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Updated: Jun 29, 2025

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

Published on: March 5, 2022

Generalized reporter score-based enrichment analysis for omics data.

Chen Peng^1,2, Qiong Chen^1,2, Shangjin Tan^3,4

¹MOE Key Laboratory of Biosystems Homeostasis & Protection, and Zhejiang Provincial Key Laboratory of Cancer Molecular Cell Biology, Life Sciences Institute, Zhejiang University, Hangzhou, Zhejiang 310030, China.

Briefings in Bioinformatics

|March 28, 2024

Summary

This summary is machine-generated.

Generalized Reporter Score-based Analysis (GRSA) offers enhanced sensitivity for multi-group and longitudinal omics data. This new method, implemented in the ReporterScore R package, improves upon existing enrichment analysis techniques.

Keywords:

customized database enrichment analysis multi-group omics data reporter score

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Enrichment analysis is crucial for understanding high-dimensional omics data in biomedical research.
Reporter score-based analysis (RSA) offers improved sensitivity over traditional methods by using P-values.
Existing RSA methods are limited in their application to complex experimental designs like multi-group and longitudinal studies.

Purpose of the Study:

To develop a novel method, Generalized Reporter Score-based Analysis (GRSA), for analyzing multi-group and longitudinal omics data.
To enhance the sensitivity and applicability of reporter score-based enrichment analysis for complex biological studies.
To provide a user-friendly R package, ReporterScore, for implementing GRSA and facilitating omics data interpretation.

Main Methods:

Development of the Generalized Reporter Score-based Analysis (GRSA) algorithm.
Application and comparison of GRSA against existing enrichment analysis methods using benchmark datasets.
Implementation of GRSA within the R package 'ReporterScore', including visualization tools and pathway databases.

Main Results:

GRSA demonstrated increased sensitivity compared to other popular enrichment analysis methods across multiple datasets.
Successful application of GRSA to microbiome, transcriptome, and metabolome data, revealing novel biological insights.
Demonstrated utility of GRSA beyond functional enrichment analysis, including its application with taxonomy databases.

Conclusions:

GRSA is a sensitive and versatile method for enrichment analysis of multi-group and longitudinal omics data.
The ReporterScore R package provides a valuable, accessible tool for the biomedical research community.
GRSA advances the systematic understanding of complex biological systems through improved omics data analysis.