DECA: harnessing interpretable transformer model for cellular deconvolution of chromatin accessibility profile
Shijie Luo, Ming Zhu, Liquan Lin, Jiajing Xie, Shihao Lin, Ying Chen, Jiali Zhu, Jialiang Huang · 2025
Read DECA: harnessing interpretable transformer model for cellular deconvolution of chromatin accessibility profile by Shijie Luo, Ming Zhu, Liquan Lin, Jiajing Xie, Shihao Lin, Ying Chen, Jiali Zhu, Jialiang Huang (2025) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-11847511.txt.
Description
A peer-reviewed research article from Europe PMC (2025). The assay for transposase-accessible chromatin with sequencing (ATAC-seq) identifies chromatin accessibility across the genome, crucial for gene expression regulating. However, bulk ATAC-seq obscures cellular heterogeneity, while single-cell ATAC-seq suffers from issues such as sparsity and costliness. To this end, we introduce DECA, a sophisticated deep learning model based on vision transformer to deconvolve cell type information from bulk chromatin accessibility profiles, utilizing single-cell ATAC-seq datasets as reference for enhanced precision and resolution.
Catalog record
| CREATOR | Shijie Luo, Ming Zhu, Liquan Lin, Jiajing Xie, Shihao Lin, Ying Chen, Jiali Zhu, Jialiang Huang |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 49 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |