1. Rossi MA, Basiri ML, McHenry JA, Kosyk O, Otis JM, van den Munkhof HE, et al. Obesity remodels activity and tran‑
scriptional state of a lateral hypothalamic brake on feeding. Science. 2019;364:1271–4.
2. Jackson HW, Fischer JR, Zanotelli VRT, Ali HR, Mechera R, Soysal SD, et al. The single-cell pathology landscape of
breast cancer. Nature. 2020;578:615–20.
3. Litviňuková M, Talavera-López C, Maatz H, Reichart D, Worth CL, Lindberg EL, et al. Cells of the adult human heart.
Nature. 2020;588:466–72.
4. Battich N, Stoeger T, Pelkmans L. Image-based transcriptomics in thousands of single human cells at single-mole‑
cule resolution. Nat Methods. 2013;10:1127–33.
5. Lubeck E, Cai L. Single-cell systems biology by super-resolution imaging and combinatorial labeling. Nat Methods.
2012;9:743–8.
6. Chen KH, Boettiger AN, Moffitt JR, Wang S, Zhuang X. Spatially resolved, highly multiplexed RNA profiling in single
cells. Science. 2015;348:aaa6090.
7. Ståhl PL, Salmén F, Vickovic S, Lundmark A, Navarro JF, Magnusson J, et al. Visualization and analysis of gene expres‑
sion in tissue sections by spatial transcriptomics. Science. 2016;353:78–82.
8. Satija R, Farrell JA, Gennert D, Schier AF, Regev A. Spatial reconstruction of single-cell gene expression data. Nat
Biotechnol. 2015;33:495–502.
9. Achim K, Pettit J-B, Saraiva LR, Gavriouchkina D, Larsson T, Arendt D, et al. High-throughput spatial mapping of
single-cell RNA-seq data to tissue of origin. Nat Biotechnol. 2015;33:503–9.
10. Halpern KB, Shenhav R, Matcovitch-Natan O, Tóth B, Lemze D, Golan M, et al. Single-cell spatial reconstruction
reveals global division of labour in the mammalian liver. Nature. 2017;542:352–6.
11. Durruthy-Durruthy R, Gottlieb A, Hartman BH, Waldhaus J, Laske RD, Altman R, et al. Reconstruction of the mouse
otocyst and early neuroblast lineage at single-cell resolution. Cell. 2014;157:964–78.
12. Durruthy-Durruthy R, Gottlieb A, Heller S. 3D computational reconstruction of tissues with hollow spherical mor‑
phologies using single-cell gene expression data. Nat Protoc. 2015;10:459–74.
13. Durruthy-Durruthy J, Wossidlo M, Pai S, Takahashi Y, Kang G, Omberg L, et al. Spatiotemporal reconstruction of
the human blastocyst by single-cell gene-expression analysis informs induction of naive pluripotency. Dev Cell.
2016;38:100–15.
14. Li J, Luo H, Wang R, Lang J, Zhu S, Zhang Z, et al. Systematic reconstruction of molecular cascades regulating GP
development using single-cell RNA-seq. Cell Rep. 2016;15:1467–80.
15. Nitzan M, Karaiskos N, Friedman N, Rajewsky N. Gene expression cartography. Nature. 2019;576:132–7.
16. Cang Z, Nie Q. Inferring spatial and signaling relationships between cells from single cell transcriptomic data. Nat
Commun. 2020;11:2084.
17. González-Blas C, Quan X-J, Duran-Romaña R, Taskiran II, Koldere D, Davie K, et al. Identification of genomic enhanc‑
ers through spatial integration of single-cell transcriptomics and epigenomics. Mol Syst Biol. 2020;16:e9438.
18. Ren X, Zhong G, Zhang Q, Zhang L, Sun Y, Zhang Z. Reconstruction of cell spatial organization from single-cell RNA
sequencing data based on ligand-receptor mediated self-assembly. Cell Res. 2020;30:763–78.
19. Rodriques SG, Stickels RR, Goeva A, Martin CA, Murray E, Vanderburg CR, et al. Slide-seq: a scalable technology for
measuring genome-wide expression at high spatial resolution. Science. 2019;363:1463–7.
20. Stickels RR, Murray E, Kumar P, Li J, Marshall JL, Bella DJD, et al. Highly sensitive spatial transcriptomics at near-cellular
resolution with Slide-seqV2. Nat Biotechnol. 2021;39:313–9.
21. Vickovic S, Eraslan G, Salmén F, Klughammer J, Stenbeck L, Schapiro D, et al. High-definition spatial transcriptomics
for in situ tissue profiling. Nat Methods. 2019;16:987–90.
22. Chen A, Liao S, Cheng M, Ma K, Wu L, Lai Y, et al. Spatiotemporal transcriptomic atlas of mouse organogenesis using
DNA nanoball-patterned arrays. Cell. 2022;185:1777–92.
23. Hu J, Li X, Coleman K, Schroeder A, Ma N, Irwin DJ, et al. SpaGCN: Integrating gene expression, spatial location and
histology to identify spatial domains and spatially variable genes by graph convolutional network. Nat Methods.
2021;18:1342–51.
24. Palla G, Spitzer H, Klein M, Fischer D, Schaar AC, Kuemmerle LB, et al. Squidpy: a scalable framework for spatial omics
analysis. Nat Methods. 2022;19:171–8.
25. Shen R, Liu L, Wu Z, Zhang Y, Yuan Z, Guo J, et al. Spatial-ID: a cell typing method for spatially resolved transcriptom‑
ics via transfer learning and spatial embedding. Nat Commun. 2022;13:7640.
26. Mori T, Takaoka H, Yamane J, Alev C, Fujibuchi W. Novel computational model of gastrula morphogenesis to identify
spatial discriminator genes by self-organizing map (SOM) clustering. Sci Rep. 2019;9:1–10.
27. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, et al. Gene ontology: tool for the unification of biol‑
ogy. Nat Genet. 2000;25:25–9.
28. The Gene Ontology Consortium. Expansion of the gene ontology knowledgebase and resources. Nucleic Acids Res.
2017;45:D331–8.
29. Kangas JA, Kohonen TK, Laaksonen JT. Variants of self-organizing maps. IEEE Trans Neural Net. 1990;1:93–9.
30. Kohonen T. Self-organized formation of topologically correct feature maps. Biol Cybern. 1982;43:59–69.
31. von der Malsburg C. Self-organization of orientation sensitive cells in the striate cortex. Kybernetik. 1973;14:85–100.
32. Turing AM. The chemical basis of morphogenesis. Philos Trans R Soc Lond B Biol Sci. 1952;237:37–72.
33. Li X, Gasteiger J, Zupan J. On the topology distortion in self-organizing feature maps. Biol Cybern. 1993;70:189–98.
34. Becht E, McInnes L, Healy J, Dutertre C-A, Kwok IWH, Ng LG, et al. Dimensionality reduction for visualizing single-cell
data using UMAP. Nat Biotechnol. 2018;37:38–44.
Page 26 of 27
Mori et al. BMC Bioinformatics
(2023) 24:252
35. Andrieu C, De Freitas N, Doucet A, Jordan MI. An Introduction to MCMC for machine learning. Mach Learn.
2003;50:5–43.
36. Peng G, Suo S, Cui G, Yu F, Wang R, Chen J, et al. Molecular architecture of lineage allocation and tissue organization
in early mouse embryo. Nature. 2019;572:528–32.
37. Swendsen RH, Wang JS. Replica monte carlo simulation of spin-glasses. Phys Rev Lett. 1986;57:2607.
38. Braeuning A, Ittrich C, Köhle C, Hailfinger S, Bonin M, Buchmann A, et al. Differential gene expression in periportal
and perivenous mouse hepatocytes. FEBS J. 2006;273:5051–61.
39. Tasic B, Yao Z, Graybuck LT, Smith KA, Nguyen TN, Bertagnolli D, et al. Shared and distinct transcriptomic cell types
across neocortical areas. Nature. 2018;563:72–8.
40. Asp M, Giacomello S, Larsson L, Wu C, Fürth D, Qian X, et al. A spatio–temporal organ-wide gene expression and cell
atlas of the developing human heart. Cell. 2019;179:1647-1660.e19.
41. de Soysa TY, Ranade SS, Okawa S, Ravichandran S, Huang Y, Salunga HT, et al. Single-cell analysis of cardiogenesis
reveals basis for organ-level developmental defects. Nature. 2019;572:120–4.
42. Hashimoto K, Kodama A, Honda T, Hanashima A, Ujihara Y, Murayama T, et al. Fam64a is a novel cell cycle promoter
of hypoxic fetal cardiomyocytes in mice. Sci Rep. 2017;7:1–17.
43. Han S, Cui C, He H, Shen X, Chen Y, Wang Y, et al. FHL1 regulates myoblast differentiation and autophagy through its
interaction with LC3. J Cell Physiol. 2020;235:4667–78.
44. Wang X, Osinska H, Gerdes AM, Robbins J. Desmin filaments and cardiac disease: establishing causality. J Card Fail.
2002;8:S287–92.
45. Gladka MM, Kohela A, Molenaar B, Versteeg D, Kooijman L, Monshouwer-Kloots J, et al. Cardiomyocytes stimulate
angiogenesis after ischemic injury in a ZEB2-dependent manner. Nat Commun. 2021;12:1–16.
46. Eghbali A, Dukes A, Toischer K, Hasenfuss G, Field LJ. Cell cycle-mediated cardiac regeneration in the mouse heart.
Curr Cardiol Rep. 2019;21:131.
47. Veres A, Faust AL, Bushnell HL, Engquist EN, Kenty JH-R, Harb G, et al. Charting cellular identity during human in vitro
β-cell differentiation. Nature. 2019;569:368–73.
48. Zhang D, Jiang W, Liu M, Sui X, Yin X, Chen S, et al. Highly efficient differentiation of human ES cells and iPS cells into
mature pancreatic insulin-producing cells. Cell Res. 2009;19:429–38.
49. Sandoval DA, D’Alessio DA. Physiology of proglucagon peptides: role of glucagon and GLP-1 in health and disease.
Physiol Rev. 2015;95:513–48.
50. Bethea M, Liu Y, Wade AK, Mullen R, Gupta R, Gelfanov V, et al. The islet-expressed Lhx1 transcription factor interacts
with Islet-1 and contributes to glucose homeostasis. Am J Physiol Endocrinol Metab. 2019;316:E397–409.
51. Haris B, Saraswathi S, Hussain K. Somatostatin analogues for the treatment of hyperinsulinaemic hypoglycaemia.
Ther Adv Endocrinol Metab. 2020;11:2042018820965068.
52. Epskamp S, Fried EI. A tutorial on regularized partial correlation networks. Psychol Methods. 2018;23:617–34.
53. Baeyens L, Lemper M, Staels W, Groef SD, Leu ND, Heremans Y, et al. (Re)generating human beta cells: status, pitfalls,
and perspectives. Physiol Rev. 2018;98:1143–67.
54. Yu X-X, Qiu W-L, Yang L, Zhang Y, He M-Y, Li L-C, et al. Defining multistep cell fate decision pathways during pancre‑
atic development at single-cell resolution. EMBO J. 2019;38: e100164.
55. Hao Y, Hao S, Andersen-Nissen E, Mauck WM III, Zheng S, Butler A, et al. Integrated analysis of multimodal single-cell
data. Cell. 2021;184:3573-3587.e29.
56. Cannoodt R, Saelens W, Deconinck L, Saeys Y. Spearheading future omics analyses using dyngen, a multi-modal
simulator of single cells. Nat Commun. 2021;12(1):3942.
57. Bhaduri A, Andrews MG, Leon WM, Jung D, Shin D, Allen D, et al. Cell stress in cortical organoids impairs molecular
subtype specification. Nature. 2020;578:142–8.
58. Blondel VD, Guillaume J-L, Lambiotte R, Lefebvre E. Fast unfolding of communities in large networks. J Stat Mech:
Theory Exp. 2008;2008:P10008.
59. Peng G, Suo S, Chen J, Chen W, Liu C, Yu F, et al. Spatial transcriptome for the molecular annotation of lineage fates
and cell identity in mid-gastrula mouse embryo. Dev Cell. 2016;36:681–97.
60. Barrett T, Wilhite SE, Ledoux P, Evangelista C, Kim IF, Tomashevsky M, et al. NCBI GEO: archive for functional genomics
data sets–update. Nucleic Acids Res. 2013;41(Database issue):D991–5.
61. Rand WM. Objective criteria for the evaluation of clustering methods. J Am Stat Assoc. 1971;66:846–50.
62. Kirkpatrick S, Gelatt CD Jr, Vacchi MP. Optimization by simulated annealing. Science. 1983;220:671–80.
63. Kursa MB, Rudnicki WR. Feature selection with the Boruta package. J Stat Softw. 2010;36:1–13.
64. Durinck S, Spellman PT, Birney E, Huber W. Mapping identifiers for the integration of genomic datasets with the R/
Bioconductor package biomaRt. Nat Protoc. 2009;4:1184–91.
65. Epskamp S, Cramer AO, Waldorp LJ, Schmittmann VD, Borsboom D. qgraph: network visualizations of relationships
in psychometric data. J Stat Softw. 2012;24(48):1–8.
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Page 27 of 27
...