A universal tool for predicting differentially active features in single-cell and spatial genomics data

1. Miao, Z., Deng, K., Wang, X. & Zhang, X. DEsingle for detecting three types of differential expression in single-cell RNA-seq data.

Bioinformatics 34, 3223–3224 (2018).

2. Nabavi, S., Schmolze, D., Maitituoheti, M., Malladi, S. & Beck, A. H. EMDomics: A robust and powerful method for the identification of genes differentially expressed between heterogeneous classes. Bioinformatics 32, 533–541 (2016).

3. Korthauer, K. D. et al. A statistical approach for identifying differential distributions in single-cell RNA-seq experiments. Genome

Biol. 17, 1–15 (2016).

4. McCarthy, D. J., Chen, Y. & Smyth, G. K. Differential expression analysis of multifactor RNA-Seq experiments with respect to

biological variation. Nucleic Acids Res. 40, 4288–4297 (2012).

5. Qiu, X. et al. Single-cell mRNA quantification and differential analysis with Census. Nat. Methods 14, 309–315 (2017).

6. Finak, G. et al. MAST: A flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in

single-cell RNA sequencing data. Genome Biol. 16, 1–13 (2015).

7. 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).

8. Soneson, C. & Robinson, M. D. Bias, robustness and scalability in single-cell differential expression analysis. Nat. Methods 15,

255–261 (2018).

9. Wang, T., Li, B., Nelson, C. E. & Nabavi, S. Comparative analysis of differential gene expression analysis tools for single-cell RNA

sequencing data. BMC Bioinform. 20, 6 (2019).

10. Rao, A., Barkley, D., França, G. S. & Yanai, I. Exploring tissue architecture using spatial transcriptomics. Nature 596, 211–220

(2021).

11. Moran, P. A. P. Notes on continuous stochastic phenomena. Biometrika 37, 17–23 (1950).

12. Dries, R. et al. Giotto: A toolbox for integrative analysis and visualization of spatial expression data. Genome Biol. 22, 1–31 (2021).

13. Edsgärd, D., Johnsson, P. & Sandberg, R. Identification of spatial expression trends in single-cell gene expression data. Nat. Methods

15, 339–342 (2018).

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

15. Sun, S., Zhu, J. & Zhou, X. Statistical analysis of spatial expression patterns for spatially resolved transcriptomic studies. Nat.

Methods 17, 193–200 (2020).

16. 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, 1–25 (2021).

17. Miller, B. F., Bambah-Mukku, D., Dulac, C., Zhuang, X. & Fan, J. Characterizing spatial gene expression heterogeneity in spatially

resolved single-cell transcriptomic data with nonuniform cellular densities. Genome Res. 31, 1843–1855 (2021).

18. Lähnemann, D. et al. Eleven grand challenges in single-cell data science. Genome Biol. 21, 1–35 (2020).

19. Vandenbon, A. & Diez, D. A clustering-independent method for finding differentially expressed genes in single-cell transcriptome

data. Nat. Commun. 11, 1–10 (2020).

20. Stickels, R. R. et al. Highly sensitive spatial transcriptomics at near-cellular resolution with Slide-seqV2. Nat. Biotechnol. 39,

313–319 (2021).

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

22. Xia, C., Fan, J., Emanuel, G., Hao, J. & Zhuang, X. Spatial transcriptome profiling by MERFISH reveals subcellular RNA compartmentalization and cell cycle-dependent gene expression. Proc. Natl. Acad. Sci. U.S.A. 116, 19490–19499 (2019).

23. Vandenbon, A. Evaluation of critical data processing steps for reliable prediction of gene co-expression from large collections of

RNA-seq data. PLoS ONE 17, 1–18 (2022).

24. Stoeckius, M. et al. Simultaneous epitope and transcriptome measurement in single cells. Nat. Methods 14, 865–868 (2017).

25. Korsunsky, I. et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat. Methods 16, 1289–1296 (2019).

26. Hie, B., Bryson, B. & Berger, B. Efficient integration of heterogeneous single-cell transcriptomes using Scanorama. Nat. Biotechnol.

37, 685–691 (2019).

27. Gayoso, A. et al. A Python library for probabilistic analysis of single-cell omics data. Nat. Biotechnol. 40, 163–166 (2022).

28. Schaum, N. et al. Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris. Nature 562, 367–372 (2018).

29. Han, X. et al. Mapping the Mouse Cell Atlas by Microwell-Seq. Cell 172, 1091–1097 (2018).

30. Cao, J. et al. A human cell atlas of fetal gene expression. Science 370, 6518 (2020).

31. Li, H., Calder, C. A. & Cressie, N. Beyond Moran’s I: Testing for spatial dependence based on the spatial autoregressive model.

Geogr. Anal. 39, 357–375 (2007).

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

33. Hao, Y. et al. Integrated analysis of multimodal single-cell data. Cell 184, 3573–3587 (2021).

34. Cao, J. et al. The single-cell transcriptional landscape of mammalian organogenesis. Nature 566, 496–502 (2019).

35. Dolgalev, I. msigdbr: MSigDB Gene Sets for Multiple Organisms in a Tidy Data Format (2022).

36. Joseph, V. R. Space-filling designs for computer experiments: A review. Qual. Eng. 28, 28–35 (2016).

37. Stuart, T., Srivastava, A., Madad, S., Lareau, C. A. & Satija, R. Single-cell chromatin state analysis with Signac. Nat. Methods 18,

1333–1341 (2021).

38. SeuratData GitHub repository. https://​github.​com/​satij​alab/​seurat-​data.

39. Bullard, J. H., Purdom, E., Hansen, K. D. & Dudoit, S. Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments. BMC Bioinform. 11, 94 (2010).

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(2023) 13:11830 |

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Vol.:(0123456789)

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40. Johnson, W. E., Li, C. & Rabinovic, A. Adjusting batch effects in microarray expression data using empirical Bayes methods.

Biostatistics 8, 118–127 (2007).

Acknowledgements

The authors would like to thank Y. Harada for secretarial assistance.

Author contributions

A.V. conceived of the project and methodology. A.V. and D.D. implemented the methods, ran the analyses and

wrote the manuscript. All authors contributed to critical revision of the manuscript.

Funding

This work was supported by JSPS KAKENHI Grant Numbers JP20K06609 (A.V.) and JP20K07538 (D.D.), and

by an Office of Directors’ Research Grant provided by the Institute for Life and Medical Sciences (Kyoto University). The funders had no role in study design, data collection and analysis, decision to publish, or preparation

of the manuscript.

Competing interests The authors declare no competing interests.

Additional information

Supplementary Information The online version contains supplementary material available at https://​doi.​org/​

10.​1038/​s41598-​023-​38965-2.

Correspondence and requests for materials should be addressed to A.V.

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