Bidirectional Mamba for Single-Cell Data: Efficient Context Learning with Biological Fidelity

• URL: http://arxiv.org/abs/2504.16956v1
• Date: Tue, 22 Apr 2025 20:34:47 GMT
• Title: Bidirectional Mamba for Single-Cell Data: Efficient Context Learning with Biological Fidelity
• Authors: Cong Qi, Hanzhang Fang, Tianxing Hu, Siqi Jiang, Wei Zhi,
• Abstract summary: We introduce GeneMamba, a scalable and efficient foundation model for single-cell transcriptomics built on state space modeling.<n>GeneMamba captures bidirectional gene context with linear-time complexity, offering substantial computational gains over transformer baselines.<n>We evaluate GeneMamba across diverse tasks, including multi-batch integration, cell type annotation, and gene-gene correlation, demonstrating strong performance, interpretability, and robustness.
• Score: 0.39945675027960637
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Single-cell RNA sequencing (scRNA-seq) enables high-resolution analysis of cellular heterogeneity, but its complexity, which is marked by high dimensionality, sparsity, and batch effects, which poses major computational challenges. Transformer-based models have made significant advances in this domain but are often limited by their quadratic complexity and suboptimal handling of long-range dependencies. In this work, we introduce GeneMamba, a scalable and efficient foundation model for single-cell transcriptomics built on state space modeling. Leveraging the Bi-Mamba architecture, GeneMamba captures bidirectional gene context with linear-time complexity, offering substantial computational gains over transformer baselines. The model is pretrained on nearly 30 million cells and incorporates biologically informed objectives, including pathway-aware contrastive loss and rank-based gene encoding. We evaluate GeneMamba across diverse tasks, including multi-batch integration, cell type annotation, and gene-gene correlation, demonstrating strong performance, interpretability, and robustness. These results position GeneMamba as a practical and powerful alternative to transformer-based methods, advancing the development of biologically grounded, scalable tools for large-scale single-cell data analysis.

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