Add spell check and vignette links

DESCRIPTION

@@ -23,7 +23,7 @@ Authors@R: c(
     person("scverse consortium", role = "spn")
   )
 Description: Bring the power and flexibility of AnnData to the R
-    ecosystem, allowing you to effortlessly manipulate and analyze your
+    ecosystem, allowing you to effortlessly manipulate and analyse your
     single-cell data. This package lets you work with backed h5ad and zarr
     files, directly access various slots (e.g. X, obs, var), or convert
     the data into SingleCellExperiment and Seurat objects.
@@ -54,6 +54,7 @@ Suggests:
     Seurat,
     SeuratObject,
     SingleCellExperiment,
+    spelling,
     SummarizedExperiment,
     testthat (>= 3.0.0),
     vctrs,
@@ -67,3 +68,4 @@ Config/testthat/edition: 3
 Encoding: UTF-8
 Roxygen: list(markdown = TRUE, r6 = TRUE)
 RoxygenNote: 7.3.3
+Language: en-GB

NEWS.md

@@ -66,7 +66,7 @@
 - Bump required R version to 4.5 in preparation for Bioconductor submission
   (PR #309)
 - Remove deprecated functions and arguments (PR #311, PR #313)
-- Minor cleanups and improvements (PR #313).
+- Minor clean-ups and improvements (PR #313).
 - Add more tests to increase coverage (PR #315)
 
 # anndataR 0.2.0
@@ -123,7 +123,7 @@
   - Most round trip tests are now enabled and pass successfully
   - Conversion helpers have been added to assist with **{reticulate}** tests
 
-# anndataR 0.1.0 (inital release candidate)
+# anndataR 0.1.0 (initial release candidate)
 
 Initial release candidate of **{anndataR}** including:
 

R/AbstractAnnData-s3methods.R

@@ -4,15 +4,15 @@
 #' making them behave like native R objects with familiar syntax.
 #'
 #' @details
-#' **Subsetting behavior**: The `[` method supports logical, integer, and character
+#' **Subsetting behaviour**: The `[` method supports logical, integer, and character
 #' subsetting for both observations (rows) and variables (columns). However, unlike
-#' standard R behavior:
+#' standard R behaviour:
 #'
 #' - Logical vectors are **not recycled** and must have the exact same length as
 #'   the dimension being subset
 #' - **Negative indices are not supported** (R's "exclude these" syntax)
 #'
-#' These design choices ensure clear and predictable subsetting behavior for
+#' These design choices ensure clear and predictable subsetting behaviour for
 #' biological data matrices, avoiding potential confusion from accidental recycling
 #' or exclusion patterns.
 #'
@@ -111,7 +111,7 @@ dimnames.AbstractAnnData <- function(x) {
 `dimnames<-.AbstractAnnData` <- function(x, value) {
   if (is.null(value)) {
     # When dimnames(x) <- NULL, generate default sequential names
-    # This mimics Python AnnData behavior but uses 1-based indexing (R convention)
+    # This mimics Python AnnData behaviour but uses 1-based indexing (R convention)
     # instead of 0-based indexing (Python convention)
     x$obs_names <- as.character(seq_len(x$n_obs()))
     x$var_names <- as.character(seq_len(x$n_vars()))

inst/WORDLIST

@@ -0,0 +1,36 @@
+AbstractAnnData
+AnnData
+AnnDataView
+Bioc
+BiocCheck
+BiocManager
+CMD
+HDF
+InMemoryAnnData
+LZF
+Lifecycle
+ORCID
+R's
+ReticulateAnnData
+SCE
+Seurat
+SeuratObject
+SingleCellExperiment
+Zarr
+anndata
+dynverse
+hdf
+knitr
+mudata
+obs
+png
+py
+rhdf
+scanpy
+scverse
+theislab
+transcriptomics
+var
+vars
+zarr
+zellkonverter

tests/spelling.R

@@ -0,0 +1,7 @@
+if (requireNamespace("spelling", quietly = TRUE)) {
+  spelling::spell_check_test(
+    vignettes = TRUE,
+    error = FALSE,
+    skip_on_cran = TRUE
+  )
+}

vignettes/anndataR.Rmd

@@ -61,7 +61,7 @@ BiocManager::install("anndataR")
 
 # Usage
 
-These sections breifly show how to use `r Biocpkg("anndataR")`.
+These sections briefly show how to use `r Biocpkg("anndataR")`.
 
 ## Read from disk
 
@@ -79,13 +79,13 @@ By default, a H5AD is read to an in-memory `AnnData` object:
 adata <- read_h5ad(h5ad_path)
 ```
 
-It can also be read as a `SingleCellExperiment` object:
+It can also be read as a `SingleCellExperiment` object (see `vignette("usage_singlecellexperiment")`):
 
 ```{r read-as-SingleCellExperiment}
 sce <- read_h5ad(h5ad_path, as = "SingleCellExperiment")
 ```
 
-Or as a `Seurat` object:
+Or as a `Seurat` object (see `vignette("usage_seurat")`):
 
 ```{r read-as-Seurat}
 obj <- read_h5ad(h5ad_path, as = "Seurat")
@@ -97,6 +97,8 @@ There is also a HDF5-backed `AnnData` object:
 adata <- read_h5ad(h5ad_path, as = "HDF5AnnData")
 ```
 
+See `r vignette("usage_python")` for interacting with a Python `AnnData` via `r CRANpkg("reticulate")`. 
+
 ## Using `AnnData` objects
 
 View structure:
@@ -118,28 +120,28 @@ See ```?`AnnData-usage` ```for more details on how to work with `AnnData` object
 
 ## Interoperability
 
-Convert the `AnnData` object to a `SingleCellExperiment` object:
+Convert the `AnnData` object to a `SingleCellExperiment` object (see `vignette("usage_singlecellexperiment")`):
 
 ```{r as-SingleCellExperiment}
 sce <- adata$as_SingleCellExperiment()
 sce
 ```
 
-Convert the `AnnData` object to a `Seurat` object:
+Convert the `AnnData` object to a `Seurat` object (see `vignette("usage_seurat")`):
 
 ```{r as-Seurat}
 obj <- adata$as_Seurat()
 obj
 ```
 
-Convert a `SingleCellExperiment` object to an `AnnData` object:
+Convert a `SingleCellExperiment` object to an `AnnData` object (see `vignette("usage_singlecellexperiment")`):
 
 ```{r as-AnnData-from-SingleCellExperiment}
 adata <- as_AnnData(sce)
 adata
 ```
 
-Convert a `Seurat` object to an `AnnData` object:
+Convert a `Seurat` object to an `AnnData` object (see `vignette("usage_seurat")`):
 
 ```{r as-AnnData-from-Seurat}
 adata <- as_AnnData(obj)

vignettes/usage_python.Rmd

@@ -70,20 +70,20 @@ sc$pp$log1p(adata)
 
 # Conversion to R objects
 
-Convert to `Seurat`:
-
-```{r convert_to_seurat}
-seurat_obj <- adata$as_Seurat()
-print(seurat_obj)
-```
-
-Convert to `SingleCellExperiment`:
+Convert to `SingleCellExperiment` (see `vignette("usage_singlecellexperiment")`):
 
 ```{r convert_to_sce}
 sce_obj <- adata$as_SingleCellExperiment()
 print(sce_obj)
 ```
 
+Convert to `Seurat` (see `vignette("usage_seurat")`):
+
+```{r convert_to_seurat}
+seurat_obj <- adata$as_Seurat()
+print(seurat_obj)
+```
+
 # Multi-modal data with _mudata_
 
 Install required Python packages if needed:

vignettes/usage_seurat.Rmd

@@ -36,7 +36,7 @@ install.packages("Seurat")
 
 Using an example `.h5ad` file included in the package, we will demonstrate how to read an `.h5ad` file and convert it to a `Seurat` object.
 
-```{r prep_file}
+```{r prep-file}
 library(anndataR)
 library(Seurat)
 
@@ -45,14 +45,14 @@ h5ad_file <- system.file("extdata", "example.h5ad", package = "anndataR")
 
 Read the `.h5ad` file as a `Seurat` object:
 
-```{r read_data}
+```{r read-data}
 seurat_obj <- read_h5ad(h5ad_file, as = "Seurat")
 seurat_obj
 ```
 
 This is equivalent to reading in the `.h5ad` file as an `AnnData` and explicitly converting:
 
-```{r convert_seurat}
+```{r convert-seurat}
 adata <- read_h5ad(h5ad_file)
 seurat_obj <- adata$as_Seurat()
 seurat_obj
@@ -85,7 +85,7 @@ Each of the mapping arguments can be provided with one of the following:
 
 See `?as_Seurat` for more details on how to customize the conversion process. For instance:
 
-```{r customize_seurat_conversion}
+```{r customize-seurat-conversion}
 seurat_obj <- adata$as_Seurat(
   layers_mapping = c("counts", "dense_counts"),
   object_metadata_mapping = c(metadata1 = "Int", metadata2 = "Float"),
@@ -106,13 +106,13 @@ The mapping arguments can also be passed directly to `read_h5ad()`.
 
 The reverse conversion is also possible, allowing you to convert the `Seurat` object back to an `AnnData` object, or to just write out the `Seurat` object as an `.h5ad` file.
 
-```{r write_seurat}
+```{r write-seurat}
 write_h5ad(seurat_obj, tempfile(fileext = ".h5ad"))
 ```
 
 This is equivalent to converting the `Seurat` object to an `AnnData` object and then writing it out:
 
-```{r convert_to_anndata}
+```{r convert-to-anndata}
 adata <- as_AnnData(seurat_obj)
 adata$write_h5ad(tempfile(fileext = ".h5ad"))
 ```
@@ -122,7 +122,7 @@ For more details, see `?as_AnnData`.
 
 Here's an example:
 
-```{r customize_anndata_conversion}
+```{r customize-anndata-conversion}
 adata <- as_AnnData(
   seurat_obj,
   assay_name = "RNA",

vignettes/usage_singlecellexperiment.Rmd

@@ -39,7 +39,7 @@ BiocManager::install("SingleCellExperiment")
 
 Using an example `.h5ad` file included in the package, we will demonstrate how to read an `.h5ad` file and convert it to a `SingleCellExperiment` object.
 
-```{r prep_h5ad_file}
+```{r prep-h5ad-file}
 library(anndataR)
 library(SingleCellExperiment)
 
@@ -48,14 +48,14 @@ h5ad_file <- system.file("extdata", "example.h5ad", package = "anndataR")
 
 Read the `.h5ad` file as a `SingleCellExperiment` object:
 
-```{r read_data}
+```{r read-data}
 sce_obj <- read_h5ad(h5ad_file, as = "SingleCellExperiment")
 sce_obj
 ```
 
 This is equivalent to reading in the `.h5ad` file as an `AnnData` and explicitly converting:
 
-```{r read_h5ad}
+```{r read-h5ad}
 adata <- read_h5ad(h5ad_file)
 sce <- adata$as_SingleCellExperiment()
 sce
@@ -86,7 +86,7 @@ Each of the mapping arguments can be provided with one of the following:
 
 See `?as_SingleCellExperiment` for more details on how to customize the conversion process. For instance:
 
-```{r ex_mapping}
+```{r ex-mapping}
 adata$as_SingleCellExperiment(
   x_mapping = "counts",
   assays_mapping = c("csc_counts"),
@@ -108,13 +108,13 @@ The mapping arguments can also be passed directly to `read_h5ad()`.
 
 The reverse conversion is also possible, allowing you to convert a `SingleCellExperiment` object back to an `AnnData` object, or to just write out the `SingleCellExperiment` object as an `.h5ad` file.
 
-```{r write_Seurat}
+```{r write-sce}
 write_h5ad(sce_obj, tempfile(fileext = ".h5ad"))
 ```
 
 This is equivalent to converting the `SingleCellExperiment` object to an `AnnData` object and then writing it out:
 
-```{r convert_and_write}
+```{r convert-and-write}
 adata <- as_AnnData(sce_obj)
 adata$write_h5ad(tempfile(fileext = ".h5ad"))
 ```
@@ -123,7 +123,7 @@ You can again customize the conversion process by providing specific mappings fo
 
 Here's an example:
 
-```{r customize_anndata_conversion}
+```{r customize-anndata-conversion}
 as_AnnData(
   sce_obj,
   x_mapping = "counts",