{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T06:15:21Z","timestamp":1784614521622,"version":"3.55.0"},"reference-count":49,"publisher":"Oxford University Press (OUP)","issue":"7","license":[{"start":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T00:00:00Z","timestamp":1783036800000},"content-version":"vor","delay-in-days":2,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82473738"],"award-info":[{"award-number":["82473738"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Young Scholars Program of Shandong University"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,7,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Graphical models have been widely used in bioinformatics to infer the conditional dependence structure among random variables, but traditional Gaussian graphical models (GGMs) are suboptimal for single-cell RNA sequencing (scRNA-seq) due to dropout events and distributional mismatch. Moreover, most existing methods estimate networks under a single condition, limiting their utility in multi-condition studies.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We propose PLNFGL (Poisson Log-Normal Fused Graphical Lasso), a joint network estimation framework for scRNA-seq data. PLNFGL uses a multivariate Poisson log-normal model to accommodate dropout effects and estimates the covariance via moment methods. A joint graphical model is then employed to infer condition-specific precision matrices. Simulations show improved estimation accuracy. Applications to scRNA-seq data of Alzheimer\u2019s disease and spatial transcriptomics of lung cancer reveal cell-type-specific interaction networks. Edge set enrichment enables pathway analysis, validating known interactions and highlighting novel disease-related targets. This work provides a powerful tool for the integrative analysis of scRNA-seq data.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and Implementation<\/jats:title>\n                    <jats:p>The R implementation of PLNFGL is available at https:\/\/github.com\/jijiadong\/PLNFGL, and an archival version is available on Zenodo at https:\/\/doi.org\/10.5281\/zenodo.20744172.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btag485","type":"journal-article","created":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T11:37:22Z","timestamp":1782992242000},"source":"Crossref","is-referenced-by-count":1,"title":["PLNFGL: joint estimation of multi-condition gene networks from single-cell RNA-seq 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