remove unnecessary examples + add tutorial

R/anglemanise_utils.R

@@ -125,8 +125,6 @@ get_dstat <- function(corr_matrix) {
 #'
 #' @param anglem_object An \code{anglem} object containing the list of FBMs.
 #' @return A new \code{\link[bigstatsr]{FBM}} object containing the mean values.
-#' @examples
-#' combined_matrix <- big_mat_list_mean(anglem_object)
 #' @importFrom bigstatsr FBM
 big_mat_list_mean <- function(anglem_object) {
   if (!inherits(anglem_object, "anglem")) {
@@ -185,8 +183,6 @@ big_mat_list_mean <- function(anglem_object) {
 #'   \code{sds_zscore}, and \code{sn_zscore}.
 #' @importFrom bigstatsr FBM big_apply
 #' @seealso \code{\link[bigstatsr]{big_apply}}, \code{\link[bigstatsr]{FBM}}
-#' @examples
-#' stats_results <- get_list_stats(anglem_object)
 #' @export
 get_list_stats <- function(anglem_object) {
   if (!inherits(anglem_object, "anglem")) {
@@ -280,8 +276,8 @@ get_list_stats <- function(anglem_object) {
 #' @examples
 #' \dontrun{
 #' gene_pairs <- data.frame(
-#'   geneA = c("Gene1", "Gene2"),
-#'   geneB = c("Gene3", "Gene4")
+#'   geneA = c("Gene1", "Gene2", "Gene3", "Gene4"),
+#'   geneB = c("Gene3", "Gene4", "Gene5", "Gene6")
 #' )
 #' unique_genes <- extract_rows_for_unique_genes(
 #'   gene_pairs,
@@ -337,19 +333,6 @@ extract_rows_for_unique_genes <- function(dt, max_n_genes) {
 #'   \item Updates the \code{integration_genes} slot of the \code{anglem_object}
 #'     with the selected genes and their statistics.
 #' }
-#' @examples
-#' \dontrun{
-#' # Assume anglem_object is already created and contains necessary statistical
-#' # matrices
-#' anglem_object <- select_genes(
-#'   anglem_object,
-#'   zscore_mean_threshold = 2,
-#'   zscore_sn_threshold = 2,
-#'   max_n_genes = 2000
-#' )
-#' # Inspect the selected genes and their statistics
-#' head(anglem_object@integration_genes$info)
-#' }
 #' @seealso \code{\link{extract_rows_for_unique_genes}},
 #'   \code{\link{intersect_genes}}, \code{\link{list_stats}}
 #' @export

R/big_anglemanise.R

@@ -59,17 +59,19 @@
 #'
 #' @examples
 #' \dontrun{
+#' 
 #' # Assuming you have an anglem_object already created
-#' anglem_object <- big_anglemanise(
-#'   anglem_object,
+#'
+#' angl <- big_anglemanise(
+#'   angl,
 #'   method = "pearson",
 #'   zscore_mean_threshold = 2,
 #'   zscore_sn_threshold = 2,
 #'   max_n_genes = 2000
 #' )
 #'
 #' # Access the selected genes
-#' selected_genes <- anglem_object@integration_genes$genes
+#' selected_genes <- extract_integration_genes(angl)
 #' }
 #'
 #' @export

R/big_extract_corr.R

@@ -35,32 +35,6 @@
 #' \code{\link[bigstatsr]{FBM}},
 #' \code{\link{big_factorise}}
 #'
-#' @examples
-#' \dontrun{
-#' # Load necessary library
-#' library(bigstatsr)
-#'
-#' # Create a random gene expression FBM for demonstration
-#' n_genes <- 1000
-#' n_samples <- 500
-#' set.seed(123)
-#' x_mat <- FBM(
-#'   n_genes,
-#'   n_samples,
-#'   init = rpois(n_genes * n_samples, lambda = 5)
-#' )
-#'
-#' # Compute the gene-gene correlation matrix using Pearson correlation
-#' corr_matrix <- big_extract_corr(x_mat, method = "pearson")
-#'
-#' # Compute the gene-gene correlation matrix using Spearman correlation
-#' corr_matrix_spearman <- big_extract_corr(x_mat, method = "spearman")
-#'
-#' # Access a subset of the correlation matrix
-#' corr_subset <- corr_matrix[1:5, 1:5]
-#' print(corr_subset)
-#' }
-#'
 #' @export
 big_extract_corr <- function(
     x_mat,

R/big_factorise.R

@@ -50,33 +50,6 @@
 #' \code{\link[bigstatsr]{big_apply}},
 #' \code{\link[bigstatsr]{FBM}}
 #'
-#' @examples
-#' \dontrun{
-#' # Load necessary packages
-#' library(bigstatsr)
-#'
-#' # Assume x_mat is a normalized and scaled FBM gene expression matrix
-#' # For example, create a random FBM for demonstration
-#' set.seed(123)
-#' n_genes <- 1000
-#' n_samples <- 500
-#' x_mat <- FBM(
-#'   n_genes,
-#'   n_samples,
-#'   init = rnorm(n_genes * n_samples)
-#' )
-#'
-#' # Run big_factorise
-#' zscore_matrix <- big_factorise(
-#'   x_mat = x_mat,
-#'   method = "pearson",
-#'   seed = 123
-#' )
-#'
-#' # View a portion of the z-score matrix
-#' zscore_submatrix <- zscore_matrix[1:5, 1:5]
-#' print(zscore_submatrix)
-#' }
 #' @export
 big_factorise <- function(
     x_mat,

R/integrate_by_features.R

@@ -65,30 +65,6 @@
 #' \code{\link[Seurat]{IntegrateData}},
 #' \code{\link[Seurat]{FindIntegrationAnchors}}
 #'
-#' @examples
-#' \dontrun{
-#' # Load required libraries
-#' library(Seurat)
-#' library(pbapply)
-#'
-#' # Assume seurat_object is a Seurat object containing all samples/batches
-#' # and anglem_object is an anglem object with integration genes identified
-#'
-#' # Integrate the Seurat object using the anglem object
-#' integrated_seurat <- integrate_by_features(
-#'   seurat_object = seurat_object,
-#'   anglem_object = anglem_object,
-#'   process = TRUE,
-#'   verbose = TRUE
-#' )
-#'
-#' # Access the integrated assay
-#' DefaultAssay(integrated_seurat) # Should be "integrated"
-#'
-#' # Visualize UMAP embedding
-#' DimPlot(integrated_seurat, reduction = "umap", group.by = "batch")
-#' }
-#'
 #' @export
 integrate_by_features <- function(
     seurat_object,
@@ -185,30 +161,6 @@ integrate_by_features <- function(
 #' \code{\link[Seurat]{RunPCA}},
 #' \code{\link[Seurat]{RunUMAP}}
 #'
-#' @examples
-#' \dontrun{
-#' # Load required libraries
-#' library(Seurat)
-#' library(pbapply)
-#'
-#' # Assume seurat_list is a list of Seurat objects representing different
-#' # samples and features is a vector of gene names used for integration
-#'
-#' # Integrate the Seurat list
-#' integrated_seurat <- integrate_seurat_list(
-#'   seurat_list = seurat_list,
-#'   features = features,
-#'   process = TRUE,
-#'   verbose = TRUE
-#' )
-#'
-#' # Access the integrated assay
-#' DefaultAssay(integrated_seurat) # Should be "integrated"
-#'
-#' # Visualize UMAP embedding
-#' DimPlot(integrated_seurat, reduction = "umap", group.by = "batch")
-#' }
-#'
 #' @export
 integrate_seurat_list <- function(
     seurat_list,