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In particular, these techniques facilitate the identifications of genes showing cell-type-specific differential expressions (DE). In this paper, we introduce MARBLES, a novel statistical model for cross-condition DE gene detection from scRNA-seq data. MARBLES employs a Markov Random Field model to borrow information across similar cell types and utilizes cell-type-specific pseudobulk count to account for sample-level variability. Our simulation results showed that MARBLES is more powerful than existing methods to detect DE genes with an appropriate control of false positive rate. Applications of MARBLES to real data identified novel disease-related DE genes and biological pathways from both a single-cell lipopolysaccharide mouse dataset with 24 381 cells and 11 076 genes and a Parkinson\u2019s disease human data set with 76 212 cells and 15 891 genes. Overall, MARBLES is a powerful tool to identify cell-type-specific DE genes across conditions from scRNA-seq data.<\/jats:p>","DOI":"10.1093\/bib\/bbac166","type":"journal-article","created":{"date-parts":[[2022,4,29]],"date-time":"2022-04-29T11:13:31Z","timestamp":1651230811000},"source":"Crossref","is-referenced-by-count":6,"title":["A Markov random field model-based approach for differentially expressed gene detection from single-cell RNA-seq data"],"prefix":"10.1093","volume":"23","author":[{"given":"Biqing","family":"Zhu","sequence":"first","affiliation":[{"name":"Program of Computational Biology and Bioinformatics , , New Haven, CT, 06511, USA"},{"name":"Yale University , , New Haven, CT, 06511, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongyu","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Biostatistics , , New Haven, CT, 06511, USA"},{"name":"School of Public Health, Yale University , , New Haven, CT, 06511, 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