add global chunk options to all episodes

episodes/cell_type_annotation.Rmd

@@ -8,24 +8,27 @@ editor_options:
 ---
 
 ::: questions
--   ScRNA-seq: Can clustering group cells by type?
--   Marker genes: Key to identifying cell types in scRNA-seq clusters?
--   What's tricky about labeling cell types in scRNA-seq data?
--   How to confirm and improve cell type labels from scRNA-seq?
+-   How to identify groups of cells with similar expression profiles?
+-   How to identify genes that drive separation between these groups of cells?
+-   How to leverage reference datasets and known marker genes for the cell type annotation of new datasets?
 :::
 
 ::: objectives
--   Identify cell types in scRNA-seq data by clustering cells based on gene expression patterns.
--   Define cell types within scRNA-seq clusters using marker genes.
--   Compare methods for assigning cell type labels in scRNA-seq data: reference datasets vs. marker genes.
--   Evaluate scRNA-seq cell type labels by analyzing marker gene distribution within clusters.
+-   Identify groups of cells by clustering cells based on gene expression patterns.
+-   Identify marker genes through testing for differential expression between clusters.
+-   Annotate cell types through annotation transfer from reference datasets.
+-   Annotate cell types through marker gene set enrichment testing.
 :::
 
 ## Setup
 
-```{r setup, message = FALSE, warning = FALSE}
-library(AUCell)
+```{r chunk-opts, include=FALSE}
+knitr::opts_chunk$set(echo = TRUE, cache = TRUE, message = FALSE, warning = FALSE)
 library(BiocStyle)
+```
+
+```{r setup}
+library(AUCell)
 library(MouseGastrulationData)
 library(SingleR)
 library(bluster)

episodes/eda_qc.Rmd

@@ -26,7 +26,7 @@ exercises: 15 # Minutes of exercises in the lesson
 ```{r chunk-opts, include=FALSE}
 rm(list = ls())
 gc()
-knitr::opts_chunk$set(echo = TRUE, cache = TRUE, warning = FALSE)
+knitr::opts_chunk$set(echo = TRUE, cache = TRUE, message = FALSE, warning = FALSE)
 library(BiocStyle)
 ```
 

episodes/hca.Rmd

@@ -94,7 +94,12 @@ BiocManager::install("CuratedAtlasQueryR")
 
 ## Package load 
 
-```{r, include = TRUE, results = "hide", message = FALSE, warning = FALSE}
+```{r chunk-opts, include=FALSE}
+knitr::opts_chunk$set(echo = TRUE, cache = TRUE, message = FALSE, warning = FALSE)
+library(BiocStyle)
+```
+
+```{r}
 library(CuratedAtlasQueryR)
 library(dplyr)
 ```

episodes/intro-sce.Rmd

@@ -22,10 +22,14 @@ exercises: 10 # Minutes of exercises in the lesson
 
 ## Setup
 
-```{r setup, message = FALSE, warning=FALSE}
+```{r chunk-opts, include=FALSE}
+knitr::opts_chunk$set(echo = TRUE, cache = TRUE, message = FALSE, warning = FALSE)
+library(BiocStyle)
+```
+
+```{r setup}
 library(SingleCellExperiment)
 library(MouseGastrulationData)
-library(BiocStyle)
 ```
 
 ## Bioconductor

episodes/large_data.Rmd

@@ -21,7 +21,8 @@ exercises: 2 # Minutes of exercises in the lesson
 
 ::::::::::::::::::::::::::::::::::::::::::::::::
 
-```{r load-styles, include=FALSE}
+```{r chunk-opts, include=FALSE}
+knitr::opts_chunk$set(echo = TRUE, cache = TRUE, message = FALSE, warning = FALSE)
 library(BiocStyle)
 ```
 
@@ -65,7 +66,7 @@ We demonstrate with a subset of 20,000 cells from the 1.3 million brain cell
 data set, as provided by the
 [TENxBrainData](https://bioconductor.org/packages/TENxBrainData) package.
 
-```{r tenx-brain, message = FALSE, warning = FALSE}
+```{r tenx-brain}
 library(TENxBrainData)
 sce.brain <- TENxBrainData20k() 
 sce.brain

episodes/multi-sample.Rmd

@@ -36,8 +36,12 @@ Note that this is a paired design in which for each biological replicate (pool 3
 
 We start by loading the data and doing a quick exploratory analysis, essentially applying the normalization and visualization techniques that we have seen in the previous lectures to all samples.
 
-```{r setup, message = FALSE, warning = FALSE}
+```{r chunk-opts, include=FALSE}
+knitr::opts_chunk$set(echo = TRUE, cache = TRUE, message = FALSE, warning = FALSE)
 library(BiocStyle)
+```
+
+```{r setup}
 library(MouseGastrulationData)
 sce <- WTChimeraData(samples=5:10, type = "processed")
 sce