单细胞和单核RNA测序（scRNA-seq和snRNA-seq，简称scnRNA-seq）等单细胞分辨率的RNA分析技术可以帮助表征组织的组成并揭示对健康和疾病组织中关键功能产生影响的细胞。然而，由于高昂的成本和严格的样本收集要求，使用这些技术在操作上具有挑战性。利用scnRNA-seq确定的细胞类型进行组分推断的计算解卷积方法可以扩展scnRNA-seq的应用，但其效果仍存在争议。我们首次对已知或使用scnRNA-seq预估组成的数据集进行了解卷积方法的系统评估。我们的分析揭示了scnRNA-seq 10X Genomics测序的共同偏差，并突显了准确和适当控制的数据预处理以及方法选择和优化的重要性。此外，我们的结果表明，联合RNA测序和scnRNA-seq的配置可以提高scnRNA-seq预处理和使用它们的解卷积方法的准确性。事实上，我们提出的方法——Single-cell RNA Quantity Informed Deconvolution（SQUID），结合了RNA测序转换和减阻最小二乘解卷积方法，在预测细胞混合物和组织样本的组成方面一直优于其他方法。我们展示了使用SQUID分析联合RNA测序和scnRNA-seq的配置可以产生准确的细胞类型丰度估计，并且这种准确性改进对于在儿童急性髓系白血病和神经母细胞瘤数据集中识别与结果相关的癌细胞亚克隆是必要的。这些结果表明，在生命科学中，解卷积准确性的提高对于其应用至关重要。 ©2023。作者(们)。

RNA profiling technologies at single-cell resolutions, including single-cell and single-nuclei RNA sequencing (scRNA-seq and snRNA-seq, scnRNA-seq for short), can help characterize the composition of tissues and reveal cells that influence key functions in both healthy and disease tissues. However, the use of these technologies is operationally challenging because of high costs and stringent sample-collection requirements. Computational deconvolution methods that infer the composition of bulk-profiled samples using scnRNA-seq-characterized cell types can broaden scnRNA-seq applications, but their effectiveness remains controversial.We produced the first systematic evaluation of deconvolution methods on datasets with either known or scnRNA-seq-estimated compositions. Our analyses revealed biases that are common to scnRNA-seq 10X Genomics assays and illustrated the importance of accurate and properly controlled data preprocessing and method selection and optimization. Moreover, our results suggested that concurrent RNA-seq and scnRNA-seq profiles can help improve the accuracy of both scnRNA-seq preprocessing and the deconvolution methods that employ them. Indeed, our proposed method, Single-cell RNA Quantity Informed Deconvolution (SQUID), which combines RNA-seq transformation and dampened weighted least-squares deconvolution approaches, consistently outperformed other methods in predicting the composition of cell mixtures and tissue samples.We showed that analysis of concurrent RNA-seq and scnRNA-seq profiles with SQUID can produce accurate cell-type abundance estimates and that this accuracy improvement was necessary for identifying outcomes-predictive cancer cell subclones in pediatric acute myeloid leukemia and neuroblastoma datasets. These results suggest that deconvolution accuracy improvements are vital to enabling its applications in the life sciences.© 2023. The Author(s).