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https://github.com/earmingol/sccellfie

scCellFie offers advanced analysis of metabolic functions from single-cell and spatial transcriptomics. Efficient and user-friendly, it runs on Python and integrates with Scanpy, enabling single-cell and spatial analysis of metabolic tasks.
https://github.com/earmingol/sccellfie

metabolic-modeling metabolic-pathways metabolism single-cell single-cell-transcriptomics spatial-transcriptomics systems-biology

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scCellFie offers advanced analysis of metabolic functions from single-cell and spatial transcriptomics. Efficient and user-friendly, it runs on Python and integrates with Scanpy, enabling single-cell and spatial analysis of metabolic tasks.

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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.



.. image:: https://github.com/earmingol/scCellFie/blob/main/scCellFie-analysis.png?raw=true

   :alt: Logo

   :width: 500

   :height: 590

   :align: center



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 ~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.



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 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

-----------



- **Metabolic activities inferred from single-cell and spatial transcriptomic atlases in health and disease**.

  *bioRxiv, 2025*. https://doi.org/10.1101/XXXXXX



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.