Merge pull request #5905 from satijalab/develop

Seurat v4.1.1

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: Seurat
-Version: 4.1.0
-Date: 2022-01-14
+Version: 4.1.1
+Date: 2022-05-01
 Title: Tools for Single Cell Genomics
 Description: A toolkit for quality control, analysis, and exploration of single cell RNA sequencing data. 'Seurat' aims to enable users to identify and interpret sources of heterogeneity from single cell transcriptomic measurements, and to integrate diverse types of single cell data. See Satija R, Farrell J, Gennert D, et al (2015) <doi:10.1038/nbt.3192>, Macosko E, Basu A, Satija R, et al (2015) <doi:10.1016/j.cell.2015.05.002>, Stuart T, Butler A, et al (2019) <doi:10.1016/j.cell.2019.05.031>, and Hao, Hao, et al (2020) <doi:10.1101/2020.10.12.335331> for more details.
 Authors@R: c(
@@ -66,7 +66,7 @@ Imports:
     scales,
     scattermore (>= 0.7),
     sctransform (>= 0.3.3),
-    SeuratObject (>= 4.0.4),
+    SeuratObject (>= 4.1.0),
     shiny,
     spatstat.core,
     spatstat.geom,

NEWS.md

@@ -1,7 +1,19 @@
+# Seurat 4.1.1 (2022-05-01)
+
+## Changes
+- Fix `giveCsparse` related warnings in `Read10X_h5`
+- Fix ident labeling for `SpatialPlot` ([#5774](https://github.com/satijalab/seurat/issues/5774))
+- Fix `ReadMtx` on Windows ([#5687](https://github.com/satijalab/seurat/issues/5687))
+- Fix `VlnPlot` to switch on rasterization only when required ([#5846](https://github.com/satijalab/seurat/pull/5846))
+- Fix `ncol` behavior in `SpatialPlot` ([#5774](https://github.com/satijalab/seurat/issues/5774))
+- Set `jitter` to FALSE in `FeatureScatter` ([#5876](https://github.com/satijalab/seurat/pull/5876))
+- Update `Cells` methods to new signature (`x, ...`)
+- Replace use of `default.stringsAsFactors()` with `getOption("stringsAsFactors")`
+
 # Seurat 4.1.0 (2022-01-14)
 ## Added
 - Add `raster.dpi` parameter to `DimPlot/FeaturePlot` to optionally rasterize individual points ([#5392](https://github.com/satijalab/seurat/pull/5392))
-- Add support for sctransform v2, differential expression on with SCT
+- Add support for sctransform v2, differential expression with SCT assay
 
 ## Changes
 - Update `ReadParseBio` to support split-pipe 0.9.6p ([#5446](https://github.com/satijalab/seurat/pull/5446))

R/generics.R

@@ -444,15 +444,15 @@ RunPCA <- function(object, ...) {
 #' Run Supervised Latent Semantic Indexing
 #'
 #' Run a supervised LSI (SLSI) dimensionality reduction supervised by a
-#' cell-cell kernel. SLSI is used to capture a linear transformation of peaks 
+#' cell-cell kernel. SLSI is used to capture a linear transformation of peaks
 #' that maximizes its dependency to the given cell-cell kernel.
 #'
 #' @param object An object
 #' @param ... Arguments passed to IRLBA irlba
 #'
 #' @return Returns Seurat object with the SLSI calculation stored in the
 #' reductions slot
-#' 
+#'
 #' @export
 #'
 #' @rdname RunSLSI
@@ -506,8 +506,9 @@ RunTSNE <- function(object, ...) {
 #' Run UMAP
 #'
 #' Runs the Uniform Manifold Approximation and Projection (UMAP) dimensional
-#' reduction technique. To run, you must first install the umap-learn python
-#' package (e.g. via \code{pip install umap-learn}). Details on this package can be
+#' reduction technique. To run using \code{umap.method="umap-learn"}, you must
+#' first install the umap-learn python package (e.g. via
+#' \code{pip install umap-learn}). Details on this package can be
 #' found here: \url{https://github.com/lmcinnes/umap}. For a more in depth
 #' discussion of the mathematics underlying UMAP, see the ArXiv paper here:
 #' \url{https://arxiv.org/abs/1802.03426}.

R/objects.R

@@ -454,6 +454,7 @@ CreateSCTAssayObject <- function(
 #' remove all DimReducs)
 #' @param graphs Only keep a subset of Graphs specified here (if NULL, remove
 #' all Graphs)
+#' @param misc Preserve the \code{misc} slot; default is \code{TRUE}
 #'
 #' @export
 #' @concept objects
@@ -466,7 +467,8 @@ DietSeurat <- function(
   features = NULL,
   assays = NULL,
   dimreducs = NULL,
-  graphs = NULL
+  graphs = NULL,
+  misc = TRUE
 ) {
   object <- UpdateSlots(object = object)
   assays <- assays %||% FilterObjects(object = object, classes.keep = "Assay")
@@ -508,6 +510,11 @@ DietSeurat <- function(
       }
     }
   }
+  # remove misc when desired
+  if (!isTRUE(x = misc)) {
+    slot(object = object, name = "misc") <- list()
+  }
+
   # remove unspecified DimReducs and Graphs
   all.objects <- FilterObjects(object = object, classes.keep = c('DimReduc', 'Graph'))
   objects.to.remove <- all.objects[!all.objects %in% c(dimreducs, graphs)]
@@ -1301,7 +1308,7 @@ as.sparse.H5Group <- function(x, ...) {
 #' @method Cells SCTModel
 #' @export
 #'
-Cells.SCTModel <- function(x) {
+Cells.SCTModel <- function(x, ...) {
   return(rownames(x = slot(object = x, name = "cell.attributes")))
 }
 
@@ -1313,7 +1320,7 @@ Cells.SCTModel <- function(x) {
 #'
 #' @seealso \code{\link[SeuratObject:Cells]{SeuratObject::Cells}}
 #'
-Cells.SlideSeq <- function(x) {
+Cells.SlideSeq <- function(x, ...) {
   return(rownames(x = GetTissueCoordinates(object = x)))
 }
 
@@ -1323,7 +1330,7 @@ Cells.SlideSeq <- function(x) {
 #' @method Cells STARmap
 #' @export
 #'
-Cells.STARmap <- function(x) {
+Cells.STARmap <- function(x, ...) {
   return(rownames(x = GetTissueCoordinates(object = x)))
 }
 
@@ -1332,7 +1339,7 @@ Cells.STARmap <- function(x) {
 #' @method Cells VisiumV1
 #' @export
 #'
-Cells.VisiumV1 <- function(x) {
+Cells.VisiumV1 <- function(x, ...) {
   return(rownames(x = GetTissueCoordinates(object = x, scale = NULL)))
 }
 
@@ -2274,7 +2281,7 @@ setMethod(
   definition = function(object) {
     cat('An AnchorSet object containing', nrow(x = slot(object = object, name = "anchors")),
         "anchors between the reference and query Seurat objects. \n",
-        "This can be used as input to TransferData.")
+        "This can be used as input to TransferData.\n")
   }
 )
 
@@ -2284,7 +2291,7 @@ setMethod(
   definition = function(object) {
     cat('An AnchorSet object containing', nrow(x = slot(object = object, name = "anchors")),
         "anchors between", length(x = slot(object = object, name = "object.list")), "Seurat objects \n",
-        "This can be used as input to IntegrateData.")
+        "This can be used as input to IntegrateData.\n")
   }
 )
 
@@ -2295,7 +2302,7 @@ setMethod(
     cat(
       'A ModalityWeights object containing modality weights between',
       paste(slot(object = object, name = "modality.assay"), collapse = " and "),
-      "assays \n", "This can be used as input to FindMultiModelNeighbors.")
+      "assays \n", "This can be used as input to FindMultiModelNeighbors.\n")
   }
 )
 
@@ -2307,7 +2314,7 @@ setMethod(
       "An sctransform model.\n",
       " Model formula: ", slot(object = object, name = "model"),
       "\n  Parameters stored for", nrow(x = SCTResults(object = object, slot = "feature.attributes")), "features,",
-      nrow(x = SCTResults(object = object, slot = "cell.attributes")), "cells")
+      nrow(x = SCTResults(object = object, slot = "cell.attributes")), "cells.\n")
   }
 )
 

R/preprocessing.R

@@ -954,7 +954,7 @@ Read10X_h5 <- function(filename, use.names = TRUE, unique.features = TRUE) {
       p = indptr[],
       x = as.numeric(x = counts[]),
       dims = shp[],
-      giveCsparse = FALSE
+      repr = "T"
     )
     if (unique.features) {
       features <- make.unique(names = features)
@@ -967,7 +967,11 @@ Read10X_h5 <- function(filename, use.names = TRUE, unique.features = TRUE) {
       types <- infile[[paste0(genome, '/features/feature_type')]][]
       types.unique <- unique(x = types)
       if (length(x = types.unique) > 1) {
-        message("Genome ", genome, " has multiple modalities, returning a list of matrices for this genome")
+        message(
+          "Genome ",
+          genome,
+          " has multiple modalities, returning a list of matrices for this genome"
+        )
         sparse.mat <- sapply(
           X = types.unique,
           FUN = function(x) {
@@ -1103,10 +1107,19 @@ ReadMtx <- function(
     "feature list" = features
   )
   for (i in seq_along(along.with = all.files)) {
-    uri <- normalizePath(all.files[[i]], mustWork = FALSE)
+    uri <- tryCatch(
+      expr = {
+        con <- url(description = all.files[[i]])
+        close(con = con)
+        all.files[[i]]
+      },
+      error = function(...) {
+        return(normalizePath(path = all.files[[i]], winslash = '/'))
+      }
+    )
     err <- paste("Cannot find", names(x = all.files)[i], "at", uri)
     uri <- build_url(url = parse_url(url = uri))
-    if (grepl(pattern = '^:///', x = uri)) {
+    if (grepl(pattern = '^[A-Z]?:///', x = uri)) {
       uri <- gsub(pattern = '^://', replacement = '', x = uri)
       if (!file.exists(uri)) {
         stop(err, call. = FALSE)

R/utilities.R

@@ -1610,7 +1610,7 @@ as.data.frame.Matrix <- function(
   row.names = NULL,
   optional = FALSE,
   ...,
-  stringsAsFactors = default.stringsAsFactors()
+  stringsAsFactors = getOption(x = "stringsAsFactors", default = FALSE)
 ) {
   return(as.data.frame(
     x = as.matrix(x = x),

R/visualization.R

@@ -1890,7 +1890,7 @@ CellScatter <- function(
 #' 100,000
 #' @param raster.dpi Pixel resolution for rasterized plots, passed to geom_scattermore().
 #' Default is c(512, 512).
-#' @param jitter Jitter for easier visualization of crowded points
+#' @param jitter Jitter for easier visualization of crowded points (default is FALSE)
 #'
 #' @return A ggplot object
 #'
@@ -1924,7 +1924,7 @@ FeatureScatter <- function(
   plot.cor = TRUE,
   raster = NULL,
   raster.dpi = c(512, 512),
-  jitter = TRUE
+  jitter = FALSE
 ) {
   cells <- cells %||% colnames(x = object)
   if (isTRUE(x = shuffle)) {
@@ -2418,7 +2418,7 @@ LinkedDimPlot <- function(
           dims = dims,
           col.by = group.by,
           alpha.by = plot.env$alpha.by
-        ) + scale_alpha_ordinal(range = alpha) + guides(alpha = FALSE)
+        ) + scale_alpha_ordinal(range = alpha) + guides(alpha = "none")
         plot.env$dimplot
       }
     )
@@ -2555,7 +2555,7 @@ LinkedFeaturePlot <- function(
           scale_fill_gradientn(name = feature, colours = cols) +
           theme(legend.position = 'top') +
           scale_alpha(range = alpha) +
-          guides(alpha = FALSE)
+          guides(alpha = "none")
         plot.env$spatialplot
       }
     )
@@ -2914,7 +2914,7 @@ ISpatialFeaturePlot <- function(
         scale_fill_gradientn(name = plot.env$feature, colours = FeaturePalettes[[plot.env$palette]]) +
         theme(legend.position = 'top') +
         scale_alpha(range = c(input$alpha, 1)) +
-        guides(alpha = FALSE)
+        guides(alpha = "none")
       plot.env$plot
     })
   }
@@ -3232,7 +3232,7 @@ SpatialPlot <- function(
           ) +
           theme(legend.position = 'top') +
           scale_alpha(range = alpha) +
-          guides(alpha = FALSE)
+          guides(alpha = "none")
       } else if (label) {
         plot <- LabelClusters(
           plot = plot,
@@ -3286,10 +3286,14 @@ SpatialPlot <- function(
   #     images = GetImage(object = object, mode = 'plotly', image = images)
   #   ))
   # }
-  if (length(x = images) > 1 && combine) {
-    plots <- wrap_plots(plots = plots, ncol = length(x = images))
-  } else if (length(x = images == 1) && combine) {
-    plots <- wrap_plots(plots = plots, ncol = ncol)
+  if (combine) {
+    if (!is.null(x = ncol)) {
+      return(wrap_plots(plots = plots, ncol = ncol))
+    }
+    if (length(x = images) > 1) {
+      return(wrap_plots(plots = plots, ncol = length(x = images)))
+    }
+    return(wrap_plots(plots = plots))
   }
   return(plots)
 }
@@ -3542,13 +3546,20 @@ DotPlot <- function(
   if (!is.null(x = id.levels)) {
     data.plot$id <- factor(x = data.plot$id, levels = id.levels)
   }
-  if (length(x = levels(x = data.plot$id)) == 1) {
+  ngroup <- length(x = levels(x = data.plot$id))
+  if (ngroup == 1) {
     scale <- FALSE
     warning(
       "Only one identity present, the expression values will be not scaled",
       call. = FALSE,
       immediate. = TRUE
     )
+  } else if (ngroup < 5 & scale) {
+    warning(
+      "Scaling data with a low number of groups may produce misleading results",
+      call. = FALSE,
+      immediate. = TRUE
+    )
   }
   avg.exp.scaled <- sapply(
     X = unique(x = data.plot$features.plot),
@@ -7240,8 +7251,9 @@ SingleExIPlot <- function(
     if ((nrow(x = data) > 1e5) & !isFALSE(raster)){
       message("Rasterizing points since number of points exceeds 100,000.",
               "\nTo disable this behavior set `raster=FALSE`")
+      # change raster to TRUE
+      raster <- TRUE
     }
-    raster <- TRUE
   }
   if (!is.null(x = seed.use)) {
     set.seed(seed = seed.use)
@@ -7666,6 +7678,7 @@ SingleSpatialPlot <- function(
       colors <- DiscretePalette(length(unique(data[[col.by]])), palette = cols)
       scale <- scale_fill_manual(values = colors, na.value = na.value)
     } else {
+      cols <- cols[names(x = cols) %in% data$ident]
       scale <- scale_fill_manual(values = cols, na.value = na.value)
     }
     plot <- plot + scale

cran-comments.md

@@ -1,19 +1,33 @@
-# Seurat v4.1.0
+# Seurat v4.1.1
 
 ## Test environments
-* local Ubuntu 20.04 install, R 4.1.2
+* local Ubuntu 20.04 install, R 4.1.3
 * Ubuntu 16.04.6 (on travis-ci), R 4.0.0, R devel
 * macOS 10.13.6 (on travis-ci), R 4.0.2
 * Windows Server 2012 R2 (on AppVeyor), R 4.1.0 Patched
 * win-builder (release, devel)
 
 ## R CMD check results
-There were no ERRORs, WARNINGs, or NOTEs
+There were no ERRORs or WARNINGs
+
+There was one NOTE:
+
+* checking CRAN incoming feasibility ... NOTE
+Maintainer: 'Paul Hoffman <seurat@nygenome.org>'
+
+Found the following (possibly) invalid URLs:
+  URL: https://www.science.org/doi/abs/10.1126/science.aad0501
+    From: man/cc.genes.Rd
+          man/cc.genes.updated.2019.Rd
+    Status: 503
+    Message: Service Unavailable
+
+This URL is valid and the service still exists. When navigating to the URL either via the documentation or directly, you are taken to the correct article
 
 ## Downstream dependencies
 
 There no packages that depend on Seurat
 
-There are sixteen packages that import Seurat: CAMML, CDSeq, CIDER, DR.SC, DUBStepR, PhitestR, Platypus, rPanglaoDB, scDiffCom, scMappR, SCRIP, scRNAstat, Signac, SignacX, SoupX, and tidyseurat; this update does not impact their functionality
+There are sixteen packages that import Seurat: CAMML, CIDER, DR.SC, DUBStepR, maple, Platypus, rPanglaoDB, scDiffCom, scMappR, SCRIP, scRNAstat, Signac, SignacX, SoupX, spruce, and tidyseurat; this update does not impact their functionality
 
-There are ten packages that suggest Seurat: BisqueRNA, ClustAssess, clustree, conos, DIscBIO, dyngen, harmony, rliger, Rmagic, and VAM; this update does not impact their functionality.
+There are twelve packages that suggest Seurat: BisqueRNA, CIARA, ClustAssess, clustree, conos, DIscBIO, dyngen, harmony, rliger, Rmagic, treefit, and VAM; this update does not impact their functionality.