Metabolic activity from single‑cell and spatial transcriptomics with scCellFie
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 from the Lewis Lab (originally in MATLAB for bulk and small single‑cell datasets), scCellFie adds multiple improvements such as marker selection, differential analysis, and cell‑cell communication inference.
Features
- Single‑cell & spatial data analysis – infer metabolic activity per single cell or spatial spot.
- Speed – analyzes ~100 k single cells in ~8 min with modest memory.
- Downstream analyses – includes marker selection, differential testing, and integration with cell‑cell communication inference.
- User‑friendly – Python-based for easier use and integration into existing workflows, including Jupyter Notebooks.
- Scanpy compatibility – fully integrated with the popular single‑cell analysis toolkit.
- Organism coverage – metabolic databases available for human and mouse.
Documentation & tutorials
- 📚 Full docs and tutorials: https://sccellfie.readthedocs.io
- 🌐 Interactive results visualizer: https://www.sccellfie.org/
Project link: https://github.com/earmingol/scCellFie