Restore the ontology chapter with fixed call to getCellOnto().

SingleR-inc · Nov 30, 2023 · 06b9fb9 · 06b9fb9
1 parent 6ad4fc8
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Showing 3 changed files with 2 additions and 6 deletions.
diff --git a/inst/book/_bookdown.yml b/inst/book/_bookdown.yml
@@ -12,6 +12,7 @@ rmd_files: ["index.Rmd",
     "scref.Rmd",
     "diagnostics.Rmd",
     "multiple.Rmd",
+    "ontology.Rmd",
     "advanced.Rmd",
     "pancreas.Rmd",
     "brain.Rmd",

diff --git a/inst/book/multiple.Rmd b/inst/book/multiple.Rmd
@@ -183,14 +183,9 @@ simplifying interpretation and ensuring that irrelevant discrepancies in labelli
 Many of the `r Biocpkg("SingleR")` reference datasets already have their labels 
 mapped to the [Cell Ontology](https://www.ebi.ac.uk/ols/ontologies/cl),
 which provides a standard vocabulary to refer to the same cell type across diverse datasets.
-The key idea is that the same term is used for the same conceptual cell type in each reference.
-To simplify interpretation, we set `cell.ont="nonna"` to remove all samples that could not be mapped to the ontology.
-
-<!-- 
 We will describe the utility of Cell Ontology terms in more detail in Chapter \@ref(exploiting-the-cell-ontology);
 at this point, the key idea is that the same term is used for the same conceptual cell type in each reference.
 To simplify interpretation, we set `cell.ont="nonna"` to remove all samples that could not be mapped to the ontology.
--->
 
 ```{r}
 hpca.ont <- HumanPrimaryCellAtlasData(ensembl=TRUE, cell.ont="nonna")

diff --git a/inst/book/ontology.Rmd b/inst/book/ontology.Rmd
@@ -24,7 +24,7 @@ that we can query for various pieces of information.
 ```{r}
 # TODO: wrap in utility function.
 library(ontoProc)
-cl <- getCellOnto()
+cl <- getOnto("cellOnto")
 cl
 ```