{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T10:32:00Z","timestamp":1780914720597,"version":"3.54.1"},"reference-count":36,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2023,5,1]],"date-time":"2023-05-01T00:00:00Z","timestamp":1682899200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["11831015"],"award-info":[{"award-number":["11831015"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["12271216"],"award-info":[{"award-number":["12271216"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,5,19]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Although many single-cell computational methods proposed use gene expression as input, recent studies show that replacing \u2018unstable\u2019 gene expression with \u2018stable\u2019 gene\u2013gene associations can greatly improve the performance of downstream analysis. To obtain accurate gene\u2013gene associations, conditional cell-specific network method (c-CSN) filters out the indirect associations of cell-specific network method (CSN) based on the conditional independence of statistics. However, when there are strong connections in networks, the c-CSN suffers from false negative problem in network construction. To overcome this problem, a new partial cell-specific network method (p-CSN) based on the partial independence of statistics is proposed in this paper, which eliminates the singularity of the c-CSN by implicitly including direct associations among estimated variables. Based on the p-CSN, single-cell network entropy (scNEntropy) is further proposed to quantify cell state. The superiorities of our method are verified on several datasets. (i) Compared with traditional gene regulatory network construction methods, the p-CSN constructs partial cell-specific networks, namely, one cell to one network. (ii) When there are strong connections in networks, the p-CSN reduces the false negative probability of the c-CSN. (iii) The input of more accurate gene\u2013gene associations further optimizes the performance of downstream analyses. (iv) The scNEntropy effectively quantifies cell state and reconstructs cell pseudo-time.<\/jats:p>","DOI":"10.1093\/bib\/bbad180","type":"journal-article","created":{"date-parts":[[2023,5,12]],"date-time":"2023-05-12T06:00:45Z","timestamp":1683871245000},"source":"Crossref","is-referenced-by-count":9,"title":["P-CSN: single-cell RNA sequencing data analysis by partial cell-specific network"],"prefix":"10.1093","volume":"24","author":[{"given":"Yan","family":"Wang","sequence":"first","affiliation":[{"name":"School of Science, Jiangnan University , Wuxi 214122 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chenxu","family":"Xuan","sequence":"additional","affiliation":[{"name":"School of Science, Jiangnan University , Wuxi 214122 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hanwen","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Science, Jiangnan University , Wuxi 214122 , 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