Introduction | List of functions | Example Analysis | Example 1: Differentially methylated probes from EWAS | Example 2: Enrichment analysis for arrays other than 450K and EPIC | Example 3: Enrichment analysis with user provided geneset | Example 4: Enrichment analysis for gene expression type of data that do not | need to combine test statistics | Gene set and pathway databases | Kyoto Encyclopedia of Genes and Genomes (KEGG) | Gene Ontology (GO) | The Molecular Signatures Database (MSigDB) | Reactome | Types of gene set enrichment analyses | Method options to combine p-value: | References

Introduction | List of functions | ENmix classes | Example Analysis | Example 1: using pipeline | Example 2: using individual function | Example 3: A more comprehensive example | Setting up the data | Quality Control | Internal control probes | Data distribution plots | QC information, outlier samples, low quality samples and probes | Filtering outliers, low quality data points, missing values and imputation | Background correction and dye-bias correction | Inter-array normalization | Probe-type bias adjustment | Batch effect correction | Principal component regression analysis plot | Multimodal CpGs or gap probes | Cell type proportion estimation | Methylation predictors | Differentially methylated regions (DMRs) | Intraclass correlation coefficient (ICC) reliability measures | 5-methylcytosine (5mC) and 5-hydroxymethylcytosine (5hmC) | Compatibility with other related R packages | References