scmidas 0.1.17

A torch-based integration method for single-cell multi-omic data.

MIDAS: A Deep Generative Model for Mosaic Integration and Knowledge Transfer of Single-Cell Multimodal Data

MIDAS turns raw mosaic data into both imputed, batch-corrected data and disentangled latent representations, powering robust downstream analysis.

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MIDAS is a powerful deep probabilistic framework designed for the mosaic integration and knowledge transfer of single-cell multimodal data. It addresses key challenges in single-cell analysis, such as modality alignment, batch effect removal, and data imputation. By leveraging self-supervised modality alignment and information-theoretic latent disentanglement, MIDAS transforms fragmented, mosaic data into a complete and harmonized dataset ready for downstream analysis.

Whether you are working with transcriptomics (RNA), proteomics (ADT), or chromatin accessibility (ATAC), MIDAS provides a versatile solution to uncover deeper biological insights from complex, multi-source datasets.

• Documentation: scmidas.readthedocs.io
• Publication: Nature Biotechnology

✨ Key Features

• Mosaic Data Integration: Seamlessly integrates datasets where different batches measure different sets of modalities (e.g., some samples have RNA and ATAC, while others have only RNA).
• Multi-Modal Support: Natively supports RNA, ADT, and ATAC data, and can be easily configured to incorporate additional modalities.
• Data Imputation: Accurately imputes missing modalities, turning incomplete data into a complete multi-modal matrix.
• Batch Correction: Effectively removes technical variations between different batches, enabling consistent and reliable analysis across datasets.
• Knowledge Transfer: Leverages a pre-trained reference atlas to enable flexible and accurate knowledge transfer to new query datasets.
• Efficient and Scalable: Built on PyTorch Lightning for highly efficient model training, with support for advanced strategies like Distributed Data Parallel (DDP).
• Advanced Visualization: Integrates with TensorBoard for real-time monitoring of training loss and UMAP visualizations.

🚀 Installation

Get started with MIDAS by setting up a conda environment.

# 1. Create and activate a new conda environment
conda create -n scmidas python=3.12
conda activate scmidas

# 2. Install MIDAS from PyPI
pip install scmidas==--version

⚡ Getting Started: A Quick Example

Here is a minimal example to get you started with a mosaic integration task. For more detailed tutorials, please refer to our documentation.

from scmidas.config import load_config
from scmidas.model import MIDAS
import lightning as L

# 1. Configure and initialize the MIDAS model
# The configuration file allows you to specify modalities, layers, and other parameters.
configs = load_config()

# 2. Load your mosaic dataset
# The input should be an AnnData object where modalities are stored.
# Different batches can have different combinations of modalities.
model = MIDAS.configure_data_from_dir(configs, 'path/to/your/data', transform={'atac':'binarize'})

# 3. Train the model on your data
model.train(max_epochs=2000)

# 4. Obtain the integrated and imputed results
# The model returns an AnnData object with a unified latent space 
# and imputed values for the missing modalities.
pred = model.predict()

# 5. Visualize the results
model.get_emb_umap()

⚡ Update: Load data from MuData

In addition to loading data from a directory, MIDAS also supports direct initialization from a MuData object. This is useful when your multimodal dataset is already organized in memory with modality-specific AnnData objects.

A typical MuData object may look like this:

# Example MuData:
# MuData object with n_obs × n_vars = 10000 × 1200
#   2 modalities
#     rna: 10000 x 1000
#       obs: 'batch'
#       uns: 'mask_batch1', 'mask_batch2'
#     adt: 8000 x 200
#       obs: 'batch'
#       uns: 'mask_batch1', 'mask_batch2'

You can configure the model from MuData as follows:

from scmidas.config import load_config
from scmidas.model import MIDAS
import lightning as L

# 1. Load model configuration
configs = load_config()

# 2. Prepare your MuData object
# Assume `mdata` is already loaded in memory.
# Each modality should be stored in mdata.mod, for example:
#   mdata.mod['rna']
#   mdata.mod['adt']
#
# The `batch_key` specifies the column in .obs that indicates batch membership.
# The `dims_x` argument defines the input feature dimension for each modality.
model = MIDAS.configure_data_from_mdata(
    mdata=mdata,
    batch_key='batch',
    dims_x={
        'rna': [1000],
        'adt': [200],
    },
    configs=configs
)

# 3. Train the model
model.train(max_epochs=2000)

# 4. Run prediction
pred = model.predict()

📈 Reproducibility

To reproduce the results from our publication, please visit the reproducibility branch of this repository: github.com/labomics/midas/tree/reproducibility

📜 Citation

If you use MIDAS in your research, please cite our paper:

He, Z., Hu, S., Chen, Y. et al. Mosaic integration and knowledge transfer of single-cell multimodal data with MIDAS. Nat Biotechnol (2024). https://doi.org/10.1038/s41587-023-02040-y

@article{he2024mosaic,
  title={Mosaic integration and knowledge transfer of single-cell multimodal data with MIDAS},
  author={He, Zhen and Hu, Shuofeng and Chen, Yaowen and An, Sijing and Zhou, Jiahao and Liu, Runyan and Shi, Junfeng and Wang, Jing and Dong, Guohua and Shi, Jinhui and others},
  journal={Nature Biotechnology},
  pages={1--12},
  year={2024},
  publisher={Nature Publishing Group US New York}
}

🙌 Contributing

We welcome contributions from the community! If you have a suggestion, bug report, or want to contribute to the code, please feel free to open an issue or submit a pull request.

📝 License

MIDAS is available under the MIT License.