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Peer-reviewedCC BY 4.0International Journal of Molecular Sciences

CCCView: A Knowledge Platform for Integrated Analysis of Spatial Transcriptomic Cell–Cell Communication

  • Ziyi Qi1,2
  • Chenyang Wu1,2
  • Qiheng Qian3,4
  • Ziyang Zeng1,2
  • Jingfa Xiao3,4,5
  • Dongyu Zhao1,2
5 affiliations
  1. 1Department of Biomedical Informatics, School of Basic Medical Sciences, Peking University, Beijing 100191, China
  2. 2State Key Laboratory of Vascular Homeostasis and Remodeling, Peking University, Beijing 100191, China
  3. 3National Genomics Data Center, China National Center for Bioinformation, Beijing 100101, China
  4. 4Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China
  5. 5University of Chinese Academy of Sciences, Beijing 100049, China
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Abstract

Spatial transcriptomics enables investigation of cell–cell communication (CCC) while preserving spatial context, yet large-scale resources that organize spatial CCC results across studies and make them readily searchable, visualizable, and comparable remain limited. Here, we present CCCView, a knowledge platform integrating 699 spatial transcriptomics samples and 7,473,444 inferred CCC interactions across 24 tissues, 182 annotated cell types, and 27 biological conditions in human and mouse. CCCView uses a unified cell-type annotation workflow and organizes results from three complementary CCC-inference approaches—COMMOT, CellNEST, and SpatialDM—while preserving method-specific outputs. The platform provides interactive search, browsing, visualization, disease-oriented exploration, data download, and online analysis functions. For cross-sample exploration, CCCView also includes MuCIA, a Tweedie regression-based statistical module for comparing inferred interaction strength across user-defined sample groups. A glioblastoma use case illustrates how CCCView can connect spatial cell-type context, inferred communication patterns, and condition-associated signals to generate biologically interpretable hypotheses. CCCView provides an integrated resource for systematic exploration of spatial transcriptomic CCC and for reuse of precomputed communication results across heterogeneous datasets.

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Cite

BibTeXRIS
BibTeX
@article{qi2026cccview,
  title = {{CCCView: A Knowledge Platform for Integrated Analysis of Spatial Transcriptomic Cell–Cell Communication}},
  author = {Ziyi Qi and Chenyang Wu and Qiheng Qian and Ziyang Zeng and Jingfa Xiao and Dongyu Zhao},
  journal = {International Journal of Molecular Sciences},
  year = {2026},
  month = oct,
  doi = {10.3390/ijms27209382},
  url = {https://www.mdpi.com/1422-0067/27/20/9382},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - CCCView: A Knowledge Platform for Integrated Analysis of Spatial Transcriptomic Cell–Cell Communication
AU  - Ziyi Qi
AU  - Chenyang Wu
AU  - Qiheng Qian
AU  - Ziyang Zeng
AU  - Jingfa Xiao
AU  - Dongyu Zhao
PY  - 2026
DA  - 2026/10/01
JO  - International Journal of Molecular Sciences
DO  - 10.3390/ijms27209382
UR  - https://www.mdpi.com/1422-0067/27/20/9382
AB  - Spatial transcriptomics enables investigation of cell–cell communication (CCC) while preserving spatial context, yet large-scale resources that organize spatial CCC results across studies and make them readily searchable, visualizable, and comparable remain limited. Here, we present CCCView, a knowledge platform integrating 699 spatial transcriptomics samples and 7,473,444 inferred CCC interactions across 24 tissues, 182 annotated cell types, and 27 biological conditions in human and mouse. CCCView uses a unified cell-type annotation workflow and organizes results from three complementary CCC-inference approaches—COMMOT, CellNEST, and SpatialDM—while preserving method-specific outputs. The platform provides interactive search, browsing, visualization, disease-oriented exploration, data download, and online analysis functions. For cross-sample exploration, CCCView also includes MuCIA, a Tweedie regression-based statistical module for comparing inferred interaction strength across user-defined sample groups. A glioblastoma use case illustrates how CCCView can connect spatial cell-type context, inferred communication patterns, and condition-associated signals to generate biologically interpretable hypotheses. CCCView provides an integrated resource for systematic exploration of spatial transcriptomic CCC and for reuse of precomputed communication results across heterogeneous datasets.
SN  - 1422-0067
ER  -