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RNA-seq

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...

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Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

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Integrating spatial transcriptomics and bulk RNA-seq: predicting gene expression with enhanced resolution through

Sudipto Baul¹, Khandakar Tanvir Ahmed¹, Qibing Jiang¹

¹Department of Computer Science, University of Central Florida, Orlando, FL 32816, United States.

Briefings in Bioinformatics

|July 3, 2024

Summary

This study introduces STGAT, a novel method using Graph Attention Networks to estimate gene expression from whole slide images and bulk RNA-seq data. STGAT accurately predicts gene expression and improves cancer sub-typing and survival analysis, especially in large datasets.

Keywords:

Graph Attention Network spatial transcriptomics spot-level gene expression estimation whole slide image

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Published on: March 12, 2021

Area of Science:

Computational biology
Genomics
Cancer research

Background:

Spatial transcriptomics offers crucial insights into tumor heterogeneity and therapeutic targets.
Large-scale cancer studies often lack spatial transcriptomics data, relying instead on bulk RNA-seq and Whole Slide Images (WSI).
A method to estimate gene expression at spot-level resolution from WSI and bulk RNA-seq is needed for reanalyzing existing cohorts.

Purpose of the Study:

To develop a novel computational approach, STGAT, for estimating gene expression at spot-level resolution using WSI and bulk RNA-seq data.
To predict tumor vs. non-tumor status for each spot in patient samples lacking spatial transcriptomics data.
To leverage Graph Attention Networks (GAT) for discerning spatial dependencies among spots.

Main Methods:

Developed STGAT, a Spatial Transcriptomics Graph Attention Network model.
Trained STGAT on existing spatial transcriptomics datasets.
Applied STGAT to predict gene expression and tissue type (tumor/non-tumor) from WSI and bulk RNA-seq data.

Main Results:

STGAT demonstrated superior performance in accurately predicting gene expression compared to existing methods on breast cancer datasets.
Gene expression profiles from STGAT-predicted tumor-only spots improved breast cancer sub-type and tumor stage prediction accuracy.
Analysis using STGAT-estimated gene expression led to improved patient survival and disease-free analyses.

Conclusions:

STGAT effectively estimates gene expression at spot-level resolution from WSI and bulk RNA-seq data.
The method enhances the utility of large-scale cancer datasets by enabling spatial analysis.
STGAT facilitates the discovery of novel biomarkers and improves clinical outcome predictions in cancer research.