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#' get_clusters
#' @title Predict Complexes
#' @param csize An integer, the minimum size of the predicted complexes.
#' Defaults to 2.
#' @param d A number, density of predicted complexes. Defaults to 0.3.
#' @param p An integer, penalty value for the inclusion of each node.
#' Defaults to 2.
#' @param max_overlap A number, specifies the maximum allowed
#' overlap between two clusters. Defaults to 0.8.
#' @param tpath A character string indicating the path to the project
#' directory that contains the interaction data. Interactions data must be
#' stored as .txt file and containing id1-id2-weight triplets.
#' @return A data.frame containing predicted complexes
#' @author Matineh Rahmatbakhsh, \email{matinerb.94@gmail.com}
#' @references Nepusz, T., Yu, H., and Paccanaro, A. (2012a).
#' Detecting overlapping protein complexes in protein-protein interaction
#' networks. Nat. Methods 9, 471.
#' @importFrom tibble rownames_to_column
#' @importFrom stringr str_replace_all
#' @description This function partitions high-confidence network to
#' putative complexes via ClusterONE clustering algorithm to identify
#' protein complex membership.
#' @export
#' @examples
#' predcpx <-
#' get_clusters(csize = 3, d = 0.3, p = 2,
#' max_overlap = 0.8,
#' tpath = file.path(system.file("extdata", package = "MACP")))
get_clusters <-
function(csize = 2,
d = 0.3,
p = 2,
max_overlap = 0.8,
tpath =
file.path(system.file("extdata", package = "MACP")))
{
# set directory to java file
fpath <- file.path(system.file("java", package = "MACP"))
tpath <- tpath
# Parameter input
max_overlap = max_overlap
d = d
p = p
txt_EX <-
paste("java -jar", paste0(fpath,"/","cluster_one-1.0.jar"),
"--seed-method",
"nodes",
"--max-overlap",
max_overlap,"-d",d, "--penalty", p, "-s", csize,
paste0(tpath,"/","ppi_input_ClusterONE.txt"))
javaOutput <-
system(txt_EX, intern = TRUE, ignore.stderr = TRUE)
df_clust <- as.data.frame(javaOutput)
df_clust[,1] <-
str_replace_all(df_clust[,1], "\t", " ")
df_clust <-
rownames_to_column(df_clust, "ClustID")
colnames(df_clust)[2] <- "Members"
return(df_clust)
}
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