Various fixes to get rid of all the CHECK warnings.

- Updated all the URLs to their new locations.
- Avoid errors due to core limits during CHECK.
- Renamed the LICENSE to respect CRAN's dislike of license copies.

.Rbuildignore

@@ -3,3 +3,4 @@
 ^Dockerfile$
 ^\.github$
 ^\.gitignore$
+^LICENSE\.txt$

DESCRIPTION

@@ -10,7 +10,7 @@ Authors@R: c(person("Dvir", "Aran", email="[email protected]", role=c("aut", "c
 Description: Performs unbiased cell type recognition from single-cell RNA
     sequencing data, by leveraging reference transcriptomic datasets of pure cell
     types to infer the cell of origin of each single cell independently. 
-License: GPL-3 + file LICENSE
+License: GPL-3
 Depends: SummarizedExperiment
 Imports: 
     methods,
@@ -60,5 +60,5 @@ SystemRequirements: C++17
 VignetteBuilder: knitr
 Encoding: UTF-8
 RoxygenNote: 7.3.2
-URL: https://github.com/LTLA/SingleR
-BugReports: https://support.bioconductor.org/
+URL: https://github.com/SingleR-inc/SingleR
+BugReports: https://github.com/SingleR-inc/SingleR/issues

R/combineRecomputedResults.R

@@ -71,7 +71,7 @@
 #' @references
 #' Lun A, Bunis D, Andrews J (2020).
 #' Thoughts on a more scalable algorithm for multiple references.
-#' \url{https://github.com/LTLA/SingleR/issues/94}
+#' \url{https://github.com/SingleR-inc/SingleR/issues/94}
 #'
 #' @examples
 #' # Making up data.

README.md

@@ -38,19 +38,19 @@ install.packages("BiocManager")
 BiocManager::install("SingleR", version = "devel")
 ```
 
-Alternatively, you can install it from GitHub using the [devtools](https://github.com/hadley/devtools "devtools") package.
+Alternatively, you can install it from GitHub using the [devtools](https://github.com/r-lib/devtools "devtools") package.
 
 ```r
 install.packages("devtools")
 library(devtools)
-install_github("LTLA/SingleR")
+install_github("SingleR-inc/SingleR")
 ```
 
 ## Usage
 
 The `SingleR()` function annotates each cell in a test dataset given a reference dataset with known labels. Documentation and basic examples can be accessed with `?SingleR`.
 
-Both basic and advanced examples can be found in the [SingleR book](https://ltla.github.io/SingleRBook/).
+Both basic and advanced examples can be found in the [SingleR book](https://bioconductor.org/books/devel/SingleRBook/).
 
 ### Usage with Seurat/SingleCellExperiment objects
 
@@ -78,4 +78,4 @@ Using broad labels can reduce the time to under 15 minutes, though run times wil
 SingleR was originally developed by Dvir Aran. 
 This refactor was initiated by Aaron Lun, with additional contributions from Daniel Bunis, Friederike Dündar, and Jared Andrews.
 
-[Issues](https://github.com/LTLA/SingleR/issues) and [pull requests](https://github.com/LTLA/SingleR/pulls) are welcome.
+[Issues](https://github.com/SingleR-inc/SingleR/issues) and [pull requests](https://github.com/SingleR-inc/SingleR/pulls) are welcome.

inst/NEWS.Rd

@@ -46,7 +46,7 @@ Restrict the PCA to the top 1000 most highly variable genes, for speed.
 \section{Version 1.4.0}{\itemize{
 \item Migrated all of the dataset getter functions to the \pkg{celldex} package.
 
-\item Streamlined the vignette to point to the book at \url{http://bioconductor.org/books/devel/SingleRBook/}.
+\item Streamlined the vignette to point to the book at \url{https://bioconductor.org/books/devel/SingleRBook/}.
 
 \item Added a \code{restrict=} argument to \code{trainSingleR()} and \code{SingleR()} to easily restrict to a subset of features.
 

tests/testthat/test-aggregate.R

@@ -108,7 +108,7 @@ test_that("aggregateReference seed setter behaves correctly", {
     setAutoBPPARAM(SerialParam())
 
     set.seed(20)
-    out <- aggregateReference(sce, labels, BPPARAM=BiocParallel::SnowParam(3))
+    out <- aggregateReference(sce, labels, BPPARAM=BiocParallel::SnowParam(2))
     expect_identical(ref, out)
 
     # The seed is unset properly for downstream applications.
@@ -117,7 +117,7 @@ test_that("aggregateReference seed setter behaves correctly", {
     test1 <- runif(10)
 
     set.seed(10)
-    aggregateReference(sce, labels, BPPARAM=BiocParallel::SnowParam(3))
+    aggregateReference(sce, labels, BPPARAM=BiocParallel::SnowParam(2))
     test2 <- runif(10)
     expect_identical(test1, test2)
 

tests/testthat/test-recomputed.R

@@ -62,7 +62,7 @@ test_that("combineRecomputedResults matrix fragmentation works as expected", {
         results=list(pred1, pred2), 
         test=test,
         trained=list(train1, train2),
-        BPPARAM=BiocParallel::MulticoreParam(3))
+        BPPARAM=BiocParallel::MulticoreParam(2))
     expect_equal(combined1, combined1x)
 
     # Testing that it works for DA's, as well as when the DA

vignettes/SingleR.Rmd

@@ -37,7 +37,7 @@ for the reference dataset, and this biological knowledge can be propagated to ne
 To keep things brief, this vignette only provides a brief summary of the basic capabilities of `r Biocpkg("SingleR")`.
 However, the package also provides more advanced functionality that includes the use of multiple references simultaneously,
 manipulating the cell ontology and improving performance on big datasets.
-Readers are referred to the [book](http://bioconductor.org/books/devel/SingleRBook/) for more details. 
+Readers are referred to the [book](https://bioconductor.org/books/devel/SingleRBook/) for more details. 
 
 # Using built-in references
 
@@ -171,7 +171,7 @@ plotHeatmap(sceG, order_columns_by="labels",
 *How can I use this with my `Seurat`, `SingleCellExperiment`, or `cell_data_set` object?*
 
 `r Biocpkg("SingleR")` is workflow agnostic - all it needs is normalized counts.
-An example showing how to map its results back to common single-cell data objects is available in the [README](https://github.com/LTLA/SingleR/blob/master/README.md).
+An example showing how to map its results back to common single-cell data objects is available in the [README](https://github.com/SingleR-inc/SingleR/blob/master/README.md).
 
 *Where can I find reference sets appropriate for my data?*
 
@@ -183,9 +183,9 @@ but have been migrated into a separate package for more general use by other Bio
 *Where can I get more help?*
 
 It is likely that your questions is already answered by the function documentation (e.g., `?SingleR`).
-Further explanations on the reasoning behind certain functions can be found in the [book](https://ltla.github.io/SingleRBook/).
+Further explanations on the reasoning behind certain functions can be found in the [book](https://bioconductor.org/books/devel/SingleRBook/).
 If this is not sufficient, we recommend posting an issue on the [Bioconductor support site](https://support.bioconductor.org)
-or [the GitHub repository](https://github.com/LTLA/SingleR) for this package.
+or [the GitHub repository](https://github.com/SingleR-inc/SingleR) for this package.
 Be sure to include your session information and a minimal reproducible example.
 
 # Session information