SnapCCESS: Ensemble deep learning of embeddings for clustering multimodal single-cell omics data.
We propose SnapCCESS for clustering cells by integrating data modalities in multimodal single-cell omics data using an unsupervised ensemble deep learning framework. By creating snapshots of embeddings of multimodality using variational autoencoders, SnapCCESS can be coupled with various clustering algorithms for generating consensus clustering of cells.
pip install snapccess --index-url https://pypi.org/simple
For detailed description of each function, please see https://github.com/PYangLab/SnapCCESS/tree/main/snapccess-py
remotes::install_github(repo='PYangLab/SnapCCESS',branch='main',subdir='snapccess-r/SnapCCESS')
For detailed description of each function, please see https://github.com/PYangLab/SnapCCESS/tree/main/snapccess-r
NOTE: This tutorial only explains how to use this package; it doesn't recommend the best parameters for your datasets. For the datasets used in the published paper associated with this package, the parameters are listed in the same paper. Please refer to the paper to guide you in finding the best parameters.
For python version of script, please see an_example_of_generate_embedding_using_SnapCCESS_python_version
For R version of script, please see SnapCCESS_R_example
Lijia Yu, Chunlei Liu, Jean Yee Hwa Yang, Pengyi Yang. Ensemble deep learning of embeddings for clustering multimodal single-cell omics data. Bioinformatics, 39(6), btad382, doi: https://doi.org/10.1093/bioinformatics/btad382, (2023).