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To address the dual challenges of data imputation and dimensionality reduction, we propose an integrated recovery framework that combines probabilistic modeling with sparse optimization. The proposed method captures latent distribution patterns underlying gene expression, enabling the reconstruction of nonlinear dependencies among genes. Furthermore, sparsity constraints and heuristic search strategies are employed to enhance recovery accuracy, particularly in regions with low expression, while maintaining global expression consistency. We evaluate the framework on in-domain breast-cancer cohorts, on mask-augmented datasets with prescribed masking ratios and known references, and on 5 heterogeneous external datasets spanning distinct protocols and scales; across these settings, the method outperforms representative baselines in reconstruction error, structural fidelity, and recovery of biologically critical genes. These results highlight the effectiveness of jointly modeling distributional structure and sparsity for the reliable restoration of single-cell expression data.<\/jats:p>","DOI":"10.34133\/csbj.0124","type":"journal-article","created":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T07:54:16Z","timestamp":1778486056000},"update-policy":"https:\/\/doi.org\/10.34133\/aaas_crossmark_01","source":"Crossref","is-referenced-by-count":0,"title":["GSCI: A Generative and Sparse Compressed Sensing Imputation Framework for Single-Cell RNA-Sequencing Dropout Recovery"],"prefix":"10.34133","volume":"35","author":[{"given":"Yan","family":"Tian","sequence":"first","affiliation":[{"name":"Jilin City Hospital of Chemical Industry","place":["P. R. 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