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Knit directory: methyl-geneset-testing/

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Gene set enrichment analysis for genome-wide DNA methylation data

This site contains the results of the analyses presented in “Gene set enrichment analysis for genome-wide DNA methylation data”. Follow the links below to view the different parts of the analysis. For details on how to reproduce the complete analysis, please see the Getting started page.

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, normalisation 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.

Authors

Jovana Maksimovic1,2,3, Alicia Oshlack1,4, Belinda Phipson1,2+

1 Peter MacCallum Cancer Centre, Melbourne, Victoria, 3000, Australia 2 Department of Pediatrics, University of Melbourne, Parkville, Victoria, 3010, Australia 3 Murdoch Children’s Research Institute, Parkville, Victoria, 3052, Australia 4 School of Biosciences, University of Melbourne, Parkville, Victoria, 3010, Australia

+ corresponding author

Analysis

  1. Explore EPIC array bias Explore the various array biases on the EPIC array that affect gene set testing.

  2. Explore 450k array bias Explore the various array biases on the 450k array that affect gene set testing.

    1. Compare FDR of different methods (KIRC data) Analyse the normal samples from a 450k array KIRC TCGA dataset using various gene set testing methods to estimate their false discovery rate control.

    2. Compare FDR of different methods (BRCA data) Analyse the normal samples from a 450k array BRCA TCGA dataset using various gene set testing methods to estimate their false discovery rate control.

    1. Generate a blood cell RNAseq "truth" set Analyse an RNAseq sorted blood cell dataset and identify the top ranked gene sets for each cell type comparison.

    2. Generate a B-cell development gene exression "truth" set Analyse Affymetrix gene expression microarray data of B-cell development and identify the top ranked gene sets for each stage comparison.

    1. Compare performance of different methods (EPIC data) Analyse an EPIC array sorted blood cell dataset using various gene set testing methods. Compare how well the different methods perform using several metrics.

    2. Compare performance of different methods (450K data) Analyse a 450k array dataset of B-cell development data using various gene set testing methods. Compare how well the different methods perform using several metrics.

  6. Compare run-time of different methods Analyse an EPIC array sorted blood cell dataset using various gene set testing methods. Compare the run-time of the different methods.

    1. Evaluate GOregion (EPIC data) Evalulate GOregion, our extension of gometh for geneset testing of differentially methylated regions (DMRs) identified by DMR finding software.

    2. Evaluate GOregion (450K data) Evalulate GOregion, our extension of gometh for geneset testing of differentially methylated regions (DMRs) identified by DMR finding software.

  8. Effect of gene set size parameters on methylGSA Analyse an EPIC array sorted blood cell dataset using various gene set testing methods. Compare the run-time of the different methods.

Licenses

The code in this analysis is covered by the MIT license and the written content on this website is covered by a Creative Commons CC-BY license.

Citations

Maksimovic J, Oshlack O, Phipson B. Gene set enrichment analysis for genome-wide DNA methylation data. bioRxiv. 2020. DOI: https://doi.org/10.1101/2020.08.24.265702.