diff --git a/content/03.results.md b/content/03.results.md
index 9f0e14a..1b03659 100644
--- a/content/03.results.md
+++ b/content/03.results.md
@@ -192,7 +192,7 @@ These submitter-provided annotations can be found in all `SingleCellExperiment`
 
 Two different methods were used for annotating cell types: `SingleR` and `CellAssign`. 
 `SingleR` is a reference-based annotation method that requires an existing bulk or single-cell RNA-seq dataset with annotations. 
-For all libraries on the Portal, we used the `BlueprintEncodeData` [@doi:10.3324/haematol.2013.094243;10.1038/nature11247] dataset from the `celldex` package [@doi:10.18129/B9.bioc.celldex], which includes a variety of normal cell types and provides both the human-readable cell name and cell ontology identifier [@url:https://www.ebi.ac.uk/ols4/ontologies/cl]. 
+For all libraries on the Portal, we used the `BlueprintEncodeData` [@doi:10.3324/haematol.2013.094243; @doi:10.1038/nature11247] dataset from the `celldex` package [@doi:10.18129/B9.bioc.celldex; @doi:10.1038/s41590-018-0276-y], which includes a variety of normal cell types and provides both the human-readable cell name and cell ontology identifier [@url:https://www.ebi.ac.uk/ols4/ontologies/cl]. 
 In contrast, `CellAssign` is a marker-gene-based annotation method that requires a binary matrix with all cell types and all associated marker genes as the reference. 
 We utilized the list of marker genes available as part of `PanglaoDB` [@doi:10.1093/database/baz046] to construct organ-specific marker gene matrices with marker genes from all cells for the specified organ. 
 Since many cancers may have infiltrating immune cells, all immune cells were included in each organ-specific reference.