remove some examples

R/CT_correlated_genes.R

@@ -33,7 +33,6 @@
 #'
 #' @examples
 #' CT_correlated_genes(gene = "MAGEA3")
-#' CT_correlated_genes("TDRD1", 0.3)
 CT_correlated_genes <- function(gene, corr_thr = 0.5,
                                 return = FALSE) {
     suppressMessages({
@@ -42,10 +41,10 @@ CT_correlated_genes <- function(gene, corr_thr = 0.5,
         CCLE_data <- CTdata::CCLE_data()
     })
 
-    stopifnot("Gene name be specified!" = !missing(gene)) 
-    stopifnot("Not a CT gene" = gene %in% CT_genes$external_gene_name) 
+    stopifnot("Gene name be specified!" = !missing(gene))
+    stopifnot("Not a CT gene" = gene %in% CT_genes$external_gene_name)
 
-    ## Need the ensembl_id 
+    ## Need the ensembl_id
     tested_ref <- rownames(CCLE_data[rowData(CCLE_data)$external_gene_name ==
         gene, ])
     tmp <- data.frame(

R/TCGA_expression.R

@@ -34,7 +34,6 @@
 #' TCGA_expression(
 #'     tumor = "LUAD", genes = c("MAGEA1", "MAGEA3"),
 #'     units = "log_TPM")
-#' TCGA_expression(tumor = "all", units = "log_TPM")
 TCGA_expression <- function(tumor = "all", genes = NULL,
                             units = "TPM", return = FALSE) {
     suppressMessages({

R/TCGA_methylation_expression_correlation.R

@@ -6,7 +6,7 @@
 #' Plots the correlation between methylation and expression values of
 #' a Cancer-Testis (CT) gene in TCGA samples.
 #'
-#' @param gene `character` selected gene. 
+#' @param gene `character` selected gene.
 #'
 #' @param tumor `character` defining the TCGA tumor type. Can be one
 #'     of "SKCM", "LUAD", "LUSC", "COAD", "ESCA", "BRCA", "HNSC", or
@@ -19,13 +19,13 @@
 #' @param nt_down `numeric(1)` indicating the number of nucleotides
 #'     downstream the TSS to define the promoter region (200 by
 #'     default)
-#'     
-#' @param min_probe_number `numeric(1)` indicating the minimum 
-#'     number of probes (with methylation values) within the selected region 
+#'
+#' @param min_probe_number `numeric(1)` indicating the minimum
+#'     number of probes (with methylation values) within the selected region
 #'     to calculate its mean methylation level. Default is 3.
-#'     
-#' @param include_normal_tissues `logical(1)`. If `TRUE`, 
-#'     the function will include normal peritumoral tissues in addition to 
+#'
+#' @param include_normal_tissues `logical(1)`. If `TRUE`,
+#'     the function will include normal peritumoral tissues in addition to
 #'     tumoral samples. Default is `FALSE`.
 #'
 #' @param return `logical(1)`. If `TRUE`, the function will return the
@@ -36,11 +36,11 @@
 #'     are found in promoter regions or if less than 1% of tumors are
 #'     positive (TPM >= 1) for the gene.
 #'
-#' @return A scatter plot representing for each TCGA sample, gene expression 
-#'     and mean methylation values of probe(s) located in its promoter region 
+#' @return A scatter plot representing for each TCGA sample, gene expression
+#'     and mean methylation values of probe(s) located in its promoter region
 #'     (defined as 1000 nucleotides upstream TSS and 200 nucleotides downstream
-#'     TSS by default). If return = TRUE, methylation and expression values 
-#'     are returned in a tibble instead. 
+#'     TSS by default). If return = TRUE, methylation and expression values
+#'     are returned in a tibble instead.
 #'
 #' @export
 #'
@@ -51,8 +51,6 @@
 #'
 #' @examples
 #' TCGA_methylation_expression_correlation("LUAD", gene = "TDRD1")
-#' TCGA_methylation_expression_correlation("all", gene = "MAGEA1", 
-#'  min_probe_number = 1, return = FALSE)
 TCGA_methylation_expression_correlation <- function(
     tumor = "all",
     gene = NULL,
@@ -63,31 +61,31 @@ TCGA_methylation_expression_correlation <- function(
     return = FALSE) {
 
     methylation_expression <- prepare_TCGA_methylation_expression(
-      tumor = tumor, 
+      tumor = tumor,
       gene = gene,
       nt_up = nt_up,
       nt_down = nt_down,
       include_normal_tissues = include_normal_tissues)
-    
+
     methylation_expression <- methylation_expression[
       methylation_expression$probe_number >= min_probe_number, ]
     methylation_expression <- methylation_expression[
       !is.na(methylation_expression$met), ]
-    
-    if (nrow(methylation_expression) == 0) stop("less than ",  
-                                                min_probe_number, 
+
+    if (nrow(methylation_expression) == 0) stop("less than ",
+                                                min_probe_number,
                                                 " probes in selected region")
-    
-    ## Evaluate correlation only if the gene is expressed (TPM >= 1) 
+
+    ## Evaluate correlation only if the gene is expressed (TPM >= 1)
     ## in at least 1% of the samples
     if (quantile(methylation_expression$TPM, 0.99) < 1) {
       message("Too few positive samples to estimate a correlation for ", gene)
       cor <- NA
     }
-    
+
     if (!is.na(cor)) cor <- cor.test(methylation_expression$met,
                                      log1p(methylation_expression$TPM))$estimate
-  
+
     p <- ggplot(
             as_tibble(methylation_expression[order(methylation_expression$type,
                 decreasing = TRUE), ]),
@@ -104,4 +102,3 @@ TCGA_methylation_expression_correlation <- function(
 
     return(p)
 }
-

R/testis_expression.R

@@ -38,7 +38,6 @@
 #'
 #' testis_expression(cells = "germ_cells",
 #'                   genes = c("MAGEA1", "MAGEA3", "MAGEA4"))
-#' testis_expression(cells = "all", scale_lims = c(0, 4))
 testis_expression <- function(cells = "all", genes = NULL,
                               scale_lims = NULL, return = FALSE) {
     suppressMessages({

R/utils.R

@@ -40,18 +40,19 @@ check_names <- function(variable, valid_vector) {
 #'
 #' Check the presence of the genes in the database then subsets the database
 #' to only keep these genes' data.
-#' 
+#'
 #' @param variable `character()` containing the names genes to keep in the
 #' data
 #'
 #' @param data `Summarized Experiment` or `SingleCellExperiment` object
 #' with valid variable names.
 #'
-#' @return A `Summarized Experiment` or `SingleCellExperiment` object with 
+#' @return A `Summarized Experiment` or `SingleCellExperiment` object with
 #' only the variables data
 #'
 #' @examples
-#' CTexploreR:::subset_database(variable = "MAGEA1", data = CTdata::GTEX_data())
+#'
+#'   CTexploreR:::subset_database(variable = "MAGEA1", data = CTdata::GTEX_data())
 subset_database <- function(variable = NULL, data) {
   if (is.null(variable)) variable <- CTdata::CT_genes()$external_gene_name
   valid_gene_names <- unique(rowData(data)$external_gene_name)
@@ -68,7 +69,7 @@ subset_database <- function(variable = NULL, data) {
 #' a gene (mean methylation of probes located in its promoter) and the
 #' expression level of the gene (TPM value).
 #'
-#' @param gene `character` selected CT gene. 
+#' @param gene `character` selected CT gene.
 #'
 #' @param tumor `character` defining the TCGA tumor type. Can be one
 #'     of "SKCM", "LUAD", "LUSC", "COAD", "ESCA", "BRCA", "HNSC", or
@@ -82,10 +83,10 @@ subset_database <- function(variable = NULL, data) {
 #'     downstream the TSS to define the promoter region (200 by
 #'     default)
 #'
-#' @param include_normal_tissues `logical(1)`. If `TRUE`, 
-#'     the function will include normal peritumoral tissues in addition 
+#' @param include_normal_tissues `logical(1)`. If `TRUE`,
+#'     the function will include normal peritumoral tissues in addition
 #'     to tumoral samples. Default is `FALSE`.
-#'     
+#'
 #' @return a Dataframe giving for each TCGA sample, the methylation level of
 #' a gene (mean methylation of probes located in its promoter) and the
 #' expression level of the gene (TPM value). The number of probes used to
@@ -100,7 +101,9 @@ subset_database <- function(variable = NULL, data) {
 #' @importFrom rlang .data
 #'
 #' @examples
-#' CTexploreR:::prepare_TCGA_methylation_expression("LUAD", gene = "TDRD1")
+#' \dontrun{
+#'   CTexploreR:::prepare_TCGA_methylation_expression("LUAD", gene = "TDRD1")
+#' }
 prepare_TCGA_methylation_expression <- function(tumor = "all",
                                                 gene = NULL,
                                                 nt_up = NULL,
@@ -111,7 +114,7 @@ prepare_TCGA_methylation_expression <- function(tumor = "all",
     TPM <- CTdata::TCGA_TPM()
     met <- CTdata::TCGA_CT_methylation()
   })
-  
+
   TPM$type <- sub("TCGA-", "", TPM$project_id)
   met$type <- sub("TCGA-", "", met$project_id)
   valid_tumor <- c(unique(TPM$type), "all")
@@ -121,27 +124,27 @@ prepare_TCGA_methylation_expression <- function(tumor = "all",
     TPM <- TPM[, TPM$type %in% type]
     met <- met[, met$type %in% type]
   }
-  
+
   valid_gene_names <- CT_genes$external_gene_name
   valid_gene_names <- valid_gene_names[valid_gene_names %in%
                                          rowData(TPM)$external_gene_name]
   gene <- check_names(gene, valid_gene_names)
   stopifnot("No valid gene name" = length(gene) == 1)
   TPM <- TPM[rowData(TPM)$external_gene_name %in% gene, ]
-  
+
   ## Rm duplicated samples
   TPM <- TPM[, !duplicated(TPM$sample)]
   met <- met[, !duplicated(met$sample)]
-  
+
   ## keep tumors for which expression and methylation data are available
   samples <- intersect(TPM$sample, met$sample)
   TPM <- TPM[, TPM$sample %in% samples]
   met <- met[, met$sample %in% samples]
-  
+
   if (is.null(nt_up)) nt_up <- 1000
   if (is.null(nt_down)) nt_down <- 200
-  
-  ## Create a Grange corresponding to promoter region 
+
+  ## Create a Grange corresponding to promoter region
   ## Calculates mean methylation value of promoter probe(s) in each sample
   gene_promoter_gr <- makeGRangesFromDataFrame(
     CT_genes |>
@@ -166,35 +169,35 @@ prepare_TCGA_methylation_expression <- function(tumor = "all",
     seqnames.field = "chr",
     start.field = "start",
     end.field = "stop")
-  
+
   met_roi <- subsetByOverlaps(met, gene_promoter_gr)
   met_mean <- colMeans(assay(met_roi), na.rm = TRUE)
-  probe_number <- colSums(!is.na(assay(met_roi))) 
-  
+  probe_number <- colSums(!is.na(assay(met_roi)))
+
   ## Keep prefix of TCGA sample names (TCGA-XX-XXXX-XXX) to join
   ## expression and methylation data
   names(met_mean) <- substr(names(met_mean), 1, 16)
   colnames(TPM) <- substr(colnames(TPM), 1, 16)
-  
+
   methylation_expression <-
     suppressMessages(left_join(
-      tibble(sample = names(met_mean), 
-             met = met_mean, 
+      tibble(sample = names(met_mean),
+             met = met_mean,
              probe_number = probe_number),
-      tibble(sample = colnames(TPM), 
+      tibble(sample = colnames(TPM),
              tissue = ifelse(TPM$shortLetterCode == "NT",
                              "Peritumoral", "Tumor"),
              type = TPM$type,
              TPM = as.vector(assay(TPM)))))
-  
-  if (!include_normal_tissues) methylation_expression <- 
+
+  if (!include_normal_tissues) methylation_expression <-
     methylation_expression[methylation_expression$tissue != "Peritumoral", ]
-  
-  
-  methylation_expression <- dplyr::select(methylation_expression, "sample", 
-                                          "tissue", "type", "probe_number", 
+
+
+  methylation_expression <- dplyr::select(methylation_expression, "sample",
+                                          "tissue", "type", "probe_number",
                                           "met", "TPM")
-  
+
 }
 
 
@@ -330,5 +333,3 @@ cell_type_colors <- c(
     "Mesenchymal_cells" = "peachpuff",
     "Pigment_cells" = "palegreen2", "Myoid" = "gray54",
     "Specialized_epithelial_cells" = "indianred1")
-
-