The COCOA Bioconductor package | Basic workflow | Quantify inter-sample epigenetic variation | Annotate variation with region sets | Permutation test | COCOA for DNA methylation data | Our data | Supervised COCOA | Quantify relationship between chosen sample phenotype and epigenetic data | Score region sets | Estimating statistical significance | Permutations | Fit gamma distribution to null distributions and get p-values | Unsupervised COCOA | Quantify relationship between latent factors and epigenetic data | Further understanding the results (visualization) | Specificity of variation to the regions of interest | The raw data | Feature contribution scores of individual regions | COCOA for chromatin accessibility data (ATAC-seq) | Supervised COCOA | Unsupervised COCOA | Additional details | Method details | Region set database | Aggregating info from individual features | Making a "meta-region" profile | Q and A | Related references

The MIRA Bioconductor package | Required inputs | DNA methylation data | Region sets | Analysis workflow | The input | Expand your regions | Aggregating methylation across regions | Calculating the MIRA score | Interpreting the results | Bonus: Loading region sets with LOLA

Biological Question | Input: | Package workflow | Initial Run-through | Choosing Appropriate Region Sizes | Full MIRA Analysis | Interpreting the Results | General Interpretation Tips and Caveats | Other Tips for Using MIRA