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OmicsML / dance

DANCE: a deep learning library and benchmark platform for single-cell analysis
https://pydance.readthedocs.io
BSD 2-Clause "Simplified" License
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benchmark bioinformatics computational-biology dance data-science deep-learning graph-neural-networks machine-learning multimodality python single-cell single-cell-rna-seq single-cell-rna-sequencing spatial-transcriptomics

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DANCE is a Python toolkit to support deep learning models for analyzing single-cell gene expression at scale. Our goal is to build up a deep learning community and benchmark platform for computational models in single-cell analysis. It includes three modules at present:

  1. Single-modality analysis
  2. Single-cell multimodal omics
  3. Spatially resolved transcriptomics

Useful links

OmicsML Homepage: https://omicsml.ai \ DANCE Open Source: https://github.com/OmicsML/dance \ DANCE Documentation: https://pydance.readthedocs.io/en/latest/ \ DANCE Tutorials: https://github.com/OmicsML/dance-tutorials \ DANCE Package Paper: https://www.biorxiv.org/content/10.1101/2022.10.19.512741v2 \ Survey Paper: https://arxiv.org/abs/2210.12385

Join the Community

Slack: https://join.slack.com/t/omicsml/shared_invite/zt-1hxdz7op3-E5K~EwWF1xDvhGZFrB9AbA \ Twitter: https://twitter.com/OmicsML \ Wechat Group Assistant: 736180290 \ Email: danceteamgnn@gmail.com

Contributing

Community-wide contribution is the key for a sustainable development and continual growth of the DANCE package. We deeply appreciate any contribution made to improve the DANCE code base. If you would like to get started, please refer to our brief guidelines about our automated quality controls, as well as setting up the dev environments.

Citation

If you find our work useful in your research, please consider citing our DANCE package or survey paper:

@article{ding2024dance,
  title={DANCE: A deep learning library and benchmark platform for single-cell analysis},
  author={Ding, Jiayuan and Liu, Renming and Wen, Hongzhi and Tang, Wenzhuo and Li, Zhaoheng and Venegas, Julian and Su, Runze and Molho, Dylan and Jin, Wei and Wang, Yixin and others},
  journal={Genome Biology},
  volume={25},
  number={1},
  pages={1--28},
  year={2024},
  publisher={BioMed Central}
}
@article{molho2024deep,
  title={Deep learning in single-cell analysis},
  author={Molho, Dylan and Ding, Jiayuan and Tang, Wenzhuo and Li, Zhaoheng and Wen, Hongzhi and Wang, Yixin and Venegas, Julian and Jin, Wei and Liu, Renming and Su, Runze and others},
  journal={ACM Transactions on Intelligent Systems and Technology},
  volume={15},
  number={3},
  pages={1--62},
  year={2024},
  publisher={ACM New York, NY}
}

Usage

Overview

In release 1.0, the main usage of the DANCE is to provide readily available experiment reproduction (see detail information about the reproduced performance below). Users can easily reproduce selected experiments presented in the original papers for the computational single-cell methods implemented in DANCE, which can be found under examples/.

Motivation

Computational methods for single-cell analysis are quickly emerging, and the field is revolutionizing the usage of single-cell data to gain biological insights. A key challenge to continually developing computational single-cell methods that achieve new state-of-the-art performance is reproducing previous benchmarks. More specifically, different studies prepare their datasets and perform evaluation differently, and not to mention the compatibility of different methods, as they could be written in different languages or using incompatible library versions.

DANCE addresses these challenges by providing a unified Python package implementing many popular computational single-cell methods (see Implemented Algorithms), as well as easily reproducible experiments by providing unified tools for

Example: run cell-type annotation benchmark using scDeepSort

Installation

Quick install

The full installation process might be a bit tedious and could involve some debugging when using CUDA enabled packages. Thus, we provide an install.sh script that simplifies the installation process, assuming the user have conda set up on their machines. The installation script creates a conda environment dance and install the DANCE package along with all its dependencies with a apseicifc CUDA version. Currently, two options are accepted: cpu and cu118. For example, to install the DANCE package using CUDA 11.8 in a dance-env conda environment, simply run:

# Clone the repository via SSH
git clone git@github.com:OmicsML/dance.git && cd dance
# Alternatively, use HTTPS if you have not set up SSH
# git clone https://github.com/OmicsML/dance.git  && cd dance

# Run the auto installation script to install DANCE and its dependencies in a conda environment
source install.sh cu118 dance-env

Note: the first argument for cuda version is mandatory, while the second argument for conda environment name is optional (default is dance).

Custom install
**Step1. Setup environment** First create a conda environment for dance (optional) ```bash conda create -n dance python=3.11 -y && conda activate dance ``` Then, install CUDA enabled packages (PyTorch, PyG, DGL): ```bash pip install torch==2.1.1 torchvision==0.16.1 --index-url https://download.pytorch.org/whl/cu118 pip install torch_geometric==2.4.0 pip install dgl==1.1.3 -f https://data/dgl.ai/wheels/cu118/repo.html ``` Alternatively, install these dependencies for CPU only: ```bash pip install torch==2.1.1 torchvision==0.16.1 --index-url https://download.pytorch.org/whl/cpu pip install torch_geometric==2.4.0 pip install dgl==1.1.3 -f https://data/dgl.ai/wheels/repo.html ``` For more information about installation or other CUDA version options, check out the installation pages for the corresponding packages - [PyTorch](https://pytorch.org/get-started/) - [PyG](https://pytorch-geometric.readthedocs.io/en/latest/notes/installation.html) - [DGL](https://www.dgl.ai/pages/start.html) **Step2. Install DANCE** Install from PyPI ```bash pip install pydance ``` Or, install the latest dev version from source ```bash git clone https://github.com/OmicsML/dance.git && cd dance pip install -e . ```

Implemented Algorithms

P1 not covered in the first release

Single Modality Module

1）Imputation

BackBone Model Algorithm Year CheckIn
GNN GraphSCI Imputing Single-cell RNA-seq data by combining Graph Convolution and Autoencoder Neural Networks 2021 ✅
GNN scGNN (2020) SCGNN: scRNA-seq Dropout Imputation via Induced Hierarchical Cell Similarity Graph 2020 P1
GNN scGNN (2021) scGNN is a novel graph neural network framework for single-cell RNA-Seq analyses 2021 ✅
GNN GNNImpute An efficient scRNA-seq dropout imputation method using graph attention network 2021 P1
Graph Diffusion MAGIC MAGIC: A diffusion-based imputation method reveals gene-gene interactions in single-cell RNA-sequencing data 2018 P1
Probabilistic Model scImpute An accurate and robust imputation method scImpute for single-cell RNA-seq data 2018 P1
GAN scGAIN scGAIN: Single Cell RNA-seq Data Imputation using Generative Adversarial Networks 2019 P1
NN DeepImpute DeepImpute: an accurate, fast, and scalable deep neural network method to impute single-cell RNA-seq data 2019 ✅
NN + TF Saver-X Transfer learning in single-cell transcriptomics improves data denoising and pattern discovery 2019 P1
Model Evaluation Metric Mouse Brain (current/reported) Mouse Embryo (current/reported) PBMC (current/reported)
DeepImpute RMSE 0.87 / N/A 1.20 / N/A 2.30 / N/A
GraphSCI RMSE 1.55 / N/A 1.81 / N/A 3.68 / N/A
scGNN2.0 MSE 1.04 / N/A 1.12 / N/A 1.22 / N/A

Note: scGNN2.0 is evaluated on 2,000 genes with highest variance following the original paper.

2）Cell Type Annotation

BackBone Model Algorithm Year CheckIn
GNN ScDeepsort Single-cell transcriptomics with weighted GNN 2021 ✅
Logistic Regression Celltypist Cross-tissue immune cell analysis reveals tissue-specific features in humans. 2021 ✅
Random Forest singleCellNet SingleCellNet: a computational tool to classify single cell RNA-Seq data across platforms and across species. 2019 ✅
Neural Network ACTINN ACTINN: automated identification of cell types in single cell RNA sequencing. 2020 ✅
Hierarchical Clustering SingleR Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage. 2019 P1
SVM SVM A comparison of automatic cell identification methods for single-cell RNA sequencing data. 2018 ✅
Model Evaluation Metric Mouse Brain 2695 (current/reported) Mouse Spleen 1759 (current/reported) Mouse Kidney 203 (current/reported)
scDeepsort ACC 0.542/0.363 0.969/0.965 0.847/0.911
Celltypist ACC 0.824/0.666 0.908/0.848 0.823/0.832
singleCellNet ACC 0.693/0.803 0.975/0.975 0.795/0.842
ACTINN ACC 0.727/0.778 0.657/0.236 0.762/0.798
SVM ACC 0.683/0.683 0.056/0.049 0.704/0.695

3）Clustering

BackBone Model Algorithm Year CheckIn
GNN graph-sc GNN-based embedding for clustering scRNA-seq data 2022 ✅
GNN scTAG ZINB-based Graph Embedding Autoencoder for Single-cell RNA-seq Interpretations 2022 ✅
GNN scDSC Deep structural clustering for single-cell RNA-seq data jointly through autoencoder and graph neural network 2022 ✅
GNN scGAC scGAC: a graph attentional architecture for clustering single-cell RNA-seq data 2022 P1
AutoEncoder scDeepCluster Clustering single-cell RNA-seq data with a model-based deep learning approach 2019 ✅
AutoEncoder scDCC Model-based deep embedding for constrained clustering analysis of single cell RNA-seq data 2021 ✅
AutoEncoder scziDesk Deep soft K-means clustering with self-training for single-cell RNA sequence data 2020 P1
Model Evaluation Metric 10x PBMC (current/reported) Mouse ES (current/reported) Worm Neuron (current/reported) Mouse Bladder (current/reported)
graph-sc ARI 0.72 / 0.70 0.82 / 0.78 0.57 / 0.46 0.68 / 0.63
scDCC ARI 0.82 / 0.81 0.98 / N/A 0.51 / 0.58 0.60 / 0.66
scDeepCluster ARI 0.81 / 0.78 0.98 / 0.97 0.51 / 0.52 0.56 / 0.58
scDSC ARI 0.72 / 0.78 0.84 / N/A 0.46 / 0.65 0.65 / 0.72
scTAG ARI 0.77 / N/A 0.96 / N/A 0.49 / N/A 0.69 / N/A

Multimodality Module

1）Modality Prediction

BackBone Model Algorithm Year CheckIn
GNN ScMoGCN Graph Neural Networks for Multimodal Single-Cell Data Integration 2022 ✅
GNN ScMoLP Link Prediction Variant of ScMoGCN 2022 P1
GNN GRAPE Handling Missing Data with Graph Representation Learning 2020 P1
Generative Model SCMM SCMM: MIXTURE-OF-EXPERTS MULTIMODAL DEEP GENERATIVE MODEL FOR SINGLE-CELL MULTIOMICS DATA ANALYSIS 2021 ✅
Auto-encoder Cross-modal autoencoders Multi-domain translation between single-cell imaging and sequencing data using autoencoders 2021 ✅
Auto-encoder BABEL BABEL enables cross-modality translation between multiomic profiles at single-cell resolution 2021 ✅
Model Evaluation Metric GEX2ADT (current/reported) ADT2GEX (current/reported) GEX2ATAC (current/reported) ATAC2GEX (current/reported)
ScMoGCN RMSE 0.3885 / 0.3885 0.3242 / 0.3242 0.1778 / 0.1778 0.2315 / 0.2315
SCMM RMSE 0.6264 / N/A 0.4458 / N/A 0.2163 / N/A 0.3730 / N/A
Cross-modal autoencoders RMSE 0.5725 / N/A 0.3585 / N/A 0.1917 / N/A 0.2551 / N/A
BABEL RMSE 0.4335 / N/A 0.3673 / N/A 0.1816 / N/A 0.2394 / N/A

2) Modality Matching

BackBone Model Algorithm Year CheckIn
GNN ScMoGCN Graph Neural Networks for Multimodal Single-Cell Data Integration 2022 ✅
GNN/Auto-ecnoder GLUE Multi-omics single-cell data integration and regulatory inference with graph-linked embedding 2021 P1
Generative Model SCMM SCMM: MIXTURE-OF-EXPERTS MULTIMODAL DEEP GENERATIVE MODEL FOR SINGLE-CELL MULTIOMICS DATA ANALYSIS 2021 ✅
Auto-encoder Cross-modal autoencoders Multi-domain translation between single-cell imaging and sequencing data using autoencoders 2021 ✅
Model Evaluation Metric GEX2ADT (current/reported) GEX2ATAC (current/reported)
ScMoGCN Accuracy 0.0827 / 0.0810 0.0600 / 0.0630
SCMM Accuracy 0.005 / N/A 5e-5 / N/A
Cross-modal autoencoders Accuracy 0.0002 / N/A 0.0002 / N/A

3) Joint Embedding

BackBone Model Algorithm Year CheckIn
GNN ScMoGCN Graph Neural Networks for Multimodal Single-Cell Data Integration 2022 ✅
Auto-encoder scMVAE Deep-joint-learning analysis model of single cell transcriptome and open chromatin accessibility data 2020 ✅
Auto-encoder scDEC Simultaneous deep generative modelling and clustering of single-cell genomic data 2021 ✅
GNN/Auto-ecnoder GLUE Multi-omics single-cell data integration and regulatory inference with graph-linked embedding 2021 P1
Auto-encoder DCCA Deep cross-omics cycle attention model for joint analysis of single-cell multi-omics data 2021 ✅
Model Evaluation Metric GEX2ADT (current/reported) GEX2ATAC (current/reported)
ScMoGCN ARI 0.706 / N/A 0.702 / N/A
ScMoGCNv2 ARI 0.734 / N/A N/A / N/A
scMVAE ARI 0.499 / N/A 0.577 / N/A
scDEC(JAE) ARI 0.705 / N/A 0.735 / N/A
DCCA ARI 0.35 / N/A 0.381 / N/A

4) Multimodal Imputation

BackBone Model Algorithm Year CheckIn
GNN ScMoLP Link Prediction Variant of ScMoGCN 2022 P1
GNN scGNN scGNN is a novel graph neural network framework for single-cell RNA-Seq analyses 2021 P1
GNN GRAPE Handling Missing Data with Graph Representation Learning 2020 P1

5) Multimodal Integration

BackBone Model Algorithm Year CheckIn
GNN ScMoGCN Graph Neural Networks for Multimodal Single-Cell Data Integration 2022 P1
GNN scGNN scGNN is a novel graph neural network framework for single-cell RNA-Seq analyses (GCN on Nearest Neighbor graph) 2021 P1
Nearest Neighbor WNN Integrated analysis of multimodal single-cell data 2021 P1
GAN MAGAN MAGAN: Aligning Biological Manifolds 2018 P1
Auto-encoder SCIM SCIM: universal single-cell matching with unpaired feature sets 2020 P1
Auto-encoder MultiMAP MultiMAP: Dimensionality Reduction and Integration of Multimodal Data 2021 P1
Generative Model SCMM SCMM: MIXTURE-OF-EXPERTS MULTIMODAL DEEP GENERATIVE MODEL FOR SINGLE-CELL MULTIOMICS DATA ANALYSIS 2021 P1

Spatial Module

1）Spatial Domain

BackBone Model Algorithm Year CheckIn
GNN SpaGCN SpaGCN: Integrating gene expression, spatial location and histology to identify spatial domains and spatially variable genes by graph convolutional network 2021 ✅
GNN STAGATE Deciphering spatial domains from spatially resolved transcriptomics with adaptive graph attention auto-encoder 2021 ✅
Bayesian BayesSpace Spatial transcriptomics at subspot resolution with BayesSpace 2021 P1
Pseudo-space-time (PST) Distance stLearn stLearn: integrating spatial location, tissue morphology and gene expression to find cell types, cell-cell interactions and spatial trajectories within undissociated tissues 2020 ✅
Heuristic Louvain Fast unfolding of community hierarchies in large networks 2008 ✅
Model Evaluation Metric 151673 (current/reported) 151676 (current/reported) 151507 (current/reported)
SpaGCN ARI 0.51 / 0.522 0.41 / N/A 0.45 / N/A
STAGATE ARI 0.59 / N/A 0.60 / 0.60 0.608 / N/A
stLearn ARI 0.30 / 0.36 0.29 / N/A 0.31 / N/A
Louvain ARI 0.31 / 0.33 0.2528 / N/A 0.28 / N/A

2）Cell Type Deconvolution

BackBone Model Algorithm Year CheckIn
GNN DSTG DSTG: deconvoluting spatial transcriptomics data through graph-based artificial intelligence 2021 ✅
logNormReg SpatialDecon Advances in mixed cell deconvolution enable quantification of cell types in spatial transcriptomic data 2022 ✅
NNMFreg SPOTlight SPOTlight: seeded NMF regression to deconvolute spatial transcriptomics spots with single-cell transcriptomes 2021 ✅
NN Linear + CAR assumption CARD Spatially informed cell-type deconvolution for spatial transcriptomics 2022 ✅
Model Evaluation Metric GSE174746 (current/reported) CARD Synthetic (current/reported) SPOTlight Synthetic (current/reported)
DSTG MSE .1722 / N/A .0239 / N/A .0315 / N/A
SpatialDecon MSE .0014 / .009 .0077 / N/A .0055 / N/A
SPOTlight MSE .0098 / N/A .0246 / 0.118 .0109 / .16
CARD MSE .0012 / N/A .0078 / 0.0062 .0076 / N/A