change

.Rbuildignore

@@ -7,6 +7,6 @@
 ^\.github$
 ^doc$
 ^Meta$
-SCIntRuler.Rproj
-^inst$
+
+
 

.gitignore

@@ -5,6 +5,7 @@
 .DS_Store
 .quarto
 docs
-inst/doc
+inst/
 /doc/
 /Meta/
+

R/CalcuSCIR.R

@@ -22,6 +22,7 @@ utils::globalVariables(c("P.value", "revDiff", "V2", "finecluster", "rarecluster
 #' CalcuSCIR(sim_result[[1]], seuratlist, sim_result[[4]])
 
 CalcuSCIR <- function(fullcluster, seuratlist, testres, p = 0.1){
+
   stopifnot(exprs = {
     is.list(fullcluster)
     is.list(seuratlist)

R/FindCell.R

@@ -40,8 +40,8 @@ FindCell <- function(seuratobj,seuratlist,fullcluster, distmat,firstn = 15){
     attid <- c()
 
     for (q in seq_len(nclust)){
-      dist1 <- as.vector(do.call(cbind, distmat[[i]][[1]][[q]])[,1:firstn])
-      dist2 <- as.vector(do.call(cbind, distmat[[i]][[2]][[q]])[,1:firstn])
+      dist1 <- as.vector(do.call(cbind, distmat[[i]][[1]][[q]])[,seq_len(firstn)])
+      dist2 <- as.vector(do.call(cbind, distmat[[i]][[2]][[q]])[,seq_len(firstn)])
       disttest[[q]] <- data.frame(rbind(cbind(dist1,rep("Internal Dist",length(dist1))),
                                         cbind(dist2,rep("External Dist",length(dist1)))))%>%
         dplyr::mutate(dist = as.numeric(dist1),group = factor(V2)) %>% dplyr::select(3,4)
@@ -52,7 +52,7 @@ FindCell <- function(seuratobj,seuratlist,fullcluster, distmat,firstn = 15){
       testres[q,2] <- round((mean(dist2) - mean(dist1))/max(mean(dist2),mean(dist1)),4)
 
       if(testres[q,1] > 0.9 & testres[q,2] < 0){
-        print(paste0("Sample",i,"_",q))
+        # print(paste0("Sample",i,"_",q))
         attid <- c(attid,paste0("Sample",i,"_",q))
       }
 

R/FindNNDist.R

@@ -56,18 +56,18 @@ FindNNDist <- function(fullcluster,normCount, meaningn = 20) {
     onemeta <- fullcluster[[j]] # 2400 * 3
 
     tmpvec <- c()
-    newcounter = 1
+    newcounter <-  1
 
     # when j = 1, the dimension of othercount is 9763 3500 study 3+ study 2
     # when j = 2, the dimension of othercount is 9763 4800 study 1+ study 3
     # when j = 3, the dimension of othercount is 9763 4800 study 2+ study 3
     for(qqq in seq_along(fullcluster)) {
       if(qqq != j) {
         if(newcounter == 1) {
-          othercount = normCount[[qqq]]
-          newcounter = newcounter + 1
+          othercount <-  normCount[[qqq]]
+          newcounter <-  newcounter + 1
         } else {
-          othercount = cbind(othercount, normCount[[qqq]])
+          othercount <-  cbind(othercount, normCount[[qqq]])
         }
       }
     }
@@ -84,9 +84,9 @@ FindNNDist <- function(fullcluster,normCount, meaningn = 20) {
     for(q in seq_along(allCT)) {
       # print(q)
 
-      ct = allCT[q]
+      ct <-  allCT[q]
 
-      onecdist_internal = onecdist_external <- list()
+      onecdist_internal <-  onecdist_external <- list()
 
       max1 <- sum(onemeta$finecluster == ct)
       max2 <- sum(onemeta$finecluster != ct)
@@ -97,16 +97,16 @@ FindNNDist <- function(fullcluster,normCount, meaningn = 20) {
       # onecount 9763 x 2400 othercount 9763 * 3500
 
       # otherc_external 9763 * 1000
-      otherc_external <- as.matrix(othercount[,sample(1:ncol(othercount), min(500*length(normCount[-j]), ncol(othercount))) ])
+      otherc_external <- as.matrix(othercount[,sample(seq_len(ncol(othercount)), min(500*length(normCount[-j]), ncol(othercount))) ])
       #                                        sample(1: 3500           , min(500* 2                   ,   3500.   )
 
       for(nnn in seq_along(ncol(subc))) {
         ## tmp dim 1 * 500
-        tmp <- sort(sqrt(Matrix::colSums(sweep(otherc, 1, subc[,nnn], "-")^2)))[1:meaningn]
+        tmp <- sort(sqrt(Matrix::colSums(sweep(otherc, 1, subc[,nnn], "-")^2)))[seq_len(meaningn)]
         onecdist_internal[[nnn]] <- tmp
 
         ## tmp1 dim 1 * 1000
-        tmp1 <- sort(sqrt(Matrix::colSums(sweep(otherc_external, 1, subc[,nnn], "-")^2)))[1:meaningn]
+        tmp1 <- sort(sqrt(Matrix::colSums(sweep(otherc_external, 1, subc[,nnn], "-")^2)))[seq_len(meaningn)]
         onecdist_external[[nnn]] <- tmp1
       }
 
@@ -125,8 +125,8 @@ FindNNDist <- function(fullcluster,normCount, meaningn = 20) {
       # ksout <- t.test(dist1, dist2)
       # onep_vec[q] <- ksout$p.value
     }
-    names(onep_internal) <- paste0("Cluster-",seq(1:length(allCT)),"-dist1")
-    names(onep_external) <- paste0("Cluster-",seq(1:length(allCT)),"-dist2")
+    names(onep_internal) <- paste0("Cluster-",seq(seq_len(length(allCT))),"-dist1")
+    names(onep_external) <- paste0("Cluster-",seq(seq_len(length(allCT))),"-dist2")
 
     dist_mat[[j]] <- list(onep_internal,onep_external)
 
@@ -135,6 +135,6 @@ FindNNDist <- function(fullcluster,normCount, meaningn = 20) {
   }
   close(pb)
 
-  names(dist_mat) <- paste0("sample-",seq(1:length(fullcluster)))
+  names(dist_mat) <- paste0("sample-",seq(seq_len(length(fullcluster))))
   return(dist_mat)
 }

R/FindNNDistC.R

@@ -45,29 +45,29 @@ FindNNDistC <- function(fullcluster,normCount, meaningn = 20) {
     onemeta <- fullcluster[[j]]
 
     tmpvec <- c()
-    newcounter = 1
+    newcounter <-  1
 
     for(qqq in seq_along(fullcluster)) {
       if(qqq != j) {
         if(newcounter == 1) {
-          othercount = normCount[[qqq]]
-          newcounter = newcounter + 1
+          othercount <-  normCount[[qqq]]
+          newcounter <-  newcounter + 1
         } else {
-          othercount = cbind(othercount, normCount[[qqq]])
+          othercount <-  cbind(othercount, normCount[[qqq]])
         }
       }
     }
 
 
     allCT <- sort(unique(onemeta$finecluster))
 
-    onep_internal = onep_external = list()
+    onep_internal <-  onep_external = list()
 
     for(q in seq_along(allCT)) {
 
       ct = allCT[q]
 
-      onecdist_internal = onecdist_external <- list()
+      onecdist_internal <-  onecdist_external <- list()
 
       max1 <- sum(onemeta$finecluster == ct)
       max2 <- sum(onemeta$finecluster != ct)

R/GetCluster.R

@@ -35,7 +35,7 @@ GetCluster <- function(seuratlist,n1 = 50,n2 = 200) {
     onedata <- Seurat::ScaleData(onedata, features = all.genes)
     onedata <- Seurat::RunPCA(onedata,
                               features = Seurat::VariableFeatures(object = onedata),seed.use = sample(1000,1))
-    onedata <- Seurat::FindNeighbors(onedata, dims = 1:20)
+    onedata <- Seurat::FindNeighbors(onedata, dims = seq_len(20))
     onedata <- Seurat::FindClusters(onedata, resolution = 0.5)
     allcluster[[i]] <- onedata$seurat_clusters
   }

R/SCIntRuler-package.R

@@ -13,7 +13,7 @@
 #' robust and accurate analyses.
 #' @author Yue Lyu
 #'
-#'
+#' @return This is the main page for SCIntRuler package.
 #' @section Features:
 #' \itemize{
 #'   \item{Informed Decision Making: Helps researchers decide on the necessity

R/data.R

@@ -5,11 +5,11 @@
 #' @format An example PBMC data with SingleCellExperiment format
 #' \describe{
 #'   \item{\code{int_elementMetadata}}{A DataFrame with 3000 rows and 1 column, storing simulated gene information.}
-#'   \item{\code{int_colData}}{A DataFrame with 800 rows and 3 columns, representing metadata for each cell such as condition, cell type, and treatment group.}
+#'   \item{\code{int_colData}}{A DataFrame with 800 rows and 3 columns, representing metadata for each cell.}
 #'   \item{\code{int_metadata}}{A list containing two elements that provide additional global metadata about the experiment.}
 #'   \item{\code{rowRanges}}{A CompressedGRangesList object providing genomic range data associated with each row/gene.}
-#'   \item{\code{colData}}{A DataFrame with 800 rows and 8 columns, detailing cell-level metadata including phenotypic traits and experimental conditions.}
-#'   \item{\code{assays}}{A SimpleAssay object with matrix dimensions 3000x800, representing the gene expression matrix with 3000 genes across 800 cells.}
+#'   \item{\code{colData}}{A DataFrame with 800 rows and 8 columns, detailing cell-level metadata.}
+#'   \item{\code{assays}}{A SimpleAssay object with matrix dimensions 3000x800, representing the gene expression matrix.}
 #'   \item{\code{elementMetadata}}{A DataFrame linked with assays, providing gene-level metadata.}
 #' }
 #' @details

tests/testthat/test-SCEtoSeurat.R

@@ -1,4 +1,3 @@
-library(S4Vectors)
 test_that("SCEtoSeurat", {
   data("sim_data_sce")