RT Journal
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
JF Genome Research 
JO Genome Research 
YR 2025 
FD January 01 
VO 35 
IS 1 
SP 147 
OP 161 
DO 10.1101/gr.278822.123 
UL http://genome.cshlp.org/content/35/1/147.abstract 
AB 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.