{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T21:37:27Z","timestamp":1780436247200,"version":"3.54.1"},"reference-count":58,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2025,10,19]],"date-time":"2025-10-19T00:00:00Z","timestamp":1760832000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001321","name":"National research foundation","doi-asserted-by":"publisher","award":["RS-2023-00276559"],"award-info":[{"award-number":["RS-2023-00276559"]}],"id":[{"id":"10.13039\/501100001321","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100009619","name":"Japan Agency for Medical Research and Development","doi-asserted-by":"publisher","award":["23tk0124003h0001, 24tk0124003h0002, and 470 25tk0124003h0003"],"award-info":[{"award-number":["23tk0124003h0001, 24tk0124003h0002, and 470 25tk0124003h0003"]}],"id":[{"id":"10.13039\/100009619","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001691","name":"Japan society for the promotion of science","doi-asserted-by":"publisher","award":["JP24H00009"],"award-info":[{"award-number":["JP24H00009"]}],"id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJMS"],"abstract":"<jats:p>Cell\u2013cell interactions play a pivotal role in maintaining tissue homeostasis and driving disease progression. Conventional Cell\u2013cell interactions modeling approaches depend on ligand\u2013receptor databases, which often fail to capture context-specific or newly emerging signaling mechanisms. To address this limitation, we propose a data-driven computational framework, data-driven cell\u2013cell interaction inference (DD-CC-II), which employs a graph-based model using eigen-cells to represent cell groups. DD-CC-II uses eigen-cells (i.e., functional module within the cell population) to characterize cell groups and construct correlation coefficient networks to model between-group associations. Correlation coefficient networks between eigen-cells are constructed, and their statistical significance is evaluated via over-representation analysis and hypergeometric testing. Monte Carlo simulations demonstrate that DD-CC-II achieves superior performance in inferring CCIs compared with ligand\u2013receptor-based methods. The application to whole-blood RNA-seq data from the Japan COVID-19 Task Force revealed severity stage-specific interaction patterns. Markers such as FOS, CXCL8, and HLA-A were associated with high severity, whereas IL1B, CD3D, and CCL5 were related to low severity. The systemic lupus erythematosus pathway emerged as a potential immune mechanism underlying disease severity. Overall, DD-CC-II provides a data-centric approach for mapping the cellular communication landscape, facilitating a better understanding of disease progression at the intercellular level.<\/jats:p>","DOI":"10.3390\/ijms262010170","type":"journal-article","created":{"date-parts":[[2025,10,20]],"date-time":"2025-10-20T08:18:54Z","timestamp":1760948334000},"page":"10170","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["DD-CC-II: Data Driven Cell\u2013Cell Interaction Inference and Its Application to COVID-19"],"prefix":"10.3390","volume":"26","author":[{"given":"Heewon","family":"Park","sequence":"first","affiliation":[{"name":"School of Mathematics Statistics and Data Science, Sungshin Women\u2019s University, Seoul 01133, Republic of Korea"},{"name":"M&D Data Science Center, Institute of Science Tokyo, Tokyo 113-8510, Japan"},{"name":"Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokane-dai, Minato-ku, Tokyo 108-0071, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1753-6616","authenticated-orcid":false,"given":"Satoru","family":"Miyano","sequence":"additional","affiliation":[{"name":"M&D Data Science Center, Institute of Science Tokyo, Tokyo 113-8510, Japan"},{"name":"Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokane-dai, Minato-ku, Tokyo 108-0071, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.semcancer.2023.07.001","article-title":"The promising application of cell\u2013cell interaction analysis in cancer from single-cell and spatial transcriptomics","volume":"95","author":"Wang","year":"2023","journal-title":"Semin. 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