1 Detailed explanation
For a detailed manual for this section please access these links:
2 Menu: Starburst plot
Starburst plot menu: Main window.
2.1 Data
Expected input is a csv file with the following pattern:
- DEA result: DEA_results_Group_subgruop1_subgroup2_pcut_0.01_logFC.cut_2.csv
- DMR result: DMR_results_Group_subgruop1_subgroup2_pcut_1e-30_meancut_0.55.csv
2.2 Thresholds control
The possible thresholds controls are:
- Log FC threshold: gene expression Log2FC threshold
- Expression FDR cut-off: gene expression FDR cut-off (y-axis)
- Mean DNA methylation difference threshold: Mean DNA methylation difference threshold
- Methylation FDR cut-off: DNA methylation FDR cut-off (x-axis)
The options Mean DNA methylation difference threshold and Log FC threshold are used to circle genes which pass the cut-offs previously defined (eg. mean methylation or FDR).
2.3 Highlight control
The possible highlight controls are:
- Show gene names: show names of significant genes
- Boxed names: show names inside a box
- Circle genes: Circle candidate biologically significant genes
2.4 Other option
- Save result: save results in a csv file
2.5 Tutorial video:
3 Menu: ELMER (Enhancer Linking by Methylation/Expression Relationship)
This sub-menu will help the user to perform an integrative analysis between DNA methylation and Gene expression using the R/Bioconductor ELMER package (L Yao et al. 2015).
3.1 Introduction
Recently, many studies suggest that enhancers play a major role as regulators of cell-specific phenotypes leading to alteration in transcriptomes related to diseases (Giorgio et al. 2015; Gröschel et al. 2014; Sur et al. 2012; Lijing Yao, Berman, and Farnham 2015). In order to investigate regulatory enhancers that can be located at long distances upstream or downstream of target genes Bioconductor offer the Enhancer Linking by Methylation/Expression Relationship (ELMER) package. This package is designed to combine DNA methylation and gene expression data from human tissues to infer multi-level cis-regulatory networks. It uses DNA methylation to identify enhancers and correlates their state with the expression of nearby genes to identify one or more transcriptional targets. Transcription factor (TF) binding site analysis of enhancers is coupled with expression analysis of all TFs to infer upstream regulators. This package can be easily applied to TCGA public available cancer data sets and custom DNA methylation and gene expression data sets (L Yao et al. 2015).
ELMER analysis has 5 main steps:
Identify distal enhancer probes on HM450K.
Identify distal enhancer probes with significantly different DNA methylation level in control group and experiment group.
Identify putative target genes for differentially methylated distal enhancer probes.
Identify enriched motifs for the distal enhancer probes which are significantly differentially methylated and linked to a putative target gene.
Identify regulatory TFs whose expression associate with DNA methylation at motifs.
3.3 Data: Create mee
The ELMER input is a mee object that contains a DNA methylation matrix, a gene expression matrix, a probe information GRanges, the gene information GRanges and a data frame summarizing the data. It should be highlighted that samples without both the gene expression and DNA methylation data will be removed from the mee object.
By default the function fetch.mee that is used to create the mee object will separate the samples into two groups, the control group (normal samples) and the experiment group (tumor samples), but the user can relabel the samples to compare different groups.
The following steps are required:
- Select DNA methylation object: select a summarized Experiment object with the DNA methylation data
- Select expression object: select a summarized Experiment object with the gene expression data
- Select group column: Select the columns with the groups. This column will be selected from the Summarized Experiment object.
- Select Experiment group: Select the columns with the subgroup 1 (experiment)
- Select Control group: Select the columns with the subgroup 2 (control)
- Select DNA methylation: Cut-off NA samples (%): By default, for the DNA methylation data will remove probes with NA values in more than 20% samples and remove the annotation data.
Also, for RNA expression data it will take the log2(expression + 1) of the expression matrix in order to linearize the relation between DNA methylation and expression.
ELMER menu: Main window.
3.3.1 Data: Select mee
Select the R object (rda) file with a mee object created in the previous step.
3.3.2 Analysis parameters
After preparing the data into a mee object, we will execute the five ELMER steps for both the hypo (distal enhancer probes hypomethylated in the experiment group) and hyper (distal enhancer probes hypermethylated in the experiment group) direction.
This box has all the available options for ELMER functions. Please see the ELMER vignette.
Also, a description of the data used by ELMER (such as the distal enhancer probes) is found in the ELMER.data vignette.
ELMER Identifies the enriched motifs for the distal enhancer probes which are significantly differentially methylated and linked to a putative target gene, it will plot the Odds Ratio (x axis) for each motif found. These motifs by default have a minimum incidence of 10 probes (that means at least 10 probes were associated with the motif) in the given probes set and the smallest lower boundary of 95% confidence interval for Odds Ratio of 1.1.
After finding the enriched motifs, ELMER identifies regulatory transcription factors (TFs) whose expression is associated with DNA methylation at motifs. ELMER automatically creates a TF ranking plot for each enriched motif. This plot shows the TF ranking plots based on the association score ( − log(Pvalue)) between TF expression and DNA methylation of the motif. We can see in Figure below that the top three TFs that are associated with a motif found.
In case, it identifies regulatory transcription factors (TFs), an object with the prefix ELMER_results will be created with the necessary data to visualize the results.
References
Giorgio, Elisa, Daniel Robyr, Malte Spielmann, Enza Ferrero, Eleonora Di Gregorio, Daniele Imperiale, Giovanna Vaula, et al. 2015. “A Large Genomic Deletion Leads to Enhancer Adoption by the Lamin B1 Gene: A Second Path to Autosomal Dominant Leukodystrophy (Adld).” Human Molecular Genetics. Oxford Univ Press, ddv065.
Gröschel, Stefan, Mathijs A Sanders, Remco Hoogenboezem, Elzo de Wit, Britta AM Bouwman, Claudia Erpelinck, Vincent HJ van der Velden, et al. 2014. “A Single Oncogenic Enhancer Rearrangement Causes Concomitant Evi1 and Gata2 Deregulation in Leukemia.” Cell 157 (2). Elsevier: 369–81.
Sur, Inderpreet Kaur, Outi Hallikas, Anna Vähärautio, Jian Yan, Mikko Turunen, Martin Enge, Minna Taipale, Auli Karhu, Lauri A Aaltonen, and Jussi Taipale. 2012. “Mice Lacking a Myc Enhancer That Includes Human Snp Rs6983267 Are Resistant to Intestinal Tumors.” Science 338 (6112). American Association for the Advancement of Science: 1360–3.
Yao, L, H Shen, PW Laird, PJ Farnham, and BP Berman. 2015. “Inferring Regulatory Element Landscapes and Transcription Factor Networks from Cancer Methylomes.” Genome Biology 16 (1): 105–5.
Yao, Lijing, Benjamin P Berman, and Peggy J Farnham. 2015. “Demystifying the Secret Mission of Enhancers: Linking Distal Regulatory Elements to Target Genes.” Critical Reviews in Biochemistry and Molecular Biology 50 (6). Taylor & Francis: 550–73.