CpGindex {biscuiteer} | R Documentation

## Measure methylation status for PRCs or PMDs

### Description

Measures hypermethylation at PRCs in CGIs or hypomethylation at WCGWs in PMDs

### Usage

    CpGindex(bsseq, CGIs = NULL, PRCs = NULL, WCGW = NULL, PMDs = NULL)

### Arguments

`bsseq` | A BSseq object
`CGIs` | A GRanges of CpG island regions - HMM CGIs if NULL (DEFAULT: NULL)
`PRCs` | A GRanges of Polycomb targets - H9 state 23 low-meth if NULL (DEFAULT: NULL)
`WCGW` | A GRanges of solo-WCGW sites - PMD WCGWs if NULL (DEFAULT: NULL)
`PMDs` | A GRanges of hypomethylating regions - PMDs if NULL (DEFAULT: NULL)

### Details

At some point in some conference call somewhere, a collaborator suggested that a simple index of Polycomb repressor complex (PRC) binding site hyper- methylation and CpG-poor "partially methylated domain" (PMD) hypomethylation would be a handy yardstick for both deterministic and stochastic changes associated with proliferation, aging, and cancer. This function provides such an index by compiling measures of aberrant hyper- and hypo-methylation along with the ratio of hyper- to hypo-methylation. (The logic for this is that while the phenomena tend to occur together, there are many exceptions) The resulting measures can provide a high-level summary of proliferation-, aging-, and/or disease-associated changes in DNA methylation across samples.

The choice of defaults is fairly straightforward: in 2006, three independent groups reported recurrent hypermethylation in cancer at sites marked by both H3K4me3 (activating) and H3K27me3 (repressive) histone marks in embryonic stem cells; these became known as "bivalent" sites. The Roadmap Epigenome project performed ChIP-seq on hundreds of normal primary tissues and cell line results from the ENCODE project to generate a systematic catalog of "chromatin states" alongside dozens of whole-genome bisulfite sequencing experiments in the same tissues. We used both to generate a default atlas of bivalent (Polycomb-associated and transcriptionally-poised) sites from H9 human embryonic stem cells which retain low DNA methylation across normal (non-placental) REMC tissues. In 2018, Zhou and Dinh (Nature Genetics) found isolated (AT)CG(AT) sites, or "solo-WCGW" motifs, in common PMDs as the most universal barometer of proliferation- and aging-associated methylation loss in mammalian cells, so we use their solo-WCGW sites in common PMDs as the default measure for hypomethylation. The resulting CpGindex is a vector of length 3 for each sample: hypermethylation, hypomethylation, and their ratio.

We suggest fitting a model for the composition of bulk samples (tumor/normal, tissue1/tissue2, or whatever is most appropriate) prior to drawing any firm conclusions from the results of this function. For example, a mixture of two-thirds normal tissue and one-third tumor tissue may produce the same or lower degree of hyper/hypomethylation than high-tumor-content cell-free DNA samples from the blood plasma of the same patient. Intuition is simply not a reliable guide in such situations, which occur with some regularity. If orthogonal estimates of purity/composition are available (flow cytometry, ploidy, yield of filtered cfDNA), it is a Very Good Idea to include them.

The default for this function is to use the HMM-defined CpG islands from Hao Wu's paper (Wu, Caffo, Jaffee, Irizarry & Feinberg, Biostatistics 2010) as generic "hypermethylation" targets inside of "bivalent" (H3K27me3+H3K4me3) sites (identified in H9 embryonic stem cells & unmethylated across normals), and the solo-WCGW sites within common partially methylated domains from Wanding Zhou and Huy Dinh's paper (Zhou, Dinh, et al, Nat Genetics 2018) as genetic "hypomethylation" targets (as above, obvious caveats about tissue specificity and user-supplied possibilities exist, but the defaults are sane for many purposes, and can be exchanged for whatever targets a user wishes).

The function returns all three components of the "CpG index", comprised of hyperCGI and hypoPMD (i.e. hyper, hypo, and their ratio). The PMD "score" is a base-coverage-weighted average of losses to solo-WCGW bases within PMDs; the PRC score is similarly base-coverage-weighted but across HMM CGI CpGs, within polycomb repressor complex sites (by default, the subset of state 23 segments in the 25-state, 12-mark ChromImpute model for H9 which have less than 10 percent CpG methylation across the CpG-island-overlapping segment in all normal primary tissues and cells from the Reference Epigenome project). By providing different targets and/or regions, users can customize as needed.

The return value is a CpGindex object, which is really just a DataFrame that knows about the regions at which it was summarized, and reminds the user of this when they implicitly call the `show` method on it.

### Value

      A CpGindex (DataFrame w/cols `hyper`, `hypo`, `ratio` + 2 GRs)

### Examples

      orig_bed <- system.file("extdata", "MCF7_Cunha_chr11p15.bed.gz",
                              package="biscuiteer")
      orig_vcf <- system.file("extdata", "MCF7_Cunha_header_only.vcf.gz",
                              package="biscuiteer")
      bisc <- readBiscuit(BEDfile = orig_bed, VCFfile = orig_vcf,
                          merged = FALSE)

      cpg <- CpGindex(bisc)

[Package biscuiteer version 1.6.0 Index]