Open Source Ecosystems

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Metabolic activity from single-cell and spatial transcriptomics with scCellFie

scCellFie is a computational tool for studying metabolic tasks using Python, inspired by the original implementation of CellFie <https://github.com/LewisLabUCSD/CellFie>, another tool originally developed in MATLAB by the Lewis Lab <https://lewislab.ucsd.edu/>. This version is designed to be compatible with single-cell and spatial data analysis using Scanpy, while including a series of improvements and new analyses.

.. image:: https://github.com/earmingol/scCellFie/blob/main/scCellFie-Logo.png?raw=true :alt: Logo :width: 350 :height: 188.31 :align: center

Installation

To install scCellFie, use pip::

pip install sccellfie

Features

Single cell and spatial data analysis: Tailored for analysis of metabolic tasks using fully single cell resolution and in space.
Speed: This implementation further leverages the original CellFie. It is now memory efficient and run much faster! A dataset of ~70k single cells can be analyzed in ~5 min.
New 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.
Scanpy compatibility: Fully integrated with Scanpy, the popular single cell analysis toolkit.
Organisms: Metabolic database and analysis available for human and mouse.

How to cite

Preprint is coming soon!

Acknowledgments

This implementation is inspired by the original CellFie tool <https://github.com/LewisLabUCSD/CellFie>_ developed by the Lewis Lab <https://lewislab.ucsd.edu/>_. Please consider citing their work if you find this 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