.png){kind=icon}
.png){kind=icon}
Return
Protocol to perform cell-type-specific transcriptomewide association study using scPrediXcan framework
.png){kind=icon}
OA
AI
DOI：10.1016/j.xpro.2025.104306.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The scPrediXcan framework enables cell-type-specific transcripto me-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.1
Keywords:
scPrediXcan
transcriptome-wide association study
cell-type-specific
gene expression prediction
deep learning
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
1.3
Papers:
392
Citations:
4.7K
View more organizations