Merge pull request #2416 from satijalab/develop

Seurat v3.1.2

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: Seurat
-Version: 3.1.1
-Date: 2019-09-23
+Version: 3.1.2
+Date: 2019-12-12
 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>, and Butler A and Satija R (2017) <doi:10.1101/164889> for more details.
 Authors@R: c(
@@ -62,7 +62,7 @@ Imports:
     tools,
     tsne,
     utils,
-    uwot
+    uwot (>= 0.1.5)
 LinkingTo: Rcpp (>= 0.11.0), RcppEigen, RcppProgress
 License: GPL-3 | file LICENSE
 LazyData: true
@@ -81,8 +81,9 @@ Collate:
     'tree.R'
     'utilities.R'
     'zzz.R'
-RoxygenNote: 6.1.1
+RoxygenNote: 7.0.2
 Encoding: UTF-8
+biocViews:
 Suggests:
     loomR,
     testthat,

NAMESPACE

@@ -4,7 +4,9 @@ S3method("$",JackStrawData)
 S3method("$",Seurat)
 S3method("$",SeuratCommand)
 S3method("$<-",Seurat)
+S3method("DefaultAssay<-",Assay)
 S3method("DefaultAssay<-",DimReduc)
+S3method("DefaultAssay<-",Graph)
 S3method("DefaultAssay<-",Seurat)
 S3method("Idents<-",Seurat)
 S3method("JS<-",DimReduc)
@@ -34,8 +36,11 @@ S3method(AddMetaData,Seurat)
 S3method(Cells,DimReduc)
 S3method(Cells,default)
 S3method(Command,Seurat)
+S3method(DefaultAssay,Assay)
 S3method(DefaultAssay,DimReduc)
+S3method(DefaultAssay,Graph)
 S3method(DefaultAssay,Seurat)
+S3method(DefaultAssay,SeuratCommand)
 S3method(Embeddings,DimReduc)
 S3method(Embeddings,Seurat)
 S3method(FindClusters,Seurat)
@@ -55,6 +60,8 @@ S3method(GetAssayData,Seurat)
 S3method(HVFInfo,Assay)
 S3method(HVFInfo,Seurat)
 S3method(Idents,Seurat)
+S3method(IsGlobal,DimReduc)
+S3method(IsGlobal,default)
 S3method(JS,DimReduc)
 S3method(JS,JackStrawData)
 S3method(Key,Assay)
@@ -175,6 +182,7 @@ export(CellCycleScoring)
 export(CellScatter)
 export(CellSelector)
 export(Cells)
+export(CellsByIdentities)
 export(CollapseEmbeddingOutliers)
 export(CollapseSpeciesExpressionMatrix)
 export(ColorDimSplit)
@@ -223,6 +231,7 @@ export(HVFInfo)
 export(HoverLocator)
 export(Idents)
 export(IntegrateData)
+export(IsGlobal)
 export(JS)
 export(JackStraw)
 export(JackStrawPlot)
@@ -265,12 +274,14 @@ export(ReadH5AD)
 export(Reductions)
 export(RegroupIdents)
 export(RelativeCounts)
+export(RenameAssays)
 export(RenameCells)
 export(RenameIdents)
 export(ReorderIdent)
 export(RestoreLegend)
 export(RidgePlot)
 export(RotatedAxis)
+export(RowMergeSparseMatrices)
 export(RunALRA)
 export(RunCCA)
 export(RunICA)
@@ -481,7 +492,6 @@ importFrom(reticulate,import)
 importFrom(reticulate,np_array)
 importFrom(reticulate,py_module_available)
 importFrom(reticulate,py_set_seed)
-importFrom(reticulate,r_to_py)
 importFrom(reticulate,tuple)
 importFrom(rlang,"!!")
 importFrom(rsvd,rsvd)

NEWS.md

@@ -3,6 +3,17 @@ All notable changes to Seurat will be documented in this file.
 The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/)
 
 
+## [3.1.2] - 2019-12-11
+### Added
+- New silent slot updater
+- New random seed options to `RunCCA`, `RunTSNE`, `WhichCells`, `HTODemux`, `AddModuleScore`, `VlnPlot`, and `RidgePlot`
+- Enhancements for dealing with `Assay`-derived objects
+
+### Changed
+- Only run `CalcN` (generates nFeatures and nCounts) when `counts` changes
+- Fix issue regarding colons in feature names
+- Change object class testing to use `inherits` or `is.*` for R 4.0 compatability
+
 ## [3.1.1] - 2019-09-20
 ### Added
 - New `RegroupIdents` function to reassign idents based on metadata column majority

R/clustering.R

@@ -158,6 +158,7 @@ FindClusters.Seurat <- function(
   weights = NULL,
   node.sizes = NULL,
   resolution = 0.8,
+  method = "matrix",
   algorithm = 1,
   n.start = 10,
   n.iter = 10,
@@ -183,6 +184,7 @@ FindClusters.Seurat <- function(
     weights = weights,
     node.sizes = node.sizes,
     resolution = resolution,
+    method = method,
     algorithm = algorithm,
     n.start = n.start,
     n.iter = n.iter,
@@ -208,7 +210,9 @@ FindClusters.Seurat <- function(
   )
   Idents(object = object) <- factor(x = Idents(object = object), levels = sort(x = levels))
   object[['seurat_clusters']] <- Idents(object = object)
-  object <- LogSeuratCommand(object)
+  cmd <- LogSeuratCommand(object = object, return.command = TRUE)
+  slot(object = cmd, name = 'assay.used') <- DefaultAssay(object = object[[graph.name]])
+  object[[slot(object = cmd, name = 'name')]] <- cmd
   return(object)
 }
 
@@ -419,7 +423,8 @@ FindNeighbors.Seurat <- function(
 ) {
   CheckDots(...)
   if (!is.null(x = dims)) {
-    assay <- assay %||% DefaultAssay(object = object)
+    # assay <- assay %||% DefaultAssay(object = object)
+    assay <- DefaultAssay(object = object[[reduction]])
     data.use <- Embeddings(object = object[[reduction]])
     if (max(dims) > ncol(x = data.use)) {
       stop("More dimensions specified in dims than have been computed")
@@ -456,6 +461,7 @@ FindNeighbors.Seurat <- function(
   }
   graph.name <- graph.name %||% paste0(assay, "_", names(x = neighbor.graphs))
   for (ii in 1:length(x = graph.name)) {
+    DefaultAssay(object = neighbor.graphs[[ii]]) <- assay
     object[[graph.name[[ii]]]] <- neighbor.graphs[[ii]]
   }
   if (do.plot) {
@@ -675,8 +681,7 @@ NNHelper <- function(data, query = data, k, method, ...) {
 # @keywords graph network igraph mvtnorm simulation
 #
 #' @importFrom leiden leiden
-#' @importFrom methods as is
-#' @importFrom reticulate py_module_available import r_to_py
+#' @importFrom reticulate py_module_available
 #' @importFrom igraph graph_from_adjacency_matrix graph_from_adj_list
 #
 # @author Tom Kelly
@@ -703,25 +708,34 @@ RunLeiden <- function(
   n.iter = 10
 ) {
   if (!py_module_available(module = 'leidenalg')) {
-    stop("Cannot find Leiden algorithm, please install through pip (e.g. pip install leidenalg).")
+    stop(
+      "Cannot find Leiden algorithm, please install through pip (e.g. pip install leidenalg).",
+      call. = FALSE
+    )
+  }
+  if (method %in% c("matrix", "igraph")) {
+    if (method == "igraph") {
+      object <- graph_from_adjacency_matrix(adjmatrix = object)
+    }
+  } else {
+    warning(
+      "method for Leiden recommended as 'matrix' or 'igraph'",
+      call. = FALSE,
+      immediate. = TRUE
+    )
+  }
+  input <- if (inherits(x = object, what = 'list')) {
+    graph_from_adj_list(adjlist = object)
+  } else if (inherits(x = object, what = c('dgCMatrix', 'matrix', "Matrix"))) {
+    graph_from_adjacency_matrix(adjmatrix = object)
+  } else if (inherits(x = object, what = 'igraph')) {
+    object
+  } else {
+    stop(
+      paste("method for Leiden not found for class", class(x = object)),
+      call. = FALSE
+    )
   }
-  switch(
-    EXPR = method,
-    #cast to dense (supported by reticulate for numpy.array)
-    "matrix" = input <- as(object, "matrix"),
-    #run as igraph object (passes to reticulate)
-    "igraph" = switch(
-      EXPR = is(object),
-      #generate igraph if needed (will handle updated snn class)
-      "Graph" = input <- graph_from_adjacency_matrix(adjmatrix = object),
-      "dgCMatrix" = input <- graph_from_adjacency_matrix(adjmatrix = object),
-      "igraph" = input <- object,
-      "matrix" = input <- graph_from_adjacency_matrix(adjmatrix = object),
-      "list" = input <- graph_from_adj_list(adjlist = object),
-      stop("SNN object must be a compatible input for igraph")
-    ),
-    stop("method for leiden must be 'matrix' or 'igraph'")
-  )
   #run leiden from CRAN package (calls python with reticulate)
   partition <- leiden(
     object = input,

R/differential_expression.R

@@ -136,7 +136,7 @@ FindAllMarkers <- function(
         return(cond$message)
       }
     )
-    if (class(x = genes.de[[i]]) == "character") {
+    if (is.character(x = genes.de[[i]])) {
       messages[[i]] <- genes.de[[i]]
       genes.de[[i]] <- NULL
     }
@@ -233,7 +233,7 @@ FindConservedMarkers <- function(
   verbose = TRUE,
   ...
 ) {
-  if (class(x = meta.method) != "function") {
+  if (!is.function(x = meta.method)) {
     stop("meta.method should be a function from the metap package. Please see https://cran.r-project.org/web/packages/metap/metap.pdf for a detailed description of the available functions.")
   }
   object.var <- FetchData(object = object, vars = grouping.var)
@@ -1412,7 +1412,7 @@ NBModelComparison <- function(y, theta, latent.data, com.fac, grp.fac) {
     ),
     silent = TRUE
   )
-  if (class(x = fit2)[1] == 'numeric' | class(x = fit4)[1] == 'numeric') {
+  if (is.numeric(x = fit2) || is.numeric(x = fit4)) {
     message('One of the glm.nb calls failed')
     return(c(rep(x = NA, 5), freqs))
   }

R/dimensional_reduction.R

@@ -314,6 +314,7 @@ ProjectDim <- function(
 #' @param standardize Standardize matrices - scales columns to have unit variance
 #' and mean 0
 #' @param num.cc Number of canonical vectors to calculate
+#' @param seed.use Random seed to set. If NULL, does not set a seed
 #' @param verbose Show progress messages
 #'
 #' @importFrom irlba irlba
@@ -326,10 +327,13 @@ RunCCA.default <- function(
   object2,
   standardize = TRUE,
   num.cc = 20,
+  seed.use = 42,
   verbose = FALSE,
   ...
 ) {
-  set.seed(seed = 42)
+  if (!is.null(x = seed.use)) {
+    set.seed(seed = seed.use)
+  }
   cells1 <- colnames(x = object1)
   cells2 <- colnames(x = object2)
   if (standardize) {
@@ -811,10 +815,10 @@ RunPCA.default <- function(
     cell.embeddings <- pca.results$v
   }
   else {
+    total.variance <- sum(RowVar(x = object))
     if (approx) {
       npcs <- min(npcs, nrow(x = object) - 1)
       pca.results <- irlba(A = t(x = object), nv = npcs, ...)
-      total.variance <- sum(RowVar(x = object))
       feature.loadings <- pca.results$v
       sdev <- pca.results$d/sqrt(max(1, ncol(object) - 1))
       if (weight.by.var) {
@@ -827,7 +831,6 @@ RunPCA.default <- function(
       pca.results <- prcomp(x = t(object), rank. = npcs, ...)
       feature.loadings <- pca.results$rotation
       sdev <- pca.results$sdev
-      total.variance <- sum(sdev)
       if (weight.by.var) {
         cell.embeddings <- pca.results$x %*% diag(pca.results$sdev[1:npcs]^2)
       } else {
@@ -858,7 +861,10 @@ RunPCA.default <- function(
   return(reduction.data)
 }
 
-#' @param features Features to compute PCA on
+#' @param features Features to compute PCA on. If features=NULL, PCA will be run 
+#' using the variable features for the Assay. Note that the features must be present
+#' in the scaled data. Any requested features that are not scaled or have 0 variance
+#' will be dropped, and the PCA will be run using the remaining features.
 #'
 #' @rdname RunPCA
 #' @export
@@ -943,7 +949,7 @@ RunPCA.Seurat <- function(
 }
 
 #' @param assay Name of assay that that t-SNE is being run on
-#' @param seed.use Random seed for the t-SNE
+#' @param seed.use Random seed for the t-SNE. If NULL, does not set the seed
 #' @param tsne.method Select the method to use to compute the tSNE. Available
 #' methods are:
 #' \itemize{
@@ -976,7 +982,9 @@ RunTSNE.matrix <- function(
   reduction.key = "tSNE_",
   ...
 ) {
-  set.seed(seed = seed.use)
+  if (!is.null(x = seed.use)) {
+    set.seed(seed = seed.use)
+  }
   tsne.data <- switch(
     EXPR = tsne.method,
     'Rtsne' = Rtsne(
@@ -1000,7 +1008,8 @@ RunTSNE.matrix <- function(
   tsne.reduction <- CreateDimReducObject(
     embeddings = tsne.data,
     key = reduction.key,
-    assay = assay
+    assay = assay,
+    global = TRUE
   )
   return(tsne.reduction)
 }
@@ -1109,7 +1118,7 @@ RunTSNE.Seurat <- function(
     )
   } else if (!is.null(x = features)) {
     RunTSNE(
-      object = as.matrix(x = GetAssayData(object = object)[features, cells]),
+      object = t(x = as.matrix(x = GetAssayData(object = object)[features, cells])),
       assay = DefaultAssay(object = object),
       seed.use = seed.use,
       tsne.method = tsne.method,
@@ -1247,7 +1256,8 @@ RunUMAP.default <- function(
   umap.reduction <- CreateDimReducObject(
     embeddings = umap.output,
     key = reduction.key,
-    assay = assay
+    assay = assay,
+    global = TRUE
   )
   return(umap.reduction)
 }
@@ -1331,7 +1341,12 @@ RunUMAP.Graph <- function(
   colnames(x = embeddings) <- paste0("UMAP_", 1:n.components)
   # center the embeddings on zero
   embeddings <- scale(x = embeddings, scale = FALSE)
-  umap <- CreateDimReducObject(embeddings = embeddings, key = reduction.key, assay = assay)
+  umap <- CreateDimReducObject(
+    embeddings = embeddings,
+    key = reduction.key,
+    assay = assay,
+    global = TRUE
+  )
   return(umap)
 }
 
@@ -1843,7 +1858,9 @@ JackRandom <- function(
   weight.by.var = weight.by.var,
   maxit = 1000
 ) {
-  set.seed(seed = seed.use)
+  if (!is.null(x = seed.use)) {
+    set.seed(seed = seed.use)
+  }
   rand.genes <- sample(
     x = rownames(x = scaled.data),
     size = nrow(x = scaled.data) * prop.use