Single-cell Foundation Models
Single-cell foundation models (scFMs) are a new class of models that leverage the power of foundation models to analyze and interpret single-cell data. They are designed to capture the complex relationships and interactions within single-cell datasets, enabling researchers to gain deeper insights into cellular heterogeneity, cell type identification, and functional characterization.
scGPT
scGPT is a generative pre-trained transformer model specifically designed for single-cell RNA sequencing (scRNA-seq) data. It leverages the power of transformer architectures to capture the intricate relationships between genes and cells, enabling accurate cell type identification, differential expression analysis, and trajectory inference.
Learn MoreGeneformer
Geneformer is a foundational transformer model pretrained on a large-scale corpus of single cell transcriptomes to enable context-aware predictions in settings with limited data in network biology.
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Single-cell variational inference (scVI) is a powerful tool for the probabilistic analysis of single-cell transcriptomics data. It uses deep generative models to address technical noise and batch effects, providing a robust framework for various downstream analysis tasks. To load the pre-trained model, use the Files and Versions tab files.
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