Understanding Heatmaps and Annotated Data with annHeatmap2 in R

annHeatmap2 is a popular package in R for creating heatmaps with annotations. However, its usage can be tricky, especially when working with datasets that require row-level annotations. In this article, we will delve into the world of annotated heatmaps using annHeatmap2 and explore how to correctly annotate rows with binary variables.

Introduction to Heatmaps

A heatmap is a graphical representation of data where values are depicted by color. It’s often used to visualize relationships between different variables in a dataset. In the context of bioinformatics, heatmaps are commonly used to display expression levels of genes across different samples or experimental conditions.

annHeatmap2: A Package for Annotated Heatmaps

annHeatmap2 is an extension of the original heatmap package that allows users to add annotations to their heatmaps. These annotations can include row and column labels, gene names, and additional metadata such as clustering status. The package provides a flexible interface for customizing the appearance and behavior of annotated heatmaps.

Working with annHeatmap2

To create an annotated heatmap using annHeatmap2, you’ll need to follow these basic steps:

1. Load the necessary libraries and datasets.
2. Prepare your data by converting it into a suitable format for annHeatmap2.
3. Create an annotation list that specifies which columns of your data should be used for annotations.
4. Set up any additional options such as clustering status.

The Problem with Existing Code

The original code provided in the Stack Overflow question attempts to create an annotated heatmap using annHeatmap2 but incorrectly annotates rows instead of columns. This is because the Row argument in the annotation list is set to display all data, including the row names and binary annotations.

## Incorrect Code
map1 = annHeatmap2(mydata_matrix[1:4,],
ann = list(Col=list(data=pData(mydata_matrix[4:7,]))),
cluster=list(Col=list(cuth=3000)))
plot(map1)

In this code snippet, the Col argument is used to specify that columns 5-7 of the data should be used for annotations. However, this results in the annotation being applied to the column labels (which are in columns 4 and 5) rather than the row values.

Solution: Using Row Annotation

To correct this issue, you can modify the code to use the Row argument instead of Col. This will display all data, including the row names and binary annotations, when creating the heatmap.

## Correct Code
map1 = annHeatmap2(mydata_matrix[1:4,],
ann = list(Row=list(data=pData(mydata_matrix[4:7,]))),
cluster=list(status="no")))
plot(map1)

In this corrected version of the code, we’ve changed the Col argument to an empty list (()), which prevents any annotations from being applied to the column labels. Instead, we’re using the Row argument with a separate data specification to display all row-level data.

Additional Options: Clustering Status

When creating an annotated heatmap, you can also specify whether or not clustering should be displayed. By default, clustering is enabled in annHeatmap2, but you can disable it by setting the status argument to "no".

## Disabling Clustering
map1 = annHeatmap2(mydata_matrix[1:4,],
ann = list(Row=list(data=pData(mydata_matrix[4:7,]))),
cluster=list(status="no")))
plot(map1)

In this code snippet, we’ve set the status argument to "no" to disable clustering. This will result in a heatmap that only displays row annotations and does not include any clustering information.

Conclusion

Creating annotated heatmaps using annHeatmap2 is a powerful tool for visualizing complex data. However, it requires careful attention to detail to ensure that the desired level of annotation and customization is achieved. By understanding how to use the Row argument and customizing the annotation list, you can create high-quality annotated heatmaps that effectively communicate your research findings.

Example Use Cases

• Visualizing gene expression levels across different samples or experimental conditions.
• Displaying clustering information for gene regulatory networks.
• Creating annotated heatmaps to illustrate relationships between different variables in a dataset.

## Example Dataset
| GeneName | wt | basal | aa basal | wt PMA 1h | aa PMA 1h | var1 | var2 | var3 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Osm | 79.04263765 | 87.7338764 | 271.0823488 | 78.54386727 | 0 | 1 | 1 | 1 |
| Il10 | 16.19566857 | 27.7348142 | 758.0504883 | 209.5772766 | 1 | 1 | 1 | 1 |
| Nr4a1 | 2850.181935 | 2867.378369 | 41778.06162 | 12366.60255 | 0 | 1 | 1 | 1 |
| Zfp36 | 202.9647756 | 150.495029 | 1385.650968 | 535.7451794 | 1 | 1 | 1 | 1 |
| var4 | 42.1356239 | 32.4596748 | 115.2345678 | 25.45667890 | 0 | 0 | 1 | 0 |

Last modified on 2023-09-01