Generalized reporter score-based enrichment analysis for omics data

Author:

Peng Chen1234,Chen Qiong1234,Tan Shangjin56,Shen Xiaotao7,Jiang Chao12348ORCID

Affiliation:

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

2. Life Sciences Institute, Zhejiang University , and Zhejiang Provincial Key Laboratory of Cancer Molecular Cell Biology, , Hangzhou, Zhejiang 310030 , China

3. State Key Laboratory for Diagnosis and Treatment of Infectious Diseases , First Affiliated Hospital, , Hangzhou, Zhejiang 310009 , China

4. Zhejiang University School of Medicine , First Affiliated Hospital, , Hangzhou, Zhejiang 310009 , China

5. BGI Research , Wuhan, Hubei 430074 , China

6. BGI Research , Shenzhen, Guangdong 518083 , China

7. Department of Genetics, Stanford University School of Medicine , Stanford, CA , USA

8. Center for Life Sciences, Shaoxing Institute, Zhejiang University , Shaoxing, Zhejiang 321000 , China

Abstract

Abstract Enrichment analysis contextualizes biological features in pathways to facilitate a systematic understanding of high-dimensional data and is widely used in biomedical research. The emerging reporter score-based analysis (RSA) method shows more promising sensitivity, as it relies on P-values instead of raw values of features. However, RSA cannot be directly applied to multi-group and longitudinal experimental designs and is often misused due to the lack of a proper tool. Here, we propose the Generalized Reporter Score-based Analysis (GRSA) method for multi-group and longitudinal omics data. A comparison with other popular enrichment analysis methods demonstrated that GRSA had increased sensitivity across multiple benchmark datasets. We applied GRSA to microbiome, transcriptome and metabolome data and discovered new biological insights in omics studies. Finally, we demonstrated the application of GRSA beyond functional enrichment using a taxonomy database. We implemented GRSA in an R package, ReporterScore, integrating with a powerful visualization module and updatable pathway databases, which is available on the Comprehensive R Archive Network (https://cran.r-project.org/web/packages/ReporterScore). We believe that the ReporterScore package will be a valuable asset for broad biomedical research fields.

Funder

National Nature Science Foundation of China

Fundamental Research Funds for the Central Universities

Publisher

Oxford University Press (OUP)

Reference51 articles.

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