fix cran issues

R/data.R

@@ -1,41 +1,3 @@
-#' Integrated scRNA-seq reference for breast tissue (subset)
-#'
-#' Integrated scRNA-seq reference using random subset of two scRNA-seq datasets for breast tissue. One sample is from Komen tissue bank.
-#' The other sample is from Gtex snRNA-seq data.
-#'
-#' @format
-#' a \code{\link[Seurat]{Seurat-class}} object with several metadata included:
-#' \describe{
-#'  \item{cellid}{cell barcodes}
-#'  \item{celltypes}{annotated cell types}
-#'  \item{subjectid}{subject id}
-#'  \item{cohort}{data cohort, either "komentissuebank" or "gtex"}
-#' }
-#'
-#' @source
-#' \url{https://gtexportal.org/home/singleCellOverviewPage}
-#' \url{https://pubmed.ncbi.nlm.nih.gov/33763657/}
-"refdata"
-
-
-
-#' Reference scRNA-seq data for mouse cortical cell taxonomy from the Allen Institute (subset)
-#'
-#' The data was generated using SMART-Seq2 protocol. 20 cells were randomly selected for each cell type. 15,000 highly variable
-#' genes were selected.
-#'
-#' @format
-#' a \code{\link[Seurat]{Seurat-class}} object with several metadata included:
-#' \describe{
-#'  \item{cellid}{cell barcodes}
-#'  \item{celltypes}{annotated cell types}
-#'  \item{subjectid}{subject id}
-#' }
-#'
-#' @source
-#' \url{https://www.nature.com/articles/nn.4216}
-"refdata_brain"
-
 #' relationship between average tpm and ffpe artifacts based on a gam model
 #'
 #' The model was learnt using seven paired FFPE-FFzn samples from BBD patients

R/deconvolution.R

@@ -41,7 +41,7 @@
 #' effect of cell type removal from reference.
 #' @param ffpe_artifacts logical value indicating whether to add simulated ffpe artifacts in the bulk data. Only applicable to simulation experiments in
 #' evaluating the effect of FFPE artifacts.
-#' @param model pre-constructed ffpe model data. Can be loaded via \code{data(ffpemodel)}.
+#' @param model pre-constructed ffpe model data. Can be downloaded from github: https://github.com/Liuy12/SCdeconR/blob/master/data/ffpemodel.rda.
 #' @param prop a matrix or data.frame of simulated cell proportion values with rows representing cell types, columns representing samples. Only applicable to simulation
 #' experiments in evaluating the effect of cell type removal from reference.
 #' @param cibersortpath full path to CIBERSORT.R script.
@@ -110,8 +110,23 @@
 #'
 #' @examples
 #' \dontrun{
-#' ## generate artificial bulk samples
-#' data(refdata)
+#' ref_list <- c(paste0(system.file("extdata", package = "SCdeconR"), "/refdata/sample1"),
+#'               paste0(system.file("extdata", package = "SCdeconR"), "/refdata/sample2"))
+#' phenopath1 <- paste0(system.file("extdata", package = "SCdeconR"),
+#' "/refdata/phenodata_sample1.txt")
+#' phenopath2 <- paste0(system.file("extdata", package = "SCdeconR"),
+#' "/refdata/phenodata_sample2.txt")
+#' phenodata_list <- c(phenopath1,phenopath2)
+#'
+#' # construct integrated reference using harmony algorithm
+#' refdata <- construct_ref(ref_list = ref_list,
+#'                       phenodata_list = phenodata_list,
+#'                       data_type = "cellranger",
+#'                       method = "harmony",
+#'                       group_var = "subjectid",
+#'                       nfeature_rna = 50,
+#'                       vars_to_regress = "percent_mt", verbose = FALSE)
+#'
 #' phenodata <- data.frame(cellid = colnames(refdata),
 #'                         celltypes = refdata$celltype,
 #'                         subjectid = refdata$subjectid)

R/differential_expression.R

@@ -28,7 +28,22 @@
 #'
 #' @examples
 #' \dontrun{
-#' data(refdata)
+#' ref_list <- c(paste0(system.file("extdata", package = "SCdeconR"), "/refdata/sample1"),
+#'               paste0(system.file("extdata", package = "SCdeconR"), "/refdata/sample2"))
+#' phenopath1 <- paste0(system.file("extdata", package = "SCdeconR"),
+#' "/refdata/phenodata_sample1.txt")
+#' phenopath2 <- paste0(system.file("extdata", package = "SCdeconR"),
+#' "/refdata/phenodata_sample2.txt")
+#' phenodata_list <- c(phenopath1,phenopath2)
+#'
+#' # construct integrated reference using harmony algorithm
+#' refdata <- construct_ref(ref_list = ref_list,
+#'                       phenodata_list = phenodata_list,
+#'                       data_type = "cellranger",
+#'                       method = "harmony",
+#'                       group_var = "subjectid",
+#'                       nfeature_rna = 50,
+#'                       vars_to_regress = "percent_mt", verbose = FALSE)
 #' phenodata <- data.frame(cellid = colnames(refdata),
 #'                         celltypes = refdata$celltype,
 #'                         subjectid = refdata$subjectid)

R/helpers.R

@@ -143,7 +143,7 @@ transformation <- function(matrix, option) {
 #' @param seed random seed used for simulating FFPE artifacts. Only applicable when ffpe_artifacts is set to TRUE.
 #' @param ffpe_artifacts logical value indicating whether to add simulated ffpe artifacts in the bulk data. Only applicable to simulation experiments in
 #' evaluating the effect of FFPE artifacts.
-#' @param model pre-constructed ffpe model data. Can be loaded via \code{data(ffpemodel)}
+#' @param model pre-constructed ffpe model data. Can be downloaded from github: https://github.com/Liuy12/SCdeconR/blob/master/data/ffpemodel.rda
 #'
 #' @return a matrix-like object with the same dimension of input object after data normalization.
 #'

R/reference_construction.R

@@ -126,7 +126,7 @@ construct_ref <- function(
     merge_data <- harmony::RunHarmony(
       object = merge_data,
       assay.use = "SCT",
-      reduction = "pca",
+      reduction.use = "pca",
       dims.use = 1:30,
       group.by.vars = group_var,
       plot_convergence = FALSE,

R/simulate_bulk.R

@@ -33,7 +33,22 @@
 #'
 #' @examples
 #' \dontrun{
-#' data(refdata)
+#' ref_list <- c(paste0(system.file("extdata", package = "SCdeconR"), "/refdata/sample1"),
+#'               paste0(system.file("extdata", package = "SCdeconR"), "/refdata/sample2"))
+#' phenopath1 <- paste0(system.file("extdata", package = "SCdeconR"),
+#' "/refdata/phenodata_sample1.txt")
+#' phenopath2 <- paste0(system.file("extdata", package = "SCdeconR"),
+#' "/refdata/phenodata_sample2.txt")
+#' phenodata_list <- c(phenopath1,phenopath2)
+#'
+#' # construct integrated reference using harmony algorithm
+#' refdata <- construct_ref(ref_list = ref_list,
+#'                       phenodata_list = phenodata_list,
+#'                       data_type = "cellranger",
+#'                       method = "harmony",
+#'                       group_var = "subjectid",
+#'                       nfeature_rna = 50,
+#'                       vars_to_regress = "percent_mt", verbose = FALSE)
 #' phenodata <- data.frame(cellid = colnames(refdata),
 #'                         celltypes = refdata$celltype,
 #'                         subjectid = refdata$subjectid)
@@ -164,7 +179,22 @@ bulk_generator <- function(
 #' @examples
 #' \dontrun{
 #' ## generate artificial bulk samples
-#' data(refdata)
+#' ref_list <- c(paste0(system.file("extdata", package = "SCdeconR"), "/refdata/sample1"),
+#'               paste0(system.file("extdata", package = "SCdeconR"), "/refdata/sample2"))
+#' phenopath1 <- paste0(system.file("extdata", package = "SCdeconR"),
+#' "/refdata/phenodata_sample1.txt")
+#' phenopath2 <- paste0(system.file("extdata", package = "SCdeconR"),
+#' "/refdata/phenodata_sample2.txt")
+#' phenodata_list <- c(phenopath1,phenopath2)
+#'
+#' # construct integrated reference using harmony algorithm
+#' refdata <- construct_ref(ref_list = ref_list,
+#'                       phenodata_list = phenodata_list,
+#'                       data_type = "cellranger",
+#'                       method = "harmony",
+#'                       group_var = "subjectid",
+#'                       nfeature_rna = 50,
+#'                       vars_to_regress = "percent_mt", verbose = FALSE)
 #' phenodata <- data.frame(cellid = colnames(refdata),
 #'                         celltypes = refdata$celltype,
 #'                         subjectid = refdata$subjectid)