CoChat
Sign Up
Cited by 184Single-cell and spatial transcriptomicsBioinformatics and Genomic NetworksGene Regulatory Network Analysis

Single-cell biological network inference using a heterogeneous graph transformer

Open access
Ma, Anjun, Wang, Xiaoying, Li, Jingxian, Wang, Cankun, Xiao, Tong, Liu, Yuntao, Cheng, Hao, Wang, Juexin +12 more·2023·Nature Communications
DOI 10.1038/s41467-023-36559-0PMC PMC9944243
Open this paper in CoChat — free accountOpen PDF

Abstract

Single-cell multi-omics (scMulti-omics) allows the quantification of multiple modalities simultaneously to capture the intricacy of complex molecular mechanisms and cellular heterogeneity. Existing tools cannot effectively infer the active biological networks in diverse cell types and the response of these networks to external stimuli. Here we present DeepMAPS for biological network inference from scMulti-omics. It models scMulti-omics in a heterogeneous graph and learns relations among cells and genes within both local and global contexts in a robust manner using a multi-head graph transformer. Benchmarking results indicate DeepMAPS performs better than existing tools in cell clustering and biological network construction. It also showcases competitive capability in deriving cell-type-specific biological networks in lung tumor leukocyte CITE-seq data and matched diffuse small lymphocytic lymphoma scRNA-seq and scATAC-seq data. In addition, we deploy a DeepMAPS webserver equipped with multiple functionalities and visualizations to improve the usability and reproducibility of scMulti-omics data analysis.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1038/s41467-023-36559-0

Cites

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Various methods have been developed to integrate single cells with different omics, uncover regulatory networks of single-cell datasets, or combine gene expression profiles with spatial information.

    “Various methods have been developed to integrate single cells with different omics [22], uncover the regulatory networks of single-cell datasets [23], or combine gene expression profiles with spatial information [24].”

    From Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis (2025) · cites this paper as [23]

  • DeepMAPS constructed a mixed graph representation of cell-gene networks, applied the heterogeneous graph transformer model to capture importance between cells and genes, and inferred gene regulatory networks to specific cell types.

    “DeepMAPS constructed a mixed graph representation of cell–gene networks, applied the heterogeneous graph transformer (HGT) model to capture the importance between cells and genes, and further inferred the gene regulatory networks to specific cell types [23].”

    From Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis (2025) · cites this paper as [23]

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

No citation lists for this paper

No citation lists for this paper yet — checked again daily.

References 79 · Cited by 184

Research works printed but not linked: 73

  1. [1]Stuart T Satija R Integrative single-cell analysis Nat. Rev. Genet. 2019 20 257 272 10.1038/s41576-019-0093-7 30696980 · DOI · Paper page
  2. [2]Ma, A., McDermaid, A., Xu, J., Chang, Y. & Ma, Q. Integrative methods and practical challenges for single-cell multi-omics. Trends Biotechnol.38, 1007–1022 (2020). PMC7442857 32818441
  3. [3]Argelaguet, R., Cuomo, A. S. E., Stegle, O. & Marioni, J. C. Computational principles and challenges in single-cell data integration. Nat. Biotechnol.39, 1202–1215 (2021). 33941931
  4. [4]S Teichmann ME Method of the year 2019: single-cell multimodal omics Nat. Methods 2020 17 1 10.1038/s41592-019-0703-5 31907477 · DOI
  5. [6]Argelaguet R MOFA+: a statistical framework for comprehensive integration of multi-modal single-cell data Genome Biol. 2020 21 111 10.1186/s13059-020-02015-1 32393329 PMC7212577 · DOI · Paper page
  6. [7]Korsunsky I Fast, sensitive and accurate integration of single-cell data with Harmony Nat. Methods 2019 16 1289 1296 10.1038/s41592-019-0619-0 31740819 PMC6884693 · DOI · Paper page
  7. [8]Gayoso A Joint probabilistic modeling of single-cell multi-omic data with totalVI Nat. Methods 2021 18 272 282 10.1038/s41592-020-01050-x 33589839 PMC7954949 · DOI · Paper page
  8. [9]Li, Y. et al. Elucidation of biological networks across complex diseases using single-cell omics. Trends Genet.36, 951–966 (2020). PMC7657957 32868128
  9. [10]Ma, Q. & Xu, D. Deep learning shapes single-cell data analysis. Nat. Rev. Mol. Cell Biol.23, 303–304 (2022). PMC8864973 35197610
  10. [11]Wang J scGNN is a novel graph neural network framework for single-cell RNA-Seq analyses Nat. Commun. 2021 12 1882 10.1038/s41467-021-22197-x 33767197 PMC7994447 · DOI
  11. [12]Hu, Z., Dong, Y., Wang, K. & Sun, Y. In Proceedings of The Web Conference 2020 2704–2710 (Association for Computing Machinery, Taipei, Taiwan; 2020).
  12. [13]Wang, X. et al. In The World Wide Web Conference 2022–2032 (Association for Computing Machinery, San Francisco, CA, USA; 2019).
  13. [15]Ma A IRIS3: integrated cell-type-specific regulon inference server from single-cell RNA-Seq Nucleic Acids Res. 2020 48 W275 W286 10.1093/nar/gkaa394 32421805 PMC7319566 · DOI
  14. [16]Han P Gopalakrishnan C Yu H Wang E Gene regulatory network rewiring in the immune cells associated with cancer Genes (Basel) 2017 8 308 10.3390/genes8110308 29112124 PMC5704221 · DOI
  15. [17]Gassner E The Steiner Forest Problem revisited J. Discret. Algorithms 2010 8 154 163 10.1016/j.jda.2009.05.002 · DOI
  16. [19]Cao Z-J Gao G Multi-omics single-cell data integration and regulatory inference with graph-linked embedding Nat. Biotechnol. 2022 40 1458 1466 10.1038/s41587-022-01284-4 35501393 PMC9546775 · DOI · Paper page
  17. [21]Joshi-Tope G Reactome: a knowledgebase of biological pathways Nucleic Acids Res. 2005 33 D428 D432 10.1093/nar/gki072 15608231 PMC540026 · DOI
  18. [22]Garcia-Alonso L Holland CH Ibrahim MM Turei D Saez-Rodriguez J Benchmark and integration of resources for the estimation of human transcription factor activities Genome Res. 2019 29 1363 1375 10.1101/gr.240663.118 31340985 PMC6673718 · DOI · Paper page
  19. [23]Han H TRRUST v2: an expanded reference database of human and mouse transcriptional regulatory interactions Nucleic Acids Res. 2018 46 D380 D386 10.1093/nar/gkx1013 29087512 PMC5753191 · DOI
  20. [24]Wang C Integrative analyses of single-cell transcriptome and regulome using MAESTRO Genome Biol. 2020 21 198 10.1186/s13059-020-02116-x 32767996 PMC7412809 · DOI
  21. [25]Jin S Inference and analysis of cell-cell communication using CellChat Nat. Commun. 2021 12 1088 10.1038/s41467-021-21246-9 33597522 PMC7889871 · DOI · Paper page
  22. [26]Ampudia J CD6-ALCAM signaling regulates multiple effector/memory T cell functions J. Immunol. 2020 204 150.113 150.113 10.4049/jimmunol.204.Supp.150.13 · DOI
  23. [27]Skonier JE Mutational analysis of the CD6 ligand binding domain Protein Eng. Des. Selection 1997 10 943 947 10.1093/protein/10.8.943 9415444 · DOI
  24. [28]Gimferrer I Relevance of CD6-mediated interactions in T cell activation and proliferation J. Immunol. (Baltim., Md.: 1950) 2004 173 2262 2270 10.4049/jimmunol.173.4.2262 15294938 · DOI
  25. [29]Johnston RJ Lee PS Strop P Smyth MJ Cancer immunotherapy and the nectin family Annu. Rev. Cancer Biol. 2021 5 203 219 10.1146/annurev-cancerbio-060920-084910 · DOI
  26. [30]Li X-Y CD155 loss enhances tumor suppression via combined host and tumor-intrinsic mechanisms J. Clin. Invest. 2018 128 2613 2625 10.1172/JCI98769 29757192 PMC5983325 · DOI
  27. [31]Gururajan M Early growth response genes regulate B cell development, proliferation, and immune response J. Immunol. (Baltim., Md.: 1950) 2008 181 4590 4602 10.4049/jimmunol.181.7.4590 PMC2592513 18802061 · DOI
  28. [32]Oh Y-K Jang E Paik D-J Youn J Early growth response-1 plays a non-redundant role in the differentiation of B cells into plasma cells Immune Netw. 2015 15 161 166 10.4110/in.2015.15.3.161 26140048 PMC4486779 · DOI
  29. [33]Brescia P MEF2B instructs germinal center development and acts as an oncogene in B cell lymphomagenesis Cancer Cell 2018 34 453 465.e459 10.1016/j.ccell.2018.08.006 30205047 PMC6223119 · DOI
  30. [34]Trøen G Constitutive expression of the AP-1 transcription factors c-jun, junD, junB, and c-fos and the marginal zone B-cell transcription factor Notch2 in splenic marginal zone lymphoma J. Mol. Diagn. 2004 6 297 307 10.1016/S1525-1578(10)60525-9 15507668 PMC1867488 · DOI
  31. [35]Sánchez-Beato M Abnormal PcG protein expression in Hodgkin’s lymphoma. Relation with E2F6 and NFkappaB transcription factors J. Pathol. 2004 204 528 537 10.1002/path.1661 15470680 · DOI
  32. [36]Saha A Robertson ES Goodrum F Mechanisms of B-cell oncogenesis induced by Epstein-Barr virus J. Virol. 2019 93 e00238 00219 10.1128/JVI.00238-19 30971472 PMC6580952 · DOI
  33. [37]Yachida S Genomic sequencing identifies ELF3 as a driver of ampullary carcinoma Cancer Cell 2016 29 229 240 10.1016/j.ccell.2015.12.012 26806338 PMC5503303 · DOI
  34. [38]Wang H Overexpression of ELF3 facilitates cell growth and metastasis through PI3K/Akt and ERK signaling pathways in non-small cell lung cancer Int. J. Biochem. Cell Biol. 2018 94 98 106 10.1016/j.biocel.2017.12.002 29208568 · DOI
  35. [39]Zhang J KLF16 affects the MYC signature and tumor growth in prostate cancer Onco Targets Ther. 2020 13 1303 1310 10.2147/OTT.S233495 32103997 PMC7025737 · DOI
  36. [40]Ma P KLF16 promotes proliferation in gastric cancer cells via regulating p21 and CDK4 Am. J. Transl. Res. 2017 9 3027 3036 28670390 PMC5489902
  37. [41]Mathas S Aberrantly expressed c-Jun and JunB are a hallmark of Hodgkin lymphoma cells, stimulate proliferation and synergize with NF-κB EMBO J. 2002 21 4104 4113 10.1093/emboj/cdf389 12145210 PMC126136 · DOI · Paper page
  38. [42]Eferl R Wagner EF AP-1: a double-edged sword in tumorigenesis Nat. Rev. Cancer 2003 3 859 868 10.1038/nrc1209 14668816 · DOI
  39. [43]Nagel D Vincendeau M Eitelhuber AC Krappmann D Mechanisms and consequences of constitutive NF-κB activation in B-cell lymphoid malignancies Oncogene 2014 33 5655 5665 10.1038/onc.2013.565 24469030 · DOI
  40. [44]Jost PJ Ruland JR Aberrant NF-κB signaling in lymphoma: mechanisms, consequences, and therapeutic implications Blood 2006 109 2700 2707 10.1182/blood-2006-07-025809 17119127 · DOI
  41. [45]Garces de Los Fayos Alonso I The role of activator protein-1 (AP-1) family members in CD30-positive lymphomas Cancers (Basel) 2018 10 93 10.3390/cancers10040093 29597249 PMC5923348 · DOI
  42. [46]Crispino JD Horwitz MS GATA factor mutations in hematologic disease Blood 2017 129 2103 2110 10.1182/blood-2016-09-687889 28179280 PMC5391620 · DOI
  43. [47]Shimizu R Engel JD Yamamoto M GATA1-related leukaemias Nat. Rev. Cancer 2008 8 279 287 10.1038/nrc2348 18354416 · DOI
  44. [48]Mosquera Orgueira A Detection of new drivers of frequent B-cell lymphoid neoplasms using an integrated analysis of whole genomes PLoS ONE 2021 16 e0248886 10.1371/journal.pone.0248886 33945543 PMC8096002 · DOI
  45. [49]Blyth K Runx1 promotes B-cell survival and lymphoma development Blood Cells Mol. Dis. 2009 43 12 19 10.1016/j.bcmd.2009.01.013 19269865 · DOI
  46. [50]Mackay F Schneider P Rennert P Browning J BAFF AND APRIL: a tutorial on B cell survival Annu Rev. Immunol. 2003 21 231 264 10.1146/annurev.immunol.21.120601.141152 12427767 · DOI
  47. [51]Smulski, C. R. & Eibel, H. BAFF and BAFF-receptor in B cell selection and survival. Front. Immunol.9, 2285 (2018). PMC6186824 30349534
  48. [52]Yang S Li JY Xu W Role of BAFF/BAFF-R axis in B-cell non-Hodgkin lymphoma Crit. Rev. Oncol. Hematol. 2014 91 113 122 10.1016/j.critrevonc.2014.02.004 24629840 · DOI
  49. [53]He B Lymphoma B cells evade apoptosis through the TNF family members BAFF/BLyS and APRIL J. Immunol. (Baltim., Md.: 1950) 2004 172 3268 3279 10.4049/jimmunol.172.5.3268 14978135 · DOI
  50. [54]Xia XZ TACI is a TRAF-interacting receptor for TALL-1, a tumor necrosis factor family member involved in B cell regulation J. Exp. Med 2000 192 137 143 10.1084/jem.192.1.137 10880535 PMC1887716 · DOI
  51. [55]Laâbi Y Egle A Strasser A TNF cytokine family: more BAFF-ling complexities Curr. Biol. 2001 11 R1013 R1016 10.1016/S0960-9822(01)00613-3 11747837 · DOI
  52. [56]Mackay F Schneider P TACI, an enigmatic BAFF/APRIL receptor, with new unappreciated biochemical and biological properties Cytokine Growth Factor Rev. 2008 19 263 276 10.1016/j.cytogfr.2008.04.006 18514565 · DOI
  53. [57]Rihacek M B-cell activating factor as a cancer biomarker and its implications in cancer-related Cachexia Biomed. Res. Int. 2015 2015 792187 792187 10.1155/2015/792187 26339644 PMC4538579 · DOI
  54. [58]Su H Chang J Xu M Sun R Wang J CDK6 overexpression resulted from microRNA‑320d downregulation promotes cell proliferation in diffuse large B‑cell lymphoma Oncol. Rep. 2019 42 321 327 31059102
  55. [59]Lee C Huang X Di Liberto M Martin P Chen-Kiang S Targeting CDK4/6 in mantle cell lymphoma Ann. Lymphoma 2020 4 1 10.21037/aol.2019.12.01 32783046 PMC7416845 · DOI
  56. [60]Otto T Sicinski P Cell cycle proteins as promising targets in cancer therapy Nat. Rev. Cancer 2017 17 93 115 10.1038/nrc.2016.138 28127048 PMC5345933 · DOI
  57. [61]Li, K. et al. cellxgene VIP unleashes full power of interactive visualization, plotting and analysis of scRNA-seq data in the scale of millions of cells. bioRxiv, 2020.2008.2028.270652 (2020).
  58. [62]Pereira, W. et al. Asc-Seurat – Analytical single-cell Seurat-based web application. bioRxiv, 2021.2003.2019.436196 (2021). PMC8600690 34794383
  59. [63]Gardeux V David FPA Shajkofci A Schwalie PC Deplancke B ASAP: a web-based platform for the analysis and interactive visualization of single-cell RNA-seq data Bioinformatics 2017 33 3123 3125 10.1093/bioinformatics/btx337 28541377 PMC5870842 · DOI
  60. [64]Li B Cumulus provides cloud-based data analysis for large-scale single-cell and single-nucleus RNA-seq Nat. Methods 2020 17 793 798 10.1038/s41592-020-0905-x 32719530 PMC7437817 · DOI
  61. [65]Hillje R Pelicci PG Luzi L Cerebro: interactive visualization of scRNA-seq data Bioinformatics 2019 36 2311 2313 10.1093/bioinformatics/btz877 PMC7141853 31764967 · DOI
  62. [66]Prompsy P Interactive analysis of single-cell epigenomic landscapes with ChromSCape Nat. Commun. 2020 11 5702 10.1038/s41467-020-19542-x 33177523 PMC7658988 · DOI
  63. [67]Bolisetty, M. T., Stitzel, M. L. & Robson, P. CellView: Interactive exploration of high dimensional single cell RNA-seq data. bioRxiv, 123810 (2017).
  64. [68]Mohanraj S CReSCENT: CanceR Single Cell ExpressioN Toolkit Nucleic Acids Res. 2020 48 W372 W379 10.1093/nar/gkaa437 32479601 PMC7319570 · DOI
  65. [69]Patel MV iS-CellR: a user-friendly tool for analyzing and visualizing single-cell RNA sequencing data Bioinformatics 2018 34 4305 4306 10.1093/bioinformatics/bty517 29982379 PMC6289135 · DOI
  66. [70]Yousif A Drou N Rowe J Khalfan M Gunsalus KC NASQAR: a web-based platform for high-throughput sequencing data analysis and visualization BMC Bioinforma. 2020 21 267 10.1186/s12859-020-03577-4 PMC7322916 32600310 · DOI
  67. [71]Zhu Q PIVOT: platform for interactive analysis and visualization of transcriptomics data BMC Bioinforma. 2018 19 6 10.1186/s12859-017-1994-0 PMC5756333 29304726 · DOI
  68. [72]Innes, B. & Bader, G. scClustViz - Single-cell RNAseq cluster assessment and visualization. F1000Res. 7, ISCB Comm J-1522 (2018). PMC6456841 31016009
  69. [73]Granja JM ArchR is a scalable software package for integrative single-cell chromatin accessibility analysis Nat. Genet. 2021 53 403 411 10.1038/s41588-021-00790-6 33633365 PMC8012210 · DOI · Paper page
  70. [74]Wan C LTMG: a novel statistical modeling of transcriptional expression states in single-cell RNA-Seq data Nucleic Acids Res. 2019 47 e111 10.1093/nar/gkz655 31372654 PMC6765121 · DOI
  71. [76]Li GS Li M Wang JX Li YH Pan Y United Neighborhood Closeness Centrality and Orthology for Predicting Essential Proteins Ieee Acm T Comput Bi 2020 17 1451 1458 30596582
  72. [77]Parisutham, N. & Rethnasamy, N. Eigenvector centrality based algorithm for finding a maximal common connected vertex induced molecular substructure of two chemical graphs. J. Mol. Struct.1244, 130980 (2021).
  73. [78]Ma A Single-cell biological network inference using a heterogeneous graph transformer Zenodo 2023 10.5281/zenodo.7559037 PMC9944243 36810839 · DOI

Authors
Ma, A., Wang, X., Li, J., Wang, C., Xiao, T., Liu, Y., Cheng, H., Wang, J., Li, Y., Chang, Y., Li, J., Wang, D., Jiang, Y., Su, L., Xin, G., Gu, S., Li, Z., Liu, B., Xu, D., & Ma, Q.
Affiliations
  • The Ohio State University (US)
  • Shandong University (CN)
  • University of Missouri (US)
  • The Ohio State University Comprehensive Cancer Center – Arthur G. James Cancer Hospital and Richard J. Solove Research Institute (US)
Corresponding author
  • Liu, Bingqiang
  • Xu, Dong
  • Ma, Qin
ORCID
Venue
Nature Communications
Publisher
Springer Science and Business Media LLC
Published
2023-02-21
Volume
14
Issue
1
Pages
964-964
ISSN
2041-1723 (ISSN-L)
DOI
10.1038/s41467-023-36559-0
PMC
PMC9944243
OpenAlex
W4321452549
Cited by
184
References
79
Citations by year
2026: 49 · 2025: 65 · 2024: 51 · 2023: 14 · 2022: 3 · 2013: 1
FWCI
16.04
Type
journal article
Language
en
Open access
Licence
Locations
  • Nature Communications (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
Foundation for the National Institutes of Health (R35-GM126985, U54-AG075931)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
Keywords
single-cell multi-omics, biological network inference, heterogeneous graph transformer, cell clustering, CITE-seq, single-cell RNA sequencing, single-cell ATAC-seq, web server
MeSH terms
Data Analysis, Electric Power Supplies, Reproducibility of Results, Cluster Analysis, Benchmarking, Single-Cell Analysis

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review