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  • Review Article
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Cell-type deconvolution methods for spatial transcriptomics

Nature Reviews Genetics volume 26, pages 828–846 (2025)Cite this article

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Abstract

Spatial transcriptomics is a powerful method for studying the spatial organization of cells, which is a critical feature in the development, function and evolution of multicellular life. However, sequencing-based spatial transcriptomics has not yet achieved cellular-level resolution, so advanced deconvolution methods are needed to infer cell-type contributions at each location in the data. Recent progress has led to diverse tools for cell-type deconvolution that are helping to describe tissue architectures in health and disease. In this Review, we describe the varied types of cell-type deconvolution methods for spatial transcriptomics, contrast their capabilities and summarize them in a web-based, interactive table to enable more efficient method selection.

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Fig. 1: Principal workflow of cell-type deconvolution for spatial transcriptomics.
Fig. 2: Classification of cell-type deconvolution methods for spatial transcriptomics.
Fig. 3: Characteristics of deconvolution methods for spatial transcriptomics.
Fig. 4: Multimodal integration for spatial transcriptomics deconvolution.

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Acknowledgements

L.C.G.-B. is supported by a Contrat Doctoral Spécifique Normalien (2022–2025), awarded by the École Normale Supérieure — PSL University. The work of L.C.G.-B. and F.M.G.C. has been performed with financial support from ITMO Cancer of Aviesan on funds Cancer 2021 administered by Inserm (ATIP-avenir), the Fondation pour la Recherche Medicale (FRM) (code dossier FRM: AJE201905008656), La Ville de Paris (programme emergence(s)), the Cancéropôle Paris region (Emergence 2021), l’INCA (2022-1-PL BIO-02-ICR-1), and La ligue contre le cancer, comité de Paris (RS23/75-43, RS24/75-67). The work of L.G., T.W. and E.B. was funded in part by the French government under management of Agence Nationale de la Recherche as part of the programmes ‘Investissements d’avenir’ (reference no. ANR-19-P3IA-0001; PRAIRIE 3IA Institute) and France 2030 (reference no. ANR-24-EXCI-0005). The authors also thank L. Chadoutaud for meaningful discussions and feedback.

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Author notes

  1. These authors contributed equally: Lucie C. Gaspard-Boulinc, Luca Gortana.

Authors and Affiliations

  1. Institut Curie, PSL University, Paris, France

    Lucie C. Gaspard-Boulinc, Luca Gortana, Thomas Walter, Emmanuel Barillot & Florence M. G. Cavalli

  2. Institut National de la Santé et de la Recherche Médicale (INSERM), U1331, Paris, France

    Lucie C. Gaspard-Boulinc, Luca Gortana, Thomas Walter, Emmanuel Barillot & Florence M. G. Cavalli

  3. Mines Paris, PSL University, CBIO — Centre for Computational Biology, Paris, France

    Lucie C. Gaspard-Boulinc, Luca Gortana, Thomas Walter, Emmanuel Barillot & Florence M. G. Cavalli

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  1. Lucie C. Gaspard-Boulinc
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Contributions

L.C.G.-B. and L.G. researched the literature. All authors contributed substantially to discussion of the content. L.C.G.-B. and L.G. wrote the article. All authors reviewed and edited the manuscript before submission.

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Correspondence to Florence M. G. Cavalli.

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Nature Reviews Genetics thanks Jean Fan; Yvan Saeys, who co-reviewed with Chananchida Sang-aram; and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary information

Glossary

Archetypal analysis

A statistical technique to identify and summarize patterns in the data by finding archetypes. In deconvolution, these archetypes represent idealized spot composed of a unique cell type. Each spot is then represented as a mixture of these archetypes.

Attention networks

Neural networks equipped with ‘attention mechanisms’ that enable the network to focus on relevant parts of the input data. Attention networks have been used extensively in Natural Language Processing and Computer Vision.

Compositional data

Quantitative descriptions of the parts of some whole, conveying relative information. Common compositional data are proportions and percentages, as encountered in most deconvolution outputs.

Dampened weighted least squares

A classical weighted least-squares regression method with two properties: weights are constrained to be greater than zero and a dampening constant is introduced to prevent infinite weights resulting from low cell-type proportions.

Digital pathology

A branch of pathology that focuses on digitizing and analysing microscopic tissue slides, such as haematoxylin and eosin-stained samples, using high-resolution scanners and advanced artificial intelligence tools.

Dimensionality reduction

A mathematical technique to represent high-dimensional data in lower dimensions. In the context of deconvolution, dimension reduction aims to represent the gene expression data as cell-type contribution in each spot.

Domain shifts

A phenomenon in machine learning that occurs when training and test data do not follow the same distribution, leading to potential performance degradation. For example, staining protocols might differ between institutes and thus may affect model performance during image analyses.

Ensemble learning

A machine-learning technique consisting in combining multiple models to obtain better predictive performances than individual models.

Foundational models

Large-scale artificial intelligence models trained on broad unlabelled datasets that can be adapted for various downstream tasks.

Fully connected neural network

A neural network in which each neuron in one layer is connected to all neurons in the next layer. Fully connected neural networks are highly flexible, but computationally expensive owing to a large number of parameters. For this reason, neural networks are often only partly fully connected.

Graph neural networks

A type of neural network designed to process data structured as graphs.

Haematoxylin and eosin

(H&E). A gold standard staining used in histology, in which the haematoxylin stains cell nuclei in purplish blue and eosin stains the extracellular matrix and the cytoplasm in pink.

Maximum a posteriori

A probabilistic estimation method that finds the most likely parameter values given the observed data and a prior distribution.

Monte Carlo algorithms

A computational method that relies on random sampling to approximate probability distributions and estimate model parameters. In Bayesian inference, Monte Carlo algorithms — such as Markov chain Monte Carlo — are used to generate samples from complex posterior distributions when direct analytical solutions are intractable.

Multilayer perceptron

A type of fully connected neural network with multiple hidden layers, typically used for classification and regression tasks.

Non-negative least squares

A type of constrained least squares problem in which the coefficients are restricted to be positive.

Optimal transport

A mathematical framework for finding the most efficient way to transform one probability distribution into another while minimizing a specified cost.

Segmentation masks

The result of a segmentation algorithm, which is used to access and further process the objects identified by the segmentation. In the case of tissue images, the segmentation mask can comprise nuclear and cellular regions, or regions of a certain type (for example, tumour or necrotic regions).

Spatial statistics

A field of applied statistics dealing with spatial data leveraging techniques to study entities using their topological, geometric or geographic properties. Spatial transcriptomics data can be analysed with the latest spatial statistics depending on the layout and features of the technology.

Topic modelling

Unsupervised statistical algorithms originally used in text mining for discovering the latent semantic structures of an extensive text body.

Tumour nests

Small clusters or groups of cancerous cells within a tumour, often surrounded by stromal or immune cells. These nests can have a role in tumour growth, invasion and metastasis.

Variational autoencoder

A generative model that learns probabilistic representations of data by mapping inputs into a latent space (encoding). Variational autoencoders are trained with a pretext task, in which a decoder reconstructs the input data from the latent space representation.

Vision transformers

A deep learning model that applies self-attention mechanisms to image patches, representing an alternative to the widely used convolutional neural networks. Vision transformers achieve high performance in computer vision tasks but require large datasets and substantial computational resources owing to the high number of parameters.

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Gaspard-Boulinc, L.C., Gortana, L., Walter, T. et al. Cell-type deconvolution methods for spatial transcriptomics. Nat Rev Genet 26, 828–846 (2025). https://doi.org/10.1038/s41576-025-00845-y

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