了解疾病相关组织的细胞组成对于疾病诊断、预后和下游治疗非常重要。单细胞 RNA 测序 (scRNA-seq) 技术的最新进展使得能够测量单个细胞的基因表达谱。然而，scRNA-seq 仍然太昂贵，无法用于大规模群体研究，而批量 RNA-seq 仍然广泛用于此类情况。一个重要的挑战是基于 scRNA-seq 数据对批量 RNA-seq 数据的细胞组成进行反卷积。在这里，我们提出了 DeepDecon，一种深度神经网络模型，它利用单细胞基因表达信息来准确预测大块组织中癌细胞的比例。它提供了一种精炼策略，其中通过一组经过训练的模型迭代估计癌细胞分数。当应用于模拟和真实癌症数据时，与现有分解方法相比，DeepDecon 在准确性方面表现出卓越的性能。© 2024 作者。

Understanding the cellular composition of a disease-related tissue is important in disease diagnosis, prognosis, and downstream treatment. Recent advances in single-cell RNA-sequencing (scRNA-seq) technique have allowed the measurement of gene expression profiles for individual cells. However, scRNA-seq is still too expensive to be used for large-scale population studies, and bulk RNA-seq is still widely used in such situations. An essential challenge is to deconvolve cellular composition for bulk RNA-seq data based on scRNA-seq data. Here, we present DeepDecon, a deep neural network model that leverages single-cell gene expression information to accurately predict the fraction of cancer cells in bulk tissues. It provides a refining strategy in which the cancer cell fraction is iteratively estimated by a set of trained models. When applied to simulated and real cancer data, DeepDecon exhibits superior performance compared to existing decomposition methods in terms of accuracy.© 2024 The Author(s).