v2.1.2

RAMEN 2.1.2

This patch focuses on the running time and memory usage of the package, and
fixes a few bugs:

• Fixed a bug in lmGE() where the function would throw an error when a
  covariate had a non-syntactic name (e.g. "cell type"). This happened whenever
  the winning model was G+E or GxE.
• Fixed a bug in lmGE() where a selected_variables object in which no VML
  had any selected genotype or environmental variable would throw an error. All
  the VML are now returned with the basal model as their winner, which is what
  already happened when only some of them were empty.
• Fixed a bug in selectVariables() where setting environmental_matrix = NULL, which the documentation describes as the way to run the variable
  selection on the genotype alone, would throw an error instead. The
  documentation of that argument was also narrowed: a genotype_matrix is
  always required, and only the environmental one can be omitted.

The performance work touches findVML(), medCorVMR(), summarizeVML(),
selectVariables(), lmGE() and nullDistGE(). Most of it resolves look-ups
that were being repeated once per locus a single time up front instead, and
avoids keeping unnecessary copies of the genotype and methylation objects. Both
the running time and the memory each parallel worker needs are reduced.

Outputs are unchanged, with one exception: summarizeVML() now computes
medians with matrixStats::colMedians(). For VML with an even number of probes
the two middle values are averaged slightly differently than by median(), so
results can differ in the last representable digit (a relative difference of
about one machine epsilon). VML with an odd number of probes, including all
sVMPs, are unaffected.

The documentation of nullDistGE() now notes that the same seed is handed to
selectVariables() in every permutation, so the cross-validation folds are
shared across permutations.

On the testing side, a test was added checking that the variables
selectVariables() reports are the ones the underlying LASSO fits select, and
the tests that register a parallel back-end now restore the sequential one when
they finish, so that the back-end no longer leaks into the test files that run
after them.

Finally, the repository was transferred to ropensci after passing the peer
review process. All repository and website links were updated to reflect this
change.

The changes in this patch were conducted with help of Claude Opus 5.

v2.1.1

RAMEN v2.1.1

This patch fixes some minor bugs in RAMEN including:

• Improved argument check - empty objects now throw errors, and all functions
  check for matched IDs across all objects.
• Fixed bug in nullDistGE() where the seed was fixed to 1 during the
  selectVariables() step instead of using the seed argument provided.
• Fixed a bug in selectVariables() where the function would throw an error
  when, in the absence of covariates, a VML had only 1 SNP. In that case, the
  variable selection for G is now not conducted and the same SNP is returned.
• Fixed a bug in lmGE() where the function would throw an error when no
  covariates were fed into the function.
• summarizeVML() now explicitly errors if the VML object has duplicate
  VML_index values, instead of silently merging them.

In addition to that, we added a section on the vignette explaining how to set
up a parallel backend, and expanding on the data assumptions/expectations
from RAMEN.

The IlluminaHumanMethylation450kanno.ilmn12.hg19 and
IlluminaHumanMethylationEPICv2anno.20a1.hg38 annotation packages have been
moved Suggests to lighten the default installation, since most
users only work with one microarray platform at a time.

This version also includes several internal performance improvements (faster
argument validation, VMR correlation computation, probe variability scoring,
and VML summarization), with no change in output.

This patch presents no changes on the user's end, other than the dependency
change and the summarizeVML() duplicate-index check noted above.

V2.1.0

This minor version presents a couple of changes that improve the integration of
RAMEN with GenomicRanges objects and Bioconductor:

• findVML() outputs a GRanges object for the identified VML instead of
  a data frame.
• Downstream functions (i.e., summarizeVML(), findCisSNPs(),
  selectVariables(), medCorVMR() and nullDistGE()) now take GRanges
  objects as an input, instead of data frames. The argument name changed from
  "VML_df" to "VML". When appropriate, the output is now also a GRanges object.
• summarizeVML() now outputs a matrix instead of a data frame.

In addition to that, the Vignette has been improved. New sections have been
added to provide guidance and recommendations to the users (e.g regarding data
pre-processing andexpectations, as RAMEN expects all data to be alread cleaned,
quality checked, and pre-processed). Also, some sections have been improved to
provide clearer instructions.

This minor version also has multiple improvements to the code, which present no
changes in the user front but have a positive impact on the internal functioning
of the package, such as:

• Optimized examples with reduced running time.
• Reduced dependencies in the package. The installation time has been
  significantly reduced.
• Implemented internal helper functions to improve the readability of the code
  and improve its debugging.
• Reduced data casting and conditionals inside the functions.
• Improved error messages across all functions.

These changes were motivated by the rOpenSci peer review process.

What's Changed

• Incorporated rOpenSci reviewer's suggestions (1st round) by @ErickNavarroD in #5

Full Changelog: v2.0.1...v2.1.0

v2.0.1

This patch version presents no changes from the user perspective. Code
improvements have been made to the package to address comments and
suggestions from the editor of the rOpenSci peer review
process. These include:

• Improving the README, adding quick usage examples, more details on the
  functionality of RAMEN, and better installation instructions.
• Fixing inconsistencies across the package documentation, such as email
  addresses, author information, and citation details.
• Adding a continuous integration check for R 4.2.0
• Minor code style improvements.
• Separating the tests into self-contained independent files (one per
  function) to improve organization and readability.

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().