A deconvolution framework that uses single-cell sequencing plus a small benchmark data set for accurate analysis of cell type ratios in complex tissue samples

作者：Shuai Guo, Xiaoqian Liu, Xuesen Cheng, Yujie Jiang, Shuangxi Ji, Qingnan Liang, Andrew Koval, Yumei Li, Leah A. Owen, Ivana K. Kim, Ana Maria Aparicio, SangHoon Lee, Anil K. Sood, Scott Kopetz, John Paul Shen, John N. Weinstein, Margaret M. DeAngelis, Rui Chen, Wenyi Wang · 发表于：Genome Research · 年份：2024 · DOI：10.1101/gr.278822.123 · 被引用次数：4 · 研究领域：Single-cell and spatial transcriptomics

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, that is, 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 data sets of healthy retinas and ovarian cancer tissues suggest much-improved deconvolution accuracy. Leveraging tissue-specific benchmark data sets, 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 data set to resolve this challenge. The developed DeMixSC framework is generally applicable for accur...