revised intro wording

episodes/intro-sce.Rmd

@@ -144,14 +144,14 @@ Before `SingleCellExperiment`, coders working with single cell data would someti
 
 ### `assays`
 
-This is arguably the most fundamental part of the object that contains the count matrix, and potentially other matrices with transformed data. We can access the _list_ of matrices with the `assays` function and individual matrices with the `assay` function. If one of these matrices is called "counts", we can use the special `counts` getter (and the analogous `logcounts`).
+This is arguably the most fundamental part of the object that contains the count matrix, and potentially other matrices with transformed data. We can access the _list_ of matrices with the `assays` function and individual matrices with the `assay` function. If one of these matrices is called "counts", we can use the special `counts` getter (likewise for `logcounts`).
 
 ```{r}
 names(assays(sce))
 counts(sce)[1:3, 1:3]
 ```
 
-You will notice that in this case we have a sparse matrix of class "dgTMatrix" inside the object. More generally, any "matrix-like" object can be used, e.g., dense matrices or HDF5-backed matrices (see "Working with large data").
+You will notice that in this case we have a sparse matrix of class "dgTMatrix" inside the object. More generally, any "matrix-like" object can be used, e.g., dense matrices or HDF5-backed matrices (see the "Working with large data" episode).
 
 ### `colData` and `rowData`
 
@@ -183,6 +183,8 @@ Here we add a column called "conservation" that is just an integer sequence from
 rowData(sce)$conservation = 1:nrow(sce)
 ```
 
+This is just a made-up example with a simple sequence of numbers, but in practice its convenient to store any sort of gene-wise information in the columns of the rowData.
+
 ::: 
 
 ::::
@@ -191,7 +193,7 @@ rowData(sce)$conservation = 1:nrow(sce)
 
 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.
+One peculiarity of `SingleCellExperiment` is its ability to store reduced dimension matrices within the object. These may include PCA, t-SNE, UMAP, etc.
 
 ```{r}
 reducedDims(sce)