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.

Key Features

• 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.

• 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.

Installation and Tutorials

Full documentation is available at: https://scmg.readthedocs.io/

The scripts to reproduce the results reported in the manuscript are available here.