pydance 0.0.1.dev0

Deep Learning for Single-cell Analysis

DANCE: A Deep Learning Library for Single-Cell Analysis

DANCE is a python toolkit to support deep learning models for analyzing single-cell gene expression at scale. It includes three modules at present:

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

Our goal is to build up a deep learning community for single cell analysis and provide GNN based architecture for users for further development in single cell analysis.

Dev installation notes

# Create fresh dev environment (optional)
conda create -n dance-dev python=3.8 -y && conda activate dance-dev

# Install PyTorch, PyG, and DGL with CUDA 10.2
conda install pytorch torchvision cudatoolkit=10.2 pyg dgl-cuda10.2 -c pytorch -c pyg -c dglteam -y

# Install dance in editable `-e` mode (under the root directory 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)
DeepImpute	MSE	0.14 / N/A	0.13 / N/A
ScGNN	MSE	0.47 / N/A	1.10 / N/A
GraphSCI	MSE	0.25 / N/A	0.87 / N/A

Note: the data split modality of DeepImpute is different from ScGNN and GraphSCI, so the results are not comparable.

2）Cell Type Annotation

BackBone	Model	Algorithm	Year	CheckIn
GNN	ScDeepsort	Single-cell transcriptomics with weighted GNN	2021	✅
Logistic Regression	Celltypist	Automated cell type annotation for scRNA-seq datasets	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	0.363/0.363	0.965 /0.965	0.901/0.911
Celltypist	ACC	0.680/xxx	0.966/xxx	0.879/xxx
singleCellNet	ACC	0.732/0.803	0.975/0.975	0.833/0.842
ACTINN	ACC	0.860/0.778	0.516/0.236	0.829/0.798
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	0.74 / 0.70	0.80 / 0.78	0.51 / 0.46	0.69 / 0.63
scDCC	ARI	0.80 / 0.81	0.97 / N/A	0.46 / 0.58	0.66 / 0.66
scDeepCluster	ARI	0.78 / 0.78	0.98 / 0.97	0.47 / 0.52	0.58 / 0.58
scDSC	ARI	0.75 / 0.78	0.88 / N/A	0.58 / 0.65	0.69 / 0.72
scTAG	ARI	0.75 / N/A	0.94 / N/A	0.56 / N/A	0.57 / 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	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
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	scGNN	scGNN is a novel graph neural network framework for single-cell RNA-Seq analyses	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	.18 / N/A	.056 / N/A	0.064 / N/A
SpatialDecon	MSE	.001 / .009	0.09 / N/A	.22 / N/A
SPOTlight	MSE	.016 / N/A	0.13 / 0.118	.21 / .16
CARD	RMSE	0.035 / N/A	0.089 / 0.079	0.087 / N/A