CHANGES IN DMRcate VERSION 1.12.1 - Peak closing sped up with Segment.R CHANGES IN DMRcate VERSION 1.10.2 - Bugfix for when there are no significant CpG sites at the given threshold CHANGES IN DMRcate VERSION 1.10.1 - Data filtering for EPIC arrays now incorporates probe information (via DMRcatedata_1.8.3) from Pidsley and Zotenko et al. (2016) Genome Biology 17(1), 208. - Two new modules are now available in cpg.annotate() in addition to "differential" and "variability": "ANOVA" and "diffVar". "ANOVA" will find whole-experiment DMRs from the entire set of contrasts in the design matrix; "diffVar" finds differentially variable regions (DVMRs) using functionality from the missMethyl package. - Class GenomicRatioSet (minfi) can now be passed to cpg.annotate(). CHANGES IN DMRcate VERSION 1.8.5 - DMRs can now be called from Illumina's EPIC array. Workflow is identical to that of 450K, just with a different annotation argument to cpg.annotate() and DMR.plot(). CHANGES IN DMRcate VERSION 1.7.2 - Major changes. WGBS pipeline is now implemented with DSS as a regression step instead of limma. 450K pipeline is the same, but with slight cosmetic changes in anticipation of the transition to the EPIC array. - DMR.plot() has been completely rewritten, now with Gviz and inbuilt transcript annotation for hg19, hg38 and mm10. - DMRs are now ranked by the Stouffer transformations of the limma- and DSS- derived FDRs of their constituent CpG sites. CHANGES IN DMRcate VERSION 1.4.1 - Extra control for Type I error through DMR constituents made commensurate with # of differential limma probes - CITATIONs added CHANGES IN DMRcate VERSION 1.0.2 BUG FIXES • annotate() renamed to cpg.annotate to avoid clashes with same-named function in ggplot2 NEW FEATURES • Kernel estimator has been rewritten from scratch without the need for ks:::kde. Now only takes moderated t-values as cpg weights, as required by the chi-square transformation. • Now allows for multi-level factor experiments, as allowed by limma. Contrasts should be specified with a contrast matrix, otherwise the design matrix MUST have an intercept. (Thanks to Tim Triche Jr. and David Martino for their advice). • A GRanges object can be produced from the results. • XY probes can also be filtered out using rmSNPandCH(). • DMR.plot() allows group median lines to be plotted to better visualise distances between groups (Thanks to Susan van Dijk and Magnus Tobiasson for their advice).