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(This article was first published on <strong><a href="http://moderndata.plot.ly/interactive-heat-maps-for-r/"> R – Modern Data</a></strong>, and kindly contributed to <a rel="nofollow" href="http://www.r-bloggers.com/">R-bloggers)</a> </div>
<p>In every statistical analysis, the first thing one should do is try and visualise the data before any modeling. In microarray studies, a common visualisation is a heatmap of gene expression data.</p>
<p>In this post I simulate some gene expression data and visualise it using the <a target="_blank" href="https://github.com/talgalili/heatmaply"><code>heatmaply</code></a> package in <code>R</code> by <a target="_blank" href="http://www.r-statistics.com/">Tal Galili</a>. This package extends the <a target="_blank" href="https://plot.ly/">plotly</a> engine to heatmaps, allowing you to inspect certain values of the data matrix by hovering the mouse over a cell. You can also zoom into a region of the heatmap by drawing a rectangle over an area of your choice</p>
<p>The following function simulates data from a multivariate normal distribution, and allows exposure dependent correlations between the data. You will need the <code>mvrnorm</code> function from the <a target="_blank" href="http://cran.r-project.org/web/packages/MASS/index.html">MASS</a> library to run this function:</p>
<pre>simulateExprData &lt;- function(n, n0, p, rho0, rho1){ 
  # n: total number of subjects 
  # n0: number of subjects with exposure 0 
  # n1: number of subjects with exposure 1 
  # p: number of genes 
  # rho0: rho between Z_i and Z_j for subjects with exposure 0 
  # rho1: rho between Z_i and Z_j for subjects with exposure 1
 
  # Simulate gene expression values according to exposure 0 or 1, 
  # according to a centered multivariate normal distribution with 
  # covariance between Z_i and Z_j being rho^|i-j| 
  n1 &lt;- n - n0 
  times &lt;- 1:p
  H &lt;- abs(outer(times, times, &quot;-&quot;)) 
  V0 &lt;- rho0^H 
  V1 &lt;- rho1^H 

  # rows are people, columns are genes 
  genes0 &lt;- MASS::mvrnorm(n = n0, mu = rep(0,p), Sigma = V0) 
  genes1 &lt;- MASS::mvrnorm(n = n1, mu = rep(0,p), Sigma = V1) 
  genes &lt;- rbind(genes0,genes1) 
  return(genes)
}

genes &lt;- simulateExprData(n = 100, n0 = 50, p = 100, 
                          rho0 = 0.01, rho1 = 0.95)</pre>

<p>Next we need to install and load the <a target="_blank" href="https://github.com/talgalili/heatmaply"><code>heatmaply</code></a> package:</p>
<pre>
install.packages('devtools')
devtools::install_github('talgalili/heatmaply')
library(heatmaply)
</pre>

<p>The syntax is extremely simple. Lets plot a few different interactive heatmaps of the data. We first plot the <code>genes</code> and specify how many groups we want for the rows (subjects) and columns (genes) of the data. In this case we specify 2 groups for both the rows and columns:</p>
<pre>heatmaply(genes, k_row = 2, k_col = 2)</pre>
<p><iframe height="450" width="450" src="https://plot.ly/~sahirbhatnagar/46.embed"></iframe></p>
<p>We see that the clustering algorithm is not able to properly cluster the subjects because we would have expected to see two equal size groups in the dendrogram on the y-axis. Let’s instead plot the correlation matrix of the genes:</p>
<pre>heatmaply(cor(genes), k_row = 2, k_col = 2)</pre>
<p><iframe height="450" width="450" src="https://plot.ly/~sahirbhatnagar/44.embed"></iframe></p>
<p>We now see two distinct groups in the dendrogram, as we would expect because that’s the way we generated the data</p>
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<div>To <strong>leave a comment</strong> for the author, please follow the link and comment on their blog: <strong><a href="http://moderndata.plot.ly/interactive-heat-maps-for-r/"> R – Modern Data</a></strong>.</div>
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