Merge remote-tracking branch 'origin/dev_Bioc2'

.Rbuildignore

@@ -3,17 +3,21 @@
 ^.*\.Rproj$
 ^\.Rproj\.user$
 
-vignettes/*.html
-vignettes/*.pdf
-vignettes/*.R
-vignettes/*.qmd
-
-
 CONTRIBUTING.md
 
 ^_pkgdown\.yml$
 ^docs$
 ^pkgdown$
-^.*\.bib$
 ^\.github$
-^vignettes/(?!reference.bib$).*\.bib$
+
+
+^.*\.Rproj$
+^\.Rproj\.user$
+.github
+^\.github$
+pkgdown
+.RData
+.pdf
+inst/extras
+^doc$
+^Meta$

.gitignore

@@ -53,3 +53,16 @@ rsconnect/
 inst/doc
 docs
 ..DS_Store
+
+
+.Rproj.user
+.Rhistory
+.RData
+.Ruserdata
+.DS_Store
+*.Rproj
+/doc/
+/Meta/
+docs/
+.DS_Store
+mia.BiocCheck/

R/LimROTS.r

@@ -1,15 +1,12 @@
-#' LimROTS: A Hybrid Method Integrating Empirical Bayes and
+#' `LimROTS`: A Hybrid Method Integrating Empirical Bayes and
 #' Reproducibility-Optimized Statistics for Robust Analysis of Proteomics and
 #' Metabolomics Data
 #'
-#' The `LimROTS` function employs a reproducibility-optimized test statistic
-#' utilising the limma and ROTS methodology to simulate complex experimental
-#' designs.
-#'
 #' @param x A \code{SummarizedExperiment} object or a matrix where rows
-#' represent features (e.g., genes, proteins) and columns represent samples.
+#' represent features (e.g., proteins, metabolites) and columns
+#' represent samples.
 #' The values should be log-transformed.
-#' @param B An integer specifying the number of bootstrap iterations.
+#' @param B An integer representing the amount of bootstrap iterations.
 #' Default is 1000.
 #' @param K An optional integer representing the top list size for ranking.
 #' If not specified, it is set to one-fourth of the number of features.
@@ -19,8 +16,8 @@
 #' If defined by the user, no optimization occurs.
 #' @param log Logical, indicating whether the data is already log-transformed.
 #' Default is \code{TRUE}.
-#' @param progress Logical, indicating whether to display a progress bar during
-#' bootstrap sampling. Default is \code{FALSE}.
+#' @param progress Logical, indicating whether to display a progress bar the
+#' function's execution. Default is \code{FALSE}.
 #' @param verbose Logical, indicating whether to display messages during the
 #' function's execution. Default is \code{TRUE}.
 #' @param meta.info A data frame containing sample-level metadata, where each
@@ -34,29 +31,33 @@
 #' if not provided, the default is 1234.
 #' @param cluster A parallel cluster object for distributed computation,
 #' e.g., created by \code{makeCluster()}. Default is 2.
-#' @param formula.str A formula string used when covariates are present in meta.
-#' info for modeling. It should include "~ 0 + ..." to exclude the
-#' intercept from the model.
+#' @param formula.str A formula string for modeling.
+#' It should include "~ 0 + ..." to exclude the intercept from the model.
+#' All the model parameters must be present in \code{meta.info}.
 #' @param robust indicating whether robust fitting should be used.
 #' Default is TRUE, see \link{eBayes}.
 #' @param trend indicating whether to include trend fitting in the
 #' differential expression analysis. Default is TRUE. see \link{eBayes}.
 #' @param permutating.group Logical, If \code{TRUE}, the permutation for
-#' calculating the null distribution is performed by permuting the target
-#' group only specified in \code{group.name}. If FALSE, the entire
-#' \code{meta.info} will be permuted (recommended to be set to FALSE).
+#' calculating the null distribution is performed by permuting the target group
+#' only specified in \code{group.name} Preserving all the other sample
+#' information. If `FALSE`, the entire sample information retrieved from
+#' \code{meta.info} will be permuted (recommended to be set to TRUE).
+#'
 #'
-#' @return A list of class `"list"` with the following elements:
+#' @return An object of class `"list"` with the following elements:
 #' \item{data}{The original data matrix.}
 #' \item{B}{The number of bootstrap samples used.}
-#' \item{d}{Differential expression statistics for each feature.}
+#' \item{d}{The optimized statistics for each feature.}
 #' \item{logfc}{Log-fold change values between groups.}
 #' \item{pvalue}{P-values computed based on the permutation samples.}
 #' \item{FDR}{False discovery rate estimates.}
 #' \item{a1}{Optimized parameter used in differential expression ranking.}
 #' \item{a2}{Optimized parameter used in differential expression ranking.}
 #' \item{k}{Top list size used for ranking.}
-#' \item{corrected.logfc}{Corrected log-fold change values, if applicable.}
+#' \item{corrected.logfc}{estimate of the log2-fold-change
+#' corresponding to the effect corrected by the s model
+#' see \link{topTable}.}
 #' \item{q_values}{Estimated q-values using the `qvalue` package.}
 #' \item{BH.pvalue}{Benjamini-Hochberg adjusted p-values.}
 #'
@@ -82,22 +83,27 @@
 #' @importFrom qvalue empPvals qvalue
 #' @import utils
 #'
-#' @details The **LimROTS** approach initially uses the
-#' \link{limma} package to simulate the intensity data of proteins and
+#' @details The **LimROTS** approach initially uses
+#' \link{limma} package functionality to simulate the intensity data of
+#' proteins and
 #' metabolites. A linear model is subsequently fitted using the design matrix.
 #' Empirical Bayes variance shrinking is then implemented. To obtain the
 #' moderated t-statistics, the adjusted standard error
 #' \eqn{SEpost = √(s2.post)
 #' \times unscaled SD} for each feature is computed, along with the regression
 #' coefficient for each feature (indicating the impact of variations in the
-#' experimental settings). The \link[ROTS]{ROTS} approach establishes optimality
+#' experimental settings). Then, by adapting a reproducibility-optimized
+#' technique known as \link[ROTS]{ROTS} to establish an optimality
 #' based on the largest overlap of top-ranked features within group-preserving
 #' bootstrap datasets, Finally based on the optimized parameters
 #' \eqn{\alpha1} and
 #' \eqn{\alpha2} this equation used to calculates the final statistics:
-#' \deqn{t_{\alpha}(g) = \frac{\beta}{\alpha1 + \alpha2 \times SEpost}}where
-#'          \eqn{t_{\alpha}(g)} is the final statistics for each feature,
-#'          \eqn{\beta} is the coefficient, and SEpost is the the adjusted
+#'
+#' \deqn{t_{\alpha_{(p)}} = \frac{\beta_{(p)}}
+#'          {\alpha1 + \alpha2 \times SEpost_{(p)}}}where
+#'          \eqn{t_{\alpha_{(p)}}} is the final statistics for each feature,
+#'          \eqn{\beta_{(p)}} is the coefficient, and \eqn{SEpost_{(p)}}
+#'          is the the adjusted
 #'          standard error. LimROTS generates p-values from permutation samples
 #'          using the implementation available in
 #'          \link{qvalue} package, along with internal implementation of FDR

vignettes/.gitignore

@@ -1 +1,3 @@
 /.quarto/
+*.html
+*.R