CRAN submission checks

DESCRIPTION

@@ -12,7 +12,7 @@ Description:
     The core limiric function processes raw scRNA-seq alignment files to predict 
     which barcodes correspond to damaged cells and outputs these 
     predictions in a csv file alongside a filtered Seurat object.
-License: GPL-3
+License: MIT + file LICENSE
 Encoding: UTF-8
 Roxygen: list(markdown = TRUE)
 RoxygenNote: 7.3.2

LICENSE

@@ -0,0 +1,2 @@
+YEAR: 2024
+COPYRIGHT HOLDER: Alicen Henning

LICENSE.note

@@ -0,0 +1,6 @@
+The `limiric` package is licensed under the MIT license.
+
+This package also includes code derived from the DropletQC package, which is licensed under the MIT license.
+The relevant code is clearly marked in the source files. The full text of the MIT license can be found in the file `LICENSE.md`.
+
+Users of the package must comply with the terms of the MIT license.

NAMESPACE

@@ -1,5 +1,6 @@
 # Generated by roxygen2: do not edit by hand
 
+export(assess_EM)
 export(create_dropletqc_plot)
 export(create_imc_plot)
 export(create_plot_grid)

R/assess_EM.R

@@ -38,6 +38,18 @@
 #' @importFrom utils data write.csv
 #'
 #' @keywords internal
+#' 
+#' @examplesIf interactive()
+#' # Example usage:
+#' # Assuming `em_result` is the result of an EM model on log10(UMI counts) and nf
+#' # and you have set thresholds for UMI and nuclear fraction.
+#' em_result <- Mclust(data = your_data)  # Replace `your_data` with actual data
+#' umi_threshold <- 30
+#' nf_threshold <- 0.1
+#' result <- assess_EM(em_result, umi_threshold, nf_threshold)
+#' print(result)
+#'
+#' @export
 
 utils::globalVariables(c("nf_means", "umi_means", "nf_check",
                          "umi_check", "check_1", "check_2",

R/create_dropletqc_plot.R

@@ -13,7 +13,7 @@
 #' @param damaged_percent A numeric value representing the percentage of damaged cells.
 #' @param initial_cells An integer representing the initial number of cells.
 #'
-#' @return A list containing the Seurat object and the limiric object.
+#' @return None. The function saves the plots as PNG files.
 #'
 #' @import cowplot
 #' @importFrom dplyr %>% pull group_by summarise mutate arrange slice case_when
@@ -25,6 +25,27 @@
 #' @export
 #'
 #' @keywords internal
+#'
+#' @examples
+#' \dontrun{
+#' # Assuming `seurat_obj` is a Seurat object
+#' # and `limiric_obj` is a Seurat object
+#'
+#' # Define inputs
+#' seurat_obj <- CreateSeuratObject(counts = your_counts)
+#' limiric_obj <- CreateSeuratObject(counts = your_lim_counts)
+#' output_path <- "path/to/save/plots"
+#' project_name <- "MyProject"
+#' damaged_percent <- 5.0
+#' initial_cells <- 1000
+#'
+#' create_dropletqc_plot(seurat_obj,
+#'                       limiric_obj,
+#'                       output_path,
+#'                       project_name,
+#'                       damaged_percent,
+#'                       initial_cells)
+#' }
 
 utils::globalVariables(c("QC", "nf", "nCount_RNA"))
 

R/create_imc_plot.R

@@ -17,6 +17,21 @@
 #' @export
 #'
 #' @keywords internal
+#' 
+#' @examples
+#' \dontrun{
+#' # Example usage:
+#' # Assuming `imc_data` is a data frame containing the immune cell data.
+#' imc_data <- data.frame(
+#'   nCount_RNA = runif(100, min = 500, max = 10000),
+#'   nFeature_RNA = runif(100, min = 200, max = 3000),
+#'   IMC = sample(c("IMC", "non-IMC"), 100, replace = TRUE)
+#' )
+#' project_name <- "ImmuneFeatureQC"
+#' IMC_percent <- 25.0
+#' plot <- create_imc_plot(imc_data, project_name, IMC_percent)
+#' print(plot)
+#' }
 
 utils::globalVariables(c("IMC", "nCount_RNA", "nFeature_RNA"))
 

R/create_plot_grid.R

@@ -17,6 +17,21 @@
 #' @export
 #'
 #' @keywords internal
+#' 
+#' @examples
+#' \dontrun{
+#' # Example usage:
+#' # Assuming `plot_list` is a list of ggplot objects.
+#' plot_list <- list(
+#'   ggplot(mtcars, aes(x = mpg, y = wt)) + geom_point(),
+#'   ggplot(mtcars, aes(x = hp, y = qsec)) + geom_point(),
+#'   ggplot(mtcars, aes(x = drat, y = wt)) + geom_point()
+#' )
+#' file_path <- "path/to/save/plot_grid.png"
+#' nrow <- 2
+#' ncol <- 2
+#' create_plot_grid(plot_list, file_path, nrow, ncol)
+#' }
 
 create_plot_grid <- function(plots, file_path, nrow, ncol = 5) {
   plot_grid <- plot_grid(plotlist = plots, ncol = ncol, nrow = nrow)

R/create_rbc_plot.R

@@ -18,6 +18,22 @@
 #' @export
 #'
 #' @keywords internal
+#' 
+#' @examples
+#' \dontrun{
+#' # Assuming `rbc_data` is a data frame containing the RBC data.
+#' rbc_data <- data.frame(
+#'   hemo.percent = runif(100, min = 0, max = 100),
+#'   ptprc.percent = runif(100, min = 0, max = 100),
+#'   RBC = sample(c("RBC", "non-RBC"), 100, replace = TRUE)
+#' )
+#' project_name <- "RBC Contamination Analysis"
+#' RBC_percent <- 15.0
+#' initial_cells <- 1000
+#' 
+#' plot <- create_rbc_plot(rbc_data, project_name, RBC_percent, initial_cells)
+#' print(plot)
+#' }
 
 utils::globalVariables(c("RBC"))
 

R/dropletqc_calculation.R

@@ -16,6 +16,15 @@
 #' @export
 #'
 #' @keywords internal
+#' 
+#' @examples
+#' \dontrun{
+#' # Example usage:
+#' # Assuming `seurat_obj` is a Seurat object containing the single-cell RNA-seq data.
+#' seurat_obj <- CreateSeuratObject(counts = your_counts)  # Replace `your_counts` with actual data
+#' updated_seurat_obj <- dropletqc_calculation(seurat_obj)
+#' print(updated_seurat_obj)
+#' }
 
 utils::globalVariables(c("nf", "nCount_RNA", "cell_status"))
 

R/identify_damage_cells.R

@@ -52,6 +52,23 @@
 #' @import stats
 #'
 #' @keywords internal
+#'
+#' @examples
+#' \dontrun{
+#' # Example usage:
+#' # Assuming `nf_umi_ed_ct` is a data frame with the required structure.
+#' nf_umi_ed_ct <- data.frame(
+#'   nf = runif(100, min = 0, max = 1),
+#'   umi = sample(100:10000, 100, replace = TRUE),
+#'   ed = sample(c("cell", "empty_droplet"), 100, replace = TRUE),
+#'   ct = sample(c("type1", "type2"), 100, replace = TRUE)
+#' )
+#' nf_sep <- 0.15
+#' umi_sep_perc <- 50
+#' verbose <- TRUE
+#' result <- identify_damage_cells(nf_umi_ed_ct, nf_sep, umi_sep_perc, verbose)
+#' print(result)
+#' }
 
 utils::globalVariables(c("nf_umi"))
 

R/identify_empty_droplets.R

@@ -41,6 +41,32 @@
 #' @export
 #'
 #' @keywords internal
+#'
+#' @examples
+#' \dontrun{
+#' # Assuming `nf_umi` is a data frame with the required structure.
+#' nf_umi <- data.frame(
+#'   nf = runif(100, min = 0, max = 1),
+#'   umi = sample(100:10000, 100, replace = TRUE)
+#' )
+#' nf_rescue <- 0.05
+#' umi_rescue <- 1000
+#' plot_name <- "empty_droplets_plot"
+#' plot_path <- "path/to/save/plot"
+#' plot_width <- 18
+#' plot_height <- 13
+#' pdf_png <- "png"
+#'
+#' result <- identify_empty_droplets(nf_umi,
+#'                                   nf_rescue,
+#'                                   umi_rescue,
+#'                                   plot_name,
+#'                                   plot_path,
+#'                                   plot_width,
+#'                                   plot_height,
+#'                                   pdf_png)
+#' print(result)
+#' }
 
 utils::globalVariables(c("umi", "nf", "non_integer_examples", "kdde_0", "kdde_1", "gradient_sign", "nf_cutoff"))
 

R/limiric_calculation.R

@@ -25,6 +25,27 @@
 #' @export
 #'
 #' @keywords internal
+#'
+#' @examples
+#' \dontrun{
+#' # Assuming `seurat_obj` is a Seurat object
+#' seurat_obj <- CreateSeuratObject(counts = your_counts)
+#' annotations <- data.frame(
+#'   gene_name = c("MT-CO1", "RPS3", "RPL5"),
+#'   gene_biotype = c("protein_coding", "protein_coding", "protein_coding"))
+#' organism <- "Hsap"
+#' initial_cells <- 1000
+#' project_name <- "MyProject"
+#' output_path <- "path/to/save/plots"
+#'
+#' result <- limiric_calculation(organism,
+#'                               seurat_obj,
+#'                               annotations,
+#'                               initial_cells,
+#'                               project_name,
+#'                               output_path)
+#' print(result)
+#' }
 
 utils::globalVariables(c("mt_plot", "complexity_plot", "rb_plot",
                          "cluster_plot", "logo", "limiric", "Cells"))

R/soupx_calculation.R

@@ -23,6 +23,18 @@
 #' @export
 #'
 #' @keywords internal
+#' 
+#' @examples
+#' \dontrun{
+#' # Assuming `raw_path` is the path to the raw data and `table_of_counts` is a matrix of counts.
+#' raw_path <- "path/to/raw/data"
+#' table_of_counts <- matrix(sample(0:100, 100, replace = TRUE), nrow = 10)
+#' min_cells <- 3
+#' project_name <- "SoupX_Project"
+#' 
+#' result <- soupx_calculation(raw_path, table_of_counts, min_cells, project_name)
+#' print(result)
+#' }
 
 utils::globalVariables(c("SeuratSoup", "meta_data", "umap_embedding", "adj_matrix", "Seurat"))