This repository serves as a curated collection of research papers focused on the application and evaluation of foundation models within the single-cell omics domain. It aims to provide researchers and practitioners with a comprehensive overview of the latest advancements, methodologies, and challenges in leveraging large-scale models for single-cell data analysis, cell type annotation, and biological discovery.

How It Works

The collection highlights papers that explore various architectures and training strategies for foundation models in single-cell analysis. These include transformer-based models, contrastive learning approaches, and multimodal integration techniques. The underlying principle is to learn generalizable representations from vast single-cell datasets, enabling zero-shot or few-shot learning for downstream tasks and improving interpretability and predictive power.

Highlighted Details

• Comprehensive coverage of foundation models for single-cell RNA sequencing, spatial transcriptomics, and proteomics.
• Includes papers evaluating model performance, robustness, and limitations, particularly concerning dataset size, diversity, and imbalanced data.
• Features research on multimodal foundation models integrating transcriptomics with other data types like genomics, proteomics, and imaging.
• Covers applications in biological discovery, drug resistance deciphering, cell niche characterization, and perturbation analysis.

Maintenance & Community

This is a static collection of research papers, not an active software project. Updates would depend on community contributions to add new relevant publications.

Licensing & Compatibility

The repository itself is a list of links to research papers. The licensing and compatibility of the underlying research papers are determined by their respective publishers and venues.

Limitations & Caveats

This repository is a literature compilation and does not provide any code, models, or direct tools for users. All practical implementation details and model access would be found within the linked research papers.