Compositional data modeling of high-dimensional single cell RNA-seq (CoDA-hd): its advantages over commonly used normalization approaches
We therefore conclude that CoDA may be a preferred scale-free model to handle scRNA-seq data for these downstream tasks. Additionally, an R package 'CoDAhd' was developed for conducting CoDA LR transformations for high dimensional scRNA-seq data. The code for implementing CoDA-hd, along with some example datasets, are available at https://github.com/GO3295/CoDAhd .
Authors
- Jinghan Huang (ORCID: https://orcid.org/0000-0003-0628-6022)
- Sheung Chi Phillip Yam (ORCID: https://orcid.org/0000-0002-4380-0919)
- Minghua Deng (ORCID: https://orcid.org/0000-0002-9143-1898)
- Nelson L.S. Tang (ORCID: https://orcid.org/0000-0002-3607-5819)
Institutions
- BGI Group (China) (CN)
- Department of Health (CN)
- Chinese University of Hong Kong (HK)
- Peking University (CN)
- Hong Kong Science and Technology Parks Corporation (HK)
- University of Hong Kong (HK)
Publication Details
- Journal
- Journal of Translational Medicine
- Published
- 2025-10-21
- DOI
- https://doi.org/10.1186/s12967-025-07157-z
- Primary Topic
- Single-cell and spatial transcriptomics
- Type
- article
- Field-Weighted Citation Impact
- 0.00