Merge pull request satijalab#414 from satijalab/fix/futureAnnoy

fix annoy search with non-multicore future plans

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: Seurat
-Version: 3.2.1.9003
-Date: 2020-09-17
+Version: 3.2.1.9004
+Date: 2020-09-18
 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 Stuart T, Butler A, et al (2019) <doi:10.1016/j.cell.2019.05.031> for more details.
 Authors@R: c(

NAMESPACE

@@ -468,6 +468,7 @@ importFrom(cowplot,plot_grid)
 importFrom(cowplot,theme_cowplot)
 importFrom(fitdistrplus,fitdist)
 importFrom(future,nbrOfWorkers)
+importFrom(future,plan)
 importFrom(future.apply,future_lapply)
 importFrom(future.apply,future_sapply)
 importFrom(ggplot2,Geom)

NEWS.md

@@ -9,6 +9,7 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/)
 - Set the seed in `WhichCells` regardless of whether or not `idents` is passed
 - Retain Graph and Neighbor objects when subsetting only on features
 - Fix data.frame input to `CreateAssayObject()` when data.frame has no rownames.
+- Default annoy search to sequential if not using multicore future plans.
 
 ## [3.2.1] - 2020-09-04
 ### Added

R/clustering.R

@@ -553,12 +553,18 @@ AnnoyBuildIndex <- function(data, metric = "euclidean", n.trees = 50) {
 # @param search.k During the query it will inspect up to search_k nodes which
 # gives you a run-time tradeoff between better accuracy and speed.
 # @ param include.distance Include the corresponding distances
+#' @importFrom future plan
+#' @importFrom future.apply future_lapply
 #
 AnnoySearch <- function(index, query, k, search.k = -1, include.distance = TRUE) {
   n <- nrow(x = query)
   idx <- matrix(nrow = n,  ncol = k)
   dist <- matrix(nrow = n, ncol = k)
   convert <- methods::is(index, "Rcpp_AnnoyAngular")
+  if (!inherits(x = plan(), what = "multicore")) {
+    oplan <- plan(strategy = "sequential")
+    on.exit(plan(oplan), add = TRUE)
+  }
   res <- future_lapply(X = 1:n, FUN = function(x) {
     res <- index$getNNsByVectorList(query[x, ], k, search.k, include.distance)
     # Convert from Angular to Cosine distance