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 Article Swipe
Shuai Guo
,
Xiaoqian Liu
,
Xuesen Cheng
,
Yujie Jiang
,
Shuangxi Ji
,
Qingnan Liang
,
Andrew Koval
,
Yumei Li
,
Leah A. Owen
,
Ivana K. Kim
,
Ana M. Aparicio
,
SangHoon Lee
,
Anil K. Sood
,
Scott Kopetz
,
John Paul Shen
,
John N. Weinstein
,
Margaret M. DeAngelis
,
Rui Chen
,
Wenyi Wang
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1101/gr.278822.123
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1101/gr.278822.123
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.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1101/gr.278822.123
- https://genome.cshlp.org/content/early/2024/11/22/gr.278822.123.full.pdf
- OA Status
- hybrid
- Cited By
- 2
- References
- 61
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4404687960
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4404687960Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1101/gr.278822.123Digital Object Identifier
- Title
-
A deconvolution framework that uses single-cell sequencing plus a small benchmark data set for accurate analysis of cell type ratios in complex tissue samplesWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-11-25Full publication date if available
- Authors
-
Shuai Guo, Xiaoqian Liu, Xuesen Cheng, Yujie Jiang, Shuangxi Ji, Qingnan Liang, Andrew Koval, Yumei Li, Leah A. Owen, Ivana K. Kim, Ana M. Aparicio, SangHoon Lee, Anil K. Sood, Scott Kopetz, John Paul Shen, John N. Weinstein, Margaret M. DeAngelis, Rui Chen, Wenyi WangList of authors in order
- Landing page
-
https://doi.org/10.1101/gr.278822.123Publisher landing page
- PDF URL
-
https://genome.cshlp.org/content/early/2024/11/22/gr.278822.123.full.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
-
https://genome.cshlp.org/content/early/2024/11/22/gr.278822.123.full.pdfDirect OA link when available
- Concepts
-
Deconvolution, Benchmark (surveying), Biology, Computer science, Computational biology, Data mining, Algorithm, Geography, GeodesyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2Per-year citation counts (last 5 years)
- References (count)
-
61Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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