Merge branch 'develop' into release/visium

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: Seurat
-Version: 3.1.1.9021
-Date: 2019-11-19
+Version: 3.1.1.9023
+Date: 2019-12-04
 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(

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/objects.R

@@ -725,7 +725,7 @@ CreateSeuratObject <- function(
       warning("Some cells in meta.data not present in provided counts matrix.")
       meta.data <- meta.data[intersect(x = rownames(x = meta.data), y = colnames(x = counts)), ]
     }
-    if (class(x = meta.data) == "data.frame") {
+    if (is.data.frame(x = meta.data)) {
       new.meta.data <- data.frame(row.names = colnames(x = counts))
       for (ii in 1:ncol(x = meta.data)) {
         new.meta.data[rownames(x = meta.data), colnames(x = meta.data)[ii]] <- meta.data[, ii, drop = FALSE]
@@ -6355,7 +6355,7 @@ setMethod( # because R doesn't allow S3-style [[<- for S4 classes
         )
       }
       # Check keyed objects
-      if (class(x = value) %in% c('Assay', 'DimReduc')) {
+      if (inherits(x = value, what = c('Assay', 'DimReduc'))) {
         if (length(x = Key(object = value)) == 0) {
           Key(object = value) <- paste0(tolower(x = i), '_')
         } else if (!grepl(pattern = '^[[:alnum:]]+_$', x = Key(object = value))) {

R/preprocessing.R

@@ -109,9 +109,9 @@ CalculateBarcodeInflections <- function(
     }
   )$x
   ## workaround for aggregate behavior noted above
-  if (class(x = whichmin_list) == 'list') { # uneven lengths
+  if (is.list(x = whichmin_list)) { # uneven lengths
     is_inflection <- unlist(x = whichmin_list)
-  } else if (class(x = whichmin_list) == 'matrix') { # even lengths
+  } else if (is.matrix(x = whichmin_list)) { # even lengths
     is_inflection <- as.vector(x = t(x = whichmin_list))
   }
   tmp <- cbind(barcode_dist_sub, is_inflection)
@@ -191,15 +191,15 @@ CreateGeneActivityMatrix <- function(
   peak.df <- as.data.frame(x = peak.df)
   colnames(x = peak.df) <- c("chromosome", 'start', 'end')
   peaks.gr <- GenomicRanges::makeGRangesFromDataFrame(df = peak.df)
-  
+
   # if any peaks start at 0, change to 1
-  # otherwise GenomicRanges::distanceToNearest will not work 
+  # otherwise GenomicRanges::distanceToNearest will not work
   BiocGenerics::start(peaks.gr[BiocGenerics::start(peaks.gr) == 0, ]) <- 1
 
   # get annotation file, select genes
   gtf <- rtracklayer::import(con = annotation.file)
   gtf <- GenomeInfoDb::keepSeqlevels(x = gtf, value = seq.levels, pruning.mode = 'coarse')
-  
+
   # change seqlevelsStyle if not the same
   if (!any(GenomeInfoDb::seqlevelsStyle(x = gtf) == GenomeInfoDb::seqlevelsStyle(x = peaks.gr))) {
     GenomeInfoDb::seqlevelsStyle(gtf) <- GenomeInfoDb::seqlevelsStyle(peaks.gr)
@@ -216,11 +216,11 @@ CreateGeneActivityMatrix <- function(
   keep.overlaps <- gene.distances[rtracklayer::mcols(x = gene.distances)$distance == 0]
   peak.ids <- peaks.gr[S4Vectors::queryHits(x = keep.overlaps)]
   gene.ids <- gtf.genes[S4Vectors::subjectHits(x = keep.overlaps)]
-  
+
   # Some GTF rows will not have gene_name attribute
   # Replace it by gene_id attribute
   gene.ids$gene_name[is.na(gene.ids$gene_name)] <- gene.ids$gene_id[is.na(gene.ids$gene_name)]
-  
+
   peak.ids$gene.name <- gene.ids$gene_name
   peak.ids <- as.data.frame(x = peak.ids)
   peak.ids$peak <- rownames(peak.matrix)[S4Vectors::queryHits(x = keep.overlaps)]
@@ -584,10 +584,10 @@ GetResidual <- function(
 #' mat_norm
 #'
 LogNormalize <- function(data, scale.factor = 1e4, verbose = TRUE) {
-  if (class(x = data) == "data.frame") {
+  if (is.data.frame(x = data)) {
     data <- as.matrix(x = data)
   }
-  if (class(x = data) != "dgCMatrix") {
+  if (!inherits(x = data, what = 'dgCMatrix')) {
     data <- as(object = data, Class = "dgCMatrix")
   }
   # call Rcpp function to normalize
@@ -1068,10 +1068,10 @@ Read10X_h5 <- function(filename, use.names = TRUE, unique.features = TRUE) {
 #' mat_norm
 #'
 RelativeCounts <- function(data, scale.factor = 1, verbose = TRUE) {
-  if (class(x = data) == "data.frame") {
+  if (is.data.frame(x = data)) {
     data <- as.matrix(x = data)
   }
-  if (class(x = data) != "dgCMatrix") {
+  if (!inherits(x = data, what = 'dgCMatrix')) {
     data <- as(object = data, Class = "dgCMatrix")
   }
   if (verbose) {
@@ -1422,10 +1422,10 @@ SubsetByBarcodeInflections <- function(object) {
 #' mat_norm <- TF.IDF(data = mat)
 #'
 TF.IDF <- function(data, verbose = TRUE) {
-  if (class(x = data) == "data.frame") {
+  if (is.data.frame(x = data)) {
     data <- as.matrix(x = data)
   }
-  if (class(x = data) != "dgCMatrix") {
+  if (!inherits(x = data, what = 'dgCMatrix')) {
     data <- as(object = data, Class = "dgCMatrix")
   }
   if (verbose) {
@@ -2116,7 +2116,7 @@ RunALRA.Seurat <- function(
 
 #' @importFrom future nbrOfWorkers
 #'
-#' @param features Vector of features names to scale/center. Default is all features
+#' @param features Vector of features names to scale/center. Default is variable features.
 #' @param vars.to.regress Variables to regress out (previously latent.vars in
 #' RegressOut). For example, nUMI, or percent.mito.
 #' @param latent.data Extra data to regress out, should be cells x latent data
@@ -2505,7 +2505,6 @@ ClassifyCells <- function(data, q) {
         message("No threshold found for ", colnames(x = data)[i], "...")
       }
     )
-    # if (class(x = model) == "character") {
     if (is.character(x = model)) {
       next
     }
@@ -2572,10 +2571,10 @@ ClassifyCells <- function(data, q) {
 # @import Matrix
 #
 CustomNormalize <- function(data, custom_function, margin, verbose = TRUE) {
-  if (class(x = data) == "data.frame") {
+  if (is.data.frame(x = data)) {
     data <- as.matrix(x = data)
   }
-  if (class(x = data) != "dgCMatrix") {
+  if (!inherits(x = data, what = 'dgCMatrix')) {
     data <- as(object = data, Class = "dgCMatrix")
   }
   myapply <- ifelse(test = verbose, yes = pbapply, no = apply)
@@ -2785,7 +2784,7 @@ NBResiduals <- function(fmla, regression.mat, gene, return.mode = FALSE) {
       data = regression.mat
     ),
     silent = TRUE)
-  if (class(fit)[1] == 'numeric') {
+  if (is.numeric(x = fit)) {
     message(sprintf('glm.nb failed for gene %s; falling back to scale(log(y+1))', gene))
     resid <- scale(x = log(x = regression.mat[, 'GENE'] + 1))[, 1]
     mode <- 'scale'