DECEPTICON: a correlation-based strategy for RNA-seq deconvolution inspired by a variation of the Anna Karenina principle
Accurately deconvoluting cellular composition from bulk RNA-seq data is pivotal for understanding the tumor microenvironment and advancing precision medicine. Existing methods often struggle to consistently and accurately quantify cell types across heterogeneous RNA-seq datasets, particularly when ground truths are unavailable. In this study, we introduce DECEPTICON, a deconvolution strategy inspired by the Anna Karenina principle, which postulates that successful outcomes share common traits, while failures are more varied. DECEPTICON selects top-performing methods by leveraging correlations between different strategies and combines them dynamically to enhance performance. Our approach demonstrates superior accuracy in predicting cell-type proportions across multiple tumor datasets, improving correlation by 23.9% and reducing root mean square error by 73.5% compared to the best of 50 analyzed strategies. Applied to The Cancer Genome Atlas (TCGA) datasets for breast carcinoma, cervical squamous cell carcinoma, and lung adenocarcinoma, DECEPTICON-based predictions showed improved differentiation between patient prognoses. This correlation-based strategy offers a reliable, flexible tool for deconvoluting complex transcriptomic data and highlights its potential in refining prognostic assessments in oncology and advancing cancer biology.
Authors
- Miaochen Wang
- Lianchong Gao
- Wantao Chen (ORCID: https://orcid.org/0000-0002-9307-162X)
- Yaoqing Chu (ORCID: https://orcid.org/0000-0001-6979-3641)
- Henry H. Y. Tong (ORCID: https://orcid.org/0000-0003-2687-741X)
- Fulan Deng
- Jiale Wu (ORCID: https://orcid.org/0009-0001-8053-9991)
- Yunfu Gu (ORCID: https://orcid.org/0000-0001-8238-9360)
- Jie Chen (ORCID: https://orcid.org/0000-0002-2658-9525)
- Xin Zou (ORCID: https://orcid.org/0000-0003-3532-5473)
- Lianjiang Tan (ORCID: https://orcid.org/0000-0002-4265-3507)
- Jiawei Zou (ORCID: https://orcid.org/0000-0003-1549-0340)
- Yuan Ji (ORCID: https://orcid.org/0000-0002-2631-9082)
- Jie Hao (ORCID: https://orcid.org/0000-0002-0646-1305)
Institutions
- Hong Kong Baptist University (HK)
- Sun Yat-sen University (CN)
- Linyi University (CN)
- Shanghai Jiao Tong University (CN)
- Shanghai Normal University (CN)
- Fudan University (CN)
- Cancer Institute (WIA) (IN)
- Shanghai Ninth People's Hospital (CN)
- Center for Excellence in Molecular Cell Science (CN)
- National Clinical Research (US)
- Zhongshan Hospital (CN)
- The First Affiliated Hospital, Sun Yat-sen University (CN)
- Shanghai Chenshan Plant Science Research Center (CN)
- University of Chinese Academy of Sciences (CN)
- Macao Polytechnic University (MO)
- Shanghai Institute of Technology (CN)
Publication Details
- Journal
- Briefings in Bioinformatics
- Published
- 2025-05-01
- DOI
- https://doi.org/10.1093/bib/bbaf234
- Citations
- 2
- Primary Topic
- Molecular Biology Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 1.61
Funders
- National Natural Science Foundation of China
- Shanghai Jiao Tong University