ArticlePatterns (New York, N.Y.)2024
DeepDecon accurately estimates cancer cell fractions in bulk RNA-seq data.
Article in Patterns (New York, N.Y.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Unveiling tissue heterogeneity through genomic interaction-encoded image representation of RNA-sequencing data.American journal of human genetics · 2025Article
- DeepDeconUQ estimates malignant cell fraction prediction intervals in bulk RNA-seq tissue.PLoS computational biology · 2025Article
- Deep learning based deconvolution methods: A systematic review.Computational and structural biotechnology journal · 2025Review
- DeepDecon accurately estimates cancer cell fractions in bulk RNA-seq data.Patterns (New York, N.Y.) · 2024Article
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Authors and funding
6 authors.
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Abstract
Understanding the cellular composition of a disease-related tissue is important in disease diagnosis, prognosis, and downstream treatment. Recent advances in single-cell RNA-sequencing (scRNA-seq) technique have allowed the measurement of gene expression profiles for individual cells. However, scRNA-seq is still too expensive to be used for large-scale population studies, and bulk RNA-seq is still widely used in such situations. An essential challenge is to deconvolve cellular composition for bulk RNA-seq data based on scRNA-seq data. Here, we present DeepDecon, a deep neural network model that leverages single-cell gene expression information to accurately predict the fraction of cancer cells in bulk tissues. It provides a refining strategy in which the cancer cell fraction is iteratively estimated by a set of trained models. When applied to simulated and real cancer data, DeepDecon exhibits superior performance compared to existing decomposition methods in terms of accuracy.
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