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Current statistical and machine learning\u2010based methods struggle with this task, often providing only visualizable embeddings, over\u2010correcting and discarding biological signal, or failing to resolve cell\u2010type\u2010specific responses. Here, we present CAPER, a matrix factorization framework that explicitly disentangles shared biological states from condition\u2010specific variations. CAPER directly outputs an interpretable, batch\u2010corrected expression matrix in which the signal of interest is preserved and isolated. The performance of CAPER is validated using extensive simulations, followed by three real\u2010world multi\u2010condition scRNA\u2010seq data applications, representing distinct signal\u2010to\u2010noise ratio (SNR) scenarios: a controlled immune stimulation in PBMCs with high SNR, a tumor\u2010microenvironment dataset from LUAD with confounded SNR, and a complex autoimmune disease dataset from T1D with low SNR. Across these settings, CAPER yields interpretable latent factors linked to relevant biology, accurately recovers key differentially expressed genes, and correctly identifies the most responsive cell populations. CAPER is a robust and interpretable tool for recovering biological signals from multi\u2010condition single\u2010cell RNA\u2010seq data, enabling reliable discovery in disease research and functional genomics.<\/jats:p>","DOI":"10.1002\/advs.76186","type":"journal-article","created":{"date-parts":[[2026,6,22]],"date-time":"2026-06-22T11:06:41Z","timestamp":1782126401000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Condition\u2010Associated Pattern Extraction and Recovery From Multi\u2010Condition Single\u2010Cell RNA\u2010seq Data With CAPER"],"prefix":"10.1002","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-9058-5352","authenticated-orcid":false,"given":"Ye","family":"Li","sequence":"first","affiliation":[{"name":"Center For Single\u2010Cell Omics and Health School of Public Health Xi'an Jiaotong University Xi'an Shaanxi P. R. 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China"},{"name":"Sun Yat\u2010Sen Memorial Hospital Sun Yat\u2010Sen University Guangzhou Guangdong P. R. China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guoliang","family":"Liu","sequence":"additional","affiliation":[{"name":"Center For Single\u2010Cell Omics and Health School of Public Health Xi'an Jiaotong University Xi'an Shaanxi P. R. China"},{"name":"Collaborative Innovation Center of Endemic Diseases and Health Promotion in Silk Road Region and NHC Key Laboratory of Environment and Endemic Diseases Xi'an Jiaotong University  Xi'an Shaanxi P. R. China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9150-6992","authenticated-orcid":false,"given":"Shiquan","family":"Sun","sequence":"additional","affiliation":[{"name":"Center For Single\u2010Cell Omics and Health School of Public Health Xi'an Jiaotong University Xi'an Shaanxi P. R. 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