pheatmap.Rmd

# Heatmaps with {pheatmap}

We will introduce package **{pheatmap}** to produce heatmaps.

First we can install **pheatmap** (bottom-right panel -> Packages -> Install).

And then we load the package: **tick** the package in the list, or load it from the console:

<br>
```{r, echo=T, fig.height=15, fig.width=10}
library(pheatmap)
```

**pheatmap** is not from the tidyverse package, and does not follow the same structure. It does not work with layers, but with multiple parameters inside a same function.

Let's read the following data file, and convert it to matrix (that is the input format for pheatmap):

```{r, echo=T, eval=T}
expr_heatmap <- read_csv("DataViz_source_files-main/files/GSE150029_rnaseq_for_heatmap.csv") %>%
  column_to_rownames(var = "gene_name") %>%
  as.matrix()
```

You can then simply plot the heatmap, giving the matrix as an input:

```{r, eval=F, fig.height=7, fig.width=6}
pheatmap(expr_heatmap)
```

Control what is clusters: rows, columns, both, or none:

```{r, eval=T, fig.height=7, fig.width=6}
pheatmap(expr_heatmap, cluster_cols=FALSE, cluster_rows=TRUE)
```

Show or hide row names:

```{r, eval=T, fig.height=7, fig.width=6}
pheatmap(expr_heatmap, show_rownames = F)
```

Change the color scale (**rainbow()** and **heat.colors()** are available from the base packages):

```{r, eval=T, fig.height=7, fig.width=6}
pheatmap(expr_heatmap, color = rainbow(10), main = "Rainbow colors")
```


```{r, eval=T, fig.height=7, fig.width=6}
pheatmap(expr_heatmap, color = heat.colors(10), main="Heat colors")
```