Bulk deconvolution with single-cell/nucleus RNA-seq data is critical for understanding heterogeneity in complex biological samples, yet the technological discrepancy across sequencing platforms limits deconvolution accuracy. To address this, we utilize an experimental design to match inter-platform biological signals, hence revealing the technological discrepancy, and then develop a deconvolution framework called DeMixSC using this well-matched, i.e., benchmark, data. Built upon a novel weighted nonnegative least-squares framework, DeMixSC identifies and adjusts genes with high technological discrepancy and aligns the benchmark data with large patient cohorts of matched-tissue-type for large-scale deconvolution. Our results using two benchmark datasets of healthy retinas and ovarian cancer tissues suggest much-improved deconvolution accuracy. Leveraging tissue-specific benchmark datasets, we applied DeMixSC to a large cohort of 453 age-related macular degeneration patients and a cohort of 30 ovarian cancer patients with various responses to neoadjuvant chemotherapy. Only DeMixSC successfully unveiled biologically meaningful differences across patient groups, demonstrating its broad applicability in diverse real-world clinical scenarios. Our findings reveal the impact of technological discrepancy on deconvolution performance and underscore the importance of a well-matched dataset to resolve this challenge. The developed DeMixSC framework is generally applicable for accurately deconvolving large cohorts of disease tissues, including cancers, when a well-matched benchmark dataset is available.

中文翻译：

---

一个反卷积框架，使用单细胞测序和一个小型基准数据集来准确分析复杂组织样本中的细胞类型比率


使用单细胞/细胞核 RNA-seq 数据进行批量反卷积对于理解复杂生物样品中的异质性至关重要，但测序平台之间的技术差异限制了反卷积的准确性。为了解决这个问题，我们利用实验设计来匹配平台间的生物信号，从而揭示技术差异，然后使用这些匹配良好的数据（即基准）开发一个名为 DeMixSC 的反卷积框架。DeMixSC 建立在新颖的加权非负最小二乘框架之上，识别和调整具有高技术差异的基因，并将基准数据与匹配组织类型的大型患者队列对齐，以进行大规模反卷积。我们使用健康视网膜和卵巢癌组织的两个基准数据集的结果表明，反卷积的准确性大大提高。利用组织特异性基准数据集，我们将 DeMixSC 应用于 453 名年龄相关性黄斑变性患者的大型队列和 30 名对新辅助化疗有不同反应的卵巢癌患者队列。只有 DeMixSC 成功地揭示了患者群体之间具有生物学意义的差异，证明了其在各种真实世界临床场景中的广泛适用性。我们的研究结果揭示了技术差异对反卷积性能的影响，并强调了匹配良好的数据集解决这一挑战的重要性。当有匹配良好的基准数据集可用时，开发的 DeMixSC 框架通常适用于准确解卷积大型疾病组织队列，包括癌症。