| Title: | Example DNAm Data for DMRsegal |
|---|---|
| Description: | Data package providing example DNA methylation files used in the DMRsegal vignette and examples. Includes a sorted beta matrix as a tab-delimited, bgzip-compressed file and a matching phenotype table. The data contains 10 healthy and 10 cancer samples, and preprocessing has already been performed on the beta values. |
| Authors: | Vasileios Lemonidis [aut, cre, cph] (ORCID: <https://orcid.org/0000-0002-6446-3536>), Center for Oncological Research, University of Antwerp [cph, fnd], Stichting Tegen Kanker [fnd] |
| Maintainer: | Vasileios Lemonidis <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 1.0.0 |
| Built: | 2026-10-10 11:21:33 UTC |
| Source: | https://github.com/bioc/DMRsegaldata |
The DMRsegaldata package provides example DNA methylation datasets for use with the DMRsegal package. It contains preprocessed Illumina HumanMethylation450K array data from 10 cancer and 10 normal/healthy samples, along with associated phenotype information, differentially methylated positions (DMPs), and array type metadata.
This data package is designed to support the DMRsegal vignette and examples, providing users with realistic methylation data to explore differential methylation analysis workflows.
| Package: | DMRsegaldata |
| Type: | Package |
| Version: | 0.99.6 |
| License: | GPL (>= 2) |
| LazyData: | yes |
The package contains four main data objects:
betaA matrix of DNA methylation beta values (proportion of methylation) for CpG sites across 20 samples. Rows represent CpG sites and columns represent samples (10 cancer: T1-T10, 10 normal: H1-H10).
phenoA data frame containing phenotype information for the 20 samples, including sample group (cancer vs. normal), age, and gender.
dmpsA data frame of differentially methylated positions identified by limma analysis comparing cancer vs. normal samples, filtered at FDR < 0.05. Includes p-values, q-values, and adjusted p-values.
array_typeA character string indicating the Illumina array platform used ("450K").
All datasets are provided as .rda files with xz compression and are loaded automatically via LazyData.
The methylation data has been preprocessed and quality controlled. The preprocessing included:
Quality control of raw Illumina 450K array data
Normalization of beta values
Filtering of low-quality probes
Calculation of differentially methylated positions using limma
The DMP analysis was performed using empirical Bayes moderated t-statistics on M-values (log2 ratio of methylated to unmethylated signal), with adjustment for age and gender covariates.
Resources can be accessed through the standard ExperimentHub interface:
library(ExperimentHub) eh <- ExperimentHub() # Query for all DMRsegaldata resources myfiles <- query(eh, "DMRsegaldata") myfiles # Load specific resources by name beta <- myfiles[["EH10275"]] pheno <- myfiles[["EH10276"]] # Or use package helper functions library(ExperimentHub) eh <- ExperimentHub() # List all resources in this package ExperimentHub::listResources(eh, "DMRsegaldata") # Load a specific resource beta <- ExperimentHub::loadResource(eh, "DMRsegaldata", "beta.rda")
This data package is designed to work seamlessly with the DMRsegal package for differential methylation region analysis. The data objects can be directly used as inputs to DMRsegal functions.
See the DMRsegal package documentation for analysis workflows using this data.
Vasileios Lemonidis
Maintainer: Vasileios Lemonidis <[email protected]>
DMRsegal package: https://github.com/CMG-UA/DMRsegal
Illumina HumanMethylation450K array documentation
Bioconductor workflows for DNA methylation analysis
beta, pheno, dmps, array_type
Related packages:
DMRsegal - Differential methylation region analysis
minfi - Illumina methylation array analysis
limma - Linear models for microarray and RNA-seq data
## Not run: ## Access via ExperimentHub (recommended for end users) library(ExperimentHub) eh <- ExperimentHub() ## List all resources in this package myfiles <- query(eh, "DMRsegaldata") myfiles ## Load resources using helper functions beta <- eh[["EH10275"]] pheno <- eh[["EH10276"]] dmps <- eh[["EH10277"]] array_type <- eh[["EH10278"]] ## Explore the data dim(beta) head(beta[, 1:5]) ## Examine phenotype data head(pheno) table(pheno$Sample_Group) ## View DMPs head(dmps) cat("Number of significant DMPs:", nrow(dmps), "\n") ## Check array type array_type ## Calculate mean methylation per sample group cancer_samples <- rownames(pheno)[pheno$Sample_Group == "cancer"] normal_samples <- rownames(pheno)[pheno$Sample_Group == "normal"] mean_cancer <- mean(colMeans(beta[, cancer_samples], na.rm = TRUE)) mean_normal <- mean(colMeans(beta[, normal_samples], na.rm = TRUE)) cat("Mean methylation (cancer):", round(mean_cancer, 4), "\n") cat("Mean methylation (normal):", round(mean_normal, 4), "\n") ## Examine a top DMP if (nrow(dmps) > 0) { top_cpg <- rownames(dmps)[1] if (top_cpg %in% rownames(beta)) { boxplot(beta[top_cpg, ] ~ pheno$Sample_Group, main = paste("Top DMP:", top_cpg), xlab = "Sample Group", ylab = "Beta Value", col = c("lightcoral", "lightblue")) } } ## End(Not run)## Not run: ## Access via ExperimentHub (recommended for end users) library(ExperimentHub) eh <- ExperimentHub() ## List all resources in this package myfiles <- query(eh, "DMRsegaldata") myfiles ## Load resources using helper functions beta <- eh[["EH10275"]] pheno <- eh[["EH10276"]] dmps <- eh[["EH10277"]] array_type <- eh[["EH10278"]] ## Explore the data dim(beta) head(beta[, 1:5]) ## Examine phenotype data head(pheno) table(pheno$Sample_Group) ## View DMPs head(dmps) cat("Number of significant DMPs:", nrow(dmps), "\n") ## Check array type array_type ## Calculate mean methylation per sample group cancer_samples <- rownames(pheno)[pheno$Sample_Group == "cancer"] normal_samples <- rownames(pheno)[pheno$Sample_Group == "normal"] mean_cancer <- mean(colMeans(beta[, cancer_samples], na.rm = TRUE)) mean_normal <- mean(colMeans(beta[, normal_samples], na.rm = TRUE)) cat("Mean methylation (cancer):", round(mean_cancer, 4), "\n") cat("Mean methylation (normal):", round(mean_normal, 4), "\n") ## Examine a top DMP if (nrow(dmps) > 0) { top_cpg <- rownames(dmps)[1] if (top_cpg %in% rownames(beta)) { boxplot(beta[top_cpg, ] ~ pheno$Sample_Group, main = paste("Top DMP:", top_cpg), xlab = "Sample Group", ylab = "Beta Value", col = c("lightcoral", "lightblue")) } } ## End(Not run)
A character string indicating the type of Illumina methylation array used for the data in this package.
A character string with value "450K", indicating the data was generated using the Illumina HumanMethylation450 BeadChip array.
This variable specifies that the methylation data in this package comes from the Illumina 450K array platform, which measures methylation at approximately 450,000 CpG sites across the human genome.
This resource can be accessed via ExperimentHub:
library(ExperimentHub) eh <- ExperimentHub() # Query and load myfiles <- query(eh, "DMRsegaldata") array_type <- myfiles[["EH10278"]]
Example data for the DMRsegal package vignette and examples.
## Not run: library(ExperimentHub) eh <- ExperimentHub() array_type <- eh[["EH10278"]] ## End(Not run)## Not run: library(ExperimentHub) eh <- ExperimentHub() array_type <- eh[["EH10278"]] ## End(Not run)
A matrix of DNA methylation beta values for CpG sites across 20 samples (10 healthy and 10 cancer samples). The matrix contains preprocessed beta values from Illumina 450K methylation array data.
A numeric matrix with CpG sites as rows and sample identifiers as columns. Beta values range from 0 to 1, representing the proportion of methylation at each CpG site.
The beta values have been preprocessed and quality controlled. The data includes measurements from 10 healthy samples (H1-H10) and 10 cancer samples (T1-T10).
The preprocessing steps included:
Quality control of raw Illumina 450K array data
Normalization of beta values
Filtering of low-quality probes
Sample QC and filtering
This resource can be accessed via ExperimentHub:
library(ExperimentHub) eh <- ExperimentHub() beta <- eh[["EH10275"]]
Example data for the DMRsegal package vignette and examples.
## Not run: library(ExperimentHub) eh <- ExperimentHub() beta <- eh[["EH10275"]] head(beta[, 1:5]) # Summary statistics summary(as.vector(beta[1:100, ])) ## End(Not run)## Not run: library(ExperimentHub) eh <- ExperimentHub() beta <- eh[["EH10275"]] head(beta[, 1:5]) # Summary statistics summary(as.vector(beta[1:100, ])) ## End(Not run)
A data frame containing differentially methylated positions (CpG sites) identified by comparing cancer and normal samples. The DMPs were identified using limma with adjusted p-value < 0.05.
A data frame with differentially methylated CpG sites as rows and the following columns:
interceptaverage expression (M-value) across all samples
pvalraw p-value from the statistical test
fF-statistic or B-statistic value
qvalq-value (FDR-adjusted p-value using Storey's method)
pval_adjadjusted p-value using Benjamini-Hochberg method
The DMPs were identified using limma's empirical Bayes moderated t-statistics on M-values (log2 ratio of methylated to unmethylated signal). Only CpG sites with adjusted p-value (Benjamini-Hochberg) < 0.05 are included. The results are sorted by p-value.
The differential methylation analysis included:
Conversion of beta values to M-values for statistical testing
Linear modeling with adjustment for age and gender covariates
Empirical Bayes moderation of t-statistics (limma)
Multiple testing correction using Benjamini-Hochberg method
Filtering at FDR < 0.05
This resource can be accessed via ExperimentHub:
library(ExperimentHub) eh <- ExperimentHub() # Query and load myfiles <- query(eh, "DMRsegaldata") dmps <- myfiles[["EH10277"]]
Derived from the beta matrix and pheno data using limma differential methylation analysis.
## Not run: library(ExperimentHub) eh <- ExperimentHub() dmps <- eh[["EH10277"]] head(dmps) nrow(dmps) # Summary of significance summary(dmps$pval_adj) sum(dmps$pval_adj < 0.01) # Very significant DMPs sum(dmps$pval_adj < 0.001) # Highly significant DMPs ## End(Not run)## Not run: library(ExperimentHub) eh <- ExperimentHub() dmps <- eh[["EH10277"]] head(dmps) nrow(dmps) # Summary of significance summary(dmps$pval_adj) sum(dmps$pval_adj < 0.01) # Very significant DMPs sum(dmps$pval_adj < 0.001) # Highly significant DMPs ## End(Not run)
A data frame containing phenotype information for 20 samples, including sample group (cancer vs. normal), age, and gender.
A data frame with 20 observations on the following 3 variables:
Sample_Groupa character vector indicating sample group: "cancer" or "normal"
Agea numeric vector containing the age of each individual
Gendera character vector indicating gender: "m" (male) or "f" (female)
The phenotype data contains information for 10 cancer samples (T1-T10) and 10 normal/healthy samples (H1-H10). Row names correspond to sample identifiers.
This resource can be accessed via ExperimentHub:
library(ExperimentHub) eh <- ExperimentHub() # Query and load myfiles <- query(eh, "DMRsegaldata") pheno <- myfiles[["EH10276"]]
Example data for the DMRsegal package vignette and examples.
## Not run: # Via ExperimentHub library(ExperimentHub) eh <- ExperimentHub() pheno <- eh[["EH10276"]] # Check sample distribution table(pheno$Sample_Group, pheno$Gender) summary(pheno$Age) ## End(Not run)## Not run: # Via ExperimentHub library(ExperimentHub) eh <- ExperimentHub() pheno <- eh[["EH10276"]] # Check sample distribution table(pheno$Sample_Group, pheno$Gender) summary(pheno$Age) ## End(Not run)