diff --git a/episodes/cell_type_annotation.Rmd b/episodes/cell_type_annotation.Rmd
index c2a8feb..78b2c02 100644
--- a/episodes/cell_type_annotation.Rmd
+++ b/episodes/cell_type_annotation.Rmd
@@ -83,17 +83,15 @@ which the cells truly reside. After annotation based on marker genes,
 the clusters can be treated as proxies for more abstract biological
 concepts such as cell types or states.
 
-Popularized by its use in
-[Seurat](https://cran.r-project.org/web/packages/Seurat/index.html),
-graph-based clustering is a flexible and scalable technique for
-clustering large scRNA-seq datasets. We first build a graph where each
-node is a cell that is connected to its nearest neighbors in the
-high-dimensional space. Edges are weighted based on the similarity
-between the cells involved, with higher weight given to cells that are
-more closely related. We then apply algorithms to identify "communities"
-of cells that are more connected to cells in the same community than
-they are to cells of different communities. Each community represents a
-cluster that we can use for downstream interpretation.
+Graph-based clustering is a flexible and scalable technique for identifying
+coherent groups of cells in large scRNA-seq datasets. We first build a graph
+where each node is a cell that is connected to its nearest neighbors in the
+high-dimensional space. Edges are weighted based on the similarity between the
+cells involved, with higher weight given to cells that are more closely related.
+We then apply algorithms to identify "communities" of cells that are more
+connected to cells in the same community than they are to cells of different
+communities. Each community represents a cluster that we can use for downstream
+interpretation.
 
 Here, we use the `clusterCells()` function from the
 [scran](https://bioconductor.org/packages/scran) package to perform
@@ -549,12 +547,6 @@ AUCell_exploreThresholds(cell.aucs[10:18], plotHist = TRUE, assign = TRUE)
 
 ::::
 
-## Session Info
-
-```{r sessionInfo}
-sessionInfo()
-```
-
 ## Exercises
 
 ::: challenge
@@ -720,3 +712,9 @@ Remember, this is an exploratory diagnostic, not the final word! At this point i
 -   The `r Biocpkg("AUCell")` package provides an enrichment
     test to identify curated marker sets that are highly expressed in each cell. 
 :::
+
+## Session Info
+
+```{r, tidy=TRUE}
+sessionInfo()
+```
diff --git a/episodes/eda_qc.Rmd b/episodes/eda_qc.Rmd
index c251066..3e64712 100644
--- a/episodes/eda_qc.Rmd
+++ b/episodes/eda_qc.Rmd
@@ -808,3 +808,10 @@ Mathematically, this would require the data to fall on a two-dimensional plane (
 [^2]: [Vallejos (2017)](learners/reference.md#litref)
 [^3]: [Lun (2016)](learners/reference.md#litref)
 
+## Session Info
+
+```{r, tidy=TRUE}
+sessionInfo()
+```
+
+
diff --git a/episodes/hca.Rmd b/episodes/hca.Rmd
index a7fbac6..a836d15 100644
--- a/episodes/hca.Rmd
+++ b/episodes/hca.Rmd
@@ -347,3 +347,8 @@ You can see we don't get very many cells given the strict set of conditions we u
 ::::::::::::::::::::::::::::::::::::::::::::::::
 
 
+## Session Info
+
+```{r, tidy=TRUE}
+sessionInfo()
+```
diff --git a/episodes/intro-sce.Rmd b/episodes/intro-sce.Rmd
index 5370dc4..2b7b6c1 100644
--- a/episodes/intro-sce.Rmd
+++ b/episodes/intro-sce.Rmd
@@ -314,3 +314,9 @@ combined_sce
 
 1. Pijuan-Sala B, Griffiths JA, Guibentif C et al. (2019). A single-cell molecular map of mouse gastrulation and early organogenesis. Nature 566, 7745:490-495.
 
+## Session Info
+
+```{r, tidy=TRUE}
+sessionInfo()
+```
+
diff --git a/episodes/large_data.Rmd b/episodes/large_data.Rmd
index 4db4a26..63d5a07 100644
--- a/episodes/large_data.Rmd
+++ b/episodes/large_data.Rmd
@@ -540,12 +540,6 @@ The resulting H5AD file can then be read into Python using scanpy's
 [read_h5ad](https://scanpy.readthedocs.io/en/stable/generated/scanpy.read_h5ad.html)
 function and then directly used in compatible Python-based analysis frameworks.
 
-## Session Info
-
-```{r sessionInfo}
-sessionInfo()
-```
-
 
 ## Exercises
 
@@ -646,3 +640,9 @@ system.time({i.out <- runPCA(sce.brain,
 - Converter functions between existing single-cell data formats enable analysis workflows that leverage complementary functionality from poplular single-cell analysis ecosystems.
 
 ::::::::::::::::::::::::::::::::::::::::::::::::
+
+## Session Info
+
+```{r, tidy=TRUE}
+sessionInfo()
+```
diff --git a/episodes/multi-sample.Rmd b/episodes/multi-sample.Rmd
index dec4520..d049f17 100644
--- a/episodes/multi-sample.Rmd
+++ b/episodes/multi-sample.Rmd
@@ -483,12 +483,6 @@ topTags(res.lfc)
 
 Addionally, the choice of \tau can be guided by other external experimental data, like a previous or a pilot experiment.
 
-## Session Info
-
-```{r, tidy=TRUE}
-sessionInfo()
-```
-
 ## Exercises
 
 
@@ -585,7 +579,7 @@ If there were large shifts in the logFC estimates or p-value distributions, that
 
 #### Extension challenge 1: Group effects
 
-Having multiple independent samples in each experimental group is always helpful, but it particularly important when it comes to batch effect correction. Why?
+Having multiple independent samples in each experimental group is always helpful, but it's particularly important when it comes to batch effect correction. Why?
 
 ::: solution
 
@@ -625,3 +619,10 @@ Imagine you had one sample that received a drug treatment and one that did not,
     limited replication - except that the counts are not of reads per gene, but
     of cells per label.
 ::::::::::::::::::::::::::::::::::::::::::::::::
+
+## Session Info
+
+```{r, tidy=TRUE}
+sessionInfo()
+```
+