1
|
Guo S, Liu X, Cheng X, Jiang Y, Ji S, Liang Q, Koval A, Li Y, Owen LA, Kim IK, Aparicio A, Shen JP, Kopetz S, Weinstein JN, DeAngelis MM, Chen R, Wang W. DeMixSC: a deconvolution framework that uses single-cell sequencing plus a small benchmark dataset for improved analysis of cell-type ratios in complex tissue samples. bioRxiv 2023:2023.10.10.561733. [PMID: 37873318 PMCID: PMC10592762 DOI: 10.1101/2023.10.10.561733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
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 introduce an experimental design to match inter-platform biological signals, hence revealing the technological discrepancy, and then develop a deconvolution framework called DeMixSC using the better-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 a benchmark dataset of healthy retinas suggest much-improved deconvolution accuracy. Further analysis of a cohort of 453 patients with age-related macular degeneration supports the broad applicability of DeMixSC. 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 deconvolving large cohorts of disease tissues, and potentially cancer.
Collapse
Affiliation(s)
- Shuai Guo
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Authors contributed equally
| | - Xiaoqian Liu
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Authors contributed equally
| | - Xuesen Cheng
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
- Authors contributed equally
| | - Yujie Jiang
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Department of Statistics, Rice University, Houston, TX, USA
| | - Shuangxi Ji
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Qingnan Liang
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Andrew Koval
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Department of Statistics, Rice University, Houston, TX, USA
| | - Yumei Li
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Leah A. Owen
- Department of Ophthalmology, Jacobs School of Medicine and Biomedical Engineering, SUNY University at Buffalo, Buffalo, NY, USA
- Department of Population Health Sciences, University of Utah School of Medicine, Salt Lake City, UT, USA
- Department of Ophthalmology and Visual Sciences, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Ivana K. Kim
- USA Retina Service, Harvard Medical School, Massachusetts Eye and Ear, Boston, MA, USA
| | - Ana Aparicio
- Department of Genitourinary Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - John Paul Shen
- Department of Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Scott Kopetz
- Department of Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - John N. Weinstein
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Margaret M. DeAngelis
- Department of Ophthalmology, Jacobs School of Medicine and Biomedical Engineering, SUNY University at Buffalo, Buffalo, NY, USA
- Department of Population Health Sciences, University of Utah School of Medicine, Salt Lake City, UT, USA
- Department of Ophthalmology and Visual Sciences, University of Utah School of Medicine, Salt Lake City, UT, USA
- VA Western New York Healthcare System, Buffalo, NY, USA
| | - Rui Chen
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
- Authors contributed equally
| | - Wenyi Wang
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Authors contributed equally
| |
Collapse
|