Uncertainty: Graphical Models

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

Recently, data-centric AI methodology has been a dominant paradigm in single-cell transcriptomics analysis, which treats data representation rather than model complexity as the fundamental bottleneck. In the review of current studies, earlier sequence methods treat cells as independent entities and adapt prevalent ML models to analyze their directly inherited sequence data. Despite their simplicity and intuition, these methods overlook the latent intercellular relationships driven by the functional mechanisms of biological systems and the inherent quality issues of the raw sequence data. Therefore, a series of structured methods has emerged. Although they employ various heuristic rules to capture intricate intercellular relationships and enhance the raw sequencing data, these methods often neglect biological prior knowledge. This omission incurs substantial overhead and yields suboptimal graph representations, thereby hindering the utility of ML models. To address them, we propose DOGMA, a holistic data-centric framework designed for the structural reshaping and semantic enhancement of raw data through multi-level biological prior knowledge. Transcending reliance on stochastic heuristics, DOGMA redefines graph construction by integrating Statistical Anchors with Cell Ontology and Phylogenetic Trees to enable deterministic structure discovery and robust cross-species alignment. Furthermore, Gene Ontology is utilized to bridge the feature-level semantic gap by incorporating functional priors. In complex multi-species and multi-organ benchmarks, DOGMA achieves SOTA performance, exhibiting superior zero-shot robustness and sample efficiency while operating with significantly lower computational cost.

Problem

• single-cell transcriptomics
• intercellular relationships
• biological prior knowledge
• graph representation
• data quality

Innovation

• data-centric AI
• biological prior knowledge
• graph construction
• single-cell transcriptomics
• cross-species alignment