add BiocStyle macros

episodes/cell_type_annotation.Rmd

@@ -492,23 +492,22 @@ TODO
     of clusters based on marker gene expression, and 2) computational annotation
     based on annotation transfer from reference datasets or marker gene set enrichment testing.
 -   For manual annotation, cells are first clustered with unsupervised methods
-    such as graph-based clustering followed community detection algorithms such
+    such as graph-based clustering followed by community detection algorithms such
     as Louvain or Leiden.
--   The `clusterCells` function from the
-    [scran](https://bioconductor.org/packages/scran) package provides different
+-   The `clusterCells` function from the `r Biocpkg("scran")` package provides different
     algorithms that are commonly used for the clustering of scRNA-seq data.
 -   Once clusters have been obtained, cell type labels are then manually
     assigned to cell clusters by matching cluster-specific upregulated marker
     genes with prior knowledge of cell-type markers.
--   The `findMarkers` function from the [scran](https://bioconductor.org/packages/scran) package
+-   The `findMarkers` function from the `r Biocpkg("scran")` package 
     package can be used to find candidate marker genes for clusters of cells by
     testing for differential expression between pairs of clusters.
 -   Computational annotation using published reference datasets or curated gene sets
     provides a fast, automated, and reproducible alternative to the manual
     annotation of cell clusters based on marker gene expression.
--   The [SingleR](https://bioconductor.org/packages/SingleR)
+-   The `r Biocpkg("SingleR")`
     package is a popular choice for reference-based annotation and assigns labels
     to cells based on the reference samples with the highest Spearman rank correlations.
--   The [AUCell](https://bioconductor.org/packages/AUCell) package provides an enrichment
+-   The `r Biocpkg("AUCell")` package provides an enrichment
     test to identify curated marker sets that are highly expressed in each cell. 
 :::

episodes/eda_qc.Rmd

@@ -201,7 +201,7 @@ While the univariate distribution of QC metrics can give some insight on the qua
 plotColData(sce, x="sum", y="subsets_Mito_percent", colour_by="discard")
 ```
 
-It could also be a good idea to perform a differential expression analysis between retained and discarded cells to check wether we are removing an unusual cell population rather than low-quality libraries (see [Section 1.5 of OSCA advanced](http://bioconductor.org/books/3.17/OSCA.advanced/quality-control-redux.html#qc-discard-cell-types)).
+It could also be a good idea to perform a differential expression analysis between retained and discarded cells to check wether we are removing an unusual cell population rather than low-quality libraries (see [Section 1.5 of OSCA advanced](https://bioconductor.org/books/release/OSCA.advanced/quality-control-redux.html#qc-discard-cell-types)).
 
 Once we are happy with the results, we can discard the low-quality cells by subsetting the original object.
 

episodes/intro-sce.Rmd

@@ -156,7 +156,7 @@ colData(sce)
 
 ### The `reducedDims`
 
-Everything that we have described so far (except for the `counts` getter) is part of the `SummarizedExperiment` class that SingleCellExperiment extends. You can find a complete lesson on the `SummarizedExperiment` class [here](https://carpentries-incubator.github.io/bioc-intro/60-next-steps.html).
+Everything that we have described so far (except for the `counts` getter) is part of the `SummarizedExperiment` class that SingleCellExperiment extends. You can find a complete lesson on the `SummarizedExperiment` class in [Introduction to data analysis with R and Bioconductor](https://carpentries-incubator.github.io/bioc-intro/60-next-steps.html) course.
 
 One of the peculiarity of SingleCellExperiment is its ability to store reduced dimension matrices within the object. These may include PCA, t-SNE, UMAP, etc.