Skip to main content

Thank you for visiting nature.com. 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:

Cell type-specific inference of differential expression in spatial transcriptomics

Nature Methods volume 19, pages 1076–1087 (2022)Cite this article

Subjects

Abstract

A central problem in spatial transcriptomics is detecting differentially expressed (DE) genes within cell types across tissue context. Challenges to learning DE include changing cell type composition across space and measurement pixels detecting transcripts from multiple cell types. Here, we introduce a statistical method, cell type-specific inference of differential expression (C-SIDE), that identifies cell type-specific DE in spatial transcriptomics, accounting for localization of other cell types. We model gene expression as an additive mixture across cell types of log-linear cell type-specific expression functions. C-SIDE’s framework applies to many contexts: DE due to pathology, anatomical regions, cell-to-cell interactions and cellular microenvironment. Furthermore, C-SIDE enables statistical inference across multiple/replicates. Simulations and validation experiments on Slide-seq, MERFISH and Visium datasets demonstrate that C-SIDE accurately identifies DE with valid uncertainty quantification. Last, we apply C-SIDE to identify plaque-dependent immune activity in Alzheimer’s disease and cellular interactions between tumor and immune cells. We distribute C-SIDE within the R package https://github.com/dmcable/spacexr.

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

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

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

Fig. 1: C-SIDE learns cell type-specific DE from spatial transcriptomics data.
Fig. 2: C-SIDE provides unbiased estimates of cell type-specific DE in simulated data.
Fig. 3: C-SIDE’s estimated cell type-specific DE is validated by HCR-FISH.
Fig. 4: C-SIDE discovers cell type-specific DE in a diverse set of problems on testes, Alzheimer’s hippocampus and hypothalamus datasets.
Fig. 5: C-SIDE enables DE discovery on diverse spatial transcriptomics technologies including Visium and MERFISH.
Fig. 6: C-SIDE enables the discovery of DE pathways in a KrasG12D/+Trp53−/− (KP) mouse model.

Similar content being viewed by others

Data availability

Slide-seq V2 data generated for this study and additional data are available at the Broad Institute Single Cell Portal https://singlecell.broadinstitute.org/single_cell/study/SCP1663. We also used the following publicly available datasets in our study. MERFISH hypothalamus dataset was accessed from Dryad https://doi.org/10.5061/dryad.8t8s248. Visium human lymph node is available at https://www.10xgenomics.com/resources/datasets/human-lymph-node-1-standard-1-1-0. Testes Slide-seq data can be accessed at https://www.dropbox.com/s/ygzpj0d0oh67br0/Testis_Slideseq_Data.zip?dl=0. Cancer Slide-seq data are available at https://singlecell.broadinstitute.org/single_cell/study/SCP1278. Hallmark gene sets were accessed from https://www.gsea-msigdb.org/.

Code availability

C-SIDE is implemented in the open-source R package spacexr, with source code freely available at https://github.com/dmcable/spacexr. Additional code used for analysis in this paper is available at https://github.com/dmcable/spacexr/tree/master/AnalysisCSIDE.

References

  1. 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 

  2. Stickels, R. R. et al. Highly sensitive spatial transcriptomics at near-cellular resolution with Slide-seqV2. Nat. Biotechnol. 39, 313–319 (2021).

    Article  CAS  PubMed  Google Scholar 

  3. Chen, K. H., Boettiger, A. N., Moffitt, J. R., Wang, S. & Zhuang, X. Spatially resolved, highly multiplexed RNA profiling in single cells. Science 348, 412 (2015).

    Article  CAS  Google Scholar 

  4. Wang, X. et al. Three-dimensional intact-tissue sequencing of single-cell transcriptional states. Science 361, 380 (2018).

    Article  CAS  Google Scholar 

  5. 10X Genomics: visium spatial gene expression. 10X Genomics https://www.10xgenomics.com/solutions/spatial-gene-expression/ (2020).

  6. Zollinger, D. R., Lingle, S. E., Sorg, K., Beechem, J. M. & Merritt, C. R. GeoMx RNA assay: High multiplex, digital, spatial analysis of RNA in FFPE tissue. In Ian A. Darby & Tim D. Hewitson (eds.) In Situ Hybridization Protocols, 331–345 (Springer, 2020).

  7. Alon, S. et al. Expansion sequencing: spatially precise in situ transcriptomics in intact biological systems. Science 371, 481 (2021).

    Article  CAS  Google Scholar 

  8. Moffitt, J. R. et al. Molecular, spatial, and functional single-cell profiling of the hypothalamic preoptic region. Science 362, 792 (2018).

    Article  CAS  Google Scholar 

  9. Chen, H. et al. Dissecting mammalian spermatogenesis using spatial transcriptomics. Cell Rep. 37, 109915 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. 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 

  11. 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 

  12. Zhu, J., Sun, S. & Zhou, X. SPARK-X: non-parametric modeling enables scalable and robust detection of spatial expression patterns for large spatial transcriptomic studies. Genome Biol. 22, 184 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Dries, R. et al. Giotto: a toolbox for integrative analysis and visualization of spatial expression data. Genome Biol. 22, 78 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija, R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36, 411–420 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 12 (2014).

    Article  CAS  Google Scholar 

  16. Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).

    Article  CAS  PubMed  Google Scholar 

  17. Haghverdi, L., Lun, A. T., Morgan, M. D. & Marioni, J. C. Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors. Nat. Biotechnol. 36, 421–427 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  19. Regev, A. et al. Science forum: the human cell atlas. eLife 6, e27041 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  20. Petukhov, V. et al. Cell segmentation in imaging-based spatial transcriptomics. Nat. Biotechnol. 40, 345–354 (2022).

    Article  CAS  PubMed  Google Scholar 

  21. Andersson, A. et al. Single-cell and spatial transcriptomics enables probabilistic inference of cell type topography. Commun. Biol. 3, 565 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  22. Dong, R. & Yuan, G. C. SpatialDWLS: accurate deconvolution of spatial transcriptomic data. Genome Biol. 22, 145 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  23. Kleshchevnikov, V. et al. Cell2location maps fine-grained cell types in spatial transcriptomics. Nat. Biotechnol. 40, 661–671 (2022).

    Article  CAS  PubMed  Google Scholar 

  24. Zhao, E. et al. Spatial transcriptomics at subspot resolution with BayesSpace. Nat. Biotechnol. 39, 1375–1384 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Hardin, J. W., Hardin, J. W., Hilbe, J. M. & Hilbe, J. Generalized Linear Models and Extensions (Stata Press, 2007).

  26. Wood, S. & Wood, M. S. Package ’mgcv’. R package version 1.29 (R Foundation for Statistical Computing, 2015).

  27. Kozareva, V. et al. A transcriptomic atlas of mouse cerebellar cortex comprehensively defines cell types. Nature 598, 214–219 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Zhao, M., Shirley, C. R., Mounsey, S. & Meistrich, M. L. Nucleoprotein transitions during spermiogenesis in mice with transition nuclear protein Tnp1 and Tnp2 mutations. Biol. Reproduction 71, 1016–1025 (2004).

    Article  CAS  Google Scholar 

  29. Hasegawa, K. & Saga, Y. Retinoic acid signaling in Sertoli cells regulates organization of the blood-testis barrier through cyclical changes in gene expression. Development 139, 4347–4355 (2012).

    Article  CAS  PubMed  Google Scholar 

  30. Xu, J. et al. Computerized spermatogenesis staging (CSS) of mouse testis sections via quantitative histomorphological analysis. Med. Image Anal. 70, 101835 (2021).

    Article  PubMed  Google Scholar 

  31. Mucke, L. et al. High-level neuronal expression of Aβ1–42 in wild-type human amyloid protein precursor transgenic mice: Synaptotoxicity without plaque formation. J. Neurosci. 20, 4050–4058 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Kraft, A. W. et al. Attenuating astrocyte activation accelerates plaque pathogenesis in APP/PS1 mice. FASEB J. 27, 187–198 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Hong, S. et al. Complement and microglia mediate early synapse loss in Alzheimer mouse models. Science 352, 712–716 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Zhou, Y. et al. Human and mouse single-nucleus transcriptomics reveal TREM2-dependent and TREM2-independent cellular responses in Alzheimer’s disease. Nat. Med. 26, 131–142 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Veerhuis, R. et al. Cytokines associated with amyloid plaques in Alzheimer’s disease brain stimulate human glial and neuronal cell cultures to secrete early complement proteins, but not C1-inhibitor. Exp. Neurol. 160, 289–299 (1999).

    Article  CAS  PubMed  Google Scholar 

  36. Bernstein, H. G. & Keilhoff, G. Putative roles of cathepsin B in Alzheimer’s disease pathology: the good, the bad, and the ugly in one? Neural Regen. Res. 13, 2100 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Sobue, A. et al. Microglial gene signature reveals loss of homeostatic microglia associated with neurodegeneration of Alzheimer's disease. Acta Neuropathol. Commun. 9, 1 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Keren-Shaul, H. et al. A unique microglia type associated with restricting development of Alzheimer’s disease. Cell 169, 1276–1290 (2017).

    Article  CAS  PubMed  Google Scholar 

  39. Serrano-Pozo, A., Das, S. & Hyman, B. T. APOE and Alzheimer’s disease: advances in genetics, pathophysiology, and therapeutic approaches. Lancet Neurol. 20, 68–80 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Mendsaikhan, A., Tooyama, I. & Walker, D. G. Microglial progranulin: involvement in Alzheimer’s disease and neurodegenerative diseases. Cells 8, 230 (2019).

    Article  CAS  PubMed Central  Google Scholar 

  41. Zhou, X. et al. Cellular and molecular properties of neural progenitors in the developing mammalian hypothalamus. Nature Commun. 11, 4063 (2020).

    Article  CAS  Google Scholar 

  42. Romanov, R. A. et al. Molecular interrogation of hypothalamic organization reveals distinct dopamine neuronal subtypes. Nat. Neurosci. 20, 176–188 (2017).

    Article  CAS  PubMed  Google Scholar 

  43. V1_Human_Lymph_Node—Datasets—Spatial Gene Expression https://support.10xgenomics.com/spatial-geneexpression/datasets/1.1.0/V1_Human_Lymph_Node (10X Genomics, 2020).

  44. Milpied, P. et al. Human germinal center transcriptional programs are de-synchronized in B cell lymphoma. Nat. Immunol. 19, 1013–1024 (2018).

    Article  CAS  PubMed  Google Scholar 

  45. Abe, Y. et al. A single-cell atlas of non-haematopoietic cells in human lymph nodes and lymphoma reveals a landscape of stromal remodelling. Nat. Cell Biol. 24, 565–578 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Weinstein, A. M. & Storkus, W. J. In Wang, X.-Y. & Fisher, P. B. (eds.) Immunotherapy of Cancer Vol. 128 Advances in Cancer Research 197–233 (Academic Press, 2015).

  47. Zhao, T. et al. Spatial genomics enables multi-modal study of clonal heterogeneity in tissues. Nature 601, 85–91 (2022).

    Article  CAS  PubMed  Google Scholar 

  48. Dang, C. V. c-Myc target genes involved in cell growth, apoptosis, and metabolism. Mol. Cell. Biol. 19, 1 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Jiménez-Sánchez, J. et al. Evolutionary dynamics at the tumor edge reveal metabolic imaging biomarkers. Proc. Natl Acad. Sci. USA 118, 110–118 (2021).

    Article  CAS  Google Scholar 

  50. Kodama, M. et al. In vivo loss-of-function screens identify KPNB1 as a new druggable oncogene in epithelial ovarian cancer. Proc. Nat. Acad. Sci. USA 114, E7301–E7310 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Chen, D. P. et al. Peritumoral monocytes induce cancer cell autophagy to facilitate the progression of human hepatocellular carcinoma. Autophagy 14, 1335–1346 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Lim, S. Y., Yuzhalin, A. E., Gordon-Weeks, A. N. & Muschel, R. J. Targeting the CCL2-CCR2 signaling axis in cancer metastasis. Oncotarget 7, 28697 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  53. Pires, B. R. et al. NF-kappaB is involved in the regulation of EMT genes in breast cancer cells. PloS ONE 12, e0169622 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  54. Dongre, A. & Weinberg, R. A. New insights into the mechanisms of epithelial-mesenchymal transition and implications for cancer. Nat. Rev. Mol. Cell Biol. 20, 69–84 (2019).

    Article  CAS  PubMed  Google Scholar 

  55. Satoh, J.-i. et al. TMEM106B expression is reduced in Alzheimer’s disease brains. Alzheimeras Res. Ther. 6, 17 (2014).

    Article  CAS  Google Scholar 

  56. Walker, D. G., Kim, S. U. & McGeer, P. L. Expression of complement C4 and C9 genes by human astrocytes. Brain Res. 809, 31–38 (1998).

    Article  CAS  PubMed  Google Scholar 

  57. Götzl, J. K. et al. Opposite microglial activation stages upon loss of PGRN or TREM 2 result in reduced cerebral glucose metabolism. EMBO Mol. Med. 11, e9711 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  58. Minami, S. S. et al. Progranulin protects against amyloid β deposition and toxicity in Alzheimer’s disease mouse models. Nat. Med. 20, 1157–1164 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Yuan, Y. X. A review of trust region algorithms for optimization. In Proc. 4th International Congress on Industrial & Applied Mathematics (ICIAM 99), Edinburgh 271–282 (Oxford Univ. Press, 2000).

  60. Van der Vaart, A. W. Asymptotic Statistics Vol. 3 (Cambridge Univ. Press, 2000).

  61. Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B Methodol. 57, 289–300 (1995).

    Google Scholar 

  62. DerSimonian, R. & Laird, N. Meta-analysis in clinical trials. Control. Clin. Trials 7, 177–188 (1986).

    Article  CAS  PubMed  Google Scholar 

  63. Green, C. D. et al. A comprehensive roadmap of murine spermatogenesis defined by single-cell RNA-seq. Dev. Cell 46, 651–667 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. 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 

  65. Bergenstråhle, J., Larsson, L. & Lundeberg, J. Seamless integration of image and molecular analysis for spatial transcriptomics workflows. BMC Genomics 21, 482 (2020).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  66. Dirks, R. M. & Pierce, N. A. Triggered amplification by hybridization chain reaction. Proc. Natl Acad. Sci. USA 101, 15275–15278 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Liberzon, A. et al. Molecular signatures database (MSigDB) 3.0. Bioinformatics 27, 1739–1740 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Irizarry, R. A., Wang, C., Zhou, Y. & Speed, T. P. Gene set enrichment analysis made simple. Stat. Methods Med. Res. 18, 565–575 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  69. Turlach, B. A. & Weingessel, A. quadprog: Functions to solve quadratic programming problems. R package version 1.5-5 (R Foundation for Statistical Computing, 2013).

Download references

Acknowledgements

We thank R. Stickels for providing valuable input on the analysis. We thank T. Zhao and Z. Chiang for generously providing the cancer Slide-seq data. We thank S. Marsh (Harvard Medical School/Boston Children’s Hospital) for kindly providing mouse J20 Alzheimer’s model samples. We thank members of the Chen laboratory, Irizarry laboratory and Macosko laboratory including T. Kamath for helpful discussions and feedback. D.M.C. was supported by a Fannie and John Hertz Foundation Fellowship and an National Science Federation Graduate Research Fellowship. This work was supported by an National Institutes of Health (NIH) Early Independence Award (DP5, 1DP5OD024583 to F.C.), the NHGRI (R01, R01HG010647 to F.C. and E.Z.M.), as well as the Burroughs Wellcome Fund, the Searle Scholars Award, and the Merkin Institute to F.C. R.A.I. was supported by NIH grant nos. R35GM131802 and R01HG005220.

Author information

Author notes

  1. These authors contributed equally: Rafael A. Irizarry and Fei Chen.

Authors and Affiliations

  1. Department of Electrical Engineering and Computer Science, MIT, Cambridge, MA, USA

    Dylan M. Cable & Michael Diao

  2. Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Dylan M. Cable, Evan Murray, Vignesh Shanmugam, Simon Zhang, Luli S. Zou, Michael Diao, Haiqi Chen, Evan Z. Macosko & Fei Chen

  3. Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA

    Dylan M. Cable, Luli S. Zou & Rafael A. Irizarry

  4. Department of Pathology, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA

    Vignesh Shanmugam

  5. Department of Biostatistics, Harvard University, Boston, MA, USA

    Luli S. Zou & Rafael A. Irizarry

  6. Cecil H. and Ida Green Center for Reproductive Biology Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA

    Haiqi Chen

  7. Department of Obstetrics and Gynecology, University of Texas Southwestern Medical Center, Dallas, TX, USA

    Haiqi Chen

  8. Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA

    Evan Z. Macosko

  9. Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA

    Fei Chen

Authors
  1. Dylan M. Cable
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Evan Murray
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Vignesh Shanmugam
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Simon Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Luli S. Zou
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Michael Diao
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Haiqi Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Evan Z. Macosko
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Rafael A. Irizarry
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Fei Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

D.M.C., R.A.I. and F.C. conceived the study. F.C., E.M., E.Z.M. and D.M.C. designed the Slide-seq, antibody stain and HCR experiments. E.M. generated the Slide-seq, antibody stain and HCR data. D.M.C., R.A.I. and F.C. developed the statistical methods. D.M.C., F.C. and R.A.I. designed the analysis. D.M.C., S.Z., L.S.Z., M.D., R.A.I. and F.C. analyzed the data. D.M.C., F.C., R.A.I., V.S. and H.C. interpreted biological results. V.S. annotated the tumor H&E stain. D.M.C., F.C. and R.A.I. wrote the manuscript and all authors read and approved the final manuscript.

Corresponding authors

Correspondence to Rafael A. Irizarry or Fei Chen.

Ethics declarations

Competing interests

E.Z.M. and F.C. are listed as inventors on a patent application related to Slide-seq. F.C. and E.Z.M. are paid consultants of Atlas Bio. The remaining authors declare no competing interests.

Peer review

Peer review information

Nature Methods thanks Pengyi Yang and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Lin Tang, in collaboration with the Nature Methods team.

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 and Figs. 1–12.

Reporting Summary

Supplementary Tables

Supplementary Table 1 Slide-seq cerebellum population-level C-SIDE significant results across three experimental replicates. Columns include cell type, mean_est (estimated loge fold-change), sd_est (standard error), Z_est (Z-score), p (P value), q_val (q-val) and sig_p (estimated standard deviation of technical and biological variation across samples). Also contains the log fold-change and standard errors for each of the three datasets. Supplementary Table 2 Slide-seq testes C-SIDE significant results. Columns include cell type, log_fc (estimated loge fold-change across two stages with maximal DE), sd (standard error), Xi (estimated log-e expression in stage i), p_val (P value). Supplementary Table 3 MERFISH hypothalamus linear C-SIDE significant results. Columns include cell type, log_fc (estimated DE loge fold-change), Z_score (Z-score), p_val (P value) and conv (convergence). Supplementary Table 4 MERFISH hypothalamus quadratic C-SIDE significant results. Columns include cell type, log_fc (estimated DE loge fold-change), Z_score (Z-score), p_val (P value) and conv (convergence). Supplementary Table 5 Visium lymph node C-SIDE significant results. Columns include cell type, log_fc (estimated DE loge fold-change), Z_score (Z-score), p_val (P value) and conv (convergence). Supplementary Table 6 Slide-seq J20 Hippocampus population-level C-SIDE significant results across four experimental replicates. Columns include cell type, mean_est (estimated log-e-fold-change), sd_est (standard error), Z_est (Z-score), p (P value), q_val (q-val) and sig_p (estimated standard deviation of technical and biological variation across samples). Also, contains the log fold-change and standard errors for each of the four datasets. Supplementary Table 7 Slide-seq tumor nonparametric C-SIDE significant results. Columns include cell type, Z_score (Z-score), p_val (P value) and conv (convergence). Supplementary Table 8 Gene set testing results on the Slide-seq tumor. Significant gene sets are shown. Supplementary Table 9 Slide-seq tumor parametric C-SIDE significant results. Columns include cell type, log_fc (estimated DE loge fold-change), Z_score (Z-score), p_val (P value) and conv (convergence). Supplementary Table 10 HCR probes used for validation experiments on the cerebellum. Supplementary Table 11 Metadata about datasets analyzed in this C-SIDE paper.

Supplementary Software 1

spacexr package manual, v.2.0.0.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cable, D.M., Murray, E., Shanmugam, V. et al. Cell type-specific inference of differential expression in spatial transcriptomics. Nat Methods 19, 1076–1087 (2022). https://doi.org/10.1038/s41592-022-01575-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41592-022-01575-3

This article is cited by

Search

Advanced search

Quick links

Nature Briefing AI and Robotics

Sign up for the Nature Briefing: AI and Robotics newsletter — what matters in AI and robotics research, free to your inbox weekly.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing: AI and Robotics