Deep Learning in Spatial Transcriptomics Analysis

Our goal is to help the scientific community by providing a (continously updated) list of machine learning/deep learning models used in the various stages of spatial transcriptomics analysis.

We would really appreciate your contributions, so please do not hesitate to do a PR with information on a new paper and/or tool.

Review Papers

Our review paper on deep learning in spatial transcriptomics can be found here [BPR open access article]. We also have a review paper on the applications of deep learning in single-cell omics analysis, which can be accessed from this link.

Methods

To provide a distinction between Deep Learning and Machine Learning/Statistical methods, we divide computational approaches for ST analysis into two sections:

Deep Learning Approaches

Stage/Category	Model (with GitHub Link)	Title of Paper	Language	Year	Reference	Additional Notes
1. Spatial Reconstruction	HematoFatePrediction	Prospective identification of hematopoietic lineage choice by deep learning	MATLAB	2017	Buggenthin F, Buettner F, Hoppe PS, Endele M, Kroiss M, Strasser M, Schwarzfischer M, Loeffler D, Kokkaliaris KD, Hilsenbeck O, et al. 2017. Prospective identification of hematopoietic lineage choice by deep learning. Nat Methods 14: 403–406.
1. Spatial Reconstruction	DEEPsc	DEEPsc: A Deep Learning-Based Map Connecting Single-Cell Transcriptomics and Spatial Imaging Data	MATLAB	2021	Maseda F, Cang Z, Nie Q. 2021. DEEPsc: A Deep Learning-Based Map Connecting Single-Cell Transcriptomics and Spatial Imaging Data. Front Genet 12: 636743.
2. Spot Deconvolution	DestVI	DestVI identifies continuums of cell types in spatial transcriptomics data	Python (PyTorch)	2022	Lopez, R., Li, B., Keren-Shaul, H. et al. DestVI identifies continuums of cell types in spatial transcriptomics data. Nat Biotechnol (2022). https://doi.org/10.1038/s41587-022-01272-8
2. Spot Deconvolution	DSTG	DSTG: deconvoluting spatial transcriptomics data through graph-based artificial in- telligence	Python/R	2021	Q. Song and J. Su, “DSTG: deconvoluting spatial transcriptomics data through graph-based artificial in- telligence,” Briefings in Bioinformatics 22 (2021), https://doi.org/10.1038/s41587-022-01272-8.
3. Data Integration (scRNAseq + ST)	Tangram	Deep Learning and Alignment of Spatially Resolved Single-Cell Transcriptomes with Tangram	Python (PyTorch)	2021	Biancalani, T., Scalia, G., Buffoni, L. et al. Deep learning and alignment of spatially resolved single-cell transcriptomes with Tangram.Nat Methods 18, 1352–1362 (2021). https://doi.org/10.1038/s41592-021-01264-7.
3. Data Integration (scRNAseq + ST)	ST-Net	Integrating Spatial Gene Expression and Breast Tumour Morphology via Deep Learning	Python (PyTorch)	2021	He B, Bergenstråhle L, Stenbeck L, Abid A, Andersson A, Borg Å, Maaskola J, Lundeberg J, Zou J. 2020. Integrating spatial gene expression and breast tumour morphology via deep learning. Nat Biomed Eng 4: 827–834.
4. Spatial Clustering	spaCell	SpaCell: integrating tissue morphology and spatial gene expression to predict disease cells	Python	2019	X. Tan, A. Su, M. Tran, and Q. Nguyen, “SpaCell: integrating tissue morphology and spatial gene expression to predict disease cells,” Bioinformatics 36, 2293– 2294 (2020).
4. Spatial Clustering	stLearn	stLearn: integrating spatial location, tissue morphology and gene expression to find cell types, cell-cell interactions and spatial trajectories within undissociated tissues	Python	2020	D. Pham, X. Tan, J. Xu, L. F. Grice, P. Y. Lam, A. Raghubar, J. Vukovic, M. J. Ruitenberg, and Q. Nguyen, “stlearn: integrating spatial location, tissue morphology and gene expression to find cell types, cell-cell interactions and spatial trajectories within undissociated tissues,” bioRxiv (2020).
4. Spatial Clustering	spaGCN	spaGCN: Integrating gene expression, spatial location and histology to identify spatial domains and spatially variable genes by graph convolutional network	Python	2021	J. Hu, X. Li, K. Coleman, A. Schroeder, N. Ma, D. J. Irwin, E. B. Lee, R. T. Shinohara, and M. Li, “spaGCN: Integrating gene expression, spatial location and histology to identify spatial domains and spatially variable genes by graph convolutional network,” Nature Methods 18, 1342–1351 (2021).
5. Integrative Tookit	GLUER	GLUER: integrative analysis of multi-omics and imaging data at single-cell resolution by deep neural networks	Python (TensorFlow)	2021	GLUER: integrative analysis of single-cell omics and imaging data by deep neural network. Tao Peng, Gregory M. Chen, KaiTan. bioRxiv 2021.01.25.427845; doi: https://doi.org/10.1101/2021.01.25.427845

Machine Learning/Statistical Approaches

Stage/Category	Model (with GitHub Link)	Title of Paper	Language	Year	Reference	Additional Notes
1. Spatial Reconstruction	Seurat	Spatial reconstruction of single-cell gene expression data	R	2015	R. Satija, J. A. Farrell, D. Gennert, A. F. Schier, and A. Regev, “Spatial reconstruction of single-cell gene expression data,” Nature Biotechnology 33, 495–502 (2015)
1. Spatial Reconstruction	novoSpaRc	Gene expression cartography	Python	2019	M. Nitzan, N. Karaiskos, N. Friedman, and N. Rajewsky, “Gene expres- sion cartography,” Nature 576, 132–137 (2019).
2. Spot Deconvolution	Steroscope	Single-cell and spatial transcriptomics enables probabilistic inference of cell type topography	Python	2020	A. Andersson, J. Bergenstråhle, M. Asp, L. Bergenstråhle, A. Jurek, J. Fernández Navarro, and J. Lundeberg, “Single-cell and spatial transcriptomics enables probabilistic inference of cell type topography,” Communications Biology 3, 565 (2020).
2. Spot Deconvolution	SPOTlight	Single-cell and spatial transcriptomics enables probabilistic inference of cell type topography	Python	2020	M. Elosua-Bayes, P. Nieto, E. Mereu, I. Gut, and H. Heyn, “SPOTlight: seeded NMF regression to deconvolute spatial tran- scriptomics spots with single-cell transcriptomes,” Nucleic Acids Research 49, e50–e50 (2021).
2. Spot Deconvolution	RTCD	Robust decomposition of cell type mixtures in spatial tran- scriptomics	R	2021	D. M. Cable, E. Murray, L. S. Zou, A. Goeva, E. Z. Macosko, F. Chen, and R. A. Irizarry, “Robust decomposition of cell type mixtures in spatial transcriptomics,” Nature Biotechnology (2021), 10.1038/s41587-021-00830- w.
2. Spot Deconvolution	SpatialDWLS	SpatialDWLS: Accurate deconvolution of spatial transcriptomic data	Python (PyTorch)	2021	R. Dong and G.-C. Yuan, “Spatialdwls: accurate deconvolution of spatial transcriptomic data,” Genome Biology 22, 145 (2021).
2. Spot Deconvolution	Cell2Location	Cell2Location maps fine-grained cell types in spatial transcriptomics	Python	2022	V. Kleshchevnikov, A. Shmatko, E. Dann, A. Aivazidis, H. W. King, T. Li, R. Elmentaite, A. Lomakin, V. Kedlian, A. Gayoso, M. S. Jain, J. S. Park, L. Ramona, E. Tuck, A. Arutyunyan, R. Vento-Tormo, M. Ger- stung, L. James, O. Stegle, and O. A. Bayraktar, “Cell2location maps fine-grained cell types in spatial transcriptomics,” Nature Biotechnology (2022), 10.1038/s41587-021-01139-4.
2. Spot Deconvolution	CARD	Spatially informed cell-type deconvolution for spatial transcriptomic	R	2022	Y. Ma and X. Zhou, “Spatially informed cell-type deconvolution for spatial transcriptomics,” Nature Biotechnology 40, 1349–1359 (2022).
3. Spatially Variable Genes ID	Trendsceek	Identification of spatial expression trends in single-cell gene expression data	R	2018	D. Edsgärd, P. Johnsson, and R. Sandberg, “Identification of spatial expression trends in single-cell gene expression data,” Nature Methods 15, 339–342 (2018).
3. Spatially Variable Genes ID	SpatialDE	SpatialDE: Identification of spatially variable genes	Python	2018	V. Svensson, S. A. Teichmann, and O. Stegle, “SpatialDE: Identification of spatially variable genes,” Nature Methods 15, 343–346 (2018).
3. Spatially Variable Genes ID	Spark	Statistical analysis of spatial expression patterns for spatially resolved transcriptomic studies	R	2020	S. Sun, J. Zhu, and X. Zhou, “Statistical analysis of spatial expression patterns for spatially resolved transcriptomic studies,” Nature Methods 17, 193–200 (2020).
4. Spatial Clustering	HMRF	Identification of spatially associated subpopulations by combining scrnaseq and sequential fluorescence in situ hybridization data	R	2018	Q. Zhu, S. Shah, R. Dries, L. Cai, and G.-C. Yuan, “Identification of spatially associated subpopulations by combining scrnaseq and sequential fluorescence in situ hybridization data,” Nature Biotechnology 36, 1183– 1190 (2018).
4. Spatial Clustering	BayesSpace	Identification of spatially associated subpopulations by combining scrnaseq and sequential fluorescence in situ hybridization data	R	2021	E. Zhao, M. R. Stone, X. Ren, J. Guenthoer, K. S. Smythe, T. Pulliam, S. R. Williams, C. R. Uytingco, S. E. B. Taylor, P. Nghiem, J. H. Bielas, and R. Gottardo, “Spatial transcriptomics at subspot resolution with bayess- pace,” Nature Biotechnology 39, 1375–1384 (2021).
5. Cell-Cell Interaction/Communication	SpaOTsc	Inferring spatial and signaling relationships between cells from single cell transcriptomic data	Python	2020	Z. Cang and Q. Nie, “Inferring spatial and signaling relationships between cells from single cell transcriptomic data,” Nature Communications 11, 2084 (2020).
5. Cell-Cell Interaction/Communication	MISTy	Explainable multi-view framework for dissecting intercellular signaling from highly multiplexed spatial data	R	2020	J. Tanevski, R. O. R. Flores, A. Gabor, D. Schapiro, and J. Saez-Rodriguez, “Explainable multi-view framework for dissecting intercellular signaling from highly multiplexed spatial data,” bioRxiv (2021).
5. Cell-Cell Interaction/Communication	Giotto	Giotto: a toolbox for integrative analysis and visualization of spatial ex- pression data	R	2021	R. Dries, Q. Zhu, R. Dong, C.-H. L. Eng, H. Li, K. Liu, Y. Fu, T. Zhao, A. Sarkar, F. Bao, R. E. George, N. Pierson, L. Cai, and G.-C. Yuan, “Giotto: a toolbox for integrative analysis and visualization of spatial expression data,” Genome Biology 22, 78 (2021).

Updates/To Do:

We will be adding a collection of ST datasets for prototyping, testing and benchmarking of ML models in the near future. If you would like to share your dataset in this repo, please do not hesitate to reach out or do a PR.

Citation and Acknowledgement

Please cite our repository/review paper if it was useful for your research:

@article{doi:10.1063/5.0091135,
author = {Heydari,A. Ali  and Sindi,Suzanne S. },
title = {Deep learning in spatial transcriptomics: Learning from the next next-generation sequencing},
journal = {Biophysics Reviews},
volume = {4},
number = {1},
pages = {011306},
year = {2023},
doi = {10.1063/5.0091135},
URL = {https://doi.org/10.1063/5.0091135},
eprint = {https://doi.org/10.1063/5.0091135}
}