Add files via upload

Author: yangRISC

DESCRIPTION

@@ -1,8 +1,9 @@
 Package: RISC
 Type: Package
 Title: Robust Integration of Single-Cell RNA-Seq Datasets
-Version: 1.6.0
-Date: 2022-11-11
+Version: 1.7
+Date: 2022-1-10
+Update: 2024-3-15
 Authors@R: c(person("Yang", "Liu", role = c("aut", "cre"), 
          email = "[email protected]"), 
          person("Deyou", "Zheng", role = "aut"), 
@@ -12,19 +13,19 @@ Description: The 'RISC' package can integrate single cell RNA sequencing data, c
 URL: https://www.biorxiv.org/content/10.1101/483297v1.article-info
         https://github.com/yangRISC/RISC
 Depends: R (>= 4.0.0)
-Imports: Rcpp, Matrix, sparseMatrixStats, MASS, Matrix.utils, pbapply,
+Imports: Rcpp, Matrix, sparseMatrixStats, MASS, pbapply, hdf5r,
         doParallel, foreach, irlba, Rtsne, umap, densityClust, FNN,
-        igraph, RColorBrewer, ggplot2, gridExtra, pheatmap, hdf5r,
-        methods, grDevices, stats, utils
+        igraph, RColorBrewer, ggplot2, gridExtra, pheatmap, methods,
+        grDevices, stats, utils
 LinkingTo: Rcpp, RcppArmadillo
 LazyData: true
 Encoding: UTF-8
-RoxygenNote: 7.2.2
+RoxygenNote: 7.3.1
 Suggests: testthat, usethis, knitr, rmarkdown
 VignetteBuilder: knitr
 NeedsCompilation: yes
 License: GPL-3 | file LICENSE
-Packaged: 2022-11-13 23:17:37 UTC; liuy128
+Packaged: 2024-03-18 13:34:09 UTC; liuy128
 Author: Yang Liu [aut, cre],
   Deyou Zheng [aut],
   Tao Wang [aut]

NAMESPACE

@@ -35,7 +35,6 @@ importFrom(Matrix,rowMeans)
 importFrom(Matrix,rowSums)
 importFrom(Matrix,spMatrix)
 importFrom(Matrix,sparseMatrix)
-importFrom(Matrix.utils,aggregate.Matrix)
 importFrom(Rcpp,evalCpp)
 importFrom(Rtsne,Rtsne)
 importFrom(densityClust,densityClust)
@@ -47,6 +46,8 @@ importFrom(grDevices,col2rgb)
 importFrom(grDevices,colorRampPalette)
 importFrom(gridExtra,grid.arrange)
 importFrom(hdf5r,H5File)
+importFrom(igraph,"E<-")
+importFrom(igraph,E)
 importFrom(igraph,cluster_louvain)
 importFrom(igraph,graph_from_data_frame)
 importFrom(igraph,simplify)
@@ -67,6 +68,7 @@ importFrom(stats,embed)
 importFrom(stats,gaussian)
 importFrom(stats,glm)
 importFrom(stats,ks.test)
+importFrom(stats,loess)
 importFrom(stats,loess.smooth)
 importFrom(stats,median)
 importFrom(stats,model.matrix)

R/Cluster.R

@@ -21,6 +21,7 @@
 #' "kd_tree", "cover_tree", "CR", "brute".
 #' @param npc The number of PCA or PLS used for cell clustering.
 #' @param k The number of cluster searched for, works in "density" method.
+#' @param res The resolution of cluster searched for, works in "louvain" method.
 #' @param dc The distance used to generate random center points which affect 
 #' clusters. If have no idea about this, do not input anything. Keep it as the 
 #' default value for most users. Work for "density" method.
@@ -30,7 +31,7 @@
 #' @name scCluster
 #' @importFrom densityClust densityClust findClusters
 #' @importFrom FNN get.knn
-#' @importFrom igraph simplify graph_from_data_frame cluster_louvain
+#' @importFrom igraph simplify graph_from_data_frame cluster_louvain E E<-
 #' @references Blondel et al., JSTAT (2008)
 #' @references Rodriguez et al., Sicence (2014)
 #' @export
@@ -44,19 +45,21 @@
 
 scCluster <- function(
   object, 
-  slot = "cell.umap", 
+  slot = "cell.pca", 
   neighbor = 10, 
   algorithm = "kd_tree", 
   method = 'louvain', 
   npc = 20, 
   k = 10, 
+  res = 0.5, 
   dc = NULL, 
   redo = TRUE, 
   random.seed = 123
   ) {
 
   set.seed(random.seed)
   k = as.integer(k)
+  res = as.numeric(res)
   neighbor = as.integer(neighbor)
   algorithm = as.character(algorithm)
   npc = as.integer(npc)
@@ -112,8 +115,9 @@ scCluster <- function(
       clust0 = get.knn(count, k = neighbor, algorithm = algorithm)
       clust1 = data.frame(NodStar = rep(1L:nrow(count), neighbor), NodEnd = as.vector(clust0$nn.index), stringsAsFactors = FALSE)
       clust1 = graph_from_data_frame(clust1, directed = FALSE)
+      E(clust1)$weight = 1/(1 + as.vector(clust0$nn.dist))
       clust1 = simplify(clust1)
-      clust1 = cluster_louvain(clust1, weights = 1/(1 + as.vector(clust0$nn.dist)))
+      clust1 = cluster_louvain(clust1, resolution = res)
       object@cluster = object@coldata$Cluster = as.factor(clust1$membership)
       names(object@cluster) = names(object@coldata$Cluster) = rownames(count)
       object@metadata[['clustering']] = data.frame(Method = 'louvain', PCs = npc, Neighbors = neighbor, stringsAsFactors = F)