Pull changes from v2.1.0

.github/workflows/R-CMD-check.yaml

@@ -24,7 +24,7 @@ jobs:
           - {os: ubuntu-latest,   r: 'devel', http-user-agent: 'release'}
           - {os: ubuntu-latest,   r: 'release'}
           - {os: ubuntu-latest,   r: 'oldrel-1'}
-          - {os: ubuntu-latest,   r: '4.2.0'}
+          - {os: ubuntu-latest,   r: '4.5.0'}
 
     env:
       GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}

DESCRIPTION

@@ -31,6 +31,7 @@ Roxygen: list(markdown = TRUE)
 Suggests: 
     BiocStyle,
     knitr,
+    magick,
     rmarkdown,
     IlluminaHumanMethylation450kanno.ilmn12.hg19,
     IlluminaHumanMethylationEPICanno.ilm10b4.hg19,
@@ -53,7 +54,7 @@ Imports:
     stats
 VignetteBuilder: knitr
 Depends: 
-    R (>= 4.2.0)
+    R (>= 4.5.0)
 LazyData: true
 URL: 
     https://ericknavarrod.github.io/RAMEN/ ,

NEWS.md

@@ -1,4 +1,4 @@
-# RAMEN 2.1.0
+# RAMEN 2.1.0 - July 14, 2026
 
 This minor version presents a couple of changes that improve the integration of
 RAMEN with GenomicRanges objects and Bioconductor: 

R/findVML.R

@@ -154,8 +154,8 @@ map_revmap_names <- function(positions, manifest_hvp) {
 #'
 #' @export
 #' @examples
-#' # Set the parallel backend to use 2 workers
-#' doParallel::registerDoParallel(2)
+#' # Evaluate sequentially
+#' foreach::registerDoSEQ()
 #' VML <- RAMEN::findVML(
 #'   methylation_data = RAMEN::test_methylation_data,
 #'   array_manifest = "IlluminaHumanMethylationEPICv1",

R/lmGE.R

@@ -121,8 +121,8 @@
 #' @importFrom foreach %do%
 #' @export
 #' @examples
-#' # Set the parallel backend to use 2 workers
-#' doParallel::registerDoParallel(2)
+#' # Evaluate sequentially
+#' foreach::registerDoSEQ()
 #' ## Find VML in test data
 #' VML <- RAMEN::findVML(
 #'   methylation_data = RAMEN::test_methylation_data,
@@ -215,7 +215,8 @@ lmGE <- function(selected_variables,
       selected_genot %in% empty_lists))
   # Select the winning model
   winning_models <- foreach::foreach(i = seq_len(nrow(selected_variables)),
-                                     .combine = "rbind") %dopar% { # For every VML
+                                     .combine = "rbind",
+                                     .export = "empty_lists") %dopar% { # For every VML
     #### Prepare data sets ####
     # Create the data frame with all the information for each VML
     VML_i <- selected_variables[i, ]

R/medCorVMR.R

@@ -23,8 +23,8 @@
 #' @export
 #'
 #' @examples
-#' # Set the parallel backend to use 2 workers
-#' doParallel::registerDoParallel(2)
+#' # Evaluate sequentially
+#' foreach::registerDoSEQ()
 #' # Create a VML object
 #' VML <- GenomicRanges::GRanges(seqnames = c("chr21", "chr21"),
 #'       ranges = IRanges::IRanges(start = c(10861376, 10862171),

R/nullDistGE.R

@@ -54,8 +54,8 @@
 #' @importFrom foreach %do%
 #' @export
 #' @examples
-#' # Set the parallel backend to use 2 workers
-#' doParallel::registerDoParallel(2)
+#' # Evaluate sequentially
+#' foreach::registerDoSEQ()
 #' ## Find VML in test data
 #' VML <- findVML(
 #'   methylation_data = test_methylation_data,
@@ -166,7 +166,7 @@ nullDistGE <- function(VML_wSNPs,
       environmental_matrix = permutated_environment,
       covariates = covariates,
       summarized_methyl_VML = summarized_methyl_VML,
-      seed = 1
+      seed = seed
     )
 
     message("Starting lmGE in permutation ", i, " of ", permutations)

R/selectVariables.R

@@ -107,8 +107,8 @@
 #'
 #' @examples
 #' ## Find VML in test data
-#' # Set the parallel backend to use 2 workers
-#' doParallel::registerDoParallel(2)
+#' # Evaluate sequentially
+#' foreach::registerDoSEQ()
 #' VML <- RAMEN::findVML(
 #'   methylation_data = RAMEN::test_methylation_data,
 #'   array_manifest = "IlluminaHumanMethylationEPICv1",
@@ -176,7 +176,12 @@ selectVariables <- function(VML_wSNPs,
   i <- NULL # To avoid R CMD check note about undefined global variable
   #### Run LASSO ####
   lasso_results <- foreach::foreach(i = VML_wSNPs$VML_index,
-                                    .combine = "rbind") %dorng% {
+                                    .combine = "rbind",
+                                    .packages = c("GenomicRanges",
+                                                  "S4Vectors",
+                                                  "IRanges",
+                                                  "glmnet"),
+                                    .export = "empty_lists") %dorng% {
     #### Prepare data sets ####
     VML_i <- VML_wSNPs[VML_wSNPs$VML_index == i]
     # Select summarized VML information

R/summarizeVML.R

@@ -22,8 +22,8 @@
 #'
 #' @examples
 #' ## Find VML in test data
-#' # Set the parallel backend to use 2 workers
-#' doParallel::registerDoParallel(2)
+#' # Evaluate sequentially
+#' foreach::registerDoSEQ()
 #' VML <- findVML(
 #'   methylation_data = test_methylation_data,
 #'   array_manifest = "IlluminaHumanMethylationEPICv1",
@@ -50,7 +50,6 @@ summarizeVML <- function(VML,
   if (!"VML_index" %in% colnames(S4Vectors::mcols(VML))) {
     S4Vectors::mcols(VML)$VML_index <- paste0("VML", seq_len(length(VML)))
   }
-
   if (!all(unique(unlist(VML$probes)) %in% rownames(methylation_data))) {
     warning(paste("Some probes listed in the VML data frame are not found in",
     "the methylation data. Please check that all probes listed in the 'probes'",
@@ -61,14 +60,12 @@ summarizeVML <- function(VML,
   if (!is.list(VML$probes)) {
     stop("Please make sure the 'probes' column in the VML object is a list")
   }
-
-  VML_index <- i <- NULL # To avoid R CMD check notes
-
+  mcols_vml <- S4Vectors::mcols(VML)
+  VML_1 <- i <- NULL # To avoid R CMD check notes
   #### Summarize VML ####
-  summarized_VML <- foreach::foreach(i = VML$VML_index,
+  summarized_VML <- foreach::foreach(i = mcols_vml$VML_index,
                                      .combine = "cbind") %dopar% {
-    probes <- VML[VML$VML_index == i]$probes |>
-      unlist()
+    probes <- unlist(mcols_vml$probes[mcols_vml$VML_index == i])
     subset_meth <-  methylation_data[probes, , drop = FALSE]
     median <- apply(subset_meth, 2, median, na.rm = TRUE)
     matrix(median, ncol = 1, dimnames = list(NULL, i))

README.Rmd

@@ -76,8 +76,11 @@ BiocManager::install("ErickNavarroD/RAMEN")
 
 ## Usage
 
-For a detailed tutorial on how to use RAMEN, please check the package's vignette
-or [website](https://ericknavarrod.github.io/RAMEN/articles/RAMEN.html). 
+For a detailed tutorial on how to use RAMEN, please check the package's vignette, 
+which you can build locally by running 
+` BiocManager::install("ErickNavarroD/RAMEN",  build_vignettes = TRUE)`
+or see externally in its 
+[website](https://ericknavarrod.github.io/RAMEN/articles/RAMEN.html). 
 Altogether, RAMEN provides a workflow that takes a set of individuals 
 with genome, exposome and DNA methylome information, and generates an estimation
 of the contribution of genetic variants and environmental exposures to its DNA 

README.md

@@ -71,7 +71,9 @@ BiocManager::install("ErickNavarroD/RAMEN")
 ## Usage
 
 For a detailed tutorial on how to use RAMEN, please check the package’s
-vignette or
+vignette, which you can build locally by running
+`BiocManager::install("ErickNavarroD/RAMEN",  build_vignettes = TRUE)`
+or see externally in its
 [website](https://ericknavarrod.github.io/RAMEN/articles/RAMEN.html).
 Altogether, RAMEN provides a workflow that takes a set of individuals
 with genome, exposome and DNA methylome information, and generates an
@@ -234,6 +236,60 @@ selected_variables <- RAMEN::selectVariables(
   summarized_methyl_VML = summarized_methyl_VML,
   seed = 1
 )
+#> Loading required package: stats4
+#> Loading required package: BiocGenerics
+#> Loading required package: generics
+#> 
+#> Attaching package: 'generics'
+#> The following object is masked from 'package:dplyr':
+#> 
+#>     explain
+#> The following objects are masked from 'package:base':
+#> 
+#>     as.difftime, as.factor, as.ordered, intersect, is.element, setdiff,
+#>     setequal, union
+#> 
+#> Attaching package: 'BiocGenerics'
+#> The following object is masked from 'package:dplyr':
+#> 
+#>     combine
+#> The following objects are masked from 'package:stats':
+#> 
+#>     IQR, mad, sd, var, xtabs
+#> The following objects are masked from 'package:base':
+#> 
+#>     anyDuplicated, aperm, append, as.data.frame, basename, cbind,
+#>     colnames, dirname, do.call, duplicated, eval, evalq, Filter, Find,
+#>     get, grep, grepl, is.unsorted, lapply, Map, mapply, match, mget,
+#>     order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank,
+#>     rbind, Reduce, rownames, sapply, saveRDS, table, tapply, unique,
+#>     unsplit, which.max, which.min
+#> Loading required package: S4Vectors
+#> 
+#> Attaching package: 'S4Vectors'
+#> The following objects are masked from 'package:dplyr':
+#> 
+#>     first, rename
+#> The following object is masked from 'package:utils':
+#> 
+#>     findMatches
+#> The following objects are masked from 'package:base':
+#> 
+#>     expand.grid, I, unname
+#> Loading required package: IRanges
+#> 
+#> Attaching package: 'IRanges'
+#> The following objects are masked from 'package:dplyr':
+#> 
+#>     collapse, desc, slice
+#> Loading required package: Seqinfo
+#> Loading required package: Matrix
+#> 
+#> Attaching package: 'Matrix'
+#> The following object is masked from 'package:S4Vectors':
+#> 
+#>     expand
+#> Loaded glmnet 5.0
 #> Loading required package: rngtools
 
 head(selected_variables)

vignettes/RAMEN.Rmd

@@ -201,6 +201,13 @@ design and the plataform, but the steps generally follow this structure:
   can be done using the ComBat function from the 
   [sva package](https://bioconductor.org/packages/release/bioc/html/sva.html). 
 
+We recommend using M values over Beta values, since M value distributions 
+provide more statistically valid properties for linear modelling, such as being
+approximately homoscedastic, unbounded, and with a lower compression at the
+methylated and unmethylated values (for more details see 
+[Pan Du, *et al.*, BMC Bioinformatics, 2010](https://doi.org/10.1186/1471-2105-11-587)). 
+
+
 ### Exposome data 
 
 The exposome is a highly heterogeneous data set, as it can encompass variables