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, scCellFie includes a series of improvements and new analyses, such as marker selection, differential analysis, and cell-cell communication inference.

Installation

To install scCellFie, use pip:

pip install sccellfie

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 ~7 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.

Quick start

A quick example of how to use scCellFie with a single-cell dataset and generate results:

import sccellfie
import scanpy as sc

# Load the dataset
adata = sc.read(filename='BALF-COVID19.h5ad',
                backup_url='https://zenodo.org/record/7535867/files/BALF-COVID19-Liao_et_al-NatMed-2020.h5ad')

# Run one-command scCellFie pipeline
results = sccellfie.run_sccellfie_pipeline(adata,
                                           organism='human',
                                           sccellfie_data_folder=None,
                                           n_counts_col='n_counts',
                                           process_by_group=False,
                                           groupby=None,
                                           neighbors_key='neighbors',
                                           n_neighbors=10,
                                           batch_key='sample',
                                           threshold_key='sccellfie_threshold',
                                           smooth_cells=True,
                                           alpha=0.33,
                                           chunk_size=5000,
                                           disable_pbar=False,
                                           save_folder=None,
                                           save_filename=None
                                          )

To access metabolic activities, we need to inspect results['adata']:

• The processed single-cell data is located in the AnnData object results['adata'].
• The reaction activities for each cell are located in the AnnData object results['adata'].reactions.
• The metabolic task activities for each cell are located in the AnnData object results['adata'].metabolic_tasks.

In particular:

• results['adata']: contains gene expression in .X.
• results['adata'].layers['gene_scores']: contains gene scores as in the original CellFie paper.
• results['adata'].uns['Rxn-Max-Genes']: contains determinant genes for each reaction per cell.
• results['adata'].reactions: contains reaction scores in .X so every scanpy function can be used on this object to visualize or compare values.
• results['adata'].metabolic_tasks: contains metabolic task scores in .X so every scanpy function can be used on this object to visualize or compare values.

Other keys in the results dictionary are associated with the scCellFie database and are already filtered for the elements present in the dataset ('gpr_rules', 'task_by_gene', 'rxn_by_gene', 'task_by_rxn', 'rxn_info', 'task_info', 'thresholds', 'organism').

Documentation

For detailed documentation and tutorials, visit the scCellFie documentation.

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

Contributing

We welcome contributions! Feel free to add requests in the issues section or directly contribute with a pull request.