Protocol to perform cell-type-specific transcriptome-wide association study using scPrediXcan framework
The scPrediXcan framework enables cell-type-specific transcriptome-wide association studies (TWASs) by integrating deep learning-based prediction of gene expression from DNA sequence and epigenetic features. We present a protocol for scPrediXcan: training cell-type-specific models for expression prediction, predicting personalized expression, and testing associations with genome-wide association study (GWAS) summary statistics. This framework produces scalable TWAS models for different cellular contexts with minimal computational burden. For complete details on the use and execution of this protocol, please refer to Zhou et al.<sup>1</sup>.
Authors
- Temidayo Adeluwa
- Lisha Zhu
- Sarah Sumner (ORCID: https://orcid.org/0000-0001-5763-4416)
- Festus M. Nyasimi (ORCID: https://orcid.org/0000-0001-5062-758X)
- Yichao Zhou (ORCID: https://orcid.org/0009-0003-8632-446X)
- M.R. Ravi
- Hoguen Kim
- Sofia Salazar-Magaña
- Mengjie Chen
- Rohit Kulkarni
- Hae Kyung Im
- Saideep Gona
- Joseph Powell
Institutions
- Argonne National Laboratory (US)
- Harvard University (US)
- Garvan Institute of Medical Research (AU)
- National University of Singapore (SG)
- University of Chicago (US)
- Igenbio (United States) (US)
Publication Details
- Journal
- STAR Protocols
- Published
- 2026-02-13
- DOI
- https://doi.org/10.1016/j.xpro.2025.104306
- Primary Topic
- Single-cell and spatial transcriptomics
- Type
- article
- Field-Weighted Citation Impact
- 0.00