TY  - JOUR
A1  - Guo, Shuai
A1  - Liu, Xiaoqian
A1  - Cheng, Xuesen
A1  - Jiang, Yujie
A1  - Ji, Shuangxi
A1  - Liang, Qingnan
A1  - Koval, Andrew
A1  - Li, Yumei
A1  - Owen, Leah A.
A1  - Kim, Ivana K.
A1  - Aparicio, Ana
A1  - Lee, Sanghoon
A1  - Sood, Anil K.
A1  - Kopetz, Scott
A1  - Shen, John Paul
A1  - Weinstein, John N.
A1  - DeAngelis, Margaret M.
A1  - Chen, Rui
A1  - Wang, Wenyi
T1  - 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
Y1  - 2025/01/01 
JF  - Genome Research 
JO  - Genome Research 
SP  - 147 
EP  - 161 
DO  - 10.1101/gr.278822.123 
VL  - 35 
IS  - 1 
UR  - http://genome.cshlp.org/content/35/1/147.abstract 
N2  - 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 accurately deconvolving large cohorts of disease tissues, including cancers, when a well-matched benchmark data set is available. 
ER  -