Increment version number to 0.1.2.9002

liangdp1984 · Jun 18, 2022 · f448617 · f448617
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commit f448617
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diff --git a/.github/workflows/R-CMD-check.yaml b/.github/workflows/R-CMD-check.yaml
@@ -30,6 +30,19 @@ jobs:
       R_KEEP_PKG_SOURCE: yes
 
     steps:
+
+      - name: Set up Linux swap space
+        if: runner.os == 'Linux'
+        uses: pierotofy/[email protected]
+        with:
+          swap-size-gb: 10
+
+      - name: Set up Windows swap space
+        if: runner.os == 'Windows'
+        uses: al-cheb/[email protected]
+        with:
+          minimum-size: 10GB
+
       - uses: actions/checkout@v2
 
       - name: Install system libraries
@@ -41,6 +54,7 @@ jobs:
         if: ${{ matrix.config.os == 'macOS-latest' }}
         run: |
             brew install openssl
+            brew install --cask xquartz
 
       - uses: r-lib/actions/setup-pandoc@v2
 

diff --git a/.github/workflows/pkgdown.yaml b/.github/workflows/pkgdown.yaml
@@ -20,6 +20,19 @@ jobs:
     env:
       GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
     steps:
+
+      - name: Set up Linux swap space
+        if: runner.os == 'Linux'
+        uses: pierotofy/[email protected]
+        with:
+          swap-size-gb: 10
+
+      - name: Set up Windows swap space
+        if: runner.os == 'Windows'
+        uses: al-cheb/[email protected]
+        with:
+          minimum-size: 10GB
+
       - uses: actions/checkout@v2
 
       - name: Install system libraries

diff --git a/DESCRIPTION b/DESCRIPTION
@@ -1,7 +1,7 @@
 Package: SCP
 Type: Package
 Title: Single Cell Pipeline
-Version: 0.1.2.9001
+Version: 0.1.2.9002
 Author: Hao Zhang
 Maintainer: Hao Zhang <[email protected]>
 Description: The SCP package provides a comprehensive set of tools for single cell data processing and downstream analysis.
@@ -56,8 +56,6 @@ Imports:
     symphony,
     umap,
     uwot,
-    harmony,
-    shiny,
     rhdf5,
     HDF5Array,
     scDblFinder,
@@ -71,14 +69,13 @@ Imports:
     harmony,
     rliger,
     png,
-    grDevices,
     ggpubr,
     ggVennDiagram,
     ggupset,
-    circlize,
     ggwordcloud,
     MatrixGenerics,
-    symphony,
+    scmap,
+    SingleR,
     shiny,
     shinycssloaders,
     styler
@@ -95,7 +92,8 @@ Suggests:
     NMF,
     MASS,
     Mfuzz,
-    magick,
     AUCell,
-    htmlwidgets
+    htmlwidgets,
+    testthat (>= 3.0.0)
 RoxygenNote: 7.2.0
+Config/testthat/edition: 3
diff --git a/R/SCP-analysis.R b/R/SCP-analysis.R
@@ -515,19 +515,19 @@ CellScoring <- function(srt, features, ncores = 1, method = "Seurat", classifica
 #' @examples
 #' library(dplyr)
 #' data(pancreas1k)
-#' pancreas1k <- RunDEtest(pancreas1k, group_by = "SubCellType")
+#' pancreas1k <- RunDEtest(pancreas1k, group_by = "CellType")
 #'
 #' # Heatmap
-#' de_filter <- filter(pancreas1k@tools$DEtest_SubCellType$AllMarkers_wilcox, p_val_adj < 0.01 & avg_log2FC > 1)
-#' ExpHeatmapPlot(pancreas1k, genes = de_filter$gene, gene_groups = de_filter$group1, cell_group_by = "SubCellType")
+#' de_filter <- filter(pancreas1k@tools$CellType$AllMarkers_wilcox, p_val_adj < 0.05 & avg_log2FC > 1)
+#' ExpHeatmapPlot(pancreas1k, genes = de_filter$gene, gene_groups = de_filter$group1, cell_group_by = "CellType")
 #'
 #' # Dot plot
 #' de_top <- de_filter %>%
 #'   group_by(gene) %>%
 #'   top_n(1, avg_log2FC) %>%
 #'   group_by(group1) %>%
 #'   top_n(3, avg_log2FC)
-#' ExpDotPlot(pancreas1k, genes = de_top$gene, gene_groups = de_top$group1, cell_group_by = "SubCellType")
+#' ExpDotPlot(pancreas1k, genes = de_top$gene, gene_groups = de_top$group1, cell_group_by = "CellType")
 #' @export
 #'
 RunDEtest <- function(srt, group_by = NULL, cell_group1 = NULL, cell_group2 = NULL,
@@ -1300,7 +1300,7 @@ RunEnrichment <- function(geneID = NULL, geneID_groups = NULL, IDtype = "symbol"
 #' if (interactive()) {
 #'   data("pancreas1k")
 #'   library(dplyr)
-#'   pancreas1k <- RunDEtest(pancreas1k, group_by = "CellType", only.pos = FALSE)
+#'   pancreas1k <- RunDEtest(pancreas1k, group_by = "CellType", only.pos = FALSE, fc.threshold = 1)
 #'   de_filter <- filter(pancreas1k@tools$DEtest_CellType$AllMarkers_wilcox, p_val_adj < 0.05)
 #'   res <- RunGSEA(geneID = de_filter$gene, geneScore = de_filter$avg_log2FC, geneID_groups = de_filter$group1, species = "Mus_musculus")
 #'   GSEAPlot(x = res$results[[1]], geneSetID = res$results[[1]]@result$ID[1])

diff --git a/R/SCP-cell_annotation.R b/R/SCP-cell_annotation.R
@@ -32,55 +32,57 @@ Preprocess <- function() {
 #' pancreas1k <- RunKNNPredict(srt_query = pancreas1k, query_group = "SubCellType", bulk_ref = SCP::ref_scMCA)
 #' ClassDimPlot(pancreas1k, group.by = "knnpredict_classification", label = TRUE)
 #'
-#' # Annotate using single cell RNA-seq data
-#' if (!require("SeuratData", quietly = TRUE)) {
-#'   devtools::install_github("zhanghao-njmu/seurat-data")
-#' }
-#' library(stringr)
-#' library(SeuratData)
-#' suppressWarnings(InstallData("panc8"))
-#' data("panc8")
+#' if (interactive()) {
+#'   # Annotate using single cell RNA-seq data
+#'   if (!require("SeuratData", quietly = TRUE)) {
+#'     devtools::install_github("zhanghao-njmu/seurat-data")
+#'   }
+#'   library(stringr)
+#'   library(SeuratData)
+#'   suppressWarnings(InstallData("panc8"))
+#'   data("panc8")
 #'
-#' # Simply convert genes from human to mouse and preprocess the data
-#' genenm <- make.unique(str_to_title(rownames(panc8)))
-#' panc8 <- RenameFeatures(panc8, newnames = genenm)
+#'   # Simply convert genes from human to mouse and preprocess the data
+#'   genenm <- make.unique(str_to_title(rownames(panc8)))
+#'   panc8 <- RenameFeatures(panc8, newnames = genenm)
 #'
-#' panc8 <- check_srtMerge(panc8, batch = "tech")[["srtMerge"]]
-#' pancreas1k <- RunKNNPredict(srt_query = pancreas1k, srt_ref = panc8, ref_group = "celltype")
-#' ClassDimPlot(pancreas1k, group.by = "knnpredict_classification", label = TRUE)
-#' ExpDimPlot(pancreas1k, features = "knnpredict_simil")
+#'   panc8 <- check_srtMerge(panc8, batch = "tech")[["srtMerge"]]
+#'   pancreas1k <- RunKNNPredict(srt_query = pancreas1k, srt_ref = panc8, ref_group = "celltype")
+#'   ClassDimPlot(pancreas1k, group.by = "knnpredict_classification", label = TRUE)
+#'   ExpDimPlot(pancreas1k, features = "knnpredict_simil")
 #'
-#' pancreas1k <- RunKNNPredict(
-#'   srt_query = pancreas1k, srt_ref = panc8,
-#'   ref_group = "celltype", ref_collapsing = FALSE
-#' )
-#' ClassDimPlot(pancreas1k, group.by = "knnpredict_classification", label = TRUE)
-#' ExpDimPlot(pancreas1k, features = "knnpredict_prob")
+#'   pancreas1k <- RunKNNPredict(
+#'     srt_query = pancreas1k, srt_ref = panc8,
+#'     ref_group = "celltype", ref_collapsing = FALSE
+#'   )
+#'   ClassDimPlot(pancreas1k, group.by = "knnpredict_classification", label = TRUE)
+#'   ExpDimPlot(pancreas1k, features = "knnpredict_prob")
 #'
-#' pancreas1k <- RunKNNPredict(
-#'   srt_query = pancreas1k, srt_ref = panc8,
-#'   query_group = "SubCellType", ref_group = "celltype",
-#'   query_collapsing = TRUE, ref_collapsing = TRUE
-#' )
-#' ClassDimPlot(pancreas1k, group.by = "knnpredict_classification", label = TRUE)
-#' ExpDimPlot(pancreas1k, features = "knnpredict_simil")
+#'   pancreas1k <- RunKNNPredict(
+#'     srt_query = pancreas1k, srt_ref = panc8,
+#'     query_group = "SubCellType", ref_group = "celltype",
+#'     query_collapsing = TRUE, ref_collapsing = TRUE
+#'   )
+#'   ClassDimPlot(pancreas1k, group.by = "knnpredict_classification", label = TRUE)
+#'   ExpDimPlot(pancreas1k, features = "knnpredict_simil")
 #'
-#' # Annotate with DE gene instead of HVF
-#' pancreas1k <- RunKNNPredict(
-#'   srt_query = pancreas1k, srt_ref = panc8,
-#'   ref_group = "celltype",
-#'   features_type = "DE", feature_source = "ref"
-#' )
-#' ClassDimPlot(pancreas1k, group.by = "knnpredict_classification", label = TRUE)
-#' ExpDimPlot(pancreas1k, features = "knnpredict_simil")
+#'   # Annotate with DE gene instead of HVF
+#'   pancreas1k <- RunKNNPredict(
+#'     srt_query = pancreas1k, srt_ref = panc8,
+#'     ref_group = "celltype",
+#'     features_type = "DE", feature_source = "ref"
+#'   )
+#'   ClassDimPlot(pancreas1k, group.by = "knnpredict_classification", label = TRUE)
+#'   ExpDimPlot(pancreas1k, features = "knnpredict_simil")
 #'
-#' pancreas1k <- RunKNNPredict(
-#'   srt_query = pancreas1k, srt_ref = panc8,
-#'   query_group = "SubCellType", ref_group = "celltype",
-#'   features_type = "DE", feature_source = "both"
-#' )
-#' ClassDimPlot(pancreas1k, group.by = "knnpredict_classification", label = TRUE)
-#' ExpDimPlot(pancreas1k, features = "knnpredict_simil")
+#'   pancreas1k <- RunKNNPredict(
+#'     srt_query = pancreas1k, srt_ref = panc8,
+#'     query_group = "SubCellType", ref_group = "celltype",
+#'     features_type = "DE", feature_source = "both"
+#'   )
+#'   ClassDimPlot(pancreas1k, group.by = "knnpredict_classification", label = TRUE)
+#'   ExpDimPlot(pancreas1k, features = "knnpredict_simil")
+#' }
 #' @importFrom methods as
 #' @importFrom Matrix colSums t rowSums
 #' @importFrom Seurat DefaultAssay GetAssayData FindVariableFeatures VariableFeatures AverageExpression
@@ -481,30 +483,32 @@ RunKNNPredict <- function(srt_query, srt_ref = NULL, bulk_ref = NULL,
 
 #' Annotate single cells using scmap.
 #' @examples
-#' if (!require("SeuratData", quietly = TRUE)) {
-#'   devtools::install_github("zhanghao-njmu/seurat-data")
-#' }
-#' library(SeuratData)
-#' library(stringr)
-#' suppressWarnings(InstallData("panc8"))
-#' data("panc8")
-#' genenm <- make.unique(str_to_title(rownames(panc8)))
-#' panc8 <- RenameFeatures(panc8, newnames = genenm)
-#' panc8 <- check_srtMerge(panc8, batch = "tech")[["srtMerge"]]
+#' if (interactive()) {
+#'   if (!require("SeuratData", quietly = TRUE)) {
+#'     devtools::install_github("zhanghao-njmu/seurat-data")
+#'   }
+#'   library(SeuratData)
+#'   library(stringr)
+#'   suppressWarnings(InstallData("panc8"))
+#'   data("panc8")
+#'   genenm <- make.unique(str_to_title(rownames(panc8)))
+#'   panc8 <- RenameFeatures(panc8, newnames = genenm)
+#'   panc8 <- check_srtMerge(panc8, batch = "tech")[["srtMerge"]]
 #'
-#' # Annotation
-#' pancreas1k <- Standard_SCP(pancreas1k)
-#' pancreas1k <- RunScmap(
-#'   srt_query = pancreas1k, srt_ref = panc8,
-#'   ref_group = "celltype", method = "scmapCluster"
-#' )
-#' ClassDimPlot(pancreas1k, group.by = "scmap_annotation")
+#'   # Annotation
+#'   pancreas1k <- Standard_SCP(pancreas1k)
+#'   pancreas1k <- RunScmap(
+#'     srt_query = pancreas1k, srt_ref = panc8,
+#'     ref_group = "celltype", method = "scmapCluster"
+#'   )
+#'   ClassDimPlot(pancreas1k, group.by = "scmap_annotation")
 #'
-#' pancreas1k <- RunScmap(
-#'   srt_query = pancreas1k, srt_ref = panc8,
-#'   ref_group = "celltype", method = "scmapCell"
-#' )
-#' ClassDimPlot(pancreas1k, group.by = "scmap_annotation")
+#'   pancreas1k <- RunScmap(
+#'     srt_query = pancreas1k, srt_ref = panc8,
+#'     ref_group = "celltype", method = "scmapCell"
+#'   )
+#'   ClassDimPlot(pancreas1k, group.by = "scmap_annotation")
+#' }
 #' @importFrom Seurat GetAssayData
 #' @export
 RunScmap <- function(srt_query, srt_ref, ref_group = NULL, method = "scmapCluster",
@@ -609,37 +613,39 @@ RunScmap <- function(srt_query, srt_ref, ref_group = NULL, method = "scmapCluste
 
 #' Annotate single cells using scmap.
 #' @examples
-#' if (!require("SeuratData", quietly = TRUE)) {
-#'   devtools::install_github("zhanghao-njmu/seurat-data")
-#' }
-#' library(SeuratData)
-#' library(stringr)
-#' suppressWarnings(InstallData("panc8"))
-#' data("panc8")
-#' genenm <- make.unique(str_to_title(rownames(panc8)))
-#' panc8 <- RenameFeatures(panc8, newnames = genenm)
-#' panc8 <- check_srtMerge(panc8, batch = "tech")[["srtMerge"]]
+#' if (interactive()) {
+#'   if (!require("SeuratData", quietly = TRUE)) {
+#'     devtools::install_github("zhanghao-njmu/seurat-data")
+#'   }
+#'   library(SeuratData)
+#'   library(stringr)
+#'   suppressWarnings(InstallData("panc8"))
+#'   data("panc8")
+#'   genenm <- make.unique(str_to_title(rownames(panc8)))
+#'   panc8 <- RenameFeatures(panc8, newnames = genenm)
+#'   panc8 <- check_srtMerge(panc8, batch = "tech")[["srtMerge"]]
 #'
-#' # Annotation
-#' pancreas1k <- Standard_SCP(pancreas1k)
-#' pancreas1k <- RunSingleR(
-#'   srt_query = pancreas1k, srt_ref = panc8,
-#'   query_group = "Standardclusters", ref_group = "celltype",
-#' )
-#' ClassDimPlot(pancreas1k, group.by = "singler_annotation")
+#'   # Annotation
+#'   pancreas1k <- Standard_SCP(pancreas1k)
+#'   pancreas1k <- RunSingleR(
+#'     srt_query = pancreas1k, srt_ref = panc8,
+#'     query_group = "Standardclusters", ref_group = "celltype",
+#'   )
+#'   ClassDimPlot(pancreas1k, group.by = "singler_annotation")
 #'
-#' pancreas1k <- RunSingleR(
-#'   srt_query = pancreas1k, srt_ref = panc8,
-#'   query_group = NULL, ref_group = "celltype"
-#' )
-#' ClassDimPlot(pancreas1k, group.by = "singler_annotation")
+#'   pancreas1k <- RunSingleR(
+#'     srt_query = pancreas1k, srt_ref = panc8,
+#'     query_group = NULL, ref_group = "celltype"
+#'   )
+#'   ClassDimPlot(pancreas1k, group.by = "singler_annotation")
+#' }
 #' @importFrom Seurat GetAssayData
 #' @export
 RunSingleR <- function(srt_query, srt_ref, query_group = NULL, ref_group = NULL,
                        genes = "de", de.method = "wilcox", sd.thresh = 1, de.n = NULL,
                        aggr.ref = FALSE, aggr.args = list(),
                        quantile = 0.8, fine.tune = TRUE, tune.thresh = 0.05, prune = TRUE,
-                       BNPARAM = BiocNeighbors::KmknnParam(), BPPARAM = BiocParallel::bpparam(),
+                       BPPARAM = BiocParallel::bpparam(),
                        query_assay = "RNA", ref_assay = "RNA", force = FALSE) {
   if (!is.null(ref_group)) {
     if (length(ref_group) == ncol(srt_ref)) {
@@ -713,7 +719,7 @@ RunSingleR <- function(srt_query, srt_ref, query_group = NULL, ref_group = NULL,
       de.method = de.method, genes = genes, sd.thresh = sd.thresh, de.n = de.n,
       aggr.ref = aggr.ref, aggr.args = aggr.args,
       quantile = quantile, fine.tune = fine.tune, tune.thresh = tune.thresh, prune = prune,
-      BNPARAM = BNPARAM, BPPARAM = BPPARAM
+      BPPARAM = BPPARAM
     )
     names(SingleRCluster_results$labels) <- levels(factor(SummarizedExperiment::colData(sce_query)[[query_group]]))
     rownames(SingleRCluster_results$scores) <- levels(factor(SummarizedExperiment::colData(sce_query)[[query_group]]))
@@ -748,7 +754,7 @@ RunSingleR <- function(srt_query, srt_ref, query_group = NULL, ref_group = NULL,
       de.method = de.method, genes = genes, sd.thresh = sd.thresh, de.n = de.n,
       aggr.ref = aggr.ref, aggr.args = aggr.args,
       quantile = quantile, fine.tune = fine.tune, tune.thresh = tune.thresh, prune = prune,
-      BNPARAM = BNPARAM, BPPARAM = BPPARAM
+      BPPARAM = BPPARAM
     )
     srt_query$singler_annotation <- if (isTRUE(prune)) {
       SingleRCell_results$pruned.labels