From 229736c4816c3db088e8a3d4dea00bbe6b469c5a Mon Sep 17 00:00:00 2001
From: Emma Rand <emma.rand@york.ac.uk>
Date: Tue, 24 Oct 2023 13:48:53 +0100
Subject: [PATCH] added frog heatmap to omics 3 workshop

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 omics/week-5/Rplot001.jpg | Bin 0 -> 4227 bytes
 omics/week-5/workshop.qmd |  23 ++++++++++++++++++++---
 2 files changed, 20 insertions(+), 3 deletions(-)
 create mode 100644 omics/week-5/Rplot001.jpg

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diff --git a/omics/week-5/workshop.qmd b/omics/week-5/workshop.qmd
index af95501..28c8ba9 100644
--- a/omics/week-5/workshop.qmd
+++ b/omics/week-5/workshop.qmd
@@ -447,41 +447,58 @@ ggsave("figures/frog-s30-pca.png",
 
 ## Visualise the expression of the most significant genes using a heatmap
 
-only should do on sig genes. but use the log 2 normalised values
+A heatmap is a common way to visualise gene expression data. Often people will create heatmaps with thousands of genes but it can be more informative to use a subset along with clustering methods. We will use the genes which are significant at the 0.01 level. 
 
+We are going to create an interactive heatmap with the **`heatmaply`** [@heatmaply] package. **`heatmaply`** takes a matrix as input so we need to convert a dataframe of the log~2~ values to a matrix. We will also set the rownames to the Xenbase gene symbols.
+
+🎬 Convert a dataframe of the log~2~ values to a matrix:
 ```{r}
 mat <- s30_results_sig0.01 |> 
   select(starts_with("log2_")) |>
   as.matrix()
 ```
 
+🎬 Set the rownames to the Xenbase gene symbols:
 ```{r}
 rownames(mat) <- s30_results_sig0.01$xenbase_gene_symbol
 ```
 
+You might want to view the matrix by clicking on it in the environment pane. 
+
+
+🎬 Load the **`heatmaply`** package:
 ```{r}
 library(heatmaply)
 ```
 
+We need to tell the clustering algorithm how many clusters to create. We will set the number of clusters for the treatments to be 2 and the number of clusters for the genes to be the same since it makes sense to see what clusters of genes correlate with the treatments.
+
+🎬 Set the number of clusters for the treatments and genes:
+
 ```{r}
 n_treatment_clusters <- 2
 n_gene_clusters <- 2
 ```
 
+
+🎬 Create the heatmap:
 ```{r}
 #| fig-height: 8
 heatmaply(mat, 
           scale = "row",
-          hide_colorbar = TRUE,
           k_col = n_treatment_clusters,
           k_row = n_gene_clusters,
-          label_names = c("Gene", "Sample", "Expression (normalised, log2)"),
           fontsize_row = 7, fontsize_col = 10,
           labCol = str_remove(colnames(mat), pattern = "log2_"),
           labRow = rownames(mat),
           heatmap_layers = theme(axis.line = element_blank()))
 ```
 
+
+On the vertical axis are genes which are differentially expressed at the 0.01 level. On the horizontal axis are samples. We can see that the FGF-treated samples cluster together and the control samples cluster together. We can also see two clusters of genes; one of these shows genes upregulated (more yellow) in the FGF-treated samples (the pink cluster) and the other shows genes down regulated (more blue, the blue cluster) in the FGF-treated samples.
+
+The heatmap will open in the viewer pane (rather than the plot pane) because it is html. You can "Show in a new window" to see it in a larger format. You can also zoom in and out and pan around the heatmap and download it as a png. You might feel the colour bars is not adding much to the plot. You can remove it by setting `hide_colorbar = TRUE,` in the `heatmaply()` function. 
+
 ## Visualise all the results with a volcano plot
 
 colour the points if padj \< 0.05 and log2FoldChange \> 1