Welcome to scDynOmics's documentation!
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**scDynOmics** is an optimized, pretrainable transformer model designed for representation learning from single-cell multiomics data.

Motivated by gene regulatory networks, scDynOmics adopts a Linformer-style attention mechanism to efficiently scale to coding-genome wide multimodal inputs.
By pretraining on paired single-cell transcriptomic and chromatin accessibility profiles, the model yields compact embeddings that represent cellular states and developmental dynamics.
scDynOmics also supports unimodal data for pretraining and/or finetuning.

For versatile downstream applications, the framework employs low-rank adaptation modules for parameter-efficient fine-tuning, enabling rapid adaptation to diverse biological tasks.

.. note::

   This project is under active development.

Key Features
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* **Scalable Architecture:** Utilizes a Linformer-style attention mechanism (via :class:`scdynomics.Masked_Pretrainer`) to handle large-scale inputs efficiently.
* **Parameter-Efficient Fine-Tuning:** Incorporates low-rank adaptation modules (:class:`scdynomics.FT_MLP_Classifier`, etc) for lightweight adaptation to diverse biological tasks.
* **Interpretable Framework:** Deciphers cellular heterogeneity and provides interpretable factors for developmental dynamics.

Citation
--------

If you use scDynOmics in your research, please cite our bioRxiv preprint:

.. code-block:: bibtex

   @misc{yu2026scdynomics,
     title={scDynOmics: An Optimized Transformer Model for Representation Learning from Single-Cell Multiomics},
     author={Yu, Gang and Ramnarine, Timothy J. S. and Klughammer, Johanna and Mages, Simon W.},
     year={2026},
     publisher={bioRxiv},
     url={https://doi.org/10.64898/2026.02.28.708160},
     doi={10.64898/2026.02.28.708160}
   }

Contents
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.. toctree::
   :maxdepth: 2
   :caption: Getting Started

   Installation <installation>
   Examples <examples>
   API Reference <api>