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

Authors

Shuai Guo
Xiaoqian Liu
Xuesen Cheng
Yujie Jiang
Shuangxi Ji
Qingnan Liang
Andrew Koval
Yumei Li
Leah A Owen
Ivana K Kim
Ana Aparicio
Sanghoon Lee
Anil K Sood
Scott Kopetz
John Paul Shen
John N Weinstein
Margaret M DeAngelis
Rui Chen
Wenyi Wang

Publication Date

1-22-2025

Journal

Genome Research

Abstract

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.

Keywords

Humans, Single-Cell Analysis, Female, Ovarian Neoplasms, Benchmarking, Macular Degeneration, Sequence Analysis, RNA, Retina, RNA-Seq

DOI

10.1101/gr.278822.123

PMID

39586714

PMCID

PMC11789644

PubMedCentral® Posted Date

1-1-2025

PubMedCentral® Full Text Version

Post-print

Published Open-Access

yes