Different distance measures are available for clustering analysis. This article describes how to perform **clustering in R using correlation** as distance metrics.

Contents:

## Prerequisites

The following R packages will be used:

`pheatmap`

[pheatmap package]: Creates pretty heatmaps.`heatmap.2()`

[gplots package]: Another alternative for drawing heatmaps.

## Demo data

Generate a demo dataset:

```
set.seed(123)
mydata <- matrix(rnorm(200), 20, 10)
mydata[1:10, seq(1, 10, 2)] = mydata[1:10, seq(1, 10, 2)] + 3
mydata[11:20, seq(2, 10, 2)] = mydata[11:20, seq(2, 10, 2)] + 2
mydata[15:20, seq(2, 10, 2)] = mydata[15:20, seq(2, 10, 2)] + 4
colnames(mydata) = paste("Sple", 1:10, sep = "")
rownames(mydata) = paste("Gene", 1:20, sep = "")
head(mydata[, 1:4], 4)
```

```
## Sple1 Sple2 Sple3 Sple4
## Gene1 2.44 -1.068 2.31 0.380
## Gene2 2.77 -0.218 2.79 -0.502
## Gene3 4.56 -1.026 1.73 -0.333
## Gene4 3.07 -0.729 5.17 -1.019
```

Prepare your data as described at : Data Preparation and R Packages for Cluster Analysis

## Draw heatmaps using pheatmap

The default is to use the euclidean distance as dissimilarity measure.

```
library("pheatmap")
pheatmap(mydata, scale = "row")
```

Use correlation as dissimilarity measures:

```
# Pairwise correlation between samples (columns)
cols.cor <- cor(mydata, use = "pairwise.complete.obs", method = "pearson")
# Pairwise correlation between rows (genes)
rows.cor <- cor(t(mydata), use = "pairwise.complete.obs", method = "pearson")
# Plot the heatmap
library("pheatmap")
pheatmap(
mydata, scale = "row",
clustering_distance_cols = as.dist(1 - cols.cor),
clustering_distance_rows = as.dist(1 - rows.cor)
)
```

## Draw heatmaps using gplots

Default heatmap using the euclidean distance as dissimilarity measure.

```
library("gplots")
heatmap.2(mydata, scale = "row", col = bluered(100),
trace = "none", density.info = "none")
```

Use correlation as dissimilarity measures:

```
# Pairwise correlation between samples (columns)
cols.cor <- cor(mydata, use = "pairwise.complete.obs", method = "pearson")
# Pairwise correlation between rows (genes)
rows.cor <- cor(t(mydata), use = "pairwise.complete.obs", method = "pearson")
## Row- and column-wise clustering using correlation
hclust.col <- hclust(as.dist(1-cols.cor))
hclust.row <- hclust(as.dist(1-rows.cor))
# Plot the heatmap
library("gplots")
heatmap.2(mydata, scale = "row", col = bluered(100),
trace = "none", density.info = "none",
Colv = as.dendrogram(hclust.col),
Rowv = as.dendrogram(hclust.row)
)
```

## Summary

In this article we introduce how perform clustering analysis and draw heatmaps in R using the `pheatmap`

and the `gplots`

package

Version: Français

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