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Deconvolution of heterogeneous tumor samples using partial reference signals
- Source :
- PLoS Computational Biology, Vol 16, Iss 11, p e1008452 (2020), PLoS Computational Biology
- Publication Year :
- 2020
- Publisher :
- Public Library of Science (PLoS), 2020.
-
Abstract
- Deconvolution of heterogeneous bulk tumor samples into distinct cellular populations is an important yet challenging problem, particularly when only partial references are available. A common approach to dealing with this problem is to deconvolve the mixed signals using available references and leverage the remaining signal as a new cell component. However, as indicated in our simulation, such an approach tends to over-estimate the proportions of known cell types and fails to detect novel cell types. Here, we propose PREDE, a partial reference-based deconvolution method using an iterative non-negative matrix factorization algorithm. Our method is verified to be effective in estimating cell proportions and expression profiles of unknown cell types based on simulated datasets at a variety of parameter settings. Applying our method to TCGA tumor samples, we found that proportions of pure cancer cells better indicate different subtypes of tumor samples. We also detected several cell types for each cancer type whose proportions successfully predicted patient survival. Our method makes a significant contribution to deconvolution of heterogeneous tumor samples and could be widely applied to varieties of high throughput bulk data. PREDE is implemented in R and is freely available from GitHub (https://xiaoqizheng.github.io/PREDE).<br />Author summary Tumor tissues are mixtures of different cell types. Identification and quantification of constitutional cell types within tumor tissues are important tasks in cancer research. The problem can be readily solved using regression-based methods if reference signals are available. But in most clinical applications, only partial references are available, which significantly reduces the deconvolution accuracy of the existing regression-based methods. In this paper, we propose a partial-reference based deconvolution model, PREDE, integrating the non-negative matrix factorization framework with an iterative optimization strategy. We conducted comprehensive evaluations for PREDE using both simulation and real data analyses, demonstrating better performance of our method than other existing methods.
- Subjects :
- 0301 basic medicine
Epidemiology
Computer science
Gene Expression
Lung and Intrathoracic Tumors
White Blood Cells
0302 clinical medicine
Animal Cells
Neoplasms
Breast Tumors
Medicine and Health Sciences
Biology (General)
Ecology
Cancer Risk Factors
Oncology
Computational Theory and Mathematics
030220 oncology & carcinogenesis
Modeling and Simulation
Deconvolution
Cellular Types
Algorithms
Research Article
QH301-705.5
Immune Cells
Immunology
Genetic Causes of Cancer
Matrix decomposition
03 medical and health sciences
Cellular and Molecular Neuroscience
Malignant Tumors
Cell Line, Tumor
Breast Cancer
Genetics
Animals
Humans
Leverage (statistics)
Molecular Biology
Ecology, Evolution, Behavior and Systematics
Blood Cells
business.industry
Gene Expression Profiling
Macrophages
Cancer type
Computational Biology
Reproducibility of Results
Cancers and Neoplasms
Biology and Life Sciences
Pattern recognition
Patient survival
Cell Biology
Rats
030104 developmental biology
Medical Risk Factors
Artificial intelligence
business
Subjects
Details
- ISSN :
- 15537358
- Volume :
- 16
- Database :
- OpenAIRE
- Journal :
- PLOS Computational Biology
- Accession number :
- edsair.doi.dedup.....11ae990bb8f05b20a298746d4e6baaef