---

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

• Data downloading
• Data (pre-)processing and transformation (e.g. graph construction)
• Model training and evaluation

Example: run cell-type annotation benchmark using scDeepSort

• Step0. Install DANCE (see Installation)
• Step1. Navigate to the folder containing the corresponding example scrtip. In this case, it is examples/single_modality/cell_type_annotation.
• Step2. Obtain command line interface (CLI) options for a particular experiment to reproduce at the end of the script. For example, the CLI options for reproducing the Mouse Brain experiment is

  python scdeepsort.py --species mouse --tissue Brain --train_dataset 753 3285 --test_dataset 2695

• Step3. Wait for the experiment to finish and check results.

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)

conda create -n dance python=3.11 -y && conda activate dance

Then, install CUDA enabled packages (PyTorch, PyG, DGL):

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:

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
• PyG
• DGL

Step2. Install DANCE

Install from PyPI

pip install pydance

Or, install the latest dev version from source

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