Benchmarking and integration of methods for deconvoluting spatial transcriptomic data
Open accessAbstract
Motivation: The rapid development of spatial transcriptomics (ST) approaches has provided new insights into understanding tissue architecture and function. However, the gene expressions measured at a spot may contain contributions from multiple cells due to the low-resolution of current ST technologies. Although many computational methods have been developed to disentangle discrete cell types from spatial mixtures, the community lacks a thorough evaluation of the performance of those deconvolution methods. Results: Here, we present a comprehensive benchmarking of 14 deconvolution methods on four datasets. Furthermore, we investigate the robustness of different methods to sequencing depth, spot size and the choice of normalization. Moreover, we propose a new ensemble learning-based deconvolution method (EnDecon) by integrating multiple individual methods for more accurate deconvolution. The major new findings include: (i) cell2loction, RCTD and spatialDWLS are more accurate than other ST deconvolution methods, based on the evaluation of three metrics: RMSE, PCC and JSD; (ii) cell2location and spatialDWLS are more robust to the variation of sequencing depth than RCTD; (iii) the accuracy of the existing methods tends to decrease as the spot size becomes smaller; (iv) most deconvolution methods perform best when they normalize ST data using the method described in their original papers; and (v) the integrative method, EnDecon, could achieve more accurate ST deconvolution. Our study provides valuable information and guideline for practically applying ST deconvolution tools and developing new and more effective methods. Availability and implementation: The benchmarking pipeline is available at https://github.com/SunXQlab/ST-deconvoulution. An R package for EnDecon is available at https://github.com/SunXQlab/EnDecon. Supplementary information: Supplementary data are available at Bioinformatics online.
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On real data sets, SpatialDWLS and RCTD performed best in overall accuracy among cell-type deconvolution methods.
“For simulated data sets, cell2location outperformed other approaches for cell-type deconvolution, but requires more computational resources, whereas for real data sets, SpatialDWLS and RCTD performed best in terms of the overall accuracy score based on four different accuracy metrics215,216.”
From Best practices for single-cell analysis across modalities (2023) · cites this paper as [216]
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Cites
Comprehensive Integration of Single-Cell Data
Stuart, T., Butler, A., Hoffman, P., Hafemeister, C., Papalexi, E., Mauck, W. M., Hao, Y., Stoeckius, M., Smibert, P., & Satija, R. (2019) · Cell · doi:10.1016/j.cell.2019.05.031 · Cited by 15,453 · see citing works
Determining cell type abundance and expression from bulk tissues with digital cytometry
Newman, A. M., Steen, C. B., Liu, C. L., Gentles, A. J., Chaudhuri, A. A., Scherer, F., Khodadoust, M. S., Esfahani, M. S., Luca, B. A., Steiner, D. F., et al. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0114-2 · Cited by 4,427 · see citing works
Visualization and analysis of gene expression in tissue sections by spatial transcriptomics
Ståhl, P. L., Salmén, F., Vickovic, S., Lundmark, A., Navarro, J. F., Magnusson, J., Giacomello, S., Asp, M., Westholm, J. O., Huss, M., et al. (2016) · Science · doi:10.1126/science.aaf2403 · Cited by 3,834 · see citing works
Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq
Zeisel, A., Muñoz-Manchado, A. B., Codeluppi, S., Lönnerberg, P., La Manno, G., Juréus, A., Marques, S., Munguba, H., He, L., Betsholtz, C., et al. (2015) · Science · doi:10.1126/science.aaa1934 · Cited by 3,124 · see citing works
Adult mouse cortical cell taxonomy revealed by single cell transcriptomics
Tasic, B., Menon, V., Nguyen, T. N., Kim, T. K., Jarsky, T., Yao, Z., Levi, B., Gray, L. T., Sorensen, S. A., Dolbeare, T., et al. (2016) · Nature Neuroscience · doi:10.1038/nn.4216 · Cited by 1,714 · see citing works
Exploring tissue architecture using spatial transcriptomics
Rao, A., Barkley, D., França, G. S., & Yanai, I. (2021) · Nature · doi:10.1038/s41586-021-03634-9 · Cited by 1,700 · see citing works
Robust decomposition of cell type mixtures in spatial transcriptomics
Cable, D. M., Murray, E., Zou, L. S., Goeva, A., Macosko, E. Z., Chen, F., & Irizarry, R. A. (2022) · Nature Biotechnology · doi:10.1038/s41587-021-00830-w · Cited by 1,487 · see citing works
Molecular, spatial, and functional single-cell profiling of the hypothalamic preoptic region
Moffitt, J. R., Bambah-Mukku, D., Eichhorn, S. W., Vaughn, E., Shekhar, K., Perez, J. D., Rubinstein, N. D., Hao, J., Regev, A., Dulac, C., et al. (2018) · Science · doi:10.1126/science.aau5324 · Cited by 1,365 · see citing works
Highly sensitive spatial transcriptomics at near-cellular resolution with Slide-seqV2
Stickels, R. R., Murray, E., Kumar, P., Li, J., Marshall, J. L., Di Bella, D. J., Arlotta, P., Macosko, E. Z., & Chen, F. (2021) · Nature Biotechnology · doi:10.1038/s41587-020-0739-1 · Cited by 1,295 · see citing works
Integrating single-cell and spatial transcriptomics to elucidate intercellular tissue dynamics
Longo, S. K., Guo, M. G., Ji, A. L., & Khavari, P. A. (2021) · Nature Reviews Genetics · doi:10.1038/s41576-021-00370-8 · Cited by 1,059 · see citing works
Moncada, R., Barkley, D., Wagner, F., Chiodin, M., Devlin, J. C., Baron, M., Hajdu, C. H., Simeone, D. M., & Yanai, I. (2020) · Nature Biotechnology · doi:10.1038/s41587-019-0392-8 · Cited by 1,045 · see citing works
Deep learning and alignment of spatially resolved single-cell transcriptomes with Tangram
Biancalani, T., Scalia, G., Buffoni, L., Avasthi, R., Lu, Z., Sanger, A., Tokcan, N., Vanderburg, C. R., Segerstolpe, Å., Zhang, M., et al. (2021) · Nature Methods · doi:10.1038/s41592-021-01264-7 · Cited by 931 · see citing works
Giotto: a toolbox for integrative analysis and visualization of spatial expression data
Dries, R., Zhu, Q., Dong, R., Eng, C.-H. L., Li, H., Liu, K., Fu, Y., Zhao, T., Sarkar, A., Bao, F., et al. (2021) · Genome biology · doi:10.1186/s13059-021-02286-2 · Cited by 921 · see citing works
PAGE: Parametric Analysis of Gene Set Enrichment
Kim, S., & Volsky, D. J. (2005) · BMC Bioinformatics · doi:10.1186/1471-2105-6-144 · Cited by 781 · see citing works
A Spatiotemporal Organ-Wide Gene Expression and Cell Atlas of the Developing Human Heart
Asp, M., Giacomello, S., Larsson, L., Wu, C., Fürth, D., Qian, X., Wärdell, E., Custodio, J., Reimegård, J., Salmén, F., et al. (2019) · Cell · doi:10.1016/j.cell.2019.11.025 · Cited by 740 · see citing works
Elosua-Bayes, M., Nieto, P., Mereu, E., Gut, I., & Heyn, H. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab043 · Cited by 732 · see citing works
Single-cell and spatial transcriptomics enables probabilistic inference of cell type topography
Andersson, A., Bergenstråhle, J., Asp, M., Bergenstråhle, L., Jurek, A., Navarro, J. F., & Lundeberg, J. (2020) · Communications Biology · doi:10.1038/s42003-020-01247-y · Cited by 463 · see citing works
Li, B., Zhang, W., Guo, C., Xu, H., Li, L., Fang, M., Hu, Y., Zhang, X., Yao, X., Tang, M., et al. (2022) · Nature Methods · doi:10.1038/s41592-022-01480-9 · Cited by 459 · see citing works
Spatially resolved transcriptomics adds a new dimension to genomics
Larsson, L., Frisén, J., & Lundeberg, J. (2021) · Nature Methods · doi:10.1038/s41592-020-01038-7 · Cited by 313 · see citing works
Danaher, P. J., Kim, Y., Nelson, B., Griswold, M., Yang, Z. G., Piazza, E., & Beechem, J. (2022) · Nature Communications · doi:10.1038/s41467-022-28020-5 · Cited by 293 · see citing works
SpatialDWLS: accurate deconvolution of spatial transcriptomic data
Dong, R., & Yuan, G. (2021) · Genome biology · doi:10.1186/s13059-021-02362-7 · Cited by 292 · see citing works
Accurate estimation of cell-type composition from gene expression data
Tsoucas, D. M., Dong, R., Chen, H., Zhu, Q., Guo, G., & Yuan, G. (2019) · Nature Communications · doi:10.1038/s41467-019-10802-z · Cited by 280 · see citing works
DestVI identifies continuums of cell types in spatial transcriptomics data
Lopez, R., Li, B., Keren-Shaul, H., Boyeau, P., Kedmi, M., Pilzer, D., Jelinski, A., Yofe, I., David, E., Wagner, A., et al. (2022) · Nature Biotechnology · doi:10.1038/s41587-022-01272-8 · Cited by 276 · see citing works
Spatial charting of single-cell transcriptomes in tissues
Wei, R., He, S., Bai, S., Sei, E., Hu, M. S., Thompson, A. M., Chen, K., Krishnamurthy, S., & Navin, N. E. (2022) · Nature Biotechnology · doi:10.1038/s41587-022-01233-1 · Cited by 270 · see citing works
Embryo-scale, single-cell spatial transcriptomics
Srivatsan, S. R., Regier, M. C., Barkan, E. R., Franks, J. M., Packer, J. S., Grosjean, P., Duran, M., Saxton, S. H., Ladd, J. J., Spielmann, M., et al. (2021) · Science · doi:10.1126/science.abb9536 · Cited by 265 · see citing works
DSTG: deconvoluting spatial transcriptomics data through graph-based artificial intelligence
Song, Q., & Su, J. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa414 · Cited by 224 · see citing works
Miller, B. F., Huang, F., Atta, L., Sahoo, A., & Fan, J. (2022) · Nature Communications · doi:10.1038/s41467-022-30033-z · Cited by 208 · see citing works
Cell-type-specific resolution epigenetics without the need for cell sorting or single-cell biology
Rahmani, E., Schweiger, R., Rhead, B. L., Criswell, L. A., Barcellos, L. F., Eskin, E., Rosset, S., Sankararaman, S., & Halperin, E. (2019) · Nature Communications · doi:10.1038/s41467-019-11052-9 · Cited by 167 · see citing works
Kleshchevnikov, V., Shmatko, A., Dann, E., Aivazidis, A., King, H. W., Li, T., Lomakin, A., Kedlian, V. R., Jain, M. S., Park, J. S., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.11.15.378125 · Cited by 88 · see citing works
Bayesian estimation of cell type–specific gene expression with prior derived from single-cell dataPreprint
Wang, J., Roeder, K., & Devlin, B. (2021) · Genome Research · doi:10.1101/gr.268722.120 · Cited by 86 · see citing works
Chen, L., Wu, C.-T., Lin, C.-H., Dai, R., Liu, C., Clarke, R. C., Yu, G., Eyk, J. E. V., Herrington, D. M., & Wang, Y. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab839 · Cited by 14 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Best practices for single-cell analysis across modalities
Heumos, L., Schaar, A. C., Lance, C., Litinetskaya, A., Drost, F., Zappia, L., Lücken, M. D., Strobl, D. C., Henao, J., Curion, F., et al. (2023) · Nature Reviews Genetics · doi:10.1038/s41576-023-00586-w · Cited by 1,051 · see citing works
“…, for example, leverage spatial correlation testing, BayesSpace 227 uses Markov random fields, spaGCN 229 uses graph neural networks to integrate gene expression data, spatial information and histology images, and sepal 235 utilizes diffusion-based modelling to identify genes with spatial patterns.”
MethodSpatial transcriptomic characterization of pathologic niches in IPF
Mayr, C. H., Santacruz, D., Jarosch, S., Bleck, M., Dalton, J., McNabola, A., Lempp, C., Neubert, L., Rath, B., Kamp, J. C., et al. (2024) · Science Advances · doi:10.1126/sciadv.adl5473 · Cited by 119 · see citing works
A data-driven single-cell and spatial transcriptomic map of the human prefrontal cortex
Huuki-Myers, L. A., Spangler, A., Eagles, N. J., Montgomery, K. D., Kwon, S. H., Guo, B., Grant‐Peters, M., Divecha, H. R., Tippani, M., Sriworarat, C., et al. (2024) · Science · doi:10.1126/science.adh1938 · Cited by 97 · see citing works
Luo, J., Deng, M., Zhang, X., & Sun, X. (2023) · Genome Research · doi:10.1101/gr.278001.123 · Cited by 78 · see citing works
Self-citationChallenges and perspectives in computational deconvolution of genomics data
Garmire, L. X., Li, Y., Huang, Q., Xu, C., Teichmann, S. A., Kaminski, N., Pellegrini, M. A., Nguyen, Q. H., & Teschendorff, A. E. (2024) · Nature Methods · doi:10.1038/s41592-023-02166-6 · Cited by 66 · see citing works
Deep learning in spatially resolved transcriptomics: a comprehensive technical view
Zahedi, R., Ghamsari, M. R. E., Argha, A., MacPhillamy, C., Beheshti, A., Alizadehsani, R., Lovell, N. H., Lotfollahi, M., & Alinejad‐Rokny, H. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae082 · Cited by 74 · see citing works
Mason, K., Sathe, A., Hess, P. R., Rong, J., Wu, C.-Y., Furth, E. E., Suszták, K., Levinsohn, J. L., Ji, H. P., & Zhang, N. R. (2024) · Genome biology · doi:10.1186/s13059-023-03159-6 · Cited by 55 · see citing works
Yu, Y., Li, Y., Zhou, L., Cheng, X., & Gong, Z. (2024) · Cancer Letters · doi:10.1016/j.canlet.2024.217243 · Cited by 54 · see citing works
Cell-type deconvolution methods for spatial transcriptomics
Gaspard-Boulinc, L., Gortana, L., Walter, T., Barillot, E., & Cavalli, F. M. G. (2025) · Nature Reviews Genetics · doi:10.1038/s41576-025-00845-y · Cited by 54 · see citing works
Spatiotemporal transcriptomic map of glial cell response in a mouse model of acute brain ischemia
Žucha, D., Abaffy, P., Kirdajová, D., Jirák, D., Kubista, M., Anděrová, M., & Valihrach, L. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2404203121 · Cited by 56 · see citing works
A spatially resolved transcriptome landscape during thyroid cancer progression
Liao, T., Zeng, Y., Xu, W., Xiao Hua, S. H. I., Shen, C., Du, Y., Zhang, M., Zhang, Y., Li, L., Ding, P., et al. (2025) · Cell Reports Medicine · doi:10.1016/j.xcrm.2025.102043 · Cited by 45 · see citing works
Sang-aram, C., Browaeys, R., Seurinck, R., & Saeys, Y. (2023) · eLife · doi:10.7554/elife.88431 · Cited by 50 · see citing works
A practical guide to spatial transcriptomics
Valihrach, L., Žucha, D., Abaffy, P., & Kubista, M. (2024) · Molecular Aspects of Medicine · doi:10.1016/j.mam.2024.101276 · Cited by 33 · see citing works
Deep learning in integrating spatial transcriptomics with other modalities
Luo, J., Fu, J., Lu, Z., & Tu, J. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae719 · Cited by 29 · see citing works
Spatial transcriptomics: a new frontier in cancer research
Huang, S., Ouyang, L., Tang, J., Qian, K., Chen, X., Xu, Z., Ming, J., & Xi, R. (2024) · Clinical Cancer Bulletin · doi:10.1007/s44272-024-00018-8 · Cited by 32 · see citing works
Yin, W., Wan, Y., & Zhou, Y. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae130 · Cited by 18 · see citing works
Yang, Y., Zhang, Y., Wu, J., Liu, Y., & Lei, X. (2025) · Frontiers in Immunology · doi:10.3389/fimmu.2025.1696914 · Cited by 20 · see citing works
He, R., Lu, J., Feng, J., Lu, Z., Shen, K., Xu, K., Luo, H., Yang, G., Chi, H., & Huang, S. (2024) · Frontiers in Immunology · doi:10.3389/fimmu.2024.1435187 · Cited by 17 · see citing works
Spatial Transcriptomics Brings New Challenges and Opportunities for Trajectory Inference
Heitz, M., Ma, Y., Kubal, S., & Schiebinger, G. R. (2024) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-040324-030052 · Cited by 19 · see citing works
scDOT: optimal transport for mapping senescent cells in spatial transcriptomics
Nguyen, N. D., Rosas, L., Оскарович, Х. Т., Jiang, P., Hasanaj, E., Ovando-Ricárdez, J. A., Bueno, M., Rahman, I., Pryhuber, G., Li, D., et al. (2024) · Genome biology · doi:10.1186/s13059-024-03426-0 · Cited by 13 · see citing works
Manukyan, A., Bahry, E., Wyler, E., Becher, E., Reguant, A. P., Plumbom, I., Dikmen, H. O., Elezkurtaj, S., Conrad, T., Altmüller, J., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.12.15.571667 · Cited by 13 · see citing works
A guide to transcriptomic deconvolution in cancer
Dai, Y., Guo, S., Pan, Y., Castignani, C., Montierth, M. D., Loo, P. V., & Wang, W. (2025) · Nature reviews. Cancer · doi:10.1038/s41568-025-00886-9 · Cited by 14 · see citing works
Next-generation deconvolution of transcriptomic data to investigate the tumor microenvironment
Merotto, L., Zopoglou, M., Zackl, C., & Finotello, F. (2023) · International review of cytology · doi:10.1016/bs.ircmb.2023.05.002 · Cited by 12 · see citing works
Slabowska, A. L. O., Pyke, C., Hvid, H., Jessen, L. E., Baumgart, S. J., & Das, V. (2024) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2024.1352594 · Cited by 11 · see citing works
Cheng, J., Yan, L., Nie, Q. H., & Sun, X. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.06.27.497696 · Cited by 11 · see citing works
Self-citationSpatialDDLS: an R package to deconvolute spatial transcriptomics data using neural networks
Mañanes, D., Rivero-García, I., Relaño‐Rupérez, C., Torres, M., Sancho, D., Jiménez‐Carretero, D., Torroja, C., & Sánchez‐Cabo, F. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae072 · Cited by 9 · see citing works
Sang-aram, C., Browaeys, R., Seurinck, R., & Saeys, Y. (2024) · eLife · doi:10.7554/elife.88431.3 · Cited by 9 · see citing works
Next‐generation spatial transcriptomics: unleashing the power to gear up translational oncology
Wang, N., Hong, W., Wu, Y., Chen, Z., Bai, M., Wang, W., & Zhu, J. Y. (2024) · MedComm · doi:10.1002/mco2.765 · Cited by 9 · see citing works
Liu, Y., Li, N., Qi, J., Xu, G., Zhao, J., Wang, N., Huang, X., Jiang, W., Wei, H. Y., Justet, A., et al. (2024) · Genome biology · doi:10.1186/s13059-024-03416-2 · Cited by 13 · see citing works
Emerging Roles of Spatial Transcriptomics in Liver Research
Fujiwara, N., Kimura, G., & Nakagawa, H. (2024) · Seminars in Liver Disease · doi:10.1055/a-2299-7880 · Cited by 11 · see citing works
Ding, J., Li, L., Lu, Q., Venegas, J., Wang, Y., Wu, L., Jin, W., Wen, H., Li, Z., Tang, W., et al. (2024) · Journal of Computational Biology · doi:10.1089/cmb.2024.0532 · Cited by 9 · see citing works
Artificial intelligence in cell annotation for high-resolution RNA sequencing data
Hou, N., Lin, X., Lin, L., Zeng, X., Zhong, Z., Wang, X., Cheng, R., Lin, X., Yang, C. J., & Song, J. T. (2024) · TrAC Trends in Analytical Chemistry · doi:10.1016/j.trac.2024.117818 · Cited by 10 · see citing works
Wang, Z., Dai, R., Wang, M., Lei, L., Zhang, Z., Han, K., Wang, Z., & Guo, Q. (2024) · Journal of genetics and genomics/Journal of Genetics and Genomics · doi:10.1016/j.jgg.2024.11.009 · Cited by 8 · see citing works
Spatial transcriptomics: integrating platforms and computational approaches for clinical insights
Song, W., Wang, D., Li, J., & Zhang, R. (2025) · Biotechnology Advances · doi:10.1016/j.biotechadv.2025.108791 · Cited by 8 · see citing works
Mapping biology in space: from spatial transcriptomics platforms to analytical tools and databases
Guo, Z., Wu, R., Li, W., Yang, K., Ying, X., Alinejad-Rokny, H., & Ye, Y. (2026) · 中国科学通报：英文版 · doi:10.1016/j.scib.2026.01.034 · Cited by 9 · see citing works
Decoding cell state transitions driven by dynamic cell–cell communication in spatial transcriptomics
Yan, L., Zhang, D., & Sun, X. (2026) · Nature Computational Science · doi:10.1038/s43588-025-00934-2 · Cited by 7 · see citing works
Self-citationSingle-cell and spatial transcriptomics in endocrine research.
Matsumoto, R., & Yamamoto, T. (2024) · Endocrine journal · doi:10.1507/endocrj.ej23-0457 · Cited by 5 · see citing works
Sainsc: A Computational Tool for Segmentation‐Free Analysis of In Situ Capture Data
Müller‐Bötticher, N., Tiesmeyer, S., Eils, R., & Ishaque, N. (2024) · Small Methods · doi:10.1002/smtd.202401123 · Cited by 7 · see citing works
A Unified Probabilistic Framework for Modeling and Inferring Spatial Transcriptomic Data
Huang, Z., Luo, S., Zhang, Z., Wang, Z., Zhou, T., & Zhang, J. (2023) · Current Bioinformatics · doi:10.2174/1574893618666230529145130 · Cited by 6 · see citing works
funkyheatmap: Visualising data frames with mixed data types
Cannoodt, R., Deconinck, L., Couckuyt, A., Markov, N. S., Zappia, L., Luecken, M. D., Interlandi, M., Saeys, Y., & Saelens, W. (2025) · The Journal of Open Source Software · doi:10.21105/joss.07698 · Cited by 5 · see citing works
Lin, H., Zhang, J., Nie, Q., & Sun, X. (2025) · Multiscale Modeling and Simulation · doi:10.1137/24m1663077 · Cited by 5 · see citing works
Self-citationOuologuem, S., Martens, L. D., Schaar, A. C., Shulman, M., Gagneur, J., & Theis, F. J. (2025) · Bioinformatics · doi:10.1093/bioinformatics/btaf268 · Cited by 5 · see citing works
Jassowicz, L., Feng, F., Warta, R., Heinicke, F., Wöllner, A., Briesch, H., Sor, F., Soller, J., Lotsch, C., Wong, J. K. L., et al. (2026) · Cancer Cell · doi:10.1016/j.ccell.2026.03.016 · Cited by 6 · see citing works
Sang-aram, C., Browaeys, R., Seurinck, R., & Saeys, Y. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.03.22.533802 · Cited by 5 · see citing works
Ding, J., Venegas, J., Lu, Q., Wang, Y., Wu, L., Jin, W., Wen, H., Liu, R., Tang, W., Li, Z., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.04.11.536333 · Cited by 4 · see citing works
Dai, R., Wang, Z., Zhang, Z., Lei, L., Wang, M., Han, K., Wang, Z., Li, Z., Zhang, J., & Guo, Q. (2025) · Journal of Molecular Medicine · doi:10.1007/s00109-025-02575-4 · Cited by 4 · see citing works
Ngassam, B. T., Niu, H., Pang, S., Shydlouskaya, V., & Andrews, T. S. (2026) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2025.1715821 · Cited by 4 · see citing works
Ariani, A. F., Aulia, K., & Arafat, L. O. A. (2024) · JATI (Jurnal Mahasiswa Teknik Informatika) · doi:10.36040/jati.v8i4.10616 · Cited by 4 · see citing works
Sang-aram, C., Browaeys, R., Seurinck, R., & Saeys, Y. (2023) · eLife · doi:10.7554/elife.88431.1 · Cited by 4 · see citing works
Zhang, D., Yu, N., Li, W., Sun, X. G., Zou, Q., Li, X., Liu, Z., Yuan, Z., Zhang, W., & Gao, R. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.02.22.581503 · Cited by 4 · see citing works
Self-citationspacedeconv: deconvolution of tissue architecture from spatial transcriptomicsPreprint
Zackl, C., Zopoglou, M., Stauffer, R., Außerhofer, M., Ijsselsteijn, M. E., Sturm, G., de Miranda, N. F. C. C., & Finotello, F. (2024) · Research Square · doi:10.21203/rs.3.rs-5102166/v1 · Cited by 5 · see citing works
Shen, W., Hu, Y., Lei, Y., Wong, H., Liu, C., Wu, S., & Zhou, X. (2026) · Nucleic Acids Research · doi:10.1093/nar/gkag410 · Cited by 4 · see citing works
Liu, Y., Li, N., Qi, J., Xu, G., Zhao, J., Wang, N., Huang, X., Jiang, W., Justet, A., Adams, T. S., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.08.24.554722 · Cited by 3 · see citing works
Sang-aram, C., Browaeys, R., Seurinck, R., & Saeys, Y. (2024) · eLife · doi:10.7554/elife.88431.2 · Cited by 3 · see citing works
Wang, N., Song, Y., Hong, W., Mo, H., Song, Z., Dai, W., Wang, L., Zhang, H., Zhang, Y., Zhang, Q., et al. (2024) · Research Square · doi:10.21203/rs.3.rs-4376986/v1 · Cited by 3 · see citing works
gwSPADE: gene frequency-weighted reference-free deconvolution in spatial transcriptomics
Xie, A., Steele, N. G., & Cui, Y. (2025) · Nucleic Acids Research · doi:10.1093/nar/gkaf966 · Cited by 2 · see citing works
Connectogenomics: edges-first spatial biology
Yasser, R., Yu, M., Qian, N., Chang, H., & Weinstein, J. A. (2025) · Trends in biotechnology · doi:10.1016/j.tibtech.2025.10.012 · Cited by 2 · see citing works
Altendorfer, S., Walker, S. J., & Daub, C. O. (2026) · International Journal of Cancer · doi:10.1002/ijc.70733 · Cited by 2 · see citing works
Wang, Z., Dai, R., Wang, M., Zhang, Z., Lei, L., Li, Z., Han, K., Wang, Z., & Guo, Q. (2026) · Interdisciplinary Sciences Computational Life Sciences · doi:10.1007/s12539-025-00811-6 · Cited by 2 · see citing works
Systematic benchmarking of omics computational tools
Kumar, S., Singh, M., Sharma, R. K., & Gupta, M. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-16092-9.00004-7 · Cited by 2 · see citing works
Wang, N., Song, Y., Hong, W., Mo, H., Song, Z., Dai, W., Wang, L., Zhang, H., Zhang, Y., Zhang, Q., et al. (2024) · Research Square · doi:10.21203/rs.3.rs-4376986/v2 · Cited by 2 · see citing works
Sun, A. Y., Azad, T., Brown, C. C., & Pritykin, Y. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.01.13.699379 · Cited by 2 · see citing works
Yang, A., Ke, M., Lin, F., Yang, Y., Chen, J., & Zeng, Z. (2025) · Molecular Carcinogenesis · doi:10.1002/mc.70013 · Cited by 1 · see citing works
Li, F., Li, H., Lin, L., & Sun, X. (2026) · Journal of Mathematical Biology · doi:10.1007/s00285-026-02380-8 · Cited by 1 · see citing works
Self-citationBEASTsim—a benchmarking and analysis platform for spatial transcriptomics simulations
García-Carpintero, T. B., Dyssel, L., Péter, K., Hansen, N. F. H., Straßer, L. J., Lio, C. T., Stahl, M., List, M., & Röttger, R. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag144 · Cited by 1 · see citing works
Guan, X., Zhi, D., Wang, L., & Chen, W. (2026) · Plant Science · doi:10.1016/j.plantsci.2026.113358 · Cited by 1 · see citing works
A Meta-Review of Spatial Transcriptomics Analysis Software
Gillespie, J. L., Pietrzak, M., Song, M., & Chung, D. (2025) · Cells · doi:10.3390/cells14141060 · Cited by 1 · see citing works
A Comprehensive Review of Cell-Type Deconvolution in Spatial Transcriptomic Data
Wang, L., Hu, Y., & Gao, L. (2025) · Big Data Mining and Analytics · doi:10.26599/bdma.2025.9020056 · Cited by 1 · see citing works
SimSpace: a comprehensive in-silico spatial omics data simulation and modeling frameworkPreprint
Zhao, T., Zhang, K., Hollenberg, M., Zhou, W. H., & Fenyö, D. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.07.18.665587 · Cited by 1 · see citing works
Progressive neurodegeneration in human dorsal root ganglion from diabetes to painful neuropathyPreprint
Sankaranarayanan, I., Mazhar, K., Barry, A. M., Shiers, S. I., Wangzhou, A., Sondermann, J. R., Xian, F., Inturi, N. N., Wilde, M. A., Meyers, E., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.01.16.700028 · Cited by 1 · see citing works
Tang, J., Ding, J., Venegas, J., Lu, Q., Wang, Y., Wu, L., Jin, W., Wen, H., Liu, R., Tang, W., et al. (2023) · Research Square · doi:10.21203/rs.3.rs-2826640/v1
Mason, K., Sathe, A., Hess, P. R., Rong, J., Wu, C.-Y., Furth, E. E., Suszták, K., Levinsohn, J. L., Ji, H. P., & Zhang, N. R. (2024) · Figshare · doi:10.6084/m9.figshare.24993682
Sui, Z., Li, Z., & Sun, W. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.08.05.606654
Cell-specific priors rescue differential gene expression in spatial spot-based technologies
Nahman, O., Few-Cooper, T. J., & Shen-Orr, S. S. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae621
Abdelkareem, A. O., Gill, G. S., Manoharan, V. T., Verhey, T. B., & Morrissy, A. S. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.08.02.668292
Mahamune, U., Jongejan, A., van Kampen, A. H. C., van Baarsen, L. G. M., & Moerland, P. D. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag594
Xie, A., & Cui, Y. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.08.26.672457
Mason, K., Sathe, A., Hess, P. R., Rong, J., Wu, C.-Y., Furth, E. E., Suszták, K., Levinsohn, J., Ji, H. P., & Zhang, N. R. (2024) · Figshare · doi:10.6084/m9.figshare.26666965
Mason, K., Sathe, A., Hess, P. R., Rong, J., Wu, C.-Y., Furth, E. E., Suszták, K., Levinsohn, J., Ji, H. P., & Zhang, N. R. (2024) · Figshare · doi:10.6084/m9.figshare.26666950
Mason, K., Sathe, A., Hess, P. R., Rong, J., Wu, C.-Y., Furth, E. E., Suszták, K., Levinsohn, J., Ji, H. P., & Zhang, N. R. (2024) · Figshare · doi:10.6084/m9.figshare.26666959
Mason, K., Sathe, A., Hess, P. R., Rong, J., Wu, C.-Y., Furth, E. E., Suszták, K., Levinsohn, J. L., Ji, H. P., & Zhang, N. R. (2024) · Figshare · doi:10.6084/m9.figshare.26666956
Mason, K., Sathe, A., Hess, P. R., Rong, J., Wu, C.-Y., Furth, E. E., Suszták, K., Levinsohn, J., Ji, H. P., & Zhang, N. R. (2024) · Figshare · doi:10.6084/m9.figshare.26666953
Mason, K., Sathe, A., Hess, P. R., Rong, J., Wu, C.-Y., Furth, E. E., Suszták, K., Levinsohn, J. L., Ji, H. P., & Zhang, N. R. (2024) · Figshare · doi:10.6084/m9.figshare.26666962
Mason, K., Sathe, A., Hess, P. R., Rong, J., Wu, C.-Y., Furth, E. E., Suszták, K., Levinsohn, J., Ji, H. P., & Zhang, N. R. (2024) · Figshare · doi:10.6084/m9.figshare.26666947
Mason, K., Sathe, A., Hess, P. R., Rong, J., Wu, C.-Y., Furth, E. E., Suszták, K., Levinsohn, J. L., Ji, H. P., & Zhang, N. R. (2024) · Figshare · doi:10.6084/m9.figshare.26666968
Mason, K., Sathe, A., Hess, P. R., Rong, J., Wu, C.-Y., Furth, E. E., Suszták, K., Levinsohn, J., Ji, H. P., & Zhang, N. R. (2024) · Figshare · doi:10.6084/m9.figshare.24993806
Mason, K., Sathe, A., Hess, P. R., Rong, J., Wu, C.-Y., Furth, E. E., Suszták, K., Levinsohn, J. L., Ji, H. P., & Zhang, N. R. (2024) · Figshare · doi:10.6084/m9.figshare.26666971
Xie, A., & Cui, Y. (2026) · Advanced Intelligent Systems · doi:10.1002/aisy.202500639
Discovering paracrine regulators of cell type composition from spatial transcriptomics using SPER
Zhao, T., & Haber, A. L. (2026) · Bioinformatics Advances · doi:10.1093/bioadv/vbag011
Spatial omics insights into tumor myeloid cells: roles in tumorigenesis, prognosis, and therapy
Budiarto, B. R., & Pohan, P. U. (2026) · Molecular Biology Reports · doi:10.1007/s11033-026-11758-2
Benchmarking cell-type-specific spatially variable gene detection methods
Yao, H., Mu, S., He, F., & Fang, Z. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag190
Tian, Y., Liang, Y., & Zheng, H. (2026) · Chemical Engineering Journal · doi:10.1016/j.cej.2026.177599
Wang, X., Xia, L. C., LIU, H. J., Du, C., Chen, L., Li, Z., Du, Y., Li, Y., & Ai, D. (2026) · Interdisciplinary Sciences Computational Life Sciences · doi:10.1007/s12539-026-00850-7
Zhu, Y., Hu, Y., Xie, M. B., Qin, H., Szul, Z. J., Young, D. M., Yuan, W., Wang, Q., Liu, Y. H., Shen, W., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.06.16.732716
wang, H., Yang, X., Wang, S., Yang, Z., Yang, X., Yang, Y., Li, Z., Ren, Y., Zhang, Q., Zhao, B., et al. (2026) · iMeta · doi:10.1002/imt2.70146
Wang, T., Zhang, W., He, Z., Song, J., Cao, B., Li, Q., & Fan, C. (2026) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2026.1822831
PRISM: Prior-enhanced Inference for Spatial Transcriptomic Cell Type Mapping
Xu, Y., Wang, X., Liu, S. J., Ge, C., Chen, X., Wang, Y., Yu, B., & Li, X.-M. (2026) · Bioinformatics · doi:10.1093/bioinformatics/btag515
Ivanova, A., Ayyadhury, S., Muñoz, D. G., Hopkins, M., Aboualizadeh, F., Spears, M., Wu, M., Pugh, T. J., Verhaak, R. G. W., Cahill, D. P., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.09.08.749421
Benchmarking segmentation methods for spatial transcriptomics
Martinez‐de‐Morentin, X., Khan, S. A., Maillo, A., Lehmann, R., Kiani, N. A., Lagani, V., Gómez-Cabrero, D., & Tegner, J. N. (2026) · Nature Methods · doi:10.1038/s41592-026-03272-x
- Authors
- Yan, L., & Sun, X.
- Affiliations
- Sun Yat-sen University (CN)
- Corresponding author
- Sun, Xiaoqiang
- ORCID
- Sun, Xiaoqiang
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2022-12-14
- Volume
- 39
- Issue
- 1
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- PMC
- PMC9825747
- OpenAlex
- W4311404503
- Cited by
- 92
- References
- 30
- Citations by year
- 2026: 25 · 2025: 17 · 2024: 38 · 2023: 11 · 2022: 1
- FWCI
- 6.04
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Bioinformatics (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- National Natural Science Foundation of China (62273364, 11931019, 11871070)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Keywords
- spatial transcriptomics, cell of origin deconvolution, benchmarking, ensemble learning, spot size, normalization methods, method comparison
- MeSH terms
- Gene Expression Profiling, Transcriptome
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