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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:
- Single-modality analysis
- Single-cell multimodal omics
- 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
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 |