RAMEN 2.0.0

RAMEN 2.0.0

In this version, we have made an important change in RAMEN terminology across all the code and documentation to more accurately reflect the biological concepts represented by the data. The term "Variably Methylated Regions (VMR)" used in RAMEN v1 has been replaced by "Variably Methylated Loci (VML)" in RAMEN v2, as not all VML are composed of 2 or more highly variable probes. VML are further composed of Variably Methylated Regions (previously named "canonical VMR" in RAMEN v1) and sparse Variably Methylated Probes (sVMPs; previously named "non-canonical VMR" in RAMENv1). To be clear, there are no changes in how these VML are identified, we only changed how we label these categories.

Updated name in RAMEN v2	Deprecated name in RAMEN v1
Variably Methylated Loci (VML)	Variably Methylated Region (VMR)
Variably Methylated Region (VMR)	canonical VMR (cVMR)
sparse Variably Methylated Probe (sVMP)	non-canonical VMR (ncVMR)

Updates

• To reflect the terminology change, the following functions had a name change: findVML() (previously named findVMRs() in RAMEN v1) and summarizeVML() (previously named summarizeVMRs() in RAMEN v1).

• findVML():

  • Output: list does not separate VMRs and sVMPs into two different list elements anymore. Now, a single element ("VML") is returned in the output list, which contains both VMRs and sVMPs, labelled accordingly under the type column; this VML element is now a data frame, and not a Genomic Ranges object to facilitate data wrangling and plotting. The function now automatically indexes the VML.

  • The user does not need to provide the array manifest anymore if working with the Illumina 450k, EPICv1 or EPICv2 array. The array_manifest argument accepts now "IlluminaHumanMethylation450k", "IlluminaHumanMethylationEPICv1" and "IlluminaHumanMethylationEPICv2".

  • There is a new method to identify VML using ultrastable probes (probes which DNA methylation is known to be stable independently of tissue and developmental stage) to discriminate Highly Variable Probes, which are then grouped into VML. This method is the default one now. For more information please see the findVML() documentation and the package vignette. The previously default method to identify Highly Variable Probes (top 10% of probes with the highest variance in the data set) is still available using the argument var_distribution = "all".

• nullDistGE(): Prints messages to keep track of the progress. Fixed a bug that made doFuture parallelization strategies crash.

• All functions have examples in the documentation.

• Added tests to reach a code coverage of >90% in all functions.

• Improved error catches to make functions stop early when the inputs are not in the right format. Fixed various bugs throughout the code (no user.

• Added news, citation and contributing files to the repository.

• Citation info is provided when loading the package.

• The package repository has now informative badges and Continuous Integration checks.

v.1.0.1

Release to link RAMEN to zenodo

v1.0.0

First version of RAMEN. The following functions are implemented to conduct a genome-exposome contribution analysis to DNA methylome variability:

• findVMRs() identifies Variable Methylated Regions (VMRs) in microarrays
• summarizeVMRs()summarizes the regional methylation state of each VMR
• findCisSNPs() identifies the SNPs in cis of each VMR
• selectVariables() conducts a LASSO-based variable selection strategy to identify potentially relevant cis SNPs and environmental variables
• lmGE() fits linear single-variable genetic (G) and environmental (E), and pairwise additive (G+E) and interaction (GxE) linear models and select the best explanatory model per VMR.
• nullDistGE() simulates a delta R squared null distribution of G and E effects on DNAme variability. Useful for filtering out poor-performing best explanatory models selected by lmGE().