scCellFie 0.2.2

A tool for studying metabolic tasks from single-cell and spatial transcriptomics

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, another tool originally developed in MATLAB by the Lewis Lab. 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.

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 developed by the Lewis Lab. 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