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Bioinform."],"abstract":"<jats:p>Single-cell RNA sequencing has provided unprecedented insights into the cellular heterogeneity of acute and chronic pulmonary injury. Lack of data-driven analytical frameworks remains a significant barrier to reproducible and biologically robust analyses. To address this challenge, we developed a modular, interactive analysis platform for single-cell RNA sequencing data processing and visualization. As a case study, we applied the framework to an immune-enriched murine lung dataset containing samples from different batches and across four conditions: control, inflammation and remodeling, fibrosis, and fibrotic lung with ozone exposure. Manual annotations performed on individual samples were used as ground-truth labels to evaluate automated cell-type annotations generated using the Tabula Muris Senis and LungMAP reference atlases. These annotations were also utilized to benchmark the performance of data preprocessing and integration combinations on batch-effect removal and the preservation of biological identity. Within the LIANA framework, we evaluated eight inference algorithms and 18 references for cell-cell communication analysis. In our results, we outlined the framework of the analysis pipeline and interactive platform. Through manual annotation, we identified 36 distinct cell populations, highlighting the limitations of automated reference-based annotations in resolving cell states missing from underlying references - particularly injury-induced monocyte-derived macrophages and eosinophils. In our dataset, Log-normalization outperformed SCTransform in both preserving cell-type separation and batch mixing, while FastMNN provided the best balance between technical batch correction and the retention of cell subtype identities. Cell-cell communication analysis revealed that permutation-based algorithms used by CellChat and CellPhoneDB improved prediction confidence, while MouseConsensus and OmniPath resources increased interaction coverage. These findings demonstrate that preprocessing, integration, and cell annotation strategies should be selected in a context-dependent, data-driven manner rather than relying on default workflows. The annotated murine lung dataset expands the existing atlas, and the modular interactive platform offers a practical framework to improve accessibility, reproducibility, and analytical precision in pulmonary research.<\/jats:p>","DOI":"10.3389\/fbinf.2026.1886929","type":"journal-article","created":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T05:36:49Z","timestamp":1789969009000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["A single-cell atlas and Shiny-based framework for murine lung injury and remodeling"],"prefix":"10.3389","volume":"6","author":[{"given":"Qiuming","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Molecular Biosciences, University of California Davis School of Veterinary Medicine","place":["Davis, CA, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jixian","family":"Li","sequence":"additional","affiliation":[{"name":"Texas Advanced Computing Center, The University of Texas at Austin","place":["Austin, TX, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Philip J.","family":"Moos","sequence":"additional","affiliation":[{"name":"Department of Pharmacology and Toxicology, University of Utah College of Pharmacy","place":["Salt Lake City, UT, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alessandro","family":"Venosa","sequence":"additional","affiliation":[{"name":"Department of Molecular Biosciences, University of California Davis School of Veterinary Medicine","place":["Davis, CA, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,21]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"194","DOI":"10.1186\/s13059-019-1795-z","article-title":"A comparison of automatic cell identification methods for single-cell RNA sequencing data","volume":"20","author":"Abdelaal","year":"2019","journal-title":"Genome Biol."},{"key":"B2","doi-asserted-by":"publisher","first-page":"eaba1983","DOI":"10.1126\/sciadv.aba1983","article-title":"Single-cell RNA-seq reveals ectopic and aberrant lung-resident cell populations in idiopathic pulmonary fibrosis","volume":"6","author":"Adams","year":"2020","journal-title":"Sci. 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