pydance 1.0.0rc0

Deep Learning 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.

Usage

Overview

In release 1.0, the main usage of the PyDANCE 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 PyDANCE, 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.

PyDANCE addresses these challenges by providing a unified Python packge 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: runing cell-type annotation benchmark using scDeepSort

• Step0. Install PyDANCE (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 --data_type scdeepsort --tissue Brain --test_data 2695
  

• Step3. Wait for the experiment to finsh 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 pydance and install the PyDANCE package along with all its dependencies with a apseicifc CUDA version. Currently, three options are accepted: cpu, cu102, and cu113. For example, to install the DANCE package using CUDA10.2, simply run:

git clone [e-mail]:OmicsML/dance.git
cd dance

source install.sh cu102

Custom install

Step1. Setup environment

First create a conda environment for pydance (optional)

conda create -n pydance python=3.8 -y && conda activate dance-dev

Then, install CUDA enabled packages (PyTorch, PyG, DGL) with CUDA 10.2:

conda install pytorch=1.12.1 torchvision cudatoolkit=10.2 -c pytorch -y
conda install dgl-cu102 -c dglteam -y
pip install torch-geometric==2.1.0 torch-scatter torch-sparse torch-cluster -f https://data.pyg.org/whl/torch-1.12.1+cu102.html

Alternatively, install these dependencies for CPU only:

conda install pytorch=1.12.1 torchvision cpuonly -c pytorch -y
conda install dgl -c dglteam
pip install torch-geometric==2.1.0 torch-scatter torch-sparse torch-cluster -f https://data.pyg.org/whl/torch-1.12.1+cpu.html

Note: If you installed PyG using conda and encountered an issue with GLIBC_2.27 when importing torch_geometric.nn, then you may need to uninstall torch-spline-conv (see https://github.com/pyg-team/pytorch_geometric/issues/3593)

pip uninstall torch-spline-conv

For more information about installation or other CUDA version options, check out the installation pages for the corresponding packges

• PyTorch
• PyG
• DGL

Step2. Install PyDANCE

Install from PyPI

pip install pydance

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)
DeepImpute	MSE	0.12 / N/A	0.12 / N/A
ScGNN	MSE	0.47 / N/A	1.10 / N/A
GraphSCI	MSE	0.42 / 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	0.680/0.666	0.966/0.848	0.879/0.832
singleCellNet	ACC	0.693/0.803	0.975/0.975	0.795/0.842
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

Note: * Benchmark datasets were renormalied before running the original implementation of Celltypist to match its form requirements.

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	0.75 / N/A	0.96 / N/A	0.53 / N/A	0.60 / 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	.172 / N/A	.0247 / N/A	.042 / 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

Dev notes

Dev installation

Install PyDANCE with extra dependencies for dev

pip install -e ."[dev]"

Make sure dependencies have specific pinned versions

pip install -r requirements.txt

Install pre-commit hooks for code quality checks

pre-commit install

Run tests

Run all tests on current environment using pytest

pytest -v

Run full test from the ground up including environment set up using tox on CPU

tox -e python3.8-cpu