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Showing content from https://github.com/TransBioInfoLab/coMethDMR below:

TransBioInfoLab/coMethDMR: Detect Regions of Concurrent Differential Methylation

Gomez L, Odom GJ, Young JI, Martin ER, Liu L, Chen X, Griswold AJ, Gao Z, Zhang L, Wang L (2019) Nucleic Acids Research, gkz590, https://doi.org/10.1093/nar/gkz590

coMethDMR is an R package that identifies genomic regions associated with continuous phenotypes by optimally leverages covariations among CpGs within predefined genomic regions. Instead of testing all CpGs within a genomic region, coMethDMR carries out an additional step that selects comethylated sub-regions first without using any outcome information. Next, coMethDMR tests association between methylation within the sub-region and continuous phenotype using a random coefficient mixed effects model, which models both variations between CpG sites within the region and differential methylation simultaneously.

Assuming you have completed all pre-processing and normalization procedures, here are the steps to analyse your Illumina EPIC or 450k DNA methylation data.

1. Your Data:
   • Ensure your methylation data is loaded into R's Global Environment as a numeric matrix in probe by sample form: with probe IDs as your row names and sample IDs as the column names
   • Your phenotype / clinical data should be a data frame with a column called Sample (spelled exactly with an upper-case "S"); these sample IDs should match the column names of the methylation data
2. Pre-Defined Regions: Load the list of pre-calculated regions of "contiguous" CpGs which matches your Illumina data type. We have pre-calculated some of these lists of regions. We used the WriteCloseByAllRegions() function with maxGap = 200 (genomic locations within 200 base pairs are placed in the same cluster) and minCpGs = 3 (we need at least 3 CpGs to retain the location). These data files are:
   • For 450k array analysis: downlaod the supplemental data file hg19 regions or hg38 regions
   • For EPIC array analysis: downlaod the supplemental data file hg19 regions or hg38 regions
   • These files were generated by the create_all_genomic_array_ranges function
3. Adjust Methylation for Covariates with the GetResiduals() function; your methylation values may be confounded by clinical variables unrelated to your treatment, such as sex, age, or even the square of age
   • Input: your methylation data and covariates from Step 1
   • Output: a matrix of methylation residuals in probe by sample form
4. Regions of Co-Methylation with the CoMethAllRegions() function
   • Input: the list of regions of interest from Step 2; the methylation residuals (or the unadjusted M-values) from Step 3
   • Output: either a list of data frames (if output = "dataframe") or a list of vectors of probe IDs (if output = "CpGs")
     • Data Frame: one data frame per region of concurrent methylation; each data frame has a row per probe and columns for the probe ID, it's enclosing region, chromosome, correlation with the surrounding probes, and an indicator for "co"-methylation
     • Vector of CpGs: one vector per region of concurrent methylation; the vector simply contains the probe IDs of the CpGs in each region
5. Detecting Regions Differential Co-Methylation with lmmTestAllRegions(); this function performs a linear mixed model test to detect, from regions of concurrent methylation, which regions have differential methylation across the majority of probes (so that one single probe cannot influence the entire region)
   • Input: the methylation and clinical data from Step 1 and the regions of concurrent methylation from Step 4
   • Output: a data frame with
     • one row per region, and
     • columns for the region chromosome and range, the number of CpGs in the region, and the linear mixed model summaries for that region (including regression slope estimate, standard error, test statistic, $p$ -value, and false discovery rate)
6. Annotate Statistically Significant Regions with AnnotateResults(); this function takes the linear mixed model summaries by region from Step 5 and adds on reference gene symbols and their relationships to known CpG islands

See details in user manuals below or from Bioconductor repository.

The coMethDMR:: package has been included in the Bioconductor repository of R packages. To install this version, please use the following code:

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

# The following initializes usage of Bioc devel
BiocManager::install(version='devel')

BiocManager::install("coMethDMR")

The development version of coMethDMR:: can also be installed from this GitHub repository by

library(devtools)
install_github("TransBioInfoLab/coMethDMR")

Please note that using compiled code from GitHub may require your computer to have additional software (Rtools for Windows or Xcode for Mac). Also note that installing this development version may result in some errors. We have outlined potential troubleshooting steps below.

You may get the following error during installation:

Error: package or namespace load failed for 'coMethDMR':
 .onLoad failed in loadNamespace() for 'coMethDMR', details:
  call: .updateHubDB(hub_bfc, .class, url, proxy, localHub)
  error: Invalid Cache: sqlite file
  Hub has not been added to cache
  Run again with 'localHub=FALSE'
Error: loading failed

If so, please fix this by running ExperimentHub::ExperimentHub() first (and type yes if you receive a prompt to create a local cache for your data), then re-installing the package. Please see this white paper for more information: https://bioconductor.org/packages/devel/bioc/vignettes/AnnotationHub/inst/doc/TroubleshootingTheCache.html.

You may also get this error during installation:

Error: package or namespace load failed for 'coMethDMR':
 .onLoad failed in loadNamespace() for 'coMethDMR', details:
  call: NULL
  error: $ operator is invalid for atomic vectors

This error is caused by a version mismatch issue for the sesameData:: (https://bioconductor.org/packages/sesameData/) package. We require sesameData:: version 1.12 or higher. To fix this, you will need Biocdonductor version 3.14 or later. The following code will assist here:

BiocManager::install(version = "3.14")
BiocManager::install("sesameData")

After successfully executing the above installation, you should be able to install coMethDMR:: from GitHub like normal.

After installation, the coMethDMR package can be loaded into R using:

The reference manual for coMethDMR can be downloaded from old repository: https://github.com/TransBioInfoLab/coMethDMR_old/tree/master/docs/. The reference manual is coMethDMR_0.0.0.9001.pdf. Two vignettes are available in the same directory: 1_Introduction_coMethDMR_10-9-2019.pdf and 2_BiocParallel_for_coMethDMR_geneBasedPipeline.pdf.

1. There are two main steps in coMethDMR: (1) identifying comethylatyed clusters (2) testing methylation levels in those comethylated clusters against a phenotype". In step 1 and 2, should we use beta value or M values for CoMethDMR?

Answer: In step (1), using M values and beta values produce similar results. See Supplementary Table 2 Comparison of using beta values or M-values for identifying co-methylated regions in first step of coMethDMR pipeline at optimal rdrop parameter value of the coMethDMR paper.

In step (2), M-values should be used because it has better statistical properties. See Du et al. (2010) Comparison of Beta-value and M-value methods for quantifying methylation levels by microarray analysis.

Our development history is at https://github.com/TransBioInfoLab/coMethDMR_old

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