foundation-model-analysis

To generate cell embeddings for single-cell RNA-seq data, this Skill applies foundation models like scGPT through a unified 6-step workflow. It automates data profiling and preprocessing validation to produce embeddings ready for downstream visualization.

Choosing between scGPT, Geneformer, and UCE for single-cell annotation is handled by intelligent model selection based on your specific task, species, and hardware constraints. The Skill evaluates these factors to recommend the most suitable foundation model architecture.

Yes, you can use this Skill for batch integration of multiple single-cell datasets. It supports dataset integration alongside cell embedding generation and annotation through its unified API managing 22 different foundation model architectures.

Yes, you need the omicverse library installed to run foundation model analysis. The Skill specifically requires the omicverse library to access the ov.fm module, which provides the underlying API for executing the 22 supported foundation models.

Single-cell data profiling validates preprocessing steps by automatically checking your data compatibility against the requirements of the selected foundation model. It suggests necessary preprocessing actions to ensure your dataset is correctly formatted before analysis.