Single Cell Manifold Generator (SCMG)
=====================================

**SCMG** is a suite of deep learning models designed to interpret, generate, and predict the molecular basis of cell states and their transitions.

.. image:: https://raw.githubusercontent.com/xingjiepan/SCMG/main/global_patterns/global_cell_type_umap.png
   :alt: Global cell type UMAP
   :align: center

Key Features
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- **Global Manifold Construction**  
  Build a well-integrated reference manifold of single-cell transcriptional states that captures cell-state relationships and gene expression patterns. The global gene expression patterns can be visualized `here <https://xingjiepan.github.io/SCMG/>`_.

- **Zero-Shot Dataset Integration**  
  Integrate new scRNA-seq datasets without the need for model retraining.

- **Zero-Shot Cell Projection**  
  Project single-cells onto the global manifold for downstream analysis and comparison.

- **Cell State Trajectory Generation**  
  Generate continuous trajectories to model transitions between cell states.

- **Causal Gene Prediction**  
  Identify candidate causal genes driving transitions between specific cell states.
  
- **Universal Decomposition of Perturbation Effects**  
  Decompose perturbation effects into universal principal axes of cell state transition and perturbation classes. 

- **Few-shot Prediction of Perturbation Effects**  
  Predict perturbation-induced cell state transition by few-shot learning.

Contents
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.. toctree::

   installation
   tutorials/index
   api/index