ELMER v.2: An R/Bioconductor package to reconstruct gene regulatory networks from DNA methylation and transcriptome profiles

In ELMER: Inferring Regulatory Element Landscapes and Transcription Factor Networks Using Cancer Methylomes

Introduction

DNA methylation can be used to identify functional changes at transcriptional enhancers and other cis-regulatory modules (CRMs) in tumors and other primary disease tissues. Our R/Bioconductor package r BiocStyle::Biocpkg("ELMER") (Enhancer Linking by Methylation/Expression Relationships) provides a systematic approach that reconstructs gene regulatory networks (GRNs) by combining methylation and gene expression data derived from the same set of samples. r BiocStyle::Biocpkg("ELMER") uses methylation changes at CRMs as the central hub of these networks, using correlation analysis to associate them with both upstream master regulator (MR) transcription factors and downstream target genes.

This package can be easily applied to TCGA public available cancer data sets and custom DNA methylation and gene expression data sets.

ELMER analyses have 5 main steps:

1. Identify distal probes on HM450K or EPIC arrays.
2. Identify distal probes with significantly different DNA methylation level between two groups
3. Identify putative target genes for differentially methylated distal probes.
4. Identify enriched motifs for the distalprobes which are significantly differentially methylated and linked to putative target gene.
5. Identify regulatory TFs whose expression associate with DNA methylation at enriched motifs.

Package workflow

The package workflow is showed in the figure below:

Main differences between ELMER v2 vs ELMER v1

Summary table

| | ELMER Version 1 | ELMER Version 2 | |--------------------------------|:----------------------------------------------|:--------------------------------------------------------------------| | Primary data structure | mee object (custom data structure) | MAE object (Bioconductor data structure) | | Auxiliary data | Manually created | Programmatically created | | Number of human TFs | 1,982 | 1,639 (curated list from Lambert, Samuel A., et al.) | | Number of TF motifs | 91 | 771 (HOCOMOCO v11 database) | | TF classification | 78 families | 82 families and 331 subfamilies \newline(TFClass database, HOCOMOCO) | | Analysis performed | Normal vs tumor samples | Group 1 vs group 2 | | Statistical grouping | Unsupervised only | Unsupervised or supervised using labeled groups | | TCGA data source | The Cancer Genome Atlas (TCGA) (not available) | The NCI's Genomic Data Commons (GDC) | | Genome of reference | GRCh37 (hg19) | GRCh37 (hg19)/GRCh38 (hg38) | | DNA methylation platforms | HM450 | EPIC and HM450 | | Graphical User Interface (GUI) | None | TCGAbiolinksGUI | | Automatic report | None | HTML summarizing results | | Annotations | None | StateHub |

Supervised vs Unsupervised mode

In ELMER v2 we introduce a new concept, the algorithm mode that can be either supervised or unsupervised. In the unsupervised mode (described in ELMER v1), it is assumed that one of the two groups is a heterogeneous mix of different (sometimes unknown) molecular phenotypes. For instance, in the example of Breast Cancer, normal breast tissues (Group A) are relatively homogenous, whereas Breast tumors fall into multiple molecular subtypes.

The assumption of the Unsupervised mode is that methylation changes may be restricted to a subset of one or more molecular subtypes, and thus only be present in a fraction of the samples in the test group. For instance, methylation changes related to estrogen signaling may only be present in LuminalA or LuminalB subtypes.

When this structure is unknown, the Unsupervised mode is the appropriate model, since it only requires changes in a subset of samples (by default, 20%). In contrast, in the Supervised mode, it is assumed that each group represents a more homogenous molecular phenotype, and thus we compare all samples in Group A vs. all samples in Group B. This can be used in the case of direct comparison of tumor subtypes (i.e. Luminal vs. Basal-like tumors), but can also be used in numerous other situations, including sorted cells of different types, or treated vs. untreated samples in perturbation experiments.

Installing and loading ELMER

To install this package from github (development version), start R and enter:

devtools::install_github(repo = "tiagochst/ELMER.data")
devtools::install_github(repo = "tiagochst/ELMER")

To install this package from Bioconductor start R and enter:

if (!requireNamespace("BiocManager", quietly=TRUE))
    install.packages("BiocManager")
BiocManager::install("ELMER")

Then, to load ELMER enter:

library(ELMER, quietly = TRUE)

Citing this work

If you used ELMER package or its results, please cite:

• Yao, L., Shen, H., Laird, P. W., Farnham, P. J., & Berman, B. P. "Inferring regulatory element landscapes and transcription factor networks from cancer methylomes." Genome Biol 16 (2015): 105.
• Yao, Lijing, Benjamin P. Berman, and Peggy J. Farnham. "Demystifying the secret mission of enhancers: linking distal regulatory elements to target genes." Critical reviews in biochemistry and molecular biology 50.6 (2015): 550-573.
• Tiago C Silva, Simon G Coetzee, Nicole Gull, Lijing Yao, Dennis J Hazelett, Houtan Noushmehr, De-Chen Lin, Benjamin P Berman; ELMER v.2: An R/Bioconductor package to reconstruct gene regulatory networks from DNA methylation and transcriptome profiles, Bioinformatics, , bty902, https://doi.org/10.1093/bioinformatics/bty902

If you get TCGA data using getTCGA function, please cite TCGAbiolinks package:

• Colaprico A, Silva TC, Olsen C, Garofano L, Cava C, Garolini D, Sabedot T, Malta TM, Pagnotta SM, Castiglioni I, Ceccarelli M, Bontempi G and Noushmehr H. "TCGAbiolinks: an R/Bioconductor package for integrative analysis of TCGA data." Nucleic acids research (2015): gkv1507.

• Silva, TC, A Colaprico, C Olsen, F D’Angelo, G Bontempi, M Ceccarelli, and H Noushmehr. 2016. “TCGA Workflow: Analyze Cancer Genomics and Epigenomics Data Using Bioconductor Packages [Version 2; Referees: 1 Approved, 1 Approved with Reservations].” F1000Research 5 (1542). doi:10.12688/f1000research.8923.2.

• Grossman, Robert L., et al. "Toward a shared vision for cancer genomic data." New England Journal of Medicine 375.12 (2016): 1109-1112.

If you get use the Graphical user interface, please cite TCGAbiolinksGUI package:

• Silva, Tiago C. and Colaprico, Antonio and Olsen, Catharina and Bontempi, Gianluca and Ceccarelli, Michele and Berman, Benjamin P. and Noushmehr, Houtan. "TCGAbiolinksGUI: A graphical user interface to analyze cancer molecular and clinical data" (bioRxiv 147496; doi: https://doi.org/10.1101/147496)

Bugs and questions

If you have questions, wants to report a bug, please use our github repository: http://www.github.com/tiagochst/ELMER

Paper supplemental material

TCGA-BRCA reports (paper supplemental material) can be found at https://tiagochst.github.io/ELMER_supplemental/

Session Info

sessionInfo()

ELMER documentation built on Nov. 8, 2020, 4:59 p.m.