Harnessing the deep learning power of foundation models in single-cell omics

Foundation models have transformed AI by leveraging large-scale data to efficiently perform diverse tasks, and their applications in bioinformatics are primarily focused on data-centric tasks like cell type annotation and gene expression analysis. However, their potential extends beyond data analysis, offering significant opportunities in software development and optimization, such as code refinement, tutorial generation, and advanced visualization. For example, models like OpenAI Codex can suggest optimized code snippets and generate well-documented, reproducible workflows, enhancing accessibility and reducing computational complexity. Despite these advantages, the use of foundation models for improving computational tool engineering in single-cell research remains underutilized. To address this gap, we developed scGNN+, a web-based platform that combines the power of graph neural networks with the capabilities of ChatGPT to enhance reproducibility, code optimization, and visualization. scGNN+ further simplifies the process for users by generating standardized, well-annotated code, making complex procedures more accessible to non-programmers. Additionally, ChatGPT integration allows users to create high-quality, customizable visualizations through natural language prompts, improving data interpretation and presentation. Ultimately, scGNN+ offers a user-friendly, reproducible, and optimized solution for single-cell research, leveraging the full potential of foundation models in bioinformatics. scGNN+ is publicly available at https://bmblx.bmi.osumc.edu/scgnn+.

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BioLLM: A Standardized Framework for Integrating and Benchmarking Single-Cell Foundation Models

Qiu,

Chen,

Qin

et al. 2024

Preprint

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The application and evaluation of single cell foundational models (scFMs) present significant challenges stemming from the heterogeneity of architectural frameworks and coding standards. To address these issues, we introduce BioLLM, a framework facilitating the integration and application of scFMs in single-cell RNA sequencing data analysis. BioLLM provides a universal interface, bridging diverse scFMs into a seamless ecosystem. By mitigating architectural disparities and coding conventions, it empowers researchers with streamlined access to scFMs. With standardized APIs and comprehensive documentation, BioLLM streamlines model switching and comparative analyses, while incorporating best practices for consistent model evaluation. Our comprehensive evaluation of scFMs revealed distinct strengths and limitations, highlighting scGPT's robust performance across all tasks, both in zero-shot and fine-tuning scenarios. Geneformer and scFoundation also demonstrated strong capabilities in gene-level tasks, benefiting from effective pretraining strategies. In contrast, scBERT underperformed relative to other models, likely attributable to its considerably smaller parameter count and the limited size of the training dataset. Ultimately, BioLLM aims to empower the scientific community to leverage the full potential of foundational models, advancing our understanding of complex biological systems through enhanced single-cell analysis.

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Harnessing the deep learning power of foundation models in single-cell omics

Cited by 3 publications

References 14 publications

Unveiling potential threats: backdoor attacks in single-cell pre-trained models

Unveiling potential threats: backdoor attacks in single-cell pre-trained models

scGNN+: Adapting ChatGPT for Seamless Tutorial and Code Optimization

BioLLM: A Standardized Framework for Integrating and Benchmarking Single-Cell Foundation Models

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