Welcome to scCellFie’s documentation!
Metabolic activity from single-cell and spatial transcriptomics with scCellFie
scCellFie is a Python-based tool for analyzing metabolic activity at different resolutions, developed at the Vento Lab. It efficiently processes both single-cell and spatial data to predict metabolic task activities. While its prediction strategy is inspired by CellFie, a tool from the Lewis Lab originally developed in MATLAB for bulk and small single-cell datasets, scCellFie includes a series of improvements and new analyses, such as marker selection, differential analysis, and cell-cell communication inference.
Features
Single cell and spatial data analysis: Inference of metabolic activity per single cell or spatial spot.
Speed: Runs fast and memory efficiently, scaling up to large datasets. ~100k single cells can be analyzed in ~8 min.
Downstream analyses: From marker selection of relevant metabolic tasks to integration with inference of cell-cell communication.
User-friendly: Python-based for easier use and integration into existing workflows, including Jupyter Notebooks.
Scanpy compatibility: Fully integrated with Scanpy, the popular single-cell analysis toolkit.
Organisms: Metabolic database and analysis available for human and mouse.
Documentation and Tutorials
For detailed documentation and tutorials, visit the scCellFie documentation.
For visualizing a summarized version of the results, visit the scCellFie Metabolic Task Visualizer.
How to Cite
Please consider citing our work if you find scCellFie useful:
Atlas-scale metabolic activities inferred from single-cell and spatial transcriptomics. bioRxiv, 2025. https://doi.org/10.1101/2025.05.09.653038
Acknowledgments
This tool is inspired by the original CellFie tool developed by the Lewis Lab. Please consider citing their work if you find our tool useful:
Model-based assessment of mammalian cell metabolic functionalities using omics data. Cell Reports Methods, 2021. https://doi.org/10.1016/j.crmeth.2021.100040
ImmCellFie: A user-friendly web-based platform to infer metabolic function from omics data. STAR Protocols, 2023. https://doi.org/10.1016/j.xpro.2023.102069
Inferring secretory and metabolic pathway activity from omic data with secCellFie. Metabolic Engineering, 2024. https://doi.org/10.1016/j.ymben.2023.12.006
Contributing
We welcome contributions! Feel free to add requests in the issues section or directly contribute with a pull request.