Understanding Clustered Heatmaps in Python with seaborn

Introduction

Clustered heatmaps are a popular visualization technique used to display the relationship between two variables. In this post, we will delve into how to create clustered heatmaps using Python and the seaborn library. We’ll explore common pitfalls and solutions, including how to order the samples in the heatmap.

Prerequisites

• Familiarity with Python and data manipulation libraries such as pandas
• Knowledge of seaborn and matplotlib for creating visualizations
• Basic understanding of hierarchical clustering and its representation in seaborn clustermaps

Problem Description

The problem at hand involves plotting a clustered heatmap using seaborn, but the order given in the dataframe does not follow the order when generating the heatmap. To tackle this issue, we’ll explore how to determine the correct ordering of samples in the heatmap.

Solution Overview

To achieve the desired output, we can modify our approach and use groupby on either axis (rows or columns) before plotting. However, this may not always be feasible depending on the structure of your data.

Understanding Clustermaps

When using seaborn’s clustermap function to create a clustered heatmap, it uses a hierarchical binary tree representation of the matrix. This top-down organizational structure makes it difficult to control the order of the samples in the heatmap, as shown in the given example.

## Determining Sample Order

The key to determining the correct ordering lies in understanding how clustermap constructs its hierarchy.

Aggregation and Ordering

One way to determine the sample order is by aggregating the data along each axis. In this case, we can use idxmax and max aggregation functions to find the row or column with the highest value for each sample.

## Example Code: Determining Sample Order using Aggregation

# Import necessary libraries
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

# Create a dataframe with some sample values
data = {
    'sscinames': ['Thermoplasmata archaeon', 'Acidobacteria bacterium', 'Planctomycetes bacterium'],
    'S3_Day90_P3': [4, 17, 5],
    'S3_Day60_P3': [0, 26, 3],
    'S3_Day0_P1': [41, 0, 0],
    'S3_Day60_P1': [1, 17, 1]
}

df = pd.DataFrame(data)

# Set the index of the dataframe to 'sscinames'
df.set_index('sscinames', inplace=True)

# Apply aggregation functions along each axis
aggregated_df = df.agg(['idxmax', 'max'], axis=1).sort_values('max', ascending=False)

print(aggregated_df)

In this example, we use groupby on either the rows or columns of the dataframe and then apply the aggregation functions to determine the correct ordering.

Plotting with Ordered Samples

Once we have determined the sample order, we can plot the clustered heatmap using seaborn’s clustermap function while specifying the ordered samples along each axis.

## Example Code: Plotting Clustered Heatmap with Ordered Samples

# Continue from previous code block...

# Sort the dataframe based on the aggregation result
df = df.sort_values(by='max', ascending=False)

# Create a clustered heatmap using seaborn's clustermap function
plt.figure(figsize=(10, 8))
sns.clustermap(df,xticklabels=True, yticklabels=True)
plt.show()

By incorporating these steps into our workflow, we can ensure that the samples in our clustered heatmap are ordered correctly.

Conclusion

Clustered heatmaps offer a powerful way to visualize complex data relationships. By understanding how to determine the correct ordering of samples and applying this knowledge to our plotting process, we can create informative and insightful visualizations that effectively communicate our findings to others.

Last modified on 2025-05-05