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awesome-deep-learning-single-cell-papers

This repository keeps track of the latest papers on the single cell analysis with deep learning methods. We categorize them based on individual tasks.

We will try to make this list updated. If you found any error or any missed paper, please don't hesitate to open an issue or pull request.

Book

  1. [Single Cell Best Practices], Fabian Theis's Lab
  2. [Basics of Single-Cell Analysis with Bioconductor], Bioconductor software based on R

Single Cell Techonoly

Single-Modality

Multimodality

Spatial Transcriptomic

  1. [2022 Nature Methods] Museum of spatial transcriptomics [paper]

Course

  1. [CSCI 1850 Deep Learning in Genomics], Brown University
  2. [Machine Learning in Genomics: Dissecting Human Disease Circuitry], MIT
  3. [ANALYSIS OF SINGLE CELL RNA-SEQ DATA], course by Orr Ashenberg, Dana Silverbush, Kirk Gosik
  4. [Analysis of single cell RNA-seq data, www.singlecellcourse.org] - step-by-step scRNA-seq analysis course. R-based, with code examples, explanations, exercises. From alignment (STAR) and QC (FASTQC) to introduction to R, SingleCellExperiment class, scater object, data exploration (reads, UMI), filtering, normalization (scran), batch effect removal (RUV, ComBat, mnnCorrect, GLM, Harmony), clustering and marker gene identification (SINCERA, SC3, tSNE, Seurat), feature selection (M3Drop::M3DropConvertData, BrenneckeGetVariableGenes), pseudotime analysis (TSCAN, Monocle, diffusion maps, SLICER, Ouija, destiny), imputation (scImpute, DrImpute, MAGIC), differential expression (Kolmogorov-Smirnov, Wilcoxon, edgeR, Monocle, MAST), data integration (scmap, cell-to-cell mapping, Metaneighbour, mnnCorrect, Seurat's canonical correllation analysis). Search for scRNA-seq data (scfind R package), as well as Hemberg group’s public datasets. Seurat chapter. "Ideal" scRNA-seq pipeline. Video lectures.
    Paper Andrews, Tallulah S., Vladimir Yu Kiselev, Davis McCarthy, and Martin Hemberg. "Tutorial: Guidelines for the Computational Analysis of Single-Cell RNA Sequencing Data." https://doi.org/10.1038/s41596-020-00409-w Nature Protocols, December 7, 2020.

Pretrained Model or LLM or Foundation Model

  1. [2023 arxiv] BiomedGPT: A Unified and Generalist Biomedical Generative Pre-trained Transformer for Vision, Language, and Multimodal Tasks [paper]
  2. [2023 arxiv] Clinical Camel: An Open-Source Expert-Level Medical Language Model with Dialogue-Based Knowledge Encoding [paper]
  3. [2023 arxiv] CancerGPT: Few-shot Drug Pair Synergy Prediction using Large Pre-trained Language Models [paper]
  4. [2023 iSchience tGPT] Generative pretraining from large-scale transcriptomes for single-cell deciphering [paper]
  5. [2023 bioRxiv] GeneGPT: Augmenting Large Language Models with Domain Tools for Improved Access to Biomedical Information [paper]
  6. [2023 Github] OpenBioMed [Github]
  7. [2023 blog] BioMedLM: a Domain-Specific Large Language Model for Biomedical Text [blog]
  8. [2023 bioRxiv] scGPT: Towards Building a Foundation Model for Single-Cell Multi-omics Using Generative AI [paper]
  9. [2023 bioRxiv] xTrimoGene: An Efficient and Scalable Representation Learner for Single-Cell RNA-Seq Data [paper]
  10. [2023 Nature Biotechnology] Large language models generate functional protein sequences across diverse families [paper]
  11. [2022 Briefings in Bioinformatics] BioGPT: generative pre-trained transformer for biomedical text generation and mining [paper]
  12. [2022 Nature Machine Intelligence] scBERT as a large-scale pretrained deep language model for cell type annotation of single-cell RNA-seq data [paper]
  13. [2022 bioRxiv] scFormer: a universal representation learning approach for single-cell data using transformers [paper]
  14. [2022 Bioinformatics] scPretrain: multi-task self-supervised learning for cell-type classification [paper]
  15. [2021 PNAS] Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences [paper]
  16. [2021 Bioinformatics] DNABERT: pre-trained Bidirectional Encoder Representations from Transformers model for DNA-language in genome [paper]
  17. [2021 Arxiv, 576 citations] Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing [paper]
  18. [2021 Arxiv, 1111 citations] Don't Stop Pretraining: Adapt Language Models to Domains and Tasks [paper]

Spatialtemporal Spatial Transcriptomic

  1. [2023 Nature Methods] Spatiotemporally resolved transcriptomics reveals the subcellular RNA kinetic landscape [paper]
  2. [2022 bioRxiv] Spateo: multidimensional spatiotemporal modeling of single-cell spatial transcriptomics [paper]

Single Cell Application

  1. [2023 Nature Communications] ASGARD is A Single-cell Guided Pipeline to Aid Repurposing of Drugs [paper]
  2. [2023 Nature Reviews Clinical Oncology] Advancing CAR T cell therapy through the use of multidimensional omics data [paper]

Single Cell RNA Tools

[Tool Summary]

  1. [2023 Nucleic Acids Research] DeepBIO: an automated and interpretable deep-learning platform for high-throughput biological sequence prediction, functional annotation and visualization analysis [paper]

Single Cell Visualization

  1. [Chanzuckerberg: An interactive explorer for single-cell transcriptomics data]
  2. [UCSC Cell Browser]
  3. [Cytoscape]
  4. [UCSC Xena]
  5. [ASAP: Automated Single-cell Analysis Pipeline]
  6. [GenePattern]
  7. [Loopy Browser]

Benchmarking

  1. [2023 Genome Biology] Meta-analysis of (single-cell method) benchmarks reveals the need for extensibility and interoperability [paper]
  2. [2023 Nature Communications] A comprehensive benchmarking with practical guidelines for cellular deconvolution of spatial transcriptomics [paper]
  3. [2023 bioRxiv] Benchmarking the Autoencoder Design for Imputing Single-Cell RNA Sequencing Data [paper]
  4. [2023 bioRxiv] Benchmarking Algorithms for Gene Set Scoring of Single-cell ATAC-seq Data [paper]
  5. [2022 Nature Communications] Comparison of methods and resources for cell-cell communication inference from single-cell RNA-Seq data [paper]
  6. [2022 Nature Methods] Benchmarking atlas-level data integration in single-cell genomics [paper]
  7. [2022 Nature Methods] Benchmarking spatial and single-cell transcriptomics integration methods for transcript distribution prediction and cell type deconvolution [paper]
  8. [2022 BioRxiv] Benchmarking Automated Cell Type Annotation Tools for Single-cell ATAC-seq Data [paper]
  9. [2022 Briefings in Bioinformatics] Benchmarking methods for detecting differential states between conditions from multi-subject single-cell RNA-seq data [paper]
  10. [2022 Nucleic Acids Research] scIMC: a platform for benchmarking comparison and visualization analysis of scRNA-seq data imputation methods [paper]
  11. [2021 Frontiers in Genetics] Evaluating the Reproducibility of Single-Cell Gene Regulatory Network Inference Algorithms [paper]
  12. [2021 Nature Communications] A benchmark study of simulation methods for single-cell RNA sequencing data [paper]
  13. [2021 Genome Biology] Benchmarking UMI-based single-cell RNA-seq preprocessing workflows [paper]
  14. [2020 Nature Methods] Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data [paper]
  15. [2020 Genome Biology] A benchmark of batch-effect correction methods for single-cell RNA sequencing data [paper]
  16. [2020 Nature Biotechnology] A multicenter study benchmarking single-cell RNA sequencing technologies using reference samples [paper]
  17. [2019 Nature Methods] Benchmarking single cell RNA-sequencing analysis pipelines using mixture control experiments [paper]

Metric Design

  1. [2019 Narure Methods] A test metric for assessing single-cell RNA-seq batch correction [paper]

Subcellular Analysis

  1. [2023 Nature Methods] Subcellular omics: a new frontier pushing the limits of resolution, complexity and throughput [paper]
  2. [2022 BioRxiv] Bento: A toolkit for subcellular analysis of spatial transcriptomics data [paper]
  3. [2022 BioRxiv] Subcellular spatially resolved gene neighborhood networks in single cells [paper]

Dimensionality Reduction and Visualization

  1. [2023 Genome Research] Complex hierarchical structures in single-cell genomics data unveiled by deep hyperbolic manifold learning [paper]
  2. [2021 Nature Communications] Deep generative model embedding of single-cell RNA-Seq profiles on hyperspheres and hyperbolic spaces [paper]
  3. [2018 Nature Communications] Interpretable dimensionality reduction of single cell transcriptome data with deep generative models [paper]

Representation Learning

  1. [2021 Current Opinion in Systems Biology] Graph representation learning for single-cell biology [paper]
  2. [2023 bioRxiv] Towards Universal Cell Embeddings: Integrating Single-cell RNA-seq Datasets across Species with SATURN [paper]

Single Cell Atlas

  1. [Cellxgene Datasets: 546 datasets by 2022]
  2. [2022 Nature Methods] Benchmarking atlas-level data integration in single-cell genomics [paper]
  3. [2022 bioRxiv] A unified analysis of atlas single cell data [paper]
  4. [2022 Nature Biotechnology] Integration of spatial and single-cell transcriptomic data elucidates mouse organogenesis [paper]
  5. [2022 bioRxiv] Supervised spatial inference of dissociated single-cell data with SageNet [paper]
  6. [2022 Nature Communications] Online single-cell data integration through projecting heterogeneous datasets into a common cell-embedding space [paper]

Single Cell Purturbation or Drug Response

  1. [2023 biorxiv Diffusion Model] The Power of Two: integrating deep diffusion models and variational autoencoders for single-cell transcriptomics analysis [paper]
  2. [2023 ICLR] Predicting Cellular Responses with Variational Causal Inference and Refined Relational Information [paper]
  3. [2022 NeurIPS] Predicting Cellular Responses to Novel Drug Perturbations at a Single-Cell Resolution [paper]
  4. [2022 biorxiv] GEARS: Predicting transcriptional outcomes of novel multi-gene perturbations [paper]
  5. [2021 biorxiv] Learning interpretable cellular responses to complex perturbations in high-throughput screens [paper]
  6. [2019 Nature Methods] scGen predicts single-cell perturbation responses [paper]

Batch Effect Correction

  1. [2023 Bioinformatics] CLAIRE: contrastive learning-based batch correction framework for better balance between batch mixing and preservation of cellular heterogeneity [paper]
  2. [2020 Genomy Biology] A benchmark of batch-effect correction methods for single-cell RNA sequencing data [paper]
  3. [2020 Nature Biotechnology] A multicenter study benchmarking single-cell RNA sequencing technologies using reference samples [paper]
  4. [2019 Nature Methods, Harmony] Fast, sensitive and accurate integration of single-cell data with Harmony [paper]
  5. [2018 Nature Biotechnology, CCA] Integrating single-cell transcriptomic data across different conditions, technologies, and species [paper]
  6. [2018 Nature Biotechnology, Mutual Nearest Neighbors] Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors [paper]
  7. [2018 Nature Methods] A test metric for assessing single-cell RNA-seq batch correction [paper]
  8. [2017 Nature Biotechnology] Multiplexed droplet single-cell RNA-sequencing using natural genetic variation [paper]

Tumor Microenvironment-TME

  1. [2023 bioRxiv] Identifying Spatial Co-occurrence in Healthy and InflAmed tissues (ISCHIA) [paper]
  2. [2023 bioRxiv] Predicting tumor immune microenvironment and checkpoint therapy response of head & neck cancer patients from blood immune single-cell transcriptomics [paper]
  3. [2022 Nature Biomedical Engineering] Graph deep learning for the characterization of tumour microenvironments from spatial protein profiles in tissue specimens [paper]
  4. [2022 Nature Communications] SOTIP is a versatile method for microenvironment modeling with spatial omics data [paper]

Cell-Cell Communication Events

  1. [2022 bioRxiv] scTensor detects many-to-many cell–cell interactions from single cell RNA-sequencing data [paper]
  2. [2022 Nature Biotechnology] Modeling intercellular communication in tissues using spatial graphs of cells [paper]
  3. [2022 bioRxiv] Decoding functional cell–cell communication events by multi-view graph learning on spatial transcriptomics [paper]
  4. [2021 Bioinformatics] Identifying signaling genes in spatial single-cell expression data [paper]
  5. [2020 Nature Methods] NicheNet: modeling intercellular communication by linking ligands to target genes [paper]
  6. [2020 Nature Communications] Predicting cell-to-cell communication networks using NATMI [paper]
  7. [2018 Nature] Single-cell reconstruction of the early maternal–fetal interface in humans [paper]

Gene Regulatory Network

  1. [2023 Bioinformatics] STGRNS: an interpretable transformer-based method for inferring gene regulatory networks from single-cell transcriptomic data [paper]
  2. [2022 Nature Machine Intelligence] Inferring transcription factor regulatory networks from single-cell ATAC-seq data based on graph neural networks [paper]
  3. [2022 Nature Biotechnology] Multi-omics single-cell data integration and regulatory inference with graph-linked embedding [paper]
  4. [2022 Biorxiv] scMEGA: Single-cell Multiomic Enhancer-based Gene Regulatory Network Inference [paper]
  5. [2022 Bioinformatics] High-performance single-cell gene regulatory network inference at scale: the Inferelator 3.0 [paper]
  6. [2022 Briefings in Bioinformatic] SIGNET: single-cell RNA-seq-based gene regulatory network prediction using multiple-layer perceptron bagging [paper]
  7. [2020 Nature Methods] Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data [paper]
  8. [2019 Genome Biology] Single-cell transcriptomics unveils gene regulatory network plasticity [paper]
  9. [2017 Cell Syst] Gene Regulatory Network Inference from Single-Cell Data Using Multivariate Information Measures [paper]

Imputation

  1. [2018 Nature Communications] An accurate and robust imputation method scImpute for single-cell RNA-seq data [paper]
  2. [2019 Genome Biology] DeepImpute: an accurate, fast, and scalable deep neural network method to impute single-cell RNA-seq data [paper]
  3. [2018 Cell] Recovering Gene Interactions from Single-Cell Data Using Data Diffusion [paper]
  4. [2018 Genome Biology] VIPER: variability-preserving imputation for accurate gene expression recovery in single-cell RNA sequencing studies [paper]
  5. [2021 PLOS Computational Biology] G2S3: A gene graph-based imputation method for single-cell RNA sequencing data [paper]
  6. [2021 Nature Communications] scGNN is a novel graph neural network framework for single-cell RNA-Seq analyses [paper]
  7. [2021 iScience] Imputing single-cell RNA-seq data by combining graph convolution and autoencoder neural networks [paper]
  8. [2022 PLOS ONE] Single-cell specific and interpretable machine learning models for sparse scChIP-seq data imputation [paper]

Spatial Domain

  1. [2023 Nature Genetics] SPICEMIX enables integrative single-cell spatial modeling of cell identity [paper]
  2. [2023 bioRxiv] CellCharter: a scalable framework to chart and compare cell niches across multiple samples and spatial -omics technologies [preprint]
  3. [2022 Genome Research] A model-based constrained deep learning clustering approach for spatially resolved single-cell data [paper]
  4. [2022 Nature Communications Biology] Deciphering tissue structure and function using spatial transcriptomics [Review paper]
  5. [2022 Genome Biology] Statistical and machine learning methods for spatially resolved transcriptomics data analysis [Review paper]
  6. [2022 Nature Communications] Deciphering spatial domains from spatially resolved transcriptomics with adaptive graph attention auto-encoder [paper]
  7. [2022 Nature Computational Science] Cell clustering for spatial transcriptomics data with graph neural networks [paper]
  8. [2022 Frontiers in Genetics] Analysis and Visualization of Spatial Transcriptomic Data [paper]
  9. [2021 Nature Methods] SpaGCN: Integrating gene expression, spatial location and histology to identify spatial domains and spatially variable genes by graph convolutional network [paper]
  10. [2021 Nature Biotechnology] Spatial transcriptomics at subspot resolution with BayesSpace [paper]
  11. [2021 Biorxiv] Unsupervised Spatially Embedded Deep Representation of Spatial Transcriptomics [paper]
  12. [2021 Genome Biology] Giotto: a toolbox for integrative analysis and visualization of spatial expression data [Tool]
  13. [2021 Biorxiv] Define and visualize pathological architectures of human tissues from spatially resolved transcriptomics using deep learning [paper]
  14. [2020 Biorxiv] stLearn: integrating spatial location, tissue morphology and gene expression to find cell types, cell-cell interactions and spatial trajectories within undissociated tissues [paper]
  15. [2018 Nature Methods] SpatialDE: Identification of Spatially Variable Genes [paper]
  16. [2018 Nature Biotechnology] Identification of Spatially Associated Subpopulations by Combining scRNAseq and Sequential Fluorescence In Situ Hybridization Data [paper]
  17. [2008 Journal of Statistical Mechanics] Fast unfolding of community hierarchies in large networks [paper]

Reference Embedding or Transfer Learning

  1. [2019 Nature Methods] Data denoising with transfer learning in single-cell transcriptomics [paper]
  2. [2018 Nature Methods] Deep generative modeling for single-cell transcriptomics [paper]
  3. [2020 Bioinformatics] Conditional out-of-distribution generation for unpaired data using transfer VAE [paper]
  4. [2021 Nature Biotechnology] Mapping single-cell data to reference atlases by transfer learning [paper]
  5. [2021 Molecular Systems Biology] Probabilistic harmonization and annotation of single-cell transcriptomics data with deep generative models [paper]
  6. [2022 bioRxiv Preprint] Biologically informed deep learning to infer gene program activity in single cells [preprint]

Cell Segmentation

  1. [2022 Cytometry A] MIRIAM: A machine and deep learning single-cell segmentation and quantification pipeline for multi-dimensional tissue images [paper][code](MIRIAM)
  2. [2021 Nature Biotechnology] Cell segmentation in imaging-based spatial transcriptomics [paper]
  3. [2021 Biorxiv] Scellseg: a style-aware cell instance segmentation tool with pre-training and contrastive fine-tuning [paper] [code]
  4. [2021 Nature Biotechnology] Cell segmentation in imaging-based spatial transcriptomics [paper] [code](Baysor)
  5. [2021 Nature Biotechnology] Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning [paper] [code](Memser)
  6. [2021 Nature Methods] Cellpose: a generalist algorithm for cellular segmentation [paper] [code](Cellpose)
  7. [2021 Molecular Systems Biology]Joint cell segmentation and cell type annotation for spatial transcriptomics [paper] [code] (JSTA)
  8. [2020 Nature Communications]A convolutional neural network segments yeast microscopy images with high accuracy [paper] [code]
  9. [2020 Medical Image Analysis] DeepDistance: A multi-task deep regression model for cell detection in inverted microscopy images [paper] (DeepDistance)
  10. [2016 Computational Biology]Deep Learning Automates the Quantitative Analysis of Individual Cells in Live-Cell Imaging Experiments [paper] [code] (Deepcell)

Cell Type Deconvolution

  1. [2023 BioRxiv] STdGCN: accurate cell-type deconvolution using graph convolutional networks in spatial transcriptomic data [paper]
  2. [2023 BioRxiv] Spotless: a reproducible pipeline for benchmarking cell type deconvolution in spatial transcriptomics [paper]
  3. [2022 Nature Biotechnology] High-resolution alignment of single-cell and spatial transcriptomes with CytoSPACE [paper]
  4. [2022 Nature Communications] Reference-free cell type deconvolution of multi-cellular pixel-resolution spatially resolved transcriptomics data [paper]
  5. [2022 Nature Biotechnology] DestVI identifies continuums of cell types in spatial transcriptomics data [paper]
  6. [2022 Biorxiv] Accurate cell type deconvolution in spatial transcriptomics using a batch effect-free strategy [paper]
  7. [2022 Nature Biotechnology] Spatially informed cell-type deconvolution for spatial transcriptomics [paper]
  8. [2022 Nature Cancer] Cell type and gene expression deconvolution with BayesPrism enables Bayesian integrative analysis across bulk and single-cell RNA sequencing in oncology [paper]
  9. [2022 Nature Communications] Advances in mixed cell deconvolution enable quantification of cell types in spatial transcriptomic data [paper]
  10. [2022 Nature Biotechnology] Cell2location maps fine-grained cell types in spatial transcriptomics [paper]
  11. [2021 Briefings in Bioinformatics] DSTG: deconvoluting spatial transcriptomics data through graph-based artificial intelligence [paper]
  12. [2021 Genome Research] Likelihood-based deconvolution of bulk gene expression data using single-cell references [paper]
  13. [2021 Genome Biology] SpatialDWLS: accurate deconvolution of spatial transcriptomic data [paper]
  14. [2021 Nucleic Acids Research] SPOTlight: seeded NMF regression to deconvolute spatial transcriptomics spots with single-cell transcriptomes [paper]
  15. [2021 Nature Biotechnology] Robust decomposition of cell type mixtures in spatial transcriptomics [paper]
  16. [2019 Nature Communications] Accurate estimation of cell-type composition from gene expression data [paper]
  17. [2019 Science] Slide-seq: A scalable technology for measuring genome-wide expression at high spatial resolution [paper]

Cell Type Annotation

  1. [2023 Nature Methods] Cue: a deep-learning framework for structural variant discovery and genotyping [paper]
  2. [2023 Nature Communications] Transformer for one stop interpretable cell type annotation [paper]
  3. [2023 Nature Biotech] TACCO unifies annotation transfer and decomposition of cell identities for single-cell and spatial omics [paper]
  4. [2022 Nature Method] Annotation of spatially resolved single-cell data with STELLAR [paper] NOTE: annotated reference cell graph + query cell graph
  5. [2022 Brief Bioinform] scIAE: an integrative autoencoder-based ensemble classification framework for single-cell RNA-seq data [paper]
  6. [2022 Nature Communications] scGCN is a graph convolutional networks algorithm for knowledge transfer in single cell omics [paper]
  7. [2022 Science] Cross-tissue immune cell analysis reveals tissue-specific features in humans [paper]
  8. [2022 Bioinformatics] CellMeSH: probabilistic cell-type identification using indexed literature [paper]
  9. [2022 Cancers] Transformer for Gene Expression Modeling (T-GEM): An Interpretable Deep Learning Model for Gene Expression-Based Phenotype Predictions [paper]
  10. [2021 Nucleic Acids Research] scDeepSort: a pre-trained cell-type annotation method for single-cell transcriptomics using deep learning with a weighted graph neural network [paper]
  11. [2021 BMC Bioinformatics] Single-cell classification using graph convolutional networks [paper]
  12. [2021 Genome Research] Semisupervised adversarial neural networks for single-cell classification [paper]
  13. [2020 BMC Bioinformatics] EnClaSC: a novel ensemble approach for accurate and robust cell-type classification of single-cell transcriptomes [paper]
  14. [2020 Bioinformatics] ACTINN: automated identification of cell types in single cell RNA sequencing [paper]
  15. [2020 Nature Communications] SciBet as a portable and fast single cell type identifier [paper]
  16. [2019 Nucleic Acids Research] SuperCT: a supervised-learning framework for enhanced characterization of single-cell transcriptomic profiles [paper]
  17. [2019 Nucleic Acids Research] CHETAH: a selective, hierarchical cell type identification method for single-cell RNA sequencing [paper]
  18. [2019 Bioinformatics] scMatch: a single-cell gene expression profile annotation tool using reference datasets [paper]
  19. [2019 Cell Systems] SingleCellNet: A Computational Tool to Classify Single Cell RNA-Seq Data Across Platforms and Across Species [paper]
  20. [2019 Genome Biology] SingleCellNet: cPred: accurate supervised method for cell-type classification from single-cell RNA-seq data [paper]

Cell Clustering

  1. [2023 Bioinformatics] scBGEDA: deep single-cell clustering analysis via a dual denoising autoencoder with bipartite graph ensemble clustering [paper]
  2. [2023 bioRxiv] G3DC: a Gene-Graph-Guided selective Deep Clustering method for single cell RNA-seq data [paper]
  3. [2022 BMC Bioinformatics] SC3s: efficient scaling of single cell consensus clustering to millions of cells [paper]
  4. [2022 Bioinformatics] GNN-based embedding for clustering scRNA-seq data [paper]
  5. [2022 AAAI] ZINB-based Graph Embedding Autoencoder for Single-cell RNA-seq Interpretations [paper]
  6. [2022 Briefings in Bioinformatics] Deep structural clustering for single-cell RNA-seq data jointly through autoencoder and graph neural network [paper]
  7. [2022 Bioinformatics] scGAC: a graph attentional architecture for clustering single-cell RNA-seq data [paper]
  8. [2022 Nature Computational Science] Cell clustering for spatial transcriptomics data with graph neural networks [paper]
  9. [2021 Nature Communications] Model-based deep embedding for constrained clustering analysis of single cell RNA-seq data [paper]
  10. [2020 NAR Genomics and Bioinformatics] Deep soft K-means clustering with self-training for single-cell RNA sequence data [paper]
  11. [2020 Nature Communications] Deep learning enables accurate clustering with batch effect removal in single-cell RNA-seq analysis [paper][website][github]
  12. [2019 Nature Machine Intelligence] Clustering single-cell RNA-seq data with a model-based deep learning approach [paper]

Disease Prediction

  1. [2018 IJCAI] Hybrid Approach of Relation Network and Localized Graph Convolutional Filtering for Breast Cancer Subtype Classification [paper]
  2. [2021 NPJ Digital Medicine] DeePaN - A deep patient graph convolutional network integratingclinico-genomic evidence to stratify lung cancers benefiting from immunotherapy [paper]
  3. [2022 Biocumputing] CloudPred: Predicting Patient Phenotypes From Single-cell RNA-seq [paper]
  4. [2022 CHIL '20: Proceedings of the ACM Conference on Health, Inference, and Learning] Disease state prediction from single-cell data using graph attention networks [paper]

Multimodal Integration

  1. [2023 Briefings in Bioinformatics] A universal framework for single-cell multi-omics data integration with graph convolutional networks [Paper]
  2. [2022 PMLR] CVQVAE: A representation learning based method for multi-omics single cell data integration [Paper]
  3. [2022 Nature Biotechnology] Multi-omics single-cell data integration and regulatory inference with graph-linked embedding [Paper]
  4. [2022 Nature Communications] Clustering of single-cell multi-omics data with a multimodal deep learning method [Survey]
  5. [2022 Genome Biology] A benchmark study of deep learning-based multi-omics data fusion methods for cancer [Survey]
  6. [2018 ICML] MAGAN: Aligning biological manifolds [paper]
  7. [2019 PLoS computational biology] Building gene regulatory networks from scATAC-seq and scRNA-seq using linked self organizing maps [paper]
  8. [2020 Bioinformatics] SCIM: universal single-cell matching with unpaired feature sets [paper]
  9. [2021 Nature communications] Multi-domain translation between single-cell imaging and sequencing data using autoencoders [paper]
  10. [2020 PNAS] BABEL enables cross-modality translation between multiomic profiles at single-cell resolution [paper]
  11. [2021 PLoS Computational Biology] Imputation of spatially-resolved transcriptomes by graph-regularized tensor completion [paper]
  12. [2021 Genome biology] Cobolt: integrative analysis of multimodal single-cell sequencing data [paper]
  13. [2021 Cell reports methods] A mixture-of-experts deep generative model for integrated analysis of single-cell multiomics data [paper]
  14. [2021 Briefings in Bioinformatics] Deep-joint-learning analysis model of single cell transcriptome and open chromatin accessibility data [paper]
  15. [2021 Bioinformatics] Deep cross-omics cycle attention model for joint analysis of single-cell multi-omics data [paper]
  16. [2022 Nature Biotechnology] scJoint integrates atlas-scale single-cell RNA-seq and ATAC-seq data with transfer learning [paper]
  17. [2022 Bioinformatics] SMILE: mutual information learning for integration of single-cell omics data [paper]
  18. [2022 SIGKDD] Graph Neural Networks for Multimodal Single-Cell Data Integration [paper]
  19. [2022 Genome biology] scDART: integrating unmatched scRNA-seq and scATAC-seq data and learning cross-modality relationship simultaneously [paper]
  20. [2019 Biorxiv] A Joint Model of RNA Expression and Surface Protein Abundance in Single Cells [paper]
  21. [2021 Biorxiv] DeepMAPS: Single-cell biological network inference using heterogeneous graph transformer [paper]
  22. [2022 Biorxiv] Adaptative Machine Translation between paired Single-Cell Multi-Omics Data [paper]
  23. [2022 Biorxiv] Multigrate: single-cell multi-omic data integration [paper]

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