suppress a couple more irrelevant messages

episodes/eda_qc.Rmd

@@ -451,12 +451,12 @@ The package `DropletTestFiles` includes the raw output from Cell Ranger of the p
 
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-- Empty droplets, i.e. droplets that do not contain intact cells and capture only ambient or background RNA, should be removed prior to an analysis. The `emptyDrops` function from the [DropletUtils](https://bioconductor.org/packages/DropletUtils) package can be used to identify empty droplets. 
-- Doublets, i.e. instances where two cells are captured in the same droplet, should also be removed prior to an analysis. The `computeDoubletDensity` and `doubletThresholding` functions from the [scDblFinder](https://bioconductor.org/packages/scDblFinder) package can be used to identify and filter out doublets. 
+- Empty droplets, i.e. droplets that do not contain intact cells and that capture only ambient or background RNA, should be removed prior to an analysis. The `emptyDrops` function from the [DropletUtils](https://bioconductor.org/packages/DropletUtils) package can be used to identify empty droplets. 
+- Doublets, i.e. instances where two cells are captured in the same droplet, should also be removed prior to an analysis. The `computeDoubletDensity` and `doubletThresholding` functions from the [scDblFinder](https://bioconductor.org/packages/scDblFinder) package can be used to identify doublets. 
 - Quality control (QC) uses metrics such as library size, number of expressed features, and mitochondrial read proportion, based on which low-quality cells can be detected and filtered out. Diagnostic plots of the chosen QC metrics are important to identify possible issues. 
 - Normalization is required to account for systematic differences in sequencing coverage between libraries and to make measurements comparable between cells. Library size normalization is the most commonly used normalization strategy, and involves dividing all counts for each cell by a cell-specific scaling factor.
 - Feature selection aims at selecting genes that contain useful information about the biology of the system while removing genes that contain only random noise. Calculate per-gene variance with the `modelGeneVar` function and select highly-variable genes with `getTopHVGs`.
-- Dimensionality reduction aims at reducing the computational work and at obtaining less noisy and more interpretable results. PCA is a simple and effective linear dimensionality reduction technique that provide interpretable results for further analysis such as clustering of cells. Non-linear approaches such as UMAP and t-SNE can be useful for visualization, but the resulting representations should not be used in downstream analysis.  
+- Dimensionality reduction aims at reducing the computational work and at obtaining less noisy and more interpretable results. PCA is a simple and effective linear dimensionality reduction technique that provides interpretable results for further analysis such as clustering of cells. Non-linear approaches such as UMAP and t-SNE can be useful for visualization, but the resulting representations should not be used in downstream analysis.  
 
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episodes/intro-sce.Rmd

@@ -14,7 +14,7 @@ exercises: 10 # Minutes of exercises in the lesson
 
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-- Install and update packages from Bioconductor. 
+- Install and update Bioconductor packages. 
 - Load data generated with common single-cell technologies as `SingleCellExperiment` objects. 
 - Inspect and manipulate `SingleCellExperiment` objects. 
 
@@ -166,7 +166,7 @@ It is more common for other functions to _store_ this information in the object,
 
 Here, we use `scater`'s `plotReducedDim` function as an example of how to extract this information _indirectly_ from the objects. Note that one could obtain the same results (somewhat less efficiently) by extracting the corresponding `reducedDim` matrix and `ggplot`.
 
-```{r, message = FALSE}
+```{r, message = FALSE, warning = FALSE}
 library(scater)
 plotReducedDim(sce, "pca.corrected.E8.5", colour_by = "celltype.mapped")
 ```
@@ -203,7 +203,7 @@ Combining two objects: The `MouseGastrulationData` package contains several data
 
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-- Bioconductor is a project that provides open-source software packages for the comprehension of high-throughput biological data.
+- The Bioconductor project provides open-source software packages for the comprehension of high-throughput biological data.
 - A `SingleCellExperiment` object is an extension of the `SummarizedExperiment` object.
 - `SingleCellExperiment` objects contain specialized data fields for storing data unique to single-cell analyses, such as the `reducedDims` field. 
 

episodes/large_data.Rmd

@@ -530,10 +530,10 @@ Use Seurat's `DimPlot` function.
 
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-- Out-of-memory representations can be used to work with single-cell datasets that are too large to fit in memory
-- Parallelization of calculations across genes or cells is an effective strategy for speeding up analysis of large single-cell datasets
-- Fast approximations for nearest neighbor search and singular value composition can speed up essential steps of single-cell analysis with minimal loss of accuracy
-- Converter functions between existing single-cell data formats enable analysis workflows that leverage complementary functionality from poplular single-cell analysis ecosystems 
+- Out-of-memory representations can be used to work with single-cell datasets that are too large to fit in memory.
+- Parallelization of calculations across genes or cells is an effective strategy for speeding up analysis of large single-cell datasets.
+- Fast approximations for nearest neighbor search and singular value composition can speed up essential steps of single-cell analysis with minimal loss of accuracy.
+- Converter functions between existing single-cell data formats enable analysis workflows that leverage complementary functionality from poplular single-cell analysis ecosystems.
 
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