Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Robust decomposition of cell type mixtures in spatial transcriptomics


A limitation of spatial transcriptomics technologies is that individual measurements may contain contributions from multiple cells, hindering the discovery of cell-type-specific spatial patterns of localization and expression. Here, we develop robust cell type decomposition (RCTD), a computational method that leverages cell type profiles learned from single-cell RNA-seq to decompose cell type mixtures while correcting for differences across sequencing technologies. We demonstrate the ability of RCTD to detect mixtures and identify cell types on simulated datasets. Furthermore, RCTD accurately reproduces known cell type and subtype localization patterns in Slide-seq and Visium datasets of the mouse brain. Finally, we show how RCTD’s recovery of cell type localization enables the discovery of genes within a cell type whose expression depends on spatial environment. Spatial mapping of cell types with RCTD enables the spatial components of cellular identity to be defined, uncovering new principles of cellular organization in biological tissue. RCTD is publicly available as an open-source R package at

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Spatial transcriptomics data present challenges for cell type learning.
Fig. 2: RCTD enables cross-platform learning of cell types.
Fig. 3: RCTD performs cross-platform detection and decomposition of doublets.
Fig. 4: RCTD applied to cell type learning in Slide-seq datasets.
Fig. 5: RCTD maps cell types and subtypes in Slide-seq hippocampus data.
Fig. 6: RCTD enables the detection of cell-type-specific spatial patterns of gene expression.

Similar content being viewed by others

Data availability

Slide-seqV2 data generated for this study are available at the Broad Institute Single Cell Portal at Additional publicly available data from other studies that were used for analysis are also included in this repository.

Code availability

RCTD is implemented in the open-source R package RCTD, with source code freely available at Additional code used for analysis in this paper is available at


  1. Stickels, R. R. et al. Sensitive spatial genome wide expression profiling at cellular resolution. Nature Biotechnology (in the press).

  2. 10x Genomics. 10x Genomics: Visium spatial gene expression (2020).

  3. Vickovic, S. et al. High-definition spatial transcriptomics for in situ tissue profiling. Nat. Methods 16, 987–990 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Pelkey, K. A. et al. Hippocampal GABAergic inhibitory interneurons. Physiol. Rev. 97, 1619–1747 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Cembrowski, M. S. et al. The subiculum is a patchwork of discrete subregions. elife 7, e37701 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  6. Edsgärd, D., Johnsson, P. & Sandberg, R. Identification of spatial expression trends in single-cell gene expression data. Nat. Methods 15, 339–342 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  7. Sun, S., Zhu, J. & Zhou, X. Statistical analysis of spatial expression patterns for spatially resolved transcriptomic studies. Nat. Methods 17, 193–200 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Svensson, V., Teichmann, S. A. & Stegle, O. SpatialDE: identification of spatially variable genes. Nat. Methods 15, 343–346 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Wagner, A., Regev, A. & Yosef, N. Revealing the vectors of cellular identity with single-cell genomics. Nat. Biotechnol. 34, 1145–1160 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  10. Regev, A. et al. Science forum: the Human Cell Atlas. eLife 6, e27041 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  11. Rodriques, S. G. et al. Slide-seq: a scalable technology for measuring genome-wide expression at high spatial resolution. Science 363, 1463–1467 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Moncada, R. et al. Integrating microarray-based spatial transcriptomics and single-cell RNA-seq reveals tissue architecture in pancreatic ductal adenocarcinomas. Nat. Biotechnol. 38, 333–342 (2020).

    Article  CAS  PubMed  Google Scholar 

  14. Townes, F. W., Hicks, S. C., Aryee, M. J. & Irizarry, R. A. Feature selection and dimension reduction for single-cell RNA-seq based on a multinomial model. Genome Biol. 20, 295 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Hafemeister, C. & Satija, R. Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression. Genome Biol. 20, 296 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Pliner, H. A., Shendure, J. & Trapnell, C. Supervised classification enables rapid annotation of cell atlases. Nat. Methods 16, 983–986 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Leek, J. T. et al. Tackling the widespread and critical impact of batch effects in high-throughput data. Nat. Rev. Genet. 11, 733–739 (2010).

    Article  CAS  PubMed  Google Scholar 

  18. Bakken, T. E. et al. Single-nucleus and single-cell transcriptomes compared in matched cortical cell types. PLoS ONE 13, e0209648 (2018).

  19. Tsoucas, D. et al. Accurate estimation of cell-type composition from gene expression data. Nat. Commun. 10, 2975 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  20. Kozareva, V. et al. A transcriptomic atlas of the mouse cerebellum reveals regional specializations and novel cell types. Preprint at bioRxiv (2020).

  21. Saunders, A. et al. Molecular diversity and specializations among the cells of the adult mouse brain. Cell 174, 1015–1030 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Brown, A. M. et al. Molecular layer interneurons shape the spike activity of cerebellar Purkinje cells. Sci. Rep. 9, 1742 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  23. Tasic, B. et al. Adult mouse cortical cell taxonomy revealed by single cell transcriptomics. Nat. Neurosci. 19, 335–346 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Zhang, M. et al. Molecular, spatial and projection diversity of neurons in primary motor cortex revealed by in situ single-cell transcriptomics. Preprint at bioRxiv (2020).

  25. Sunkin, S. M. et al. Allen Brain Atlas: an integrated spatio-temporal portal for exploring the central nervous system. Nucleic Acids Res. 41, D996–D1008 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  26. Capogna, M. Neurogliaform cells and other interneurons of stratum lacunosum-moleculare gate entorhinal–hippocampal dialogue. J. Physiol. 589, 1875–1883 (2011).

    Article  CAS  PubMed  Google Scholar 

  27. Leão, R. N. et al. OLM interneurons differentially modulate CA3 and entorhinal inputs to hippocampal CA1 neurons. Nat. Neurosci. 15, 1524–1530 (2012).

    Article  PubMed Central  Google Scholar 

  28. Gampe, K. et al. NTPDase2 and purinergic signaling control progenitor cell proliferation in neurogenic niches of the adult mouse brain. Stem Cells 33, 253–264 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Dikow, N. et al. 3p25.3 microdeletion of GABA transporters SLC6A1 and SLC6A11 results in intellectual disability, epilepsy and stereotypic behavior. Am. J. Med. Genet. A 164, 3061–3068 (2014).

    Article  CAS  Google Scholar 

  30. Lee, T.-S. et al. GAT1 and GAT3 expression are differently localized in the human epileptogenic hippocampus. Acta Neuropathol. 111, 351–363 (2006).

    Article  CAS  PubMed  Google Scholar 

  31. Kulkarni, A., Anderson, A. G., Merullo, D. P. & Konopka, G. Beyond bulk: a review of single cell transcriptomics methodologies and applications. Curr. Opin. Biotechnol. 58, 129–136 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Halpern, K. B. et al. Paired-cell sequencing enables spatial gene expression mapping of liver endothelial cells. Nat. Biotechnol. 36, 962–970 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Sakamoto, Y., Ishiguro, M. & Kitagawa, G. Akaike Information Criterion Statistics 1st edn, Vol. 1 (Springer Netherlands, 1986).

  34. Zhou, M., Li, L., Dunson, D. & Carin, L. Lognormal and gamma mixed negative binomial regression. Proc. Int. Conf. Mach. Learn. 2012, 1343–1350 (2012).

    PubMed  PubMed Central  Google Scholar 

  35. Swami, A. Non-Gaussian mixture models for detection and estimation in heavy-tailed noise. In Proceedings of the 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing 3802–3805 (IEEE, 2000).

  36. Turlach, B. A. & Weingessel, A. quadprog: functions to solve quadratic programming problems. R package version 1.5-5 (2013).

  37. Duchi, J. Sequential convex programming, notes for EE364b: Convex Optimization II (Stanford University, 2018).

  38. SatijaLab. Analysis, visualization, and integration of spatial datasets with Seurat. (2020).

Download references


We thank R. Stickels for providing valuable input on the analysis. We thank members of the Chen lab, Irizarry lab and Macosko lab for helpful discussions. D.M.C. was supported by a Fannie and John Hertz Foundation Fellowship and an NSF Graduate Research Fellowship. This work was supported by an NIH Early Independence Award (1DP5OD024583 to F.C.), the Burroughs Wellcome Fund (F.C.) and the NHGRI (R01HG010647 to E.Z.M. and F.C.) as well as the Schmidt Fellows Program at the Broad Institute and the Stanley Center for Psychiatric Research. R.A.I. was supported by NIH grants R35GM131802 and R01HG005220.


Open Access funding enabled and organized by Projekt DEAL.

Author information

Authors and Affiliations



D.M.C., F.C., R.A.I. and E.Z.M. conceived the study. F.C., E.M. and E.Z.M. designed the Slide-seq experiment. E.M. generated the Slide-seq data. D.M.C., R.A.I. and F.C. developed the statistical methods. D.M.C., F.C., R.A.I. and E.Z.M. designed the analysis. D.M.C., R.A.I., F.C., A.G. and L.S.Z. analyzed the data. D.M.C., F.C., R.A.I., E.Z.M. and L.S.Z. wrote the manuscript. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Fei Chen or Rafael A. Irizarry.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Methods, Table 1 and Figs. 1–27.

Reporting Summary

Supplementary Table 2

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cable, D.M., Murray, E., Zou, L.S. et al. Robust decomposition of cell type mixtures in spatial transcriptomics. Nat Biotechnol 40, 517–526 (2022).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:

This article is cited by


Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research