Gene set enrichment analysis for genome-wide DNA methylation data

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Abstract

DNA methylation is one of the most commonly studied epigenetic marks, due to its role in disease and development. Illumina methylation arrays have been extensively used to measure methylation across the human genome. Methylation array analysis has primarily focused on preprocessing, normalization, and identification of differentially methylated CpGs and regions. GOmeth and GOregion are new methods for performing unbiased gene set testing following differential methylation analysis. Benchmarking analyses demonstrate GOmeth outperforms other approaches, and GOregion is the first method for gene set testing of differentially methylated regions. Both methods are publicly available in the missMethyl Bioconductor R package.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13059-021-02388-x.

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Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

Yoav Benjamini, Yosef Hochberg (1995)

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Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

A. Subramanian, P. Tamayo, V. K. Mootha … (2005)

Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.

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limma powers differential expression analyses for RNA-sequencing and microarray studies

Matthew E. Ritchie, Belinda Phipson, Di Wu … (2015)

limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.

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Author and article information

Contributors

Jovana Maksimovic: Jovana.Maksimovic@petermac.org

Alicia Oshlack: Alicia.Oshlack@petermac.org

Belinda Phipson:

ORCID: http://orcid.org/0000-0002-1711-7454

Belinda.Phipson@petermac.org

Journal

Journal ID (nlm-ta): Genome Biol

Journal ID (iso-abbrev): Genome Biol

Title: Genome Biology

Publisher: BioMed Central (London )

ISSN (Print): 1474-7596

ISSN (Electronic): 1474-760X

Publication date (Electronic): 8 June 2021

Publication date PMC-release: 8 June 2021

Publication date Collection: 2021

Volume: 22

Electronic Location Identifier: 173

Affiliations

[1 ]GRID grid.1055.1, ISNI 0000000403978434, Peter MacCallum Cancer Centre, ; Melbourne, Victoria 3000 Australia

[2 ]GRID grid.1008.9, ISNI 0000 0001 2179 088X, Department of Pediatrics, , University of Melbourne, ; Parkville, Victoria 3010 Australia

[3 ]GRID grid.1058.c, ISNI 0000 0000 9442 535X, Murdoch Children’s Research Institute, ; Parkville, Victoria 3052 Australia

[4 ]GRID grid.1008.9, ISNI 0000 0001 2179 088X, School of Biosciences, , University of Melbourne, ; Parkville, Victoria 3010 Australia

[5 ]GRID grid.1008.9, ISNI 0000 0001 2179 088X, Sir Peter MacCallum Department of Oncology, , University of Melbourne, ; Parkville, Victoria 3010 Australia

Author information

Belinda Phipson http://orcid.org/0000-0002-1711-7454

Article

Publisher ID: 2388

DOI: 10.1186/s13059-021-02388-x

PMC ID: 8186068

PubMed ID: 34103055

SO-VID: 2e01bb3e-6260-4219-bfe2-f41c1fbba60e

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Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

History

Date received : 25 August 2020

Date accepted : 24 May 2021

Funding

Funded by: FundRef http://dx.doi.org/10.13039/501100000925, National Health and Medical Research Council;

Award ID: GNT1175653

Award ID: GNT1126157

Award Recipient : Alicia Oshlack Belinda Phipson

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ScienceOpen disciplines: Genetics

Keywords: dna methylation,gene set analysis,differential methylation,statistical analysis

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ScienceOpen disciplines: Genetics

Keywords: dna methylation, gene set analysis, differential methylation, statistical analysis

Gene set enrichment analysis for genome-wide DNA methylation data

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Most cited references 63

Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

limma powers differential expression analyses for RNA-sequencing and microarray studies

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