Provide info about Supplementary and some edits

Author: mblue9

vignettes/supplementary.Rmd

@@ -53,7 +53,7 @@ counts_tt <-
 	# shorten sample name
 	mutate(sample=str_remove(sample, "SRR1039")) %>%
 
-	# convert to tidybulk object
+	# convert to tidybulk tibble
 	tidybulk(.sample=sample, .transcript=geneID, .abundance=counts)
 ```
 
@@ -67,8 +67,7 @@ counts_tt %>%
 
 We can also check how many counts we have for each sample by making a bar plot. This helps us see whether there are any major discrepancies between the samples more easily.
 
-```{r}
-# make barplot of counts
+```{r out.width = "40%"}
 ggplot(counts_tt, aes(x=sample, weight=counts, fill=sample)) + 
 	geom_bar() +
 	theme_bw()
@@ -78,14 +77,14 @@ As we are using ggplot2, we can also easily view by any other variable that's a
 
 We can colour by dex treatment.
 
-```{r}
+```{r out.width = "40%"}
 ggplot(counts_tt, aes(x=sample, weight=counts, fill=dex)) + 
 	geom_bar() +
 	theme_bw()
 ```
 We can colour by cell line.
 
-```{r}
+```{r out.width = "40%"}
 ggplot(counts_tt, aes(x=sample, weight=counts, fill=cell)) + 
 	geom_bar() +
 	theme_bw()
@@ -94,7 +93,7 @@ ggplot(counts_tt, aes(x=sample, weight=counts, fill=cell)) +
 
 ## How to examine normalised counts with boxplots
 
-```{r}
+```{r out.width = "40%"}
 # scale counts
 counts_scaled <- counts_tt %>% scale_abundance(factor_of_interest = dex)
 
@@ -112,7 +111,7 @@ counts_scaled %>%
 
 ## How to create MDS plot
 
-```{r}
+```{r out.width = "40%"}
 airway %>%
 	tidybulk() %>%
 	scale_abundance(factor_of_interest=dex) %>%
@@ -127,7 +126,7 @@ airway %>%
 
 MA plots enable us to visualise amount of expression (logCPM) versus logFC. Highly expressed genes are towards the right of the plot. We can also colour significant genes (e.g. genes with FDR < 0.05) 
 
-```{r}
+```{r out.width = "40%"}
 # perform differential testing
 counts_de <- 
 	counts_tt %>%
@@ -148,7 +147,7 @@ counts_de %>%
 
 A more informative MA plot, integrating some of the packages in tidyverse.
 
-```{r warning=FALSE}
+```{r out.width = "40%", warning=FALSE}
 counts_de %>%
 	pivot_transcript() %>%
 	
@@ -167,9 +166,9 @@ counts_de %>%
 ```
 
 
-## How to perform gene set analysis
+## How to perform gene enrichment analysis
 
-To run below you'll need the `clusterProfiler` and `org.Hs.eg.db` packages. This is just one suggestion, if you have other suggestions for how to do a 'tidy' pathway analysis feel free to [let us know](https://github.com/stemangiola/bioc_2020_tidytranscriptomics/blob/master/CONTRIBUTING.md).
+To run below you'll need the `clusterProfiler` and `org.Hs.eg.db` packages. This is just one suggestion, adapted from [here](https://simon-anders.github.io/data_analysis_course/lecture9.html). If you have other suggestions for how to do a 'tidy' pathway analysis feel free to [let us know](https://github.com/stemangiola/bioc_2020_tidytranscriptomics/blob/master/CONTRIBUTING.md).
 
 ```{r eval=FALSE}
 library(clusterProfiler)

vignettes/tidytranscriptomics.Rmd

@@ -82,7 +82,7 @@ The tidytranscriptomics approach abstracts out the coding-related complexity and
 * Create plots that summarise the information content of the data and analysis results
 
 ### Acknowledgements
-This material was adapted from an R for RNA sequencing workshop first run [here](http://combine-australia.github.io/2016-05-11-RNAseq/).
+This material was adapted from an R for RNA sequencing workshop first run [here](http://combine-australia.github.io/2016-05-11-RNAseq/). Use of the airway and pasilla datasets was inspired by the [DESeq2 vignette](http://bioconductor.org/packages/devel/bioc/vignettes/DESeq2/inst/doc/DESeq2.html).
 
 ```{r, echo=FALSE, out.width = "100px"}
 knitr::include_graphics("../inst/vignettes/tidybulk_logo.png")
@@ -537,6 +537,9 @@ strip_chart %>% ggplotly(tooltip = c("label", "y"))
 - Dimensionality reduction (PCA or MDS) plots are very important for exploring the data
 - Density plots, volcano plots, strip-charts and heatmaps are useful visualisation tools for evaluating the hypothesis testing.
 
+## Supplementary
+Some things we didn't have time to cover in this workshop can be found in the [Supplementary material](https://stemangiola.github.io/bioc_2020_tidytranscriptomics/articles/supplementary.html).
+
 ## Exercises
 
 Try to apply what you've learned to another dataset. This dataset was generated from the pasilla package, which obtained the data from the paper by [@brooks2011conservation]. Here we provide it as a SummarizedExperiment object. The dataset has 7 samples from Drosophila (fruitfly): 3 treated with siRNA knockdown of the pasilla gene and 4 untreated controls.