Framework improves cell type identification and expression analysis in single-cell RNA-seq data, suggesting enhanced performance across analytical tasks.
ZILLNB provides a principled framework for addressing technical artifacts in scRNA-seq data while preserving biological variation. The integration of statistical modeling with deep learning enables robust performance across diverse analytical tasks, including cell type identification, differential expression analysis, and rare cell population discovery, demonstrating utility across common single-cell analysis tasks.
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Luo et al. (2025) studied this question.
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