Merge pull request #21 from AliYoussef96/dev_jeba

corrections to the package

.github/workflows/rworkflows.yml

@@ -0,0 +1,57 @@
+name: rworkflows
+'on':
+  push:
+    branches:
+    - master
+    - main
+    - devel
+    - RELEASE_**
+  pull_request:
+    branches:
+    - master
+    - main
+    - devel
+    - RELEASE_**
+jobs:
+  rworkflows:
+    permissions: write-all
+    runs-on: ${{ matrix.config.os }}
+    name: ${{ matrix.config.os }} (${{ matrix.config.r }})
+    container: ${{ matrix.config.cont }}
+    strategy:
+      fail-fast: ${{ false }}
+      matrix:
+        config:
+        - os: ubuntu-latest
+          bioc: devel
+          r: auto
+          cont: ghcr.io/bioconductor/bioconductor_docker:devel
+          rspm: ~
+        - os: macOS-latest
+          bioc: devel
+          r: auto
+          cont: ~
+          rspm: ~
+        - os: windows-latest
+          bioc: devel
+          r: auto
+          cont: ~
+          rspm: ~
+    steps:
+    - uses: neurogenomics/rworkflows@master
+      with:
+        run_bioccheck: ${{ false }}
+        run_rcmdcheck: ${{ true }}
+        as_cran: ${{ true }}
+        run_vignettes: ${{ true }}
+        has_testthat: ${{ true }}
+        run_covr: ${{ true }}
+        run_pkgdown: ${{ true }}
+        has_runit: ${{ false }}
+        has_latex: ${{ false }}
+        GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+        run_docker: ${{ false }}
+        DOCKER_TOKEN: ${{ secrets.DOCKER_TOKEN }}
+        runner_os: ${{ runner.os }}
+        cache_version: cache-v1
+        docker_registry: ghcr.io

DESCRIPTION

@@ -14,10 +14,18 @@ Authors@R:
     person(given = "Eleanor", family = "Coffey", role = c("aut", "ths"),
     email = "[email protected]",
     comment = c(ORCID = "0000-0002-9717-5610")))
-Description: Differential expression analysis is a prevalent method utilised in the examination of diverse biological data.
-            The reproducibility-optimized test statistic (ROTS) modifies a t-statistic based on the data's intrinsic characteristics and ranks features according to their statistical significance for differential expression between two or more groups (f-statistic). 
-            Focussing on proteomics and metabolomics, the current ROTS implementation cannot account for technical or biological covariates such as MS batches or gender differences among the samples. 
-            Consequently, we developed LimROTS, which employs a reproducibility-optimized test statistic utilising the limma methodology to simulate complex experimental designs.
+Description: Differential expression analysis is a prevalent method utilised in 
+            the examination of diverse biological data.The 
+            reproducibility-optimized test statistic (ROTS) modifies a 
+            t-statistic based on the data's intrinsic characteristics and ranks 
+            features according to their statistical significance for 
+            differential expression between two or more groups (f-statistic). 
+            Focussing on proteomics and metabolomics, the current ROTS 
+            implementation cannot account for technical or biological 
+            covariates such as MS batches or gender differences among 
+            the samples.Consequently, we developed LimROTS, which employs a 
+            reproducibility-optimized test statistic utilising the limma 
+            methodology to simulate complex experimental designs.
 License: Artistic-2.0
 Encoding: UTF-8
 Roxygen: list(markdown = TRUE)
@@ -40,8 +48,10 @@ Imports:
     utils,
     stats,
     doRNG,
+    magick,
     dplyr
 Suggests: 
+    BiocStyle,
     ggplot2,
     testthat (>= 3.0.0),
     knitr,

R/Bootstrap_functions.r

@@ -1,43 +1,62 @@
 #' Generate Bootstrap Samples with Optional Pairing
 #'
-#' This function generates bootstrap samples from the input metadata. It samples with replacement
-#' within each group defined in the metadata, and optionally adjusts for paired groups.
+#' This function generates bootstrap samples from the input metadata. It samples
+#'  with replacement within each group defined in the metadata, and optionally
+#'  adjusts for paired groups.
 #'
 #' @param B Integer. The number of bootstrap samples to generate.
-#' @param meta.info Data frame. Metadata containing sample information, where each row corresponds to a sample.
-#' @param group.name Character. The name of the column in `meta.info` that defines the grouping variable for the samples.
-#' @param paired Logical. If `TRUE`, the function ensures the bootstrap samples are paired between two groups.
+#' @param meta.info Data frame. Metadata containing sample information, where
+#' each row corresponds to a sample.
+#' @param group.name Character. The name of the column in `meta.info` that
+#' defines the grouping variable for the samples.
+#' @param paired Logical. If `TRUE`, the function ensures the bootstrap samples
+#'  are paired between two groups.
 #'
 #' @details
-#' The function works by resampling the row names of the metadata for each group separately. If `paired` is `TRUE`,
-#' it assumes there are exactly two groups and samples the second group based on the positions of the first group to maintain pairing.
+#' The function works by resampling the row names of the metadata for each group
+#'  separately. If `paired` is `TRUE`, it assumes there are exactly two groups
+#'  and samples the second group based on the positions of the first group to
+#'  maintain pairing.
 #'
-#' @return A matrix of dimension \code{B} x \code{n}, where \code{n} is the number of samples. Each row corresponds
-#' to a bootstrap sample, and each entry is a resampled row name from the metadata.
+#' @return A matrix of dimension \code{B} x \code{n}, where \code{n} is the
+#' number of samples. Each row corresponds to a bootstrap sample, and each
+#' entry is a resampled row name from the metadata.
 #'
 #' @export
 #' @examples
 #' # Example usage:
 #' set.seed(123)
-#' meta.info <- data.frame(group = rep(c("A", "B"), each = 5), row.names = paste0("Sample", 1:10))
-#' bootstrapS(B = 10, meta.info = meta.info, group.name = "group", paired = FALSE)
+#' meta.info <- data.frame(
+#'     group = rep(c("A", "B"), each = 5),
+#'     row.names = paste0("Sample", 1:10)
+#' )
+#' bootstrapS(
+#'     B = 10, meta.info = meta.info, group.name = "group",
+#'     paired = FALSE
+#' )
 #'
 #' # Paired bootstrap sampling
-#' bootstrapS(B = 10, meta.info = meta.info, group.name = "group", paired = TRUE)
+#' bootstrapS(
+#'     B = 10, meta.info = meta.info, group.name = "group",
+#'     paired = TRUE
+#' )
+#'
 bootstrapS <- function(B, meta.info, group.name, paired) {
     groups <- meta.info[, group.name]
     bootsamples <- matrix(nrow = B, ncol = length(groups))
     for (i in seq_len(B)) {
         for (g in unique(groups)) {
             g.names <- row.names(meta.info)[which(groups == g)]
-            bootsamples[i, which(groups == g)] <- sample(g.names, length(g.names), replace = TRUE)
+            bootsamples[i, which(groups == g)] <-
+                sample(g.names, length(g.names), replace = TRUE)
         }
     }
     if (paired) {
         for (i in seq_len(B)) {
             g.names1 <- bootsamples[i, which(groups == unique(groups)[1])]
             g.names2 <- match(g.names1, row.names(meta.info)) + length(g.names1)
-            bootsamples[i, which(groups == unique(groups)[2])] <- row.names(meta.info)[g.names2]
+            bootsamples[i, which(groups == unique(groups)[2])] <-
+                row.names(meta.info)[g.names2]
         }
     }
     return(bootsamples)
@@ -46,26 +65,32 @@ bootstrapS <- function(B, meta.info, group.name, paired) {
 
 #' Generate Permutated Samples
 #'
-#' This function generates permuted samples by shuffling the row names of the metadata.
+#' This function generates permuted samples by shuffling the row names of the
+#' metadata.
 #'
-#' @param meta.info Data frame. Metadata containing sample information, where each row corresponds to a sample.
+#' @param meta.info Data frame. Metadata containing sample information, where
+#' each row corresponds to a sample.
 #' @param B Integer. The number of permutations to generate.
 #'
 #' @details
-#' The function creates a matrix where each row is a permuted version of the row names from `meta.info`.
-#' This can be used to generate null distributions or perform randomization-based tests.
+#' The function creates a matrix where each row is a permuted version of the
+#' row names from `meta.info`.This can be used to generate null distributions
+#' or perform randomization-based tests.
 #'
-#' @return A matrix of dimension \code{B} x \code{n}, where \code{n} is the number of samples (i.e., rows in `meta.info`).
-#' Each row is a permutation of the row names of the metadata.
+#' @return A matrix of dimension \code{B} x \code{n}, where \code{n} is the
+#' number of samples (i.e., rows in `meta.info`).Each row is a permutation of
+#' the row names of the metadata.
 #'
 #' @export
 #' @examples
 #' # Example usage:
 #' set.seed(123)
-#' meta.info <- data.frame(group = rep(c("A", "B"), each = 5), row.names = paste0("Sample", 1:10))
+#' meta.info <- data.frame(
+#'     group = rep(c("A", "B"), each = 5),
+#'     row.names = paste0("Sample", 1:10)
+#' )
 #' permutatedS(meta.info = meta.info, B = 10)
-permutatedS <- function(meta.info, B)
-{
+permutatedS <- function(meta.info, B) {
     persamples <- matrix(nrow = B, ncol = nrow(meta.info))
     for (i in seq_len(B)) {
         persamples[i, ] <- sample(row.names(meta.info))
@@ -77,32 +102,42 @@ permutatedS <- function(meta.info, B)
 
 #' Generate Stratified Bootstrap Samples for limRots
 #'
-#' This function generates stratified bootstrap samples based on the groupings and additional factors in the metadata.
-#' The function ensures that samples are drawn proportionally based on strata defined by the interaction of factor columns in the metadata.
+#' This function generates stratified bootstrap samples based on the groupings
+#' and additional factors in the metadata. The function ensures that samples
+#' are drawn proportionally based on strata defined by the interaction of
+#' factor columns in the metadata.
 #'
 #' @param B Integer. The number of bootstrap samples to generate.
-#' @param meta.info Data frame. Metadata containing sample information, where each row corresponds to a sample. Factor columns in `meta.info` are used to define strata for sampling.
-#' @param group.name Character. The name of the column in `meta.info` that defines the grouping variable for the samples.
+#' @param meta.info Data frame. Metadata containing sample information,
+#' where each row corresponds to a sample. Factor columns in `meta.info`
+#' are used to define strata for sampling.
+#' @param group.name Character. The name of the column in `meta.info` that
+#' defines the grouping variable for the samples.
 #'
 #' @details
-#' The function works by first identifying the factors in the `meta.info` data frame that are used to create strata for sampling.
-#' Within each group defined by `group.name`, the function samples according to the strata proportions, ensuring that samples are drawn
-#' from the correct groups and strata in a proportional manner.
+#' The function works by first identifying the factors in the `meta.info` data
+#' frame that are used to create strata for sampling. Within each group defined
+#' by `group.name`, the function samples according to the strata proportions,
+#' ensuring that samples are drawn from the correct groups and strata in a
+#' proportional manner.
 #'
-#' @return A matrix of dimension \code{B} x \code{n}, where \code{n} is the number of samples. Each row corresponds
-#' to a bootstrap sample, and each entry is a resampled row name from the metadata, stratified by group and additional factors.
+#' @return A matrix of dimension \code{B} x \code{n}, where \code{n} is the
+#' number of samples. Each row corresponds to a bootstrap sample, and each
+#' entry is a resampled row name from the metadata, stratified by group and
+#' additional factors.
 #'
 #' @export
 #' @examples
 #' # Example usage:
 #' set.seed(123)
-#' meta.info <- data.frame(group = rep(c(1, 2), each = 5),
+#' meta.info <- data.frame(
+#'     group = rep(c(1, 2), each = 5),
 #'     batch = rep(c("A", "B"), 5),
-#'     row.names = paste0("Sample", 1:10))
+#'     row.names = paste0("Sample", 1:10)
+#' )
 #' meta.info$batch <- as.factor(meta.info$batch)
 #' bootstrapSamples.limRots(B = 10, meta.info = meta.info, group.name = "group")
-bootstrapSamples.limRots <- function(B, meta.info, group.name)
-{
+bootstrapSamples.limRots <- function(B, meta.info, group.name) {
     labels <- as.numeric(meta.info[, group.name])
     samples <- matrix(nrow = B, ncol = length(labels))
     for (i in seq_len(B)) {
@@ -112,7 +147,8 @@ bootstrapSamples.limRots <- function(B, meta.info, group.name)
             meta.info.factors <- c()
             for (j in seq_len(ncol(meta.info))) {
                 if (is.factor(meta.info.pos[, j])) {
-                    meta.info.factors <- c(meta.info.factors, colnames(meta.info.pos)[j])
+                    meta.info.factors <-
+                        c(meta.info.factors, colnames(meta.info.pos)[j])
                 }
             }
             if (is.null(meta.info.factors)) {
@@ -124,14 +160,21 @@ bootstrapSamples.limRots <- function(B, meta.info, group.name)
                 )
                 return(samples)
             }
-            meta.info.factors <- meta.info.factors[meta.info.factors != group.name]
-            meta.info.pos$stratum <- interaction(meta.info.pos[, meta.info.factors])
+            meta.info.factors <-
+                meta.info.factors[meta.info.factors != group.name]
+            meta.info.pos$stratum <-
+                interaction(meta.info.pos[, meta.info.factors])
             stratum_sizes <- table(meta.info.pos$stratum)
             stratum_samples <- round(length(pos) * prop.table(stratum_sizes))
-            sampled_indices <- unlist(lapply(names(stratum_samples), function(stratum) {
-                stratum_indices <- row.names(meta.info.pos)[which(meta.info.pos$stratum == stratum)]
-                sample(stratum_indices, stratum_samples[stratum], replace = TRUE)
-            }))
+            sampled_indices <-
+                unlist(lapply(names(stratum_samples), function(stratum) {
+                    stratum_indices <-
+                        row.names(meta.info.pos)[which(meta.info.pos$stratum ==
+                            stratum)]
+                    sample(stratum_indices, stratum_samples[stratum],
+                        replace = TRUE
+                    )
+                }))
             samples[i, pos] <- sampled_indices
         }
     }