Metadata-Version: 2.4
Name: scMMDI
Version: 0.0.1
Summary: A deep learning framework for single cell multimode data mosaic integration
Home-page: https://github.com/NMD-CompBio/scMMDI
Author: Mengdi Nan
Author-email: 2139698547@qq.com
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Dynamic: author
Dynamic: author-email
Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
Dynamic: home-page
Dynamic: license-file
Dynamic: requires-python
Dynamic: summary

# scMMDI_py
scMMDI: A deep learning framework for single cell multimode data mosaic integration
This is the Python implementation of the scMMDI algorithm. Note that this implementation supports GPU acceleration.

## 1. Installation
You can use the following command to install scMMDI:
```
pip install scMMDI
```

## 2. Usage
scMMDI accepts a muon object as input and populates the obsm and uns fields with embeddings and dictionaries, respectively.
The pre-processed PBMC dataset is available for download here. It is recommended that a GPU be used to run the model.
To initialize and run the scMMDI model, the following code should be used:

```
from scMMDI.model import scMMDI
import mudata as md
import scanpy as sc

# Load data into a Muon object.
mdata = md.read_h5mu("my_data.h5mu")

# Initialize and train the model.
model = scMMDI(latent_dim=30)
model.fit(mdata)

# Visualize the embedding with UMAP.
sc.pp.neighbors(mdata, use_rep="scMMDI_z")
sc.tl.umap(mdata)
sc.pl.umap(mdata)
```

'scMMDI' class also has other methods, you can use the 'help' or '?' command for more details explanations of the methods.

