M3
M3 is a deep generative framework for condition-aware integration, patient-level inference, and multi-resolution interpretation of multimodal single-cell omics data across many biological conditions and samples.
From one shared, condition-aware representation, M3 delivers six downstream tasks: factorised dimension reduction, condition-aware batch correction, mosaic integration + imputation, patient-level condition inference, patient/sample generation, and multi-resolution attribution.
📖 Documentation & tutorials: https://pyanglab.github.io/M3/
Install
Python
pip install m3-sc # imported as `import m3`
PyTorch comes along automatically. If you need a build matched to a specific CUDA version, install it first and pip will keep it:
pip install torch --index-url https://download.pytorch.org/whl/cu121
pip install m3-sc
For the development version: pip install "git+https://github.com/PYangLab/M3.git".
R
install.packages("remotes")
remotes::install_github("PYangLab/M3", subdir = "m3-r")
The R package provisions its own Python engine through basilisk on first use — no manual Python / PyTorch setup, and R and Python produce identical results.
Quickstart (Python)
import m3
data = m3.datasets.liu_demo() # built-in demo
model = m3.M3(data, condition_keys=["cond_group"],
celltype_key="mergedcelltype").train()
emb = model.embedding(part="bio") # integrated embedding
Repository layout
| Path | What |
|---|---|
src/m3/ |
the m3-sc Python package (PyTorch engine vendored under _engine/) |
m3-r/ |
the R package — library(m3), basilisk-wrapped, same engine |
website/ |
documentation site (MkDocs Material); mkdocs serve -f website/mkdocs.yml |
.github/workflows/deploy.yml |
builds + publishes the docs to GitHub Pages |
tests/ |
Python test suite |
License
See LICENSE.
2 maintainers
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