<p align="center">
<img
src="https://github.com/OmicsML/dance/blob/main/imgs/dance_logo.jpg"
style="width:100%; height:100%; object-fit:cover;"
/>
</p>
______________________________________________________________________
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[](https://omicsml.slack.com)
[](https://twitter.com/OmicsML)
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**
1. **Single-cell multimodal omics**
1. **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](CONTRIBUTING.md) 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:
```bibtex
@article{ding2022dance,
title={DANCE: A Deep Learning Library and Benchmark for Single-Cell Analysis},
author={Ding, Jiayuan and Wen, Hongzhi and Tang, Wenzhuo and Liu, Renming and Li, Zhaoheng and Venegas, Julian and Su, Runze and Molho, Dylan and Jin, Wei and Zuo, Wangyang and others},
journal={bioRxiv},
year={2022},
publisher={Cold Spring Harbor Laboratory}
}
```
```bibtex
@article{molho2022deep,
title={Deep Learning in Single-Cell Analysis},
author={Molho, Dylan and Ding, Jiayuan and Li, Zhaoheng and Wen, Hongzhi and Tang, Wenzhuo and Wang, Yixin and Venegas, Julian and Jin, Wei and Liu, Renming and Su, Runze and others},
journal={arXiv preprint arXiv:2210.12385},
year={2022}
}
```
## 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](#implemented-algorithms)).
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/`](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](#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](#installation))
- Step1. Navigate to the folder containing the corresponding example scrtip.
In this case, it is [`examples/single_modality/cell_type_annotation`](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](examples/single_modality/cell_type_annotation/scdeepsort.py).
For example, the CLI options for reproducing the `Mouse Brain` experiment is
```bash
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
<H3>Quick install</H3>
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](https://conda.io/projects/conda/en/latest/index.html) 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:
```bash
# 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`).
<details>
<summary><H3>Custom install</H3></summary>
<br>
**Step1. Setup environment**
First create a conda environment for dance (optional)
```bash
conda create -n dance python=3.8 -y && conda activate dance
```
Then, install CUDA enabled packages (PyTorch, PyG, DGL):
```bash
conda install pytorch=2.0.1 torchvision torchaudio pytorch-cuda=11.8 -c pytorch -c nvidia -y
conda install pyg=2.3.1 -c pyg -y
conda install dgl=1.1.2 -c dglteam/label/cu118 -y
```
Alternatively, install these dependencies for CPU only:
```bash
conda install pytorch=2.0.1 torchvision torchaudio cpuonly -c pytorch -y
conda install pyg=2.3.1 -c pyg -y
conda install dgl=1.1.2 -c dglteam -y
```
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 .
```
</details>
## 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 |
Raw data
{
"_id": null,
"home_page": "https://github.com/OmicsML/dance",
"name": "pydance",
"maintainer": "",
"docs_url": null,
"requires_python": ">=3.8",
"maintainer_email": "",
"keywords": "Single-cell Biology,Deep Learning,Graph Neural Networks",
"author": "\"DANCE Team\"",
"author_email": "\"danceteamgnn@gmail.com\"",
"download_url": "https://files.pythonhosted.org/packages/57/9e/157055a4bf86b56dcdaccbb6e6a5b5c41e8c70a26d401364e447050f6b8f/pydance-1.0.1.tar.gz",
"platform": null,
"description": "<p align=\"center\">\n <img\n src=\"https://github.com/OmicsML/dance/blob/main/imgs/dance_logo.jpg\"\n style=\"width:100%; height:100%; object-fit:cover;\"\n />\n</p>\n\n______________________________________________________________________\n\n[](https://badge.fury.io/py/pydance)\n[](https://opensource.org/licenses/BSD-2-Clause)\n[](https://pydance.readthedocs.io/en/latest/?badge=latest)\n[](https://github.com/OmicsML/dance/actions/workflows/test_examples.yml)\n\n[](https://omicsml.slack.com)\n[](https://twitter.com/OmicsML)\n\nDANCE 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:\n\n1. **Single-modality analysis**\n1. **Single-cell multimodal omics**\n1. **Spatially resolved transcriptomics**\n\n### Useful links\n\nOmicsML Homepage: https://omicsml.ai \\\nDANCE Open Source: https://github.com/OmicsML/dance \\\nDANCE Documentation: https://pydance.readthedocs.io/en/latest/ \\\nDANCE Tutorials: https://github.com/OmicsML/dance-tutorials \\\nDANCE Package Paper: https://www.biorxiv.org/content/10.1101/2022.10.19.512741v2 \\\nSurvey Paper: https://arxiv.org/abs/2210.12385\n\n### Join the Community\n\nSlack: https://join.slack.com/t/omicsml/shared_invite/zt-1hxdz7op3-E5K~EwWF1xDvhGZFrB9AbA \\\nTwitter: https://twitter.com/OmicsML \\\nWechat Group Assistant: 736180290 \\\nEmail: danceteamgnn@gmail.com\n\n### Contributing\n\nCommunity-wide contribution is the key for a sustainable development and\ncontinual growth of the DANCE package. We deeply appreciate any contribution\nmade to improve the DANCE code base. If you would like to get started, please\nrefer to our brief [guidelines](CONTRIBUTING.md) about our automated quality\ncontrols, as well as setting up the `dev` environments.\n\n## Citation\n\nIf you find our work useful in your research, please consider citing our DANCE package or survey paper:\n\n```bibtex\n@article{ding2022dance,\n title={DANCE: A Deep Learning Library and Benchmark for Single-Cell Analysis},\n author={Ding, Jiayuan and Wen, Hongzhi and Tang, Wenzhuo and Liu, Renming and Li, Zhaoheng and Venegas, Julian and Su, Runze and Molho, Dylan and Jin, Wei and Zuo, Wangyang and others},\n journal={bioRxiv},\n year={2022},\n publisher={Cold Spring Harbor Laboratory}\n}\n```\n\n```bibtex\n@article{molho2022deep,\n title={Deep Learning in Single-Cell Analysis},\n author={Molho, Dylan and Ding, Jiayuan and Li, Zhaoheng and Wen, Hongzhi and Tang, Wenzhuo and Wang, Yixin and Venegas, Julian and Jin, Wei and Liu, Renming and Su, Runze and others},\n journal={arXiv preprint arXiv:2210.12385},\n year={2022}\n}\n```\n\n## Usage\n\n### Overview\n\nIn release 1.0, the main usage of the DANCE is to provide readily available experiment reproduction\n(see detail information about the reproduced performance [below](#implemented-algorithms)).\nUsers 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/`](examples).\n\n### Motivation\n\nComputational methods for single-cell analysis are quickly emerging, and the field is revolutionizing the usage of single-cell data to gain biological insights.\nA key challenge to continually developing computational single-cell methods that achieve new state-of-the-art performance is reproducing previous benchmarks.\nMore specifically, different studies prepare their datasets and perform evaluation differently,\nand not to mention the compatibility of different methods, as they could be written in different languages or using incompatible library versions.\n\nDANCE addresses these challenges by providing a unified Python package implementing many popular computational single-cell methods (see [Implemented Algorithms](#implemented-algorithms)),\nas well as easily reproducible experiments by providing unified tools for\n\n- Data downloading\n- Data (pre-)processing and transformation (e.g. graph construction)\n- Model training and evaluation\n\n### Example: run cell-type annotation benchmark using scDeepSort\n\n- Step0. Install DANCE (see [Installation](#installation))\n- Step1. Navigate to the folder containing the corresponding example scrtip.\n In this case, it is [`examples/single_modality/cell_type_annotation`](examples/single_modality/cell_type_annotation).\n- Step2. Obtain command line interface (CLI) options for a particular experiment to reproduce at the end of the\n [script](examples/single_modality/cell_type_annotation/scdeepsort.py).\n For example, the CLI options for reproducing the `Mouse Brain` experiment is\n ```bash\n python scdeepsort.py --species mouse --tissue Brain --train_dataset 753 3285 --test_dataset 2695\n ```\n- Step3. Wait for the experiment to finish and check results.\n\n## Installation\n\n<H3>Quick install</H3>\n\nThe full installation process might be a bit tedious and could involve some debugging when using CUDA enabled packages.\nThus, we provide an `install.sh` script that simplifies the installation process, assuming the user have [conda](https://conda.io/projects/conda/en/latest/index.html) set up on their machines.\nThe installation script creates a conda environment `dance` and install the DANCE package along with all its dependencies with a apseicifc CUDA version.\nCurrently, two options are accepted: `cpu` and `cu118`.\nFor example, to install the DANCE package using CUDA 11.8 in a `dance-env` conda environment, simply run:\n\n```bash\n# Clone the repository via SSH\ngit clone git@github.com:OmicsML/dance.git && cd dance\n# Alternatively, use HTTPS if you have not set up SSH\n# git clone https://github.com/OmicsML/dance.git && cd dance\n\n# Run the auto installation script to install DANCE and its dependencies in a conda environment\nsource install.sh cu118 dance-env\n```\n\n**Note**: the first argument for cuda version is mandatory, while the second argument for conda environment name is optional (default is `dance`).\n\n<details>\n<summary><H3>Custom install</H3></summary>\n<br>\n\n**Step1. Setup environment**\n\nFirst create a conda environment for dance (optional)\n\n```bash\nconda create -n dance python=3.8 -y && conda activate dance\n```\n\nThen, install CUDA enabled packages (PyTorch, PyG, DGL):\n\n```bash\nconda install pytorch=2.0.1 torchvision torchaudio pytorch-cuda=11.8 -c pytorch -c nvidia -y\nconda install pyg=2.3.1 -c pyg -y\nconda install dgl=1.1.2 -c dglteam/label/cu118 -y\n```\n\nAlternatively, install these dependencies for CPU only:\n\n```bash\nconda install pytorch=2.0.1 torchvision torchaudio cpuonly -c pytorch -y\nconda install pyg=2.3.1 -c pyg -y\nconda install dgl=1.1.2 -c dglteam -y\n```\n\nFor more information about installation or other CUDA version options, check out the installation pages for the corresponding packages\n\n- [PyTorch](https://pytorch.org/get-started/)\n- [PyG](https://pytorch-geometric.readthedocs.io/en/latest/notes/installation.html)\n- [DGL](https://www.dgl.ai/pages/start.html)\n\n**Step2. Install DANCE**\n\nInstall from PyPI\n\n```bash\npip install pydance\n```\n\nOr, install the latest dev version from source\n\n```bash\ngit clone https://github.com/OmicsML/dance.git\ncd dance\npip install -e .\n```\n\n</details>\n\n## Implemented Algorithms\n\n**P1** not covered in the first release\n\n### Single Modality Module\n\n#### 1\uff09Imputation\n\n| BackBone | Model | Algorithm | Year | CheckIn |\n| ------------------- | ------------ | ------------------------------------------------------------------------------------------------------------ | ---- | ------- |\n| GNN | GraphSCI | Imputing Single-cell RNA-seq data by combining Graph Convolution and Autoencoder Neural Networks | 2021 | \u2705 |\n| GNN | scGNN (2020) | SCGNN: scRNA-seq Dropout Imputation via Induced Hierarchical Cell Similarity Graph | 2020 | P1 |\n| GNN | scGNN (2021) | scGNN is a novel graph neural network framework for single-cell RNA-Seq analyses | 2021 | \u2705 |\n| GNN | GNNImpute | An efficient scRNA-seq dropout imputation method using graph attention network | 2021 | P1 |\n| Graph Diffusion | MAGIC | MAGIC: A diffusion-based imputation method reveals gene-gene interactions in single-cell RNA-sequencing data | 2018 | P1 |\n| Probabilistic Model | scImpute | An accurate and robust imputation method scImpute for single-cell RNA-seq data | 2018 | P1 |\n| GAN | scGAIN | scGAIN: Single Cell RNA-seq Data Imputation using Generative Adversarial Networks | 2019 | P1 |\n| NN | DeepImpute | DeepImpute: an accurate, fast, and scalable deep neural network method to impute single-cell RNA-seq data | 2019 | \u2705 |\n| NN + TF | Saver-X | Transfer learning in single-cell transcriptomics improves data denoising and pattern discovery | 2019 | P1 |\n\n| Model | Evaluation Metric | Mouse Brain (current/reported) | Mouse Embryo (current/reported) | PBMC (current/reported) |\n| ---------- | ----------------- | ------------------------------ | ------------------------------- | ----------------------- |\n| DeepImpute | RMSE | 0.87 / N/A | 1.20 / N/A | 2.30 / N/A |\n| GraphSCI | RMSE | 1.55 / N/A | 1.81 / N/A | 3.68 / N/A |\n| scGNN2.0 | MSE | 1.04 / N/A | 1.12 / N/A | 1.22 / N/A |\n\n**Note**: scGNN2.0 is evaluated on 2,000 genes with highest variance following the original paper.\n\n#### 2\uff09Cell Type Annotation\n\n| BackBone | Model | Algorithm | Year | CheckIn |\n| ----------------------- | ------------- | ------------------------------------------------------------------------------------------------------------- | ---- | ------- |\n| GNN | ScDeepsort | Single-cell transcriptomics with weighted GNN | 2021 | \u2705 |\n| Logistic Regression | Celltypist | Cross-tissue immune cell analysis reveals tissue-specific features in humans. | 2021 | \u2705 |\n| Random Forest | singleCellNet | SingleCellNet: a computational tool to classify single cell RNA-Seq data across platforms and across species. | 2019 | \u2705 |\n| Neural Network | ACTINN | ACTINN: automated identification of cell types in single cell RNA sequencing. | 2020 | \u2705 |\n| Hierarchical Clustering | SingleR | Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage. | 2019 | P1 |\n| SVM | SVM | A comparison of automatic cell identification methods for single-cell RNA sequencing data. | 2018 | \u2705 |\n\n| Model | Evaluation Metric | Mouse Brain 2695 (current/reported) | Mouse Spleen 1759 (current/reported) | Mouse Kidney 203 (current/reported) |\n| ------------- | ----------------- | ----------------------------------- | ------------------------------------ | ----------------------------------- |\n| scDeepsort | ACC | 0.542/0.363 | 0.969/0.965 | 0.847/0.911 |\n| Celltypist | ACC | 0.824/0.666 | 0.908/0.848 | 0.823/0.832 |\n| singleCellNet | ACC | 0.693/0.803 | 0.975/0.975 | 0.795/0.842 |\n| ACTINN | ACC | 0.727/0.778 | 0.657/0.236 | 0.762/0.798 |\n| SVM | ACC | 0.683/0.683 | 0.056/0.049 | 0.704/0.695 |\n\n#### 3\uff09Clustering\n\n| BackBone | Model | Algorithm | Year | CheckIn |\n| ----------- | ------------- | ------------------------------------------------------------------------------------------------------------ | ---- | ------- |\n| GNN | graph-sc | GNN-based embedding for clustering scRNA-seq data | 2022 | \u2705 |\n| GNN | scTAG | ZINB-based Graph Embedding Autoencoder for Single-cell RNA-seq Interpretations | 2022 | \u2705 |\n| GNN | scDSC | Deep structural clustering for single-cell RNA-seq data jointly through autoencoder and graph neural network | 2022 | \u2705 |\n| GNN | scGAC | scGAC: a graph attentional architecture for clustering single-cell RNA-seq data | 2022 | P1 |\n| AutoEncoder | scDeepCluster | Clustering single-cell RNA-seq data with a model-based deep learning approach | 2019 | \u2705 |\n| AutoEncoder | scDCC | Model-based deep embedding for constrained clustering analysis of single cell RNA-seq data | 2021 | \u2705 |\n| AutoEncoder | scziDesk | Deep soft K-means clustering with self-training for single-cell RNA sequence data | 2020 | P1 |\n\n| Model | Evaluation Metric | 10x PBMC (current/reported) | Mouse ES (current/reported) | Worm Neuron (current/reported) | Mouse Bladder (current/reported) |\n| ------------- | ----------------- | --------------------------- | --------------------------- | ------------------------------ | -------------------------------- |\n| graph-sc | ARI | 0.72 / 0.70 | 0.82 / 0.78 | 0.57 / 0.46 | 0.68 / 0.63 |\n| scDCC | ARI | 0.82 / 0.81 | 0.98 / N/A | 0.51 / 0.58 | 0.60 / 0.66 |\n| scDeepCluster | ARI | 0.81 / 0.78 | 0.98 / 0.97 | 0.51 / 0.52 | 0.56 / 0.58 |\n| scDSC | ARI | 0.72 / 0.78 | 0.84 / N/A | 0.46 / 0.65 | 0.65 / 0.72 |\n| scTAG | ARI | 0.77 / N/A | 0.96 / N/A | 0.49 / N/A | 0.69 / N/A |\n\n### Multimodality Module\n\n#### 1\uff09Modality Prediction\n\n| BackBone | Model | Algorithm | Year | CheckIn |\n| ---------------- | ------------------------ | -------------------------------------------------------------------------------------------------- | ---- | ------- |\n| GNN | ScMoGCN | Graph Neural Networks for Multimodal Single-Cell Data Integration | 2022 | \u2705 |\n| GNN | ScMoLP | Link Prediction Variant of ScMoGCN | 2022 | P1 |\n| GNN | GRAPE | Handling Missing Data with Graph Representation Learning | 2020 | P1 |\n| Generative Model | SCMM | SCMM: MIXTURE-OF-EXPERTS MULTIMODAL DEEP GENERATIVE MODEL FOR SINGLE-CELL MULTIOMICS DATA ANALYSIS | 2021 | \u2705 |\n| Auto-encoder | Cross-modal autoencoders | Multi-domain translation between single-cell imaging and sequencing data using autoencoders | 2021 | \u2705 |\n| Auto-encoder | BABEL | BABEL enables cross-modality translation between multiomic profiles at single-cell resolution | 2021 | \u2705 |\n\n| Model | Evaluation Metric | GEX2ADT (current/reported) | ADT2GEX (current/reported) | GEX2ATAC (current/reported) | ATAC2GEX (current/reported) |\n| ------------------------ | ----------------- | -------------------------- | -------------------------- | --------------------------- | --------------------------- |\n| ScMoGCN | RMSE | 0.3885 / 0.3885 | 0.3242 / 0.3242 | 0.1778 / 0.1778 | 0.2315 / 0.2315 |\n| SCMM | RMSE | 0.6264 / N/A | 0.4458 / N/A | 0.2163 / N/A | 0.3730 / N/A |\n| Cross-modal autoencoders | RMSE | 0.5725 / N/A | 0.3585 / N/A | 0.1917 / N/A | 0.2551 / N/A |\n| BABEL | RMSE | 0.4335 / N/A | 0.3673 / N/A | 0.1816 / N/A | 0.2394 / N/A |\n\n#### 2) Modality Matching\n\n| BackBone | Model | Algorithm | Year | CheckIn |\n| ---------------- | ------------------------ | -------------------------------------------------------------------------------------------------- | ---- | ------- |\n| GNN | ScMoGCN | Graph Neural Networks for Multimodal Single-Cell Data Integration | 2022 | \u2705 |\n| GNN/Auto-ecnoder | GLUE | Multi-omics single-cell data integration and regulatory inference with graph-linked embedding | 2021 | P1 |\n| Generative Model | SCMM | SCMM: MIXTURE-OF-EXPERTS MULTIMODAL DEEP GENERATIVE MODEL FOR SINGLE-CELL MULTIOMICS DATA ANALYSIS | 2021 | \u2705 |\n| Auto-encoder | Cross-modal autoencoders | Multi-domain translation between single-cell imaging and sequencing data using autoencoders | 2021 | \u2705 |\n\n| Model | Evaluation Metric | GEX2ADT (current/reported) | GEX2ATAC (current/reported) |\n| ------------------------ | ----------------- | -------------------------- | --------------------------- |\n| ScMoGCN | Accuracy | 0.0827 / 0.0810 | 0.0600 / 0.0630 |\n| SCMM | Accuracy | 0.005 / N/A | 5e-5 / N/A |\n| Cross-modal autoencoders | Accuracy | 0.0002 / N/A | 0.0002 / N/A |\n\n#### 3) Joint Embedding\n\n| BackBone | Model | Algorithm | Year | CheckIn |\n| ---------------- | ------- | ----------------------------------------------------------------------------------------------------- | ---- | ------- |\n| GNN | ScMoGCN | Graph Neural Networks for Multimodal Single-Cell Data Integration | 2022 | \u2705 |\n| Auto-encoder | scMVAE | Deep-joint-learning analysis model of single cell transcriptome and open chromatin accessibility data | 2020 | \u2705 |\n| Auto-encoder | scDEC | Simultaneous deep generative modelling and clustering of single-cell genomic data | 2021 | \u2705 |\n| GNN/Auto-ecnoder | GLUE | Multi-omics single-cell data integration and regulatory inference with graph-linked embedding | 2021 | P1 |\n| Auto-encoder | DCCA | Deep cross-omics cycle attention model for joint analysis of single-cell multi-omics data | 2021 | \u2705 |\n\n| Model | Evaluation Metric | GEX2ADT (current/reported) | GEX2ATAC (current/reported) |\n| ---------- | ----------------- | -------------------------- | --------------------------- |\n| ScMoGCN | ARI | 0.706 / N/A | 0.702 / N/A |\n| ScMoGCNv2 | ARI | 0.734 / N/A | N/A / N/A |\n| scMVAE | ARI | 0.499 / N/A | 0.577 / N/A |\n| scDEC(JAE) | ARI | 0.705 / N/A | 0.735 / N/A |\n| DCCA | ARI | 0.35 / N/A | 0.381 / N/A |\n\n#### 4) Multimodal Imputation\n\n| BackBone | Model | Algorithm | Year | CheckIn |\n| -------- | ------ | -------------------------------------------------------------------------------- | ---- | ------- |\n| GNN | ScMoLP | Link Prediction Variant of ScMoGCN | 2022 | P1 |\n| GNN | scGNN | scGNN is a novel graph neural network framework for single-cell RNA-Seq analyses | 2021 | P1 |\n| GNN | GRAPE | Handling Missing Data with Graph Representation Learning | 2020 | P1 |\n\n#### 5) Multimodal Integration\n\n| BackBone | Model | Algorithm | Year | CheckIn |\n| ---------------- | -------- | ---------------------------------------------------------------------------------------------------------------- | ---- | ------- |\n| GNN | ScMoGCN | Graph Neural Networks for Multimodal Single-Cell Data Integration | 2022 | P1 |\n| GNN | scGNN | scGNN is a novel graph neural network framework for single-cell RNA-Seq analyses (GCN on Nearest Neighbor graph) | 2021 | P1 |\n| Nearest Neighbor | WNN | Integrated analysis of multimodal single-cell data | 2021 | P1 |\n| GAN | MAGAN | MAGAN: Aligning Biological Manifolds | 2018 | P1 |\n| Auto-encoder | SCIM | SCIM: universal single-cell matching with unpaired feature sets | 2020 | P1 |\n| Auto-encoder | MultiMAP | MultiMAP: Dimensionality Reduction and Integration of Multimodal Data | 2021 | P1 |\n| Generative Model | SCMM | SCMM: MIXTURE-OF-EXPERTS MULTIMODAL DEEP GENERATIVE MODEL FOR SINGLE-CELL MULTIOMICS DATA ANALYSIS | 2021 | P1 |\n\n### Spatial Module\n\n#### 1\uff09Spatial Domain\n\n| BackBone | Model | Algorithm | Year | CheckIn |\n| -------------------------------- | ---------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---- | ------- |\n| GNN | SpaGCN | SpaGCN: Integrating gene expression, spatial location and histology to identify spatial domains and spatially variable genes by graph convolutional network | 2021 | \u2705 |\n| GNN | STAGATE | Deciphering spatial domains from spatially resolved transcriptomics with adaptive graph attention auto-encoder | 2021 | \u2705 |\n| Bayesian | BayesSpace | Spatial transcriptomics at subspot resolution with BayesSpace | 2021 | P1 |\n| 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 | \u2705 |\n| Heuristic | Louvain | Fast unfolding of community hierarchies in large networks | 2008 | \u2705 |\n\n| Model | Evaluation Metric | 151673 (current/reported) | 151676 (current/reported) | 151507 (current/reported) |\n| ------- | ----------------- | ------------------------- | ------------------------- | ------------------------- |\n| SpaGCN | ARI | 0.51 / 0.522 | 0.41 / N/A | 0.45 / N/A |\n| STAGATE | ARI | 0.59 / N/A | 0.60 / 0.60 | 0.608 / N/A |\n| stLearn | ARI | 0.30 / 0.36 | 0.29 / N/A | 0.31 / N/A |\n| Louvain | ARI | 0.31 / 0.33 | 0.2528 / N/A | 0.28 / N/A |\n\n#### 2\uff09Cell Type Deconvolution\n\n| BackBone | Model | Algorithm | Year | CheckIn |\n| -------------------------- | ------------ | ------------------------------------------------------------------------------------------------------------- | ---- | ------- |\n| GNN | DSTG | DSTG: deconvoluting spatial transcriptomics data through graph-based artificial intelligence | 2021 | \u2705 |\n| logNormReg | SpatialDecon | Advances in mixed cell deconvolution enable quantification of cell types in spatial transcriptomic data | 2022 | \u2705 |\n| NNMFreg | SPOTlight | SPOTlight: seeded NMF regression to deconvolute spatial transcriptomics spots with single-cell transcriptomes | 2021 | \u2705 |\n| NN Linear + CAR assumption | CARD | Spatially informed cell-type deconvolution for spatial transcriptomics | 2022 | \u2705 |\n\n| Model | Evaluation Metric | GSE174746 (current/reported) | CARD Synthetic (current/reported) | SPOTlight Synthetic (current/reported) |\n| ------------ | ----------------- | ---------------------------- | --------------------------------- | -------------------------------------- |\n| DSTG | MSE | .1722 / N/A | .0239 / N/A | .0315 / N/A |\n| SpatialDecon | MSE | .0014 / .009 | .0077 / N/A | .0055 / N/A |\n| SPOTlight | MSE | .0098 / N/A | .0246 / 0.118 | .0109 / .16 |\n| CARD | MSE | .0012 / N/A | .0078 / 0.0062 | .0076 / N/A |\n\n\n",
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