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>.

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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
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article

Protocol to perform cell-type-specific transcriptome-wide association study using scPrediXcan framework

Temidayo Adeluwa, Lisha Zhu, Sarah Sumner, Festus M. Nyasimi et al.
STAR Protocols
Single-cell and spatial transcriptomics
article

Protocol to perform cell-type-specific transcriptome-wide association study using scPrediXcan framework

Temidayo Adeluwa, Lisha Zhu, Sarah Sumner, Festus M. Nyasimi, Yichao Zhou, M.R. Ravi, Hoguen Kim, Sofia Salazar-Magaña, Mengjie Chen, Rohit Kulkarni, Hae Kyung Im, Saideep Gona, Joseph Powell
article en

Abstract

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>.

STAR ProtocolsVol. 7(1)
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)
Openalex Percentile: Top 57%
Single-cell and spatial transcriptomics
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