Add examples again: trying to make BiocCheck work

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: anglemania
 Title: Feature Extraction for scRNA-seq Dataset Integration
-Version: 0.0.0.9000
+Version: 0.99.0
 Description: anglemania extracts genes from multi-batch scRNA-seq experiments for downstream dataset integration. It improves conventional usage of highly-variable genes for integration tasks.
 Authors@R: c(
     person("Aaron", "Kollotzek", email = "[email protected]", role = c("aut", "cre"),
@@ -16,6 +16,7 @@ Roxygen: list(markdown = TRUE)
 RoxygenNote: 7.3.2
 Imports:
     Matrix,
+    stats,
     checkmate,
     Seurat,
     SeuratObject,
@@ -31,6 +32,6 @@ Suggests:
     covr,
     DT
 VignetteBuilder: knitr
-biocViews: SingleCell, BatchEffect, MultipleComparison
+biocViews: SingleCell, BatchEffect, MultipleComparison, FeatureExtraction
 BiocType: Software
 Config/testthat/edition: 3

NAMESPACE

@@ -1,10 +1,6 @@
 # Generated by roxygen2: do not edit by hand
 
 export("%>%")
-export("angl_weights<-")
-export("intersect_genes<-")
-export("list_stats<-")
-export("matrix_list<-")
 export(add_unique_batch_key)
 export(angl_weights)
 export(anglemania)
@@ -58,4 +54,5 @@ importFrom(dplyr,select)
 importFrom(magrittr,"%>%")
 importFrom(pbapply,pblapply)
 importFrom(pbapply,pboptions)
+importFrom(stats,quantile)
 importFrom(tidyr,unite)

R/anglemania.R

@@ -54,9 +54,16 @@
 #'   \url{https://arxiv.org/abs/1306.0256}
 #'
 #' @examples
-#' \dontrun{
+#' \donttest{
+#' load(system.file(
+#'  "extdata",
+#'  "seurat_splatter_sim.RData",
+#'  package = "anglemania"))
 #' 
-#' # Assuming you have an anglemania_object already created
+#' angl <- create_anglemaniaObject(se,
+#'  batch_key = batch_key,
+#'  min_cells_per_gene = 1
+#'  )
 #'
 #' angl <- anglemania(
 #'   angl,
@@ -67,9 +74,9 @@
 #' )
 #'
 #' # Access the selected genes
-#' selected_genes <- extract_integration_genes(angl)
+#' selected_genes <- get_anglemania_genes(angl)
 #' }
-#'
+#' selected_genes[1:10]
 #' @export
 anglemania <- function(
     anglemania_object,

R/anglemanise_utils.R

@@ -12,6 +12,11 @@
 #' @return An \code{\link[bigstatsr]{FBM}} object from the \pkg{bigstatsr}
 #'  package.
 #' @importFrom bigstatsr FBM
+#' @examples
+#' s_mat <- Matrix::rsparsematrix(nrow = 10, ncol = 5, density = 0.3)
+#' # Convert the sparse matrix to an FBM using your function
+#' fbm_mat <- sparse_to_fbm(s_mat)
+#' fbm_mat
 #' @export
 sparse_to_fbm <- function(s_mat) {
   n <- nrow(s_mat)
@@ -41,6 +46,13 @@ sparse_to_fbm <- function(s_mat) {
 #' @return A list with statistical measures including \code{mean}, \code{sd},
 #'   \code{var}, \code{sn}, \code{min}, and \code{max}.
 #' @importFrom bigstatsr big_apply
+#' @examples
+#' s_mat <- Matrix::rsparsematrix(nrow = 10, ncol = 5, density = 0.3)
+#' # Convert the sparse matrix to an FBM using your function
+#' fbm_mat <- sparse_to_fbm(s_mat)
+#' result <- get_dstat(fbm_mat)
+#' str(result)
+#' result
 #' @seealso \code{\link[bigstatsr]{big_apply}}, \code{\link[bigstatsr]{FBM}}
 #' @export
 get_dstat <- function(corr_matrix) {
@@ -129,6 +141,29 @@ get_dstat <- function(corr_matrix) {
 #'  In this case, the FBMs are the angle matrices computed in \code{factorise}.
 #' @return A new \code{\link[bigstatsr]{FBM}} object containing the mean values.
 #' @importFrom bigstatsr FBM
+#' @examples
+#' \donttest{
+#' # Create FBMs
+#' mat1 <- matrix(1:9, nrow = 3)
+#' mat2 <- matrix(1:3, nrow = 3)
+#'
+#' fbm1 <- bigstatsr::FBM(nrow = nrow(mat1), ncol = ncol(mat1), init = mat1)
+#' fbm2 <- bigstatsr::FBM(nrow = nrow(mat2), ncol = ncol(mat2), init = mat2)
+#'
+#' # Create weights
+#' weights <- c(batch1 = 0.5, batch2 = 0.5)
+#'
+#' # Create the list of FBMs
+#' fbm_list <- list(batch1 = fbm1, batch2 = fbm2)
+#'
+#' # Construct the anglemaniaObject
+#' anglemania_object <- new(
+#'   "anglemaniaObject",
+#'   weights = weights,
+#'   matrix_list = fbm_list
+#' )
+#' big_mat_list_mean(anglemania_object)
+#' }
 #' @export
 big_mat_list_mean <- function(anglemania_object) {
   if (!inherits(anglemania_object, "anglemaniaObject")) {
@@ -186,6 +221,18 @@ big_mat_list_mean <- function(anglemania_object) {
 #' @return A list containing three matrices: \code{mean_zscore},
 #'   \code{sds_zscore}, and \code{sn_zscore}.
 #' @importFrom bigstatsr FBM big_apply
+#' @examples
+#' \donttest{
+#' load(system.file(
+#' "extdata",
+#'  "seurat_splatter_sim.RData",
+#'  package = "anglemania")
+#' )
+#' anglemania_object <- create_anglemaniaObject(se, batch_key = "Batch")
+#' anglemania_object <- anglemania(anglemania_object)
+#' list_stats(anglemania_object) <- get_list_stats(anglemania_object)
+#' str(list_stats(anglemania_object))
+#' }
 #' @seealso \code{\link[bigstatsr]{big_apply}}, \code{\link[bigstatsr]{FBM}}
 #' @export
 get_list_stats <- function(anglemania_object) {
@@ -278,7 +325,7 @@ get_list_stats <- function(anglemania_object) {
 #' \code{max_n_genes} exceeds the number of unique genes available, all unique
 #' genes are returned.
 #' @examples
-#' \dontrun{
+#' \donttest{
 #' gene_pairs <- data.frame(
 #'   geneA = c("Gene1", "Gene2", "Gene3", "Gene4"),
 #'   geneB = c("Gene3", "Gene4", "Gene5", "Gene6")
@@ -323,6 +370,7 @@ extract_rows_for_unique_genes <- function(dt, max_n_genes) {
 #' @return The input \code{anglemaniaObject} with the
 #'   \code{integration_genes} slot updated to include the selected genes and
 #'   their statistical information.
+#' @importFrom stats quantile
 #' @details
 #' The function performs the following steps:
 #' \enumerate{
@@ -339,6 +387,15 @@ extract_rows_for_unique_genes <- function(dt, max_n_genes) {
 #'   \code{anglemaniaObject}
 #'     with the selected genes and their statistics.
 #' }
+#' @examples
+#' \donttest{
+#' angl <- select_genes(angl,
+#'                       zscore_mean_threshold = 2,
+#'                      zscore_sn_threshold = 2,
+#'                      max_n_genes = 2000)
+#' anglemania_genes <- get_anglemania_genes(angl)
+#' # View the selected genes and use for integration
+#' }
 #' @seealso \code{\link{extract_rows_for_unique_genes}},
 #'   \code{\link{intersect_genes}}, \code{\link{list_stats}}
 #' @export
@@ -366,12 +423,12 @@ select_genes <- function(
   # Adjust thresholds if no genes passed the cutoff
   if (nrow(gene_ind) == 0) {
     message("No genes passed the cutoff.")
-    quantile95mean <- quantile(
+    quantile95mean <- stats::quantile(
       abs(list_stats(anglemania_object)$mean_zscore),
       0.95,
       na.rm = TRUE
     )
-    quantile95sn <- quantile(
+    quantile95sn <- stats::quantile(
       list_stats(anglemania_object)$sn_zscore,
       0.95,
       na.rm = TRUE

R/extract_angles.R

@@ -28,7 +28,27 @@
 #'   The diagonal elements are set to \code{NA}.
 #'
 #' @importFrom bigstatsr FBM big_apply big_transpose big_cor
+#' @examples 
+#' \donttest{
+#' mat <- matrix(
+#'  c(
+#'      5, 3, 0, 0,
+#'      0, 0, 0, 3,
+#'      2, 1, 3, 4,
+#'      0, 0, 1, 0,
+#'      1, 2, 1, 2,
+#'      3, 4, 3, 4
+#'    ),
+#'    nrow = 6, # 6 genes
+#'    ncol = 4, # 4 cells
+#'    byrow = TRUE
+#' )
 #'
+#' mat <- bigstatsr::FBM(nrow = nrow(mat), ncol = ncol(mat), init = mat)
+#'
+#' angle_mat <- extract_angles(mat)
+#' angle_mat[]
+#' }
 #' @seealso
 #' \code{\link[bigstatsr]{big_apply}},
 #' \code{\link[bigstatsr]{big_cor}},

R/factorise.R

@@ -45,6 +45,28 @@
 #' @importFrom bigstatsr FBM big_apply
 #' @importFrom checkmate assertClass assertString assertChoice
 #'
+#' @examples
+#' \donttest{
+#' mat <- matrix(
+#'  c(
+#'      5, 3, 0, 0,
+#'      0, 0, 0, 3,
+#'      2, 1, 3, 4,
+#'      0, 0, 1, 0,
+#'      1, 2, 1, 2,
+#'      3, 4, 3, 4
+#'    ),
+#'    nrow = 6, # 6 genes
+#'    ncol = 4, # 4 cells
+#'    byrow = TRUE
+#' )
+#'
+#' mat <- bigstatsr::FBM(nrow = nrow(mat), ncol = ncol(mat), init = mat)
+#'
+#' # Run factorise with method "pearson" and a fixed seed
+#' result_fbm <- factorise(mat, method = "pearson", seed = 1)
+#' result_fbm[]
+#' }
 #' @seealso
 #' \code{\link{extract_angles}},
 #' \code{\link{get_dstat}},

R/integrate_by_features.R

@@ -10,7 +10,7 @@
 #' `integrate_by_features` integrates samples or batches within a Seurat
 #' object using canonical correlation analysis (CCA) based on a set of
 #' selected features (genes). The function utilizes an `anglemaniaObject` to
-#' extract integration genes and handles the integration process, including
+#' extract anglemania genes and handles the integration process, including
 #' optional downstream processing steps such as scaling, PCA, and UMAP
 #' visualization.
 #'
@@ -35,7 +35,7 @@
 #'
 #' @param seurat_object A \code{\link[Seurat]{Seurat}} object containing
 #'   all samples or batches to be integrated.
-#' @param anglem_object An \code{\link{anglemaniaObject}} previously generated
+#' @param anglemania_object An \code{\link{anglemaniaObject}} previously generated
 #'   using \code{\link{create_anglemaniaObject}} and \code{\link{anglemania}}.
 #'   It is important that the \code{dataset_key} and \code{batch_key} are
 #'   correctly set in the \code{anglemaniaObject}.
@@ -57,11 +57,19 @@
 #'   IntegrateData ScaleData RunPCA RunUMAP DefaultAssay
 #' @importFrom pbapply pblapply
 #' @importFrom checkmate assertClass assertLogical testFALSE
-#'
+#' @examples 
+#' \donttest{
+#' # Integrate samples using anglemaniaObject 
+#' # Automatically reads the batch key from anglemaniaObject
+#' #  splits the seurat object into batches and integrates them
+#' #  using CCA integration and anglemania genes previously extracted
+#' #  with anglemania() or select_genes()
+#' integrated_object <- integrate_by_features(seurat_object, anglemania_object)
+#' }
 #' @seealso
 #' \code{\link{create_anglemaniaObject}},
 #' \code{\link{anglemania}},
-#' \code{\link{extract_integration_genes}},
+#' \code{\link{get_anglemania_genes}},
 #' \code{\link{integrate_seurat_list}},
 #' \code{\link[Seurat]{IntegrateData}},
 #' \code{\link[Seurat]{FindIntegrationAnchors}}
@@ -166,7 +174,14 @@ integrate_by_features <- function(
 #'   ScaleData RunPCA RunUMAP DefaultAssay
 #' @importFrom pbapply pblapply
 #' @importFrom checkmate assertClass assertCharacter assertLogical
-#'
+#' @examples 
+#' \donttest{
+#' # Integrate a list of seurat object using selected 
+#' #  features (e.g. anglemania genes or HVGs)
+#' #  and CCA integration method
+#' seurat_list <- list(seurat_object1, seurat_object2)
+#' integrated_seurat <- integrate_seurat_list(seurat_list, features)
+#' }
 #' @seealso
 #' \code{\link{integrate_by_features}},
 #' \code{\link[Seurat]{IntegrateData}},