R/ClusterSet_class.R
In dputhier/dbfmcl: The scigenex package (Single-Cell Informative GENe Explorer)
#################################################################
## DEFINITION OF A SPECIFIC CLASS OBJECT : ClusterSet
#################################################################
#' @title
#' ClusterSet-class
#' @description
#' This class is a representation of a partitioning algorithm and is intented to store gene clusters.
#' @slot data A matrix containing the filtered and partitioned data.
#' @slot gene_clusters A list contains the partitioned genes of the dataset. Each element of the list corresponds to a cluster, and contains the indices of the genes assigned to that cluster.
#' @slot top_genes A list contains the top genes from the gene clusters. Each element of the list corresponds to a cluster, and contains the indices of the genes assigned to that cluster ranked by their correlation value within their cluster.
#' @slot gene_clusters_metadata A list contains metadata related to the gene clusters such as the number of gene clusters, their ID, and the number of genes contained in each of them.
#' @slot gene_cluster_annotations A list contains the result obtained from the GO enrichment analysis of gene clusters.
#' @slot cells_metadata A list containing metadata related to the cell clusters such as the clustering results the number of cell clusters, their order, colors associated to each cluster,...
#' @slot dbf_output A list containing the intermediates outputs of the DBF function : dknn, simulated distances, critical distance and fdr values.
#' @slot parameters A list containing the parameter used. Each element of the list correspond to a parameter.
#'
#' @return A ClusterSet object.
#' @export
#'
#' @examples
#' library(Seurat)
#' load_example_dataset("7871581/files/pbmc3k_medium")
#'
#' # Select informative genes
#' res <- select_genes(pbmc3k_medium)
#'
#' # Cluster informative features
#' res <- gene_clustering(res, inflation=1.6)
#' is(res)
#'
#' # Plot heatmap of gene clusters
#' plot_heatmap(res, row_labels = FALSE, line_size_horizontal = 2)
#' plot_heatmap(res[1,], row_labels = FALSE, line_size_horizontal = 2)
#' plot_heatmap(res[1:2, ], row_labels = FALSE, line_size_horizontal = 2)
#' plot_heatmap(res[1:2, 1:15], row_labels = FALSE, line_size_horizontal = 2)
#'
#' # plot the profiles
#' idents <- Seurat::Idents(pbmc3k_medium)
#' plot_profiles(res,
#' ident = idents)
#'
#' # Some methods of the ClusterSet object
#' x <- ncol(res)
#' x <- nrow(res)
#' x <- dim(res)
#' x <- col_names(res)
#' x <- row_names(res)
#' x <- get_genes(res)
#' x<- clust_size(res)
#' x <- c("IL32", "CCL5") %in% res
#' x <- which_clust(res, genes = c("IL32", "CCL5"))
#' res <- top_genes(res, top=5)
#' res <- res[2:3, ]
#' res <- rename_clust(res)
#' clust_names(res)
#' res <- res[, col_names(res)[1:10]]
#' show(res)
#' show_methods(res)
setClass("ClusterSet",
representation = list(
data = "matrix",
gene_clusters = "list",
top_genes = "list",
gene_clusters_metadata = "list",
gene_cluster_annotations = "list",
cells_metadata = "data.frame",
dbf_output = "list",
parameters = "list"
),
prototype = list(
data = matrix(nr = 0, nc = 0),
gene_clusters = list(),
top_genes = list(),
gene_clusters_metadata = list(),
gene_cluster_annotations = list(),
cells_metadata = data.frame(),
dbf_output = list(),
parameters = list()
)
)
#################################################################
## REDEFINE SHOW METHOD FOR CLASS OBJECT : ClusterSet
#################################################################
#' @title
#' The show method of a ClusterSet.
#' @description
#' The show method of a ClusterSet.
#' @param object A ClusterSet object.
#' @export show
#' @keywords internal
setMethod(
"show", signature("ClusterSet"),
function(object) {
cat("\t\tAn object of class ClusterSet\n")
cat("\t\tName:", slot(object, "parameters")$name, "\n")
cat("\t\tMemory used: ", object.size(object), "\n")
cat("\t\tNumber of cells: ", ncol(slot(object, "data")), "\n")
cat(
"\t\tNumber of informative genes: ",
nrow(slot(object, "data")), "\n"
)
cat("\t\tNumber of gene clusters: ", slot(object, "gene_clusters_metadata")$number, "\n")
cat("\t\tThis object contains the following informations:\n")
for (i in slotNames(object)) {
cat("\t\t\t - ", i, "\n")
}
if (length(slot(object, "parameters")) > 0) {
for (i in 1:length(slot(object, "parameters"))) {
cat(
"\t\t\t\t * ", names(slot(object, "parameters"))[[i]],
" = ", slot(object, "parameters")[[i]], "\n"
)
}
}
}
)
################################################################################
## NCOL/NROW/DIM METHOD FOR CLASS OBJECT : ClusterSet
################################################################################
#' @title
#' ncol.ClusterSet
#' @description
#' The number of column of a ClusterSet object.
#' @param x The ClusterSet object
#' @keywords internal
ncol.ClusterSet <- function (x) {
ncol(x@data)
}
#' @title
#' nrow.ClusterSet
#' @description
#' The number of rows of a ClusterSet object.
#' @param x The ClusterSet object
#' @keywords internal
nrow.ClusterSet <- function (x) {
nrow(x@data)
}
#' @title Names of gene clusters stored in the ClusterSet object
#' @description
#' The names of the gene clusters stored in the ClusterSet object.
#' @param x The ClusterSet object.
#' @export clust_names
#' @keywords internal
setGeneric("clust_names",
function(x)
standardGeneric("clust_names")
)
#' @title The names of the gene clusters stored in the ClusterSet object
#' @description
#' The names of the gene clusters stored in the ClusterSet object.
#' @param x The ClusterSet object
#' @export clust_names
setMethod("clust_names",
"ClusterSet",
function(x){
return(names(x@gene_clusters))
}
)
#' @title Dimension of a ClusterSet object.
#' dim
#' @description
#' The dimension of a ClusterSet object.
#' @param x The ClusterSet object
#' @keywords internal
setMethod("dim",signature(x="ClusterSet"),
function(x) {
dim(x@data)
}
)
#' @title Column names of an object
#' @description
#' The column names of a ClusterSet object.
#' @param x The ClusterSet object
#' @export col_names
#' @keywords internal
setGeneric(name="col_names", def=function(x) standardGeneric("col_names"))
#' @title Column names of a ClusterSet object.
#' @description
#' The column names of a ClusterSet object.
#' @param x The ClusterSet object
#' @export col_names
setMethod(f="col_names",
signature="ClusterSet",
definition=function(x) colnames(x@data))
#' @title Row names of a ClusterSet object.
#' row_names
#' @description
#' The row names of a ClusterSet object.
#' @param x The ClusterSet object
#' @export row_names
#' @keywords internal
setGeneric("row_names",
function(x)
standardGeneric("row_names"))
#' @title Row names of a ClusterSet object.
#' row_names
#' @description
#' The row names of a ClusterSet object.
#' @param x The ClusterSet object
#' @export row_names
setMethod("row_names", "ClusterSet",
function(x)
rownames(x@data)
)
################################################################################
## Method for function"[". Subsetting
## ClusterSet object
################################################################################
#' @title Subsetting operator of a ClusterSet object
#' Extract
#' @description
#' The subsetting operator of a ClusterSet object.
#' The i axis correspond to clusters and j to column/cells
#' @param i indices specifying rows to extract. Indices are numeric or character vectors or empty (missing) or NULL.
#' @param j indices specifying column to extract. Indices are numeric or character vectors or empty (missing) or NULL.
#' @param ... See ?'['. Not functionnal here.
#' @param drop For matrices and arrays. If TRUE the result is coerced to the lowest possible dimension. Not functionnal here.
#' @keywords internal
setMethod("[", signature(x = "ClusterSet"),
function (x, i, j, ..., drop = FALSE) {
if(is.null(names(x@gene_clusters_metadata$cluster_id)))
names(x@gene_clusters_metadata$cluster_id) <- names(x@gene_clusters)
if(is.null(names(x@gene_clusters_metadata$size)))
names(x@gene_clusters_metadata$size) <- names(x@gene_clusters)
if (missing(j)) {
if (missing(i)) {
n_data <- x@data
n_gene_clusters <- x@gene_clusters
n_top_genes <- x@top_genes
n_gene_clusters_metadata <- x@gene_clusters_metadata
n_gene_cluster_annotations <- x@gene_cluster_annotations
n_cells_metadata <- x@cells_metadata
n_dbf_output <- x@dbf_output
}else {
n_data <- x@data[unlist(x@gene_clusters[i]), , drop=FALSE]
n_gene_clusters <- x@gene_clusters[i]
if(length(x@top_genes)){
n_top_genes <- x@top_genes[i]
}else{
n_top_genes <- x@top_genes
}
n_gene_clusters_metadata <- x@gene_clusters_metadata
n_gene_clusters_metadata$cluster_id <-
x@gene_clusters_metadata$cluster_id[i]
n_gene_clusters_metadata$number <- length(n_gene_clusters)
n_gene_clusters_metadata$size <- x@gene_clusters_metadata$size[i]
if(length(x@gene_cluster_annotations) > 0){
n_gene_cluster_annotations <- x@gene_cluster_annotations[i]
}else{
n_gene_cluster_annotations <- x@gene_cluster_annotations
}
n_cells_metadata <- x@cells_metadata
n_dbf_output <- x@dbf_output
n_dbf_output$center <- n_dbf_output$center[i, , drop=FALSE]
}
} else {
if (missing(i)) {
n_data <- x@data[,j]
n_gene_clusters <- x@gene_clusters
n_top_genes <- x@top_genes
n_gene_clusters_metadata <- x@gene_clusters_metadata
n_gene_cluster_annotations <- x@gene_cluster_annotations
n_cells_metadata <- x@cells_metadata[j, , drop=FALSE]
n_dbf_output <- x@dbf_output
n_dbf_output$center <- n_dbf_output$center[ , j, drop=FALSE]
}else {
n_data <- x@data[unlist(x@gene_clusters[i]), j, drop=FALSE]
n_gene_clusters <- x@gene_clusters[i]
if(length(x@top_genes)){
n_top_genes <- x@top_genes[i]
}else{
n_top_genes <- x@top_genes
}
n_gene_clusters_metadata <- x@gene_clusters_metadata
n_gene_clusters_metadata$cluster_id <- x@gene_clusters_metadata$cluster_id[i]
n_gene_clusters_metadata$number <- length(i)
n_gene_clusters_metadata$size <- x@gene_clusters_metadata$size[i]
if(length(x@gene_cluster_annotations) > 0){
n_gene_cluster_annotations <- x@gene_cluster_annotations[i]
}else{
n_gene_cluster_annotations <- x@gene_cluster_annotations
}
n_cells_metadata <- x@cells_metadata[j, , drop=FALSE]
n_dbf_output <- x@dbf_output
n_dbf_output$center <- n_dbf_output$center[i, j, drop=FALSE]
}
}
new(
"ClusterSet",
data = n_data,
gene_clusters = n_gene_clusters,
top_genes = n_top_genes,
gene_clusters_metadata = n_gene_clusters_metadata,
gene_cluster_annotations = n_gene_cluster_annotations,
cells_metadata = n_cells_metadata,
dbf_output = n_dbf_output,
parameters = x@parameters
)
})
################################################################################
## Method nclust/clust_size for a
## ClusterSet object
################################################################################
#' @title Number of clusters in a ClusterSet object.
#' nclust
#' @description
#' The number of clusters in a ClusterSet object.
#' @param x The ClusterSet object
#' @export nclust
#' @examples
#' load_example_dataset('7871581/files/pbmc3k_medium_clusters')
#' n_clust <- nclust(pbmc3k_medium_clusters)
#' @keywords internal
setGeneric("nclust",
function(x)
standardGeneric("nclust")
)
#' @title Number of clusters in a ClusterSet object.
#' nclust
#' @description
#' The number of clusters in a ClusterSet object.
#' @param x The ClusterSet object
#' @export nclust
#' @examples
#' # load a dataset
#' load_example_dataset('7871581/files/pbmc3k_medium_clusters')
#' n_clust <- nclust(pbmc3k_medium_clusters)
setMethod(
"nclust", signature("ClusterSet"),
function(x) {
length(x@gene_clusters)
}
)
#' @title Sizes of the clusters stored in a ClusterSet object
#' clust_size
#' @description
#' The sizes of the clusters stored in a ClusterSet object.
#' @param x A ClusterSet object.
#' @export clust_size
#' @examples
#' # load a dataset
#' load_example_dataset('7871581/files/pbmc3k_medium_clusters')
#' x <- clust_size(pbmc3k_medium_clusters)
#' @keywords internal
setGeneric("clust_size",
function(x)
standardGeneric("clust_size")
)
#' @title Sizes of the clusters stored in a ClusterSet object
#' clust_size
#' @description
#' The sizes of the clusters stored in a ClusterSet object.
#' @param x A ClusterSet object.
#' @export
#' @examples
#' # load a dataset
#' load_example_dataset('7871581/files/pbmc3k_medium_clusters')
#' x <- clust_size(pbmc3k_medium_clusters)
setMethod(
"clust_size", signature("ClusterSet"),
function(x) {
x@gene_clusters_metadata$size
}
)
#################################################################
## Define gene_cluster function for ClusterSet object
#################################################################
#' @title The gene clusters stored in a ClusterSet.
#' gene_cluster
#' @description
#' Returns a named vector (gene as names) and cluster
#' as value.
#' @param object a ClusterSet object.
#' @param cluster The cluster of interest. 0 means all cluster. Otherwise a non-null integer value.
#' @param as_string Return cluster names as strings.
#' @examples
#' # load a dataset
#' load_example_dataset('7871581/files/pbmc3k_medium_clusters')
#' g_clust <- gene_cluster(pbmc3k_medium_clusters)
#' @export gene_cluster
#' @keywords internal
setGeneric("gene_cluster",
function(object,
cluster = 0,
as_string=FALSE)
standardGeneric("gene_cluster")
)
#' @title The gene clusters stored in a ClusterSet.
#' gene_cluster
#' @description
#' Returns a named vector (gene as names) and cluster
#' as value.
#' @param object a ClusterSet object.
#' @param cluster The cluster of interest. 0 means all cluster. Otherwise a non-null integer value.
#' @param as_string Return cluster names as strings.
#' @examples
#' # load a dataset
#' load_example_dataset('7871581/files/pbmc3k_medium_clusters')
#' g_clust <- gene_cluster(pbmc3k_medium_clusters)
#' @export gene_cluster
setMethod(
"gene_cluster", signature("ClusterSet"),
function(object,
cluster = 0,
as_string=FALSE) {
if(!is.null(object@gene_clusters)){
nb_clust <- length(object@gene_clusters)
}else{
print_msg("There is no cluster in this object.",
msg_type = 'STOP')
}
if(!is.numeric(cluster))
print_msg("Please provide a numeric value.",
msg_type = 'STOP')
cluster <- unique(cluster)
if (!all(cluster-floor(cluster)==0) | any(cluster < 0 | any(cluster > nb_clust)))
print_msg("Please provide a zero (all clusters) or positive integer in the required range.",
msg_type = 'STOP')
if(length(cluster) == 1){
if (cluster == 0){
cluster <- 1:length(object@gene_clusters)
}
}
if(length(cluster) > 1){
if(length(cluster[cluster == 0]))
print_msg("Zero is out of range.",
msg_type = 'STOP')
}
if(nb_clust) {
if(!as_string){
cluster_as_int <- unlist(mapply(rep,
cluster,
lapply(object@gene_clusters[cluster], length),
SIMPLIFY = TRUE))
cluster_as_int <- as.vector(as.matrix(cluster_as_int))
names(cluster_as_int) <-
unlist(object@gene_clusters[cluster])
return(cluster_as_int)
}else{
cluster_as_str <- unlist(mapply(rep,
clust_names(object)[cluster],
lapply(object@gene_clusters[cluster], length),
SIMPLIFY = TRUE))
cluster_as_str <- as.vector(as.matrix(cluster_as_str))
names(cluster_as_str) <-
unlist(object@gene_clusters[cluster])
return(cluster_as_str)
}
} else{
return(NULL)
}
})
################################################################################
## Method for function matching genes in a ClusterSet.
################################################################################
#' @title Match operator of a ClusterSet object
#' @description The match operator of a ClusterSet object
#' @param x The gene to be searched;
#' @param table The ClusterSet object.
#' @keywords internal
#' @export `%in%`
setMethod("%in%", signature(x = "character", table = "ClusterSet"), function(x, table) {
x %in% names(gene_cluster(table))
})
#' @title Which clusters contain a set of genes.
#' @description Which clusters contain a set of genes.
#' @param object a ClusterSet object.
#' @param genes The genes to be searched in the ClusterSet.
#' @export which_clust
#' @examples
#' # load a dataset
#' load_example_dataset('7871581/files/pbmc3k_medium_clusters')
#' hit <- which_clust(pbmc3k_medium_clusters, genes = c("TJP2", "GLA", "UNKNOWN"))
#' @keywords internal
setGeneric("which_clust",
function(object,
genes = NULL)
standardGeneric("which_clust")
)
#' @title Which clusters contain a set of genes.
#' @description Which clusters contain a set of genes.
#' @param object a ClusterSet object.
#' @param genes The genes to be searched in the ClusterSet.
#' @export which_clust
#' @examples
#' # load a dataset
#' load_example_dataset('7871581/files/pbmc3k_medium_clusters')
#' hit <- which_clust(pbmc3k_medium_clusters, genes = c("TJP2", "GLA", "UNKNOWN"))
setMethod("which_clust",
signature("ClusterSet"),
function(object, genes) {
check_format_cluster_set(object)
gc <- gene_cluster(object)
tmp <- gc[which(names(gc) %in% genes)]
tmp[genes]
})
################################################################################
## Method for renaming clusters from a clusterSet
################################################################################
#' @title Rename the gene clusters of a ClusterSet
#' @description Rename the gene clusters of a ClusterSet.
#' @param object a ClusterSet object.
#' @param new_names The new names for the clusters.
#' @export rename_clust
#' @examples
#' # load a dataset
#' load_example_dataset('7871581/files/pbmc3k_medium_clusters')
#' new_obj <- rename_clust(pbmc3k_medium_clusters, new_names = letters[1:nclust(pbmc3k_medium_clusters)])
#' @keywords internal
setGeneric("rename_clust",
function(object, new_names=NULL)
standardGeneric("rename_clust")
)
#' @title Rename the gene clusters of a ClusterSet
#' @description Rename the gene clusters of a ClusterSet.
#' @param object a ClusterSet object.
#' @param new_names The new names for the clusters.
#' @export rename_clust
#' @examples
#' # load a dataset
#' load_example_dataset('7871581/files/pbmc3k_medium_clusters')
#' new_obj <- rename_clust(pbmc3k_medium_clusters, new_names = letters[1:nclust(pbmc3k_medium_clusters)])
setMethod("rename_clust",
signature("ClusterSet"),
function(object,
new_names=NULL) {
check_format_cluster_set(object)
if(is.null(new_names)){
if(length(object@gene_clusters)){
new_names <- 1:nclust(object)
}else{
new_names <- NULL
}
}
if(length(new_names) != nclust(object))
print_msg("The number of labels should be the same a the number of clusters.")
names(object@gene_clusters) <- new_names
if(length(object@top_genes) > 0)
names(object@top_genes) <- new_names
object@gene_clusters_metadata$cluster_id <- new_names
names(object@gene_clusters_metadata$size) <- new_names
if(length(object@gene_cluster_annotations) > 0)
names(object@gene_cluster_annotations) <- new_names
rownames(object@dbf_output$center) <- new_names
return(object)
})
################################################################################
## Method for writing gene list into an excel sheet.
################################################################################
#' @title Write Cluster-Set gene lists into an excel sheet.
#' @description Write gene lists from a Cluster-Set object into an excel sheet.
#' @param object The ClusterSet object.
#' @param file_path The file path.
#' @keywords internal
#' @export cluster_set_to_xls
setGeneric("cluster_set_to_xls",
function(object,
file_path = NULL)
standardGeneric("cluster_set_to_xls")
)
#' @title Write Cluster-Set gene lists into an excel sheet.
#' @description Write gene lists from a Cluster-Set object into an excel sheet.
#' @param object The ClusterSet object.
#' @param file_path The file path.
#' @importFrom WriteXLS WriteXLS
#' @examples
#' #' Load an example dataset
#' load_example_dataset('7871581/files/pbmc3k_medium_clusters')
#'
#' # Write gene lists to file
#' tp_dir <- tempdir()
#' dir.create(tp_dir, showWarnings = FALSE)
#' cluster_set_to_xls(pbmc3k_medium_clusters, file.path(tp_dir, "test.xls"))
#' @export cluster_set_to_xls
setMethod("cluster_set_to_xls",
signature("ClusterSet"),
function(object,
file_path = NULL) {
check_format_cluster_set(object)
object <- reorder_genes(object)
dir_n <- dirname(file_path)
if (!dir.exists(dir_n))
print_msg("Directory does not exist. Exiting.", msg_type = "STOP")
if (file.exists(file_path))
print_msg("File already exist. Exiting.", msg_type = "STOP")
gnc <- gene_cluster(object)
df_list <- list(x=data.frame(All_modules = unname(gnc),
"official_gene_symbol" = names(gnc)))
tmp <- lapply(object@gene_clusters, as.data.frame)
for(i in 1:length(tmp)){
colnames(tmp[[i]]) <- paste0("Module ", i)
}
df_list <- append(df_list, tmp)
WriteXLS::WriteXLS(
x=df_list,
ExcelFileName = file_path,
SheetNames = c("All_modules", paste0("Module ", 1:length(object@gene_clusters)))
)
})
################################################################################
## Method for reordering clusters from a clusterSet
################################################################################
#' @title Reorder the clusters from a ClusterSet
#' @description Reorder the clusters from a ClusterSet based on their names
#' @param object a ClusterSet object.
#' @param new_order The names from the clusterSet in an alternative order.
#' @export reorder_clust
#' @examples
#' # load a dataset
#' load_example_dataset('7871581/files/pbmc3k_medium_clusters')
#' clust_size(pbmc3k_medium_clusters)
#' new_obj <- reorder_clust(pbmc3k_medium_clusters, new_order = 15:1)
#' clust_size(pbmc3k_medium_clusters)
#' @keywords internal
setGeneric("reorder_clust",
function(object, new_order=NULL)
standardGeneric("reorder_clust")
)
#' @title Reorder the clusters from a ClusterSet
#' @description Reorder the clusters from a ClusterSet based on their names
#' @param object a ClusterSet object.
#' @param new_order The names from the clusterSet in an alternative order.
#' @export reorder_clust
#' @examples
#' # load a dataset
#' load_example_dataset('7871581/files/pbmc3k_medium_clusters')
#' clust_size(pbmc3k_medium_clusters)
#' new_obj <- reorder_clust(pbmc3k_medium_clusters, new_order = 15:1)
#' clust_size(new_obj)
setMethod("reorder_clust",
signature("ClusterSet"),
function(object,
new_order=NULL) {
check_format_cluster_set(object)
if(is.null(new_order)){
print_msg('Please provide new_order argument.')
}
if(length(new_order) != nclust(object))
print_msg("The number of labels should be the same a the number of clusters.")
if(!all(sort(new_order) == sort(clust_names(object))))
print_msg("The labels should be the same in an alternative order.")
new_pos <- match(new_order, clust_names(object))
object@gene_clusters <- object@gene_clusters[new_pos]
if(length(object@top_genes) > 0)
object@top_genes <- object@top_genes[new_pos]
object@gene_clusters_metadata$cluster_id <- object@gene_clusters_metadata$cluster_id[new_pos]
object@gene_clusters_metadata$size <- object@gene_clusters_metadata$size[new_pos]
if(length(object@gene_cluster_annotations) > 0)
object@gene_cluster_annotations <- object@gene_cluster_annotations[new_pos]
object@dbf_output$center <- object@dbf_output$center[new_pos,]
return(object)
})
################################################################################
## Method for searching genes using REGEXP
################################################################################
#' @title Search genes within ClusterSet using a REGEXP.
#' @description Search genes within ClusterSet using a REGEXP.
#' @param object a ClusterSet object.
#' @param regexp The regular expression
#' @param val if FALSE, a vector containing the (integer) indices of the matches determined
#' by grep is returned, and if TRUE, a vector containing the matching elements themselves
#' is returned.
#' @export grep_clust
#' @examples
#' # load a dataset
#' load_example_dataset('7871581/files/pbmc3k_medium_clusters')
#' grep_clust(pbmc3k_medium_clusters, "[Kk][Rr][Tt]")
#' @keywords internal
setGeneric("grep_clust",
function(object, regexp=NULL, val=TRUE)
standardGeneric("grep_clust")
)
#' @title Search genes within ClusterSet using a REGEXP.
#' @description Search genes within ClusterSet using a REGEXP.
#' @param object a ClusterSet object.
#' @param regexp The regular expression
#' @param val if FALSE, a vector containing the (integer) indices of the matches determined
#' by grep is returned, and if TRUE, a vector containing the matching elements themselves
#' is returned.
#' @export grep_clust
#' @examples
#' # load a dataset
#' load_example_dataset('7871581/files/pbmc3k_medium_clusters')
#' grep_clust(pbmc3k_medium_clusters, "^CD")
setMethod("grep_clust",
signature("ClusterSet"),
function(object,
regexp=NULL, val=TRUE) {
check_format_cluster_set(object)
if(is.null(regexp)){
print_msg('Please provide regexp argument.')
}
fgrep <- function(x, regexp, val){grep(regexp, x, value=val)}
lapply(object@gene_clusters, fgrep, regexp, val)
})
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#################################################################
## DEFINITION OF A SPECIFIC CLASS OBJECT : ClusterSet
#################################################################
#' @title
#' ClusterSet-class
#' @description
#' This class is a representation of a partitioning algorithm and is intented to store gene clusters.
#' @slot data A matrix containing the filtered and partitioned data.
#' @slot gene_clusters A list contains the partitioned genes of the dataset. Each element of the list corresponds to a cluster, and contains the indices of the genes assigned to that cluster.
#' @slot top_genes A list contains the top genes from the gene clusters. Each element of the list corresponds to a cluster, and contains the indices of the genes assigned to that cluster ranked by their correlation value within their cluster.
#' @slot gene_clusters_metadata A list contains metadata related to the gene clusters such as the number of gene clusters, their ID, and the number of genes contained in each of them.
#' @slot gene_cluster_annotations A list contains the result obtained from the GO enrichment analysis of gene clusters.
#' @slot cells_metadata A list containing metadata related to the cell clusters such as the clustering results the number of cell clusters, their order, colors associated to each cluster,...
#' @slot dbf_output A list containing the intermediates outputs of the DBF function : dknn, simulated distances, critical distance and fdr values.
#' @slot parameters A list containing the parameter used. Each element of the list correspond to a parameter.
#'
#' @return A ClusterSet object.
#' @export
#'
#' @examples
#' library(Seurat)
#' load_example_dataset("7871581/files/pbmc3k_medium")
#'
#' # Select informative genes
#' res <- select_genes(pbmc3k_medium)
#'
#' # Cluster informative features
#' res <- gene_clustering(res, inflation=1.6)
#' is(res)
#'
#' # Plot heatmap of gene clusters
#' plot_heatmap(res, row_labels = FALSE, line_size_horizontal = 2)
#' plot_heatmap(res[1,], row_labels = FALSE, line_size_horizontal = 2)
#' plot_heatmap(res[1:2, ], row_labels = FALSE, line_size_horizontal = 2)
#' plot_heatmap(res[1:2, 1:15], row_labels = FALSE, line_size_horizontal = 2)
#'
#' # plot the profiles
#' idents <- Seurat::Idents(pbmc3k_medium)
#' plot_profiles(res,
#' ident = idents)
#'
#' # Some methods of the ClusterSet object
#' x <- ncol(res)
#' x <- nrow(res)
#' x <- dim(res)
#' x <- col_names(res)
#' x <- row_names(res)
#' x <- get_genes(res)
#' x<- clust_size(res)
#' x <- c("IL32", "CCL5") %in% res
#' x <- which_clust(res, genes = c("IL32", "CCL5"))
#' res <- top_genes(res, top=5)
#' res <- res[2:3, ]
#' res <- rename_clust(res)
#' clust_names(res)
#' res <- res[, col_names(res)[1:10]]
#' show(res)
#' show_methods(res)
setClass("ClusterSet",
representation = list(
data = "matrix",
gene_clusters = "list",
top_genes = "list",
gene_clusters_metadata = "list",
gene_cluster_annotations = "list",
cells_metadata = "data.frame",
dbf_output = "list",
parameters = "list"
),
prototype = list(
data = matrix(nr = 0, nc = 0),
gene_clusters = list(),
top_genes = list(),
gene_clusters_metadata = list(),
gene_cluster_annotations = list(),
cells_metadata = data.frame(),
dbf_output = list(),
parameters = list()
)
)
#################################################################
## REDEFINE SHOW METHOD FOR CLASS OBJECT : ClusterSet
#################################################################
#' @title
#' The show method of a ClusterSet.
#' @description
#' The show method of a ClusterSet.
#' @param object A ClusterSet object.
#' @export show
#' @keywords internal
setMethod(
"show", signature("ClusterSet"),
function(object) {
cat("\t\tAn object of class ClusterSet\n")
cat("\t\tName:", slot(object, "parameters")$name, "\n")
cat("\t\tMemory used: ", object.size(object), "\n")
cat("\t\tNumber of cells: ", ncol(slot(object, "data")), "\n")
cat(
"\t\tNumber of informative genes: ",
nrow(slot(object, "data")), "\n"
)
cat("\t\tNumber of gene clusters: ", slot(object, "gene_clusters_metadata")$number, "\n")
cat("\t\tThis object contains the following informations:\n")
for (i in slotNames(object)) {
cat("\t\t\t - ", i, "\n")
}
if (length(slot(object, "parameters")) > 0) {
for (i in 1:length(slot(object, "parameters"))) {
cat(
"\t\t\t\t * ", names(slot(object, "parameters"))[[i]],
" = ", slot(object, "parameters")[[i]], "\n"
)
}
}
}
)
################################################################################
## NCOL/NROW/DIM METHOD FOR CLASS OBJECT : ClusterSet
################################################################################
#' @title
#' ncol.ClusterSet
#' @description
#' The number of column of a ClusterSet object.
#' @param x The ClusterSet object
#' @keywords internal
ncol.ClusterSet <- function (x) {
ncol(x@data)
}
#' @title
#' nrow.ClusterSet
#' @description
#' The number of rows of a ClusterSet object.
#' @param x The ClusterSet object
#' @keywords internal
nrow.ClusterSet <- function (x) {
nrow(x@data)
}
#' @title Names of gene clusters stored in the ClusterSet object
#' @description
#' The names of the gene clusters stored in the ClusterSet object.
#' @param x The ClusterSet object.
#' @export clust_names
#' @keywords internal
setGeneric("clust_names",
function(x)
standardGeneric("clust_names")
)
#' @title The names of the gene clusters stored in the ClusterSet object
#' @description
#' The names of the gene clusters stored in the ClusterSet object.
#' @param x The ClusterSet object
#' @export clust_names
setMethod("clust_names",
"ClusterSet",
function(x){
return(names(x@gene_clusters))
}
)
#' @title Dimension of a ClusterSet object.
#' dim
#' @description
#' The dimension of a ClusterSet object.
#' @param x The ClusterSet object
#' @keywords internal
setMethod("dim",signature(x="ClusterSet"),
function(x) {
dim(x@data)
}
)
#' @title Column names of an object
#' @description
#' The column names of a ClusterSet object.
#' @param x The ClusterSet object
#' @export col_names
#' @keywords internal
setGeneric(name="col_names", def=function(x) standardGeneric("col_names"))
#' @title Column names of a ClusterSet object.
#' @description
#' The column names of a ClusterSet object.
#' @param x The ClusterSet object
#' @export col_names
setMethod(f="col_names",
signature="ClusterSet",
definition=function(x) colnames(x@data))
#' @title Row names of a ClusterSet object.
#' row_names
#' @description
#' The row names of a ClusterSet object.
#' @param x The ClusterSet object
#' @export row_names
#' @keywords internal
setGeneric("row_names",
function(x)
standardGeneric("row_names"))
#' @title Row names of a ClusterSet object.
#' row_names
#' @description
#' The row names of a ClusterSet object.
#' @param x The ClusterSet object
#' @export row_names
setMethod("row_names", "ClusterSet",
function(x)
rownames(x@data)
)
################################################################################
## Method for function"[". Subsetting
## ClusterSet object
################################################################################
#' @title Subsetting operator of a ClusterSet object
#' Extract
#' @description
#' The subsetting operator of a ClusterSet object.
#' The i axis correspond to clusters and j to column/cells
#' @param i indices specifying rows to extract. Indices are numeric or character vectors or empty (missing) or NULL.
#' @param j indices specifying column to extract. Indices are numeric or character vectors or empty (missing) or NULL.
#' @param ... See ?'['. Not functionnal here.
#' @param drop For matrices and arrays. If TRUE the result is coerced to the lowest possible dimension. Not functionnal here.
#' @keywords internal
setMethod("[", signature(x = "ClusterSet"),
function (x, i, j, ..., drop = FALSE) {
if(is.null(names(x@gene_clusters_metadata$cluster_id)))
names(x@gene_clusters_metadata$cluster_id) <- names(x@gene_clusters)
if(is.null(names(x@gene_clusters_metadata$size)))
names(x@gene_clusters_metadata$size) <- names(x@gene_clusters)
if (missing(j)) {
if (missing(i)) {
n_data <- x@data
n_gene_clusters <- x@gene_clusters
n_top_genes <- x@top_genes
n_gene_clusters_metadata <- x@gene_clusters_metadata
n_gene_cluster_annotations <- x@gene_cluster_annotations
n_cells_metadata <- x@cells_metadata
n_dbf_output <- x@dbf_output
}else {
n_data <- x@data[unlist(x@gene_clusters[i]), , drop=FALSE]
n_gene_clusters <- x@gene_clusters[i]
if(length(x@top_genes)){
n_top_genes <- x@top_genes[i]
}else{
n_top_genes <- x@top_genes
}
n_gene_clusters_metadata <- x@gene_clusters_metadata
n_gene_clusters_metadata$cluster_id <-
x@gene_clusters_metadata$cluster_id[i]
n_gene_clusters_metadata$number <- length(n_gene_clusters)
n_gene_clusters_metadata$size <- x@gene_clusters_metadata$size[i]
if(length(x@gene_cluster_annotations) > 0){
n_gene_cluster_annotations <- x@gene_cluster_annotations[i]
}else{
n_gene_cluster_annotations <- x@gene_cluster_annotations
}
n_cells_metadata <- x@cells_metadata
n_dbf_output <- x@dbf_output
n_dbf_output$center <- n_dbf_output$center[i, , drop=FALSE]
}
} else {
if (missing(i)) {
n_data <- x@data[,j]
n_gene_clusters <- x@gene_clusters
n_top_genes <- x@top_genes
n_gene_clusters_metadata <- x@gene_clusters_metadata
n_gene_cluster_annotations <- x@gene_cluster_annotations
n_cells_metadata <- x@cells_metadata[j, , drop=FALSE]
n_dbf_output <- x@dbf_output
n_dbf_output$center <- n_dbf_output$center[ , j, drop=FALSE]
}else {
n_data <- x@data[unlist(x@gene_clusters[i]), j, drop=FALSE]
n_gene_clusters <- x@gene_clusters[i]
if(length(x@top_genes)){
n_top_genes <- x@top_genes[i]
}else{
n_top_genes <- x@top_genes
}
n_gene_clusters_metadata <- x@gene_clusters_metadata
n_gene_clusters_metadata$cluster_id <- x@gene_clusters_metadata$cluster_id[i]
n_gene_clusters_metadata$number <- length(i)
n_gene_clusters_metadata$size <- x@gene_clusters_metadata$size[i]
if(length(x@gene_cluster_annotations) > 0){
n_gene_cluster_annotations <- x@gene_cluster_annotations[i]
}else{
n_gene_cluster_annotations <- x@gene_cluster_annotations
}
n_cells_metadata <- x@cells_metadata[j, , drop=FALSE]
n_dbf_output <- x@dbf_output
n_dbf_output$center <- n_dbf_output$center[i, j, drop=FALSE]
}
}
new(
"ClusterSet",
data = n_data,
gene_clusters = n_gene_clusters,
top_genes = n_top_genes,
gene_clusters_metadata = n_gene_clusters_metadata,
gene_cluster_annotations = n_gene_cluster_annotations,
cells_metadata = n_cells_metadata,
dbf_output = n_dbf_output,
parameters = x@parameters
)
})
################################################################################
## Method nclust/clust_size for a
## ClusterSet object
################################################################################
#' @title Number of clusters in a ClusterSet object.
#' nclust
#' @description
#' The number of clusters in a ClusterSet object.
#' @param x The ClusterSet object
#' @export nclust
#' @examples
#' load_example_dataset('7871581/files/pbmc3k_medium_clusters')
#' n_clust <- nclust(pbmc3k_medium_clusters)
#' @keywords internal
setGeneric("nclust",
function(x)
standardGeneric("nclust")
)
#' @title Number of clusters in a ClusterSet object.
#' nclust
#' @description
#' The number of clusters in a ClusterSet object.
#' @param x The ClusterSet object
#' @export nclust
#' @examples
#' # load a dataset
#' load_example_dataset('7871581/files/pbmc3k_medium_clusters')
#' n_clust <- nclust(pbmc3k_medium_clusters)
setMethod(
"nclust", signature("ClusterSet"),
function(x) {
length(x@gene_clusters)
}
)
#' @title Sizes of the clusters stored in a ClusterSet object
#' clust_size
#' @description
#' The sizes of the clusters stored in a ClusterSet object.
#' @param x A ClusterSet object.
#' @export clust_size
#' @examples
#' # load a dataset
#' load_example_dataset('7871581/files/pbmc3k_medium_clusters')
#' x <- clust_size(pbmc3k_medium_clusters)
#' @keywords internal
setGeneric("clust_size",
function(x)
standardGeneric("clust_size")
)
#' @title Sizes of the clusters stored in a ClusterSet object
#' clust_size
#' @description
#' The sizes of the clusters stored in a ClusterSet object.
#' @param x A ClusterSet object.
#' @export
#' @examples
#' # load a dataset
#' load_example_dataset('7871581/files/pbmc3k_medium_clusters')
#' x <- clust_size(pbmc3k_medium_clusters)
setMethod(
"clust_size", signature("ClusterSet"),
function(x) {
x@gene_clusters_metadata$size
}
)
#################################################################
## Define gene_cluster function for ClusterSet object
#################################################################
#' @title The gene clusters stored in a ClusterSet.
#' gene_cluster
#' @description
#' Returns a named vector (gene as names) and cluster
#' as value.
#' @param object a ClusterSet object.
#' @param cluster The cluster of interest. 0 means all cluster. Otherwise a non-null integer value.
#' @param as_string Return cluster names as strings.
#' @examples
#' # load a dataset
#' load_example_dataset('7871581/files/pbmc3k_medium_clusters')
#' g_clust <- gene_cluster(pbmc3k_medium_clusters)
#' @export gene_cluster
#' @keywords internal
setGeneric("gene_cluster",
function(object,
cluster = 0,
as_string=FALSE)
standardGeneric("gene_cluster")
)
#' @title The gene clusters stored in a ClusterSet.
#' gene_cluster
#' @description
#' Returns a named vector (gene as names) and cluster
#' as value.
#' @param object a ClusterSet object.
#' @param cluster The cluster of interest. 0 means all cluster. Otherwise a non-null integer value.
#' @param as_string Return cluster names as strings.
#' @examples
#' # load a dataset
#' load_example_dataset('7871581/files/pbmc3k_medium_clusters')
#' g_clust <- gene_cluster(pbmc3k_medium_clusters)
#' @export gene_cluster
setMethod(
"gene_cluster", signature("ClusterSet"),
function(object,
cluster = 0,
as_string=FALSE) {
if(!is.null(object@gene_clusters)){
nb_clust <- length(object@gene_clusters)
}else{
print_msg("There is no cluster in this object.",
msg_type = 'STOP')
}
if(!is.numeric(cluster))
print_msg("Please provide a numeric value.",
msg_type = 'STOP')
cluster <- unique(cluster)
if (!all(cluster-floor(cluster)==0) | any(cluster < 0 | any(cluster > nb_clust)))
print_msg("Please provide a zero (all clusters) or positive integer in the required range.",
msg_type = 'STOP')
if(length(cluster) == 1){
if (cluster == 0){
cluster <- 1:length(object@gene_clusters)
}
}
if(length(cluster) > 1){
if(length(cluster[cluster == 0]))
print_msg("Zero is out of range.",
msg_type = 'STOP')
}
if(nb_clust) {
if(!as_string){
cluster_as_int <- unlist(mapply(rep,
cluster,
lapply(object@gene_clusters[cluster], length),
SIMPLIFY = TRUE))
cluster_as_int <- as.vector(as.matrix(cluster_as_int))
names(cluster_as_int) <-
unlist(object@gene_clusters[cluster])
return(cluster_as_int)
}else{
cluster_as_str <- unlist(mapply(rep,
clust_names(object)[cluster],
lapply(object@gene_clusters[cluster], length),
SIMPLIFY = TRUE))
cluster_as_str <- as.vector(as.matrix(cluster_as_str))
names(cluster_as_str) <-
unlist(object@gene_clusters[cluster])
return(cluster_as_str)
}
} else{
return(NULL)
}
})
################################################################################
## Method for function matching genes in a ClusterSet.
################################################################################
#' @title Match operator of a ClusterSet object
#' @description The match operator of a ClusterSet object
#' @param x The gene to be searched;
#' @param table The ClusterSet object.
#' @keywords internal
#' @export `%in%`
setMethod("%in%", signature(x = "character", table = "ClusterSet"), function(x, table) {
x %in% names(gene_cluster(table))
})
#' @title Which clusters contain a set of genes.
#' @description Which clusters contain a set of genes.
#' @param object a ClusterSet object.
#' @param genes The genes to be searched in the ClusterSet.
#' @export which_clust
#' @examples
#' # load a dataset
#' load_example_dataset('7871581/files/pbmc3k_medium_clusters')
#' hit <- which_clust(pbmc3k_medium_clusters, genes = c("TJP2", "GLA", "UNKNOWN"))
#' @keywords internal
setGeneric("which_clust",
function(object,
genes = NULL)
standardGeneric("which_clust")
)
#' @title Which clusters contain a set of genes.
#' @description Which clusters contain a set of genes.
#' @param object a ClusterSet object.
#' @param genes The genes to be searched in the ClusterSet.
#' @export which_clust
#' @examples
#' # load a dataset
#' load_example_dataset('7871581/files/pbmc3k_medium_clusters')
#' hit <- which_clust(pbmc3k_medium_clusters, genes = c("TJP2", "GLA", "UNKNOWN"))
setMethod("which_clust",
signature("ClusterSet"),
function(object, genes) {
check_format_cluster_set(object)
gc <- gene_cluster(object)
tmp <- gc[which(names(gc) %in% genes)]
tmp[genes]
})
################################################################################
## Method for renaming clusters from a clusterSet
################################################################################
#' @title Rename the gene clusters of a ClusterSet
#' @description Rename the gene clusters of a ClusterSet.
#' @param object a ClusterSet object.
#' @param new_names The new names for the clusters.
#' @export rename_clust
#' @examples
#' # load a dataset
#' load_example_dataset('7871581/files/pbmc3k_medium_clusters')
#' new_obj <- rename_clust(pbmc3k_medium_clusters, new_names = letters[1:nclust(pbmc3k_medium_clusters)])
#' @keywords internal
setGeneric("rename_clust",
function(object, new_names=NULL)
standardGeneric("rename_clust")
)
#' @title Rename the gene clusters of a ClusterSet
#' @description Rename the gene clusters of a ClusterSet.
#' @param object a ClusterSet object.
#' @param new_names The new names for the clusters.
#' @export rename_clust
#' @examples
#' # load a dataset
#' load_example_dataset('7871581/files/pbmc3k_medium_clusters')
#' new_obj <- rename_clust(pbmc3k_medium_clusters, new_names = letters[1:nclust(pbmc3k_medium_clusters)])
setMethod("rename_clust",
signature("ClusterSet"),
function(object,
new_names=NULL) {
check_format_cluster_set(object)
if(is.null(new_names)){
if(length(object@gene_clusters)){
new_names <- 1:nclust(object)
}else{
new_names <- NULL
}
}
if(length(new_names) != nclust(object))
print_msg("The number of labels should be the same a the number of clusters.")
names(object@gene_clusters) <- new_names
if(length(object@top_genes) > 0)
names(object@top_genes) <- new_names
object@gene_clusters_metadata$cluster_id <- new_names
names(object@gene_clusters_metadata$size) <- new_names
if(length(object@gene_cluster_annotations) > 0)
names(object@gene_cluster_annotations) <- new_names
rownames(object@dbf_output$center) <- new_names
return(object)
})
################################################################################
## Method for writing gene list into an excel sheet.
################################################################################
#' @title Write Cluster-Set gene lists into an excel sheet.
#' @description Write gene lists from a Cluster-Set object into an excel sheet.
#' @param object The ClusterSet object.
#' @param file_path The file path.
#' @keywords internal
#' @export cluster_set_to_xls
setGeneric("cluster_set_to_xls",
function(object,
file_path = NULL)
standardGeneric("cluster_set_to_xls")
)
#' @title Write Cluster-Set gene lists into an excel sheet.
#' @description Write gene lists from a Cluster-Set object into an excel sheet.
#' @param object The ClusterSet object.
#' @param file_path The file path.
#' @importFrom WriteXLS WriteXLS
#' @examples
#' #' Load an example dataset
#' load_example_dataset('7871581/files/pbmc3k_medium_clusters')
#'
#' # Write gene lists to file
#' tp_dir <- tempdir()
#' dir.create(tp_dir, showWarnings = FALSE)
#' cluster_set_to_xls(pbmc3k_medium_clusters, file.path(tp_dir, "test.xls"))
#' @export cluster_set_to_xls
setMethod("cluster_set_to_xls",
signature("ClusterSet"),
function(object,
file_path = NULL) {
check_format_cluster_set(object)
object <- reorder_genes(object)
dir_n <- dirname(file_path)
if (!dir.exists(dir_n))
print_msg("Directory does not exist. Exiting.", msg_type = "STOP")
if (file.exists(file_path))
print_msg("File already exist. Exiting.", msg_type = "STOP")
gnc <- gene_cluster(object)
df_list <- list(x=data.frame(All_modules = unname(gnc),
"official_gene_symbol" = names(gnc)))
tmp <- lapply(object@gene_clusters, as.data.frame)
for(i in 1:length(tmp)){
colnames(tmp[[i]]) <- paste0("Module ", i)
}
df_list <- append(df_list, tmp)
WriteXLS::WriteXLS(
x=df_list,
ExcelFileName = file_path,
SheetNames = c("All_modules", paste0("Module ", 1:length(object@gene_clusters)))
)
})
################################################################################
## Method for reordering clusters from a clusterSet
################################################################################
#' @title Reorder the clusters from a ClusterSet
#' @description Reorder the clusters from a ClusterSet based on their names
#' @param object a ClusterSet object.
#' @param new_order The names from the clusterSet in an alternative order.
#' @export reorder_clust
#' @examples
#' # load a dataset
#' load_example_dataset('7871581/files/pbmc3k_medium_clusters')
#' clust_size(pbmc3k_medium_clusters)
#' new_obj <- reorder_clust(pbmc3k_medium_clusters, new_order = 15:1)
#' clust_size(pbmc3k_medium_clusters)
#' @keywords internal
setGeneric("reorder_clust",
function(object, new_order=NULL)
standardGeneric("reorder_clust")
)
#' @title Reorder the clusters from a ClusterSet
#' @description Reorder the clusters from a ClusterSet based on their names
#' @param object a ClusterSet object.
#' @param new_order The names from the clusterSet in an alternative order.
#' @export reorder_clust
#' @examples
#' # load a dataset
#' load_example_dataset('7871581/files/pbmc3k_medium_clusters')
#' clust_size(pbmc3k_medium_clusters)
#' new_obj <- reorder_clust(pbmc3k_medium_clusters, new_order = 15:1)
#' clust_size(new_obj)
setMethod("reorder_clust",
signature("ClusterSet"),
function(object,
new_order=NULL) {
check_format_cluster_set(object)
if(is.null(new_order)){
print_msg('Please provide new_order argument.')
}
if(length(new_order) != nclust(object))
print_msg("The number of labels should be the same a the number of clusters.")
if(!all(sort(new_order) == sort(clust_names(object))))
print_msg("The labels should be the same in an alternative order.")
new_pos <- match(new_order, clust_names(object))
object@gene_clusters <- object@gene_clusters[new_pos]
if(length(object@top_genes) > 0)
object@top_genes <- object@top_genes[new_pos]
object@gene_clusters_metadata$cluster_id <- object@gene_clusters_metadata$cluster_id[new_pos]
object@gene_clusters_metadata$size <- object@gene_clusters_metadata$size[new_pos]
if(length(object@gene_cluster_annotations) > 0)
object@gene_cluster_annotations <- object@gene_cluster_annotations[new_pos]
object@dbf_output$center <- object@dbf_output$center[new_pos,]
return(object)
})
################################################################################
## Method for searching genes using REGEXP
################################################################################
#' @title Search genes within ClusterSet using a REGEXP.
#' @description Search genes within ClusterSet using a REGEXP.
#' @param object a ClusterSet object.
#' @param regexp The regular expression
#' @param val if FALSE, a vector containing the (integer) indices of the matches determined
#' by grep is returned, and if TRUE, a vector containing the matching elements themselves
#' is returned.
#' @export grep_clust
#' @examples
#' # load a dataset
#' load_example_dataset('7871581/files/pbmc3k_medium_clusters')
#' grep_clust(pbmc3k_medium_clusters, "[Kk][Rr][Tt]")
#' @keywords internal
setGeneric("grep_clust",
function(object, regexp=NULL, val=TRUE)
standardGeneric("grep_clust")
)
#' @title Search genes within ClusterSet using a REGEXP.
#' @description Search genes within ClusterSet using a REGEXP.
#' @param object a ClusterSet object.
#' @param regexp The regular expression
#' @param val if FALSE, a vector containing the (integer) indices of the matches determined
#' by grep is returned, and if TRUE, a vector containing the matching elements themselves
#' is returned.
#' @export grep_clust
#' @examples
#' # load a dataset
#' load_example_dataset('7871581/files/pbmc3k_medium_clusters')
#' grep_clust(pbmc3k_medium_clusters, "^CD")
setMethod("grep_clust",
signature("ClusterSet"),
function(object,
regexp=NULL, val=TRUE) {
check_format_cluster_set(object)
if(is.null(regexp)){
print_msg('Please provide regexp argument.')
}
fgrep <- function(x, regexp, val){grep(regexp, x, value=val)}
lapply(object@gene_clusters, fgrep, regexp, val)
})
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