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Related Experiment Video

Updated: Feb 16, 2026

Isolation and Transcriptome Analysis of Plant Cell Types

Isolation and Transcriptome Analysis of Plant Cell Types

Published on: April 7, 2023

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

Yichao Zhou¹, Sarah Sumner², Temidayo Adeluwa¹

¹Committee of Genetic, Genomics, and Systems Biology, University of Chicago, Chicago, IL, USA.

|February 14, 2026

Summary

This summary is machine-generated.

The scPrediXcan framework allows cell-type-specific transcriptome-wide association studies (TWASs) using deep learning. This approach integrates gene expression prediction from DNA sequence and epigenetic data for scalable analysis.

Keywords:

Bioinformatics Genetics gene expression genomics sequence analysis

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Last Updated: Feb 16, 2026

Isolation and Transcriptome Analysis of Plant Cell Types

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Published on: April 7, 2023

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Published on: February 24, 2023

iCLIP - Transcriptome-wide Mapping of Protein-RNA Interactions with Individual Nucleotide Resolution

iCLIP - Transcriptome-wide Mapping of Protein-RNA Interactions with Individual Nucleotide Resolution

Published on: April 30, 2011

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Transcriptome-wide association studies (TWASs) identify gene-trait associations.
Existing TWAS methods often lack cell-type specificity.
Integrating gene expression prediction with genetic data is crucial for understanding disease mechanisms.

Purpose of the Study:

To present a protocol for scPrediXcan, a novel framework for cell-type-specific TWAS.
To enable accurate prediction of gene expression using deep learning from sequence and epigenetic data.
To facilitate scalable and computationally efficient TWAS across diverse cellular contexts.

Main Methods:

Training cell-type-specific deep learning models for gene expression prediction.
Predicting personalized gene expression profiles.
Testing associations between predicted expression and genome-wide association study (GWAS) summary statistics.

Main Results:

The scPrediXcan framework provides scalable TWAS models tailored to specific cell types.
The protocol integrates deep learning for gene expression prediction from genomic and epigenetic features.
Minimal computational burden is required for analyzing diverse cellular contexts.

Conclusions:

scPrediXcan offers a powerful approach for cell-type-specific TWAS.
The framework enhances the biological relevance of TWAS by considering cellular context.
This protocol facilitates deeper insights into the genetic architecture of complex traits across different cell types.