Heatmaps

Write a description of the lesson here.

Keywords

keyword_1, keyword_2, keyword_3, keyword_4, keyword_5, keyword_6

Approximate time: XX minutes

Learning Objectives

In this lesson, we will:

• Learning Objective 1
• Learning Objective 2
• Learning Objective 3

Overview of lesson

When doing XYZ…

Heatmaps

# Load the data for heatmaps
import pandas as pd
rpkm_ordered = pd.read_csv("data/ordered_counts_rpkm.csv", index_col=0)
rpkm_ordered.head()

	sample1	sample2	sample3	sample4	sample5	sample6	sample7	sample8	sample9	sample10	sample11	sample12
ENSMUSG00000000001	19.784800	19.265000	20.889500	24.076700	23.722200	20.819800	2.61161	5.849540	6.512630	24.198100	24.046500	26.915800
ENSMUSG00000000003	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000
ENSMUSG00000000028	0.937792	1.032290	0.892183	0.827891	0.826954	1.168630	1.13441	0.698754	0.925117	1.045920	0.975327	0.673563
ENSMUSG00000000031	0.035963	0.000000	0.000000	0.000000	0.000000	0.051193	0.00000	0.029845	0.059773	0.000000	0.000000	0.020438
ENSMUSG00000000037	0.151417	0.056033	0.146196	0.180883	0.047324	0.143884	0.00000	0.068594	0.049415	0.017004	0.020640	0.066232

First let us identify the top 6 most highly expressed genes across all of our samples. We can do this by calculating the average expression for each gene across all samples and then selecting the top 6 genes with the highest average expression:

# Calculate the average expression for each gene across all samples
gene_means = rpkm_ordered.mean(axis=1)

# Get the top 6 most highly expressed genes
important_genes = gene_means.sort_values(ascending=False).head(6).index
important_genes

Index(['ENSMUSG00000098973', 'ENSMUSG00000098178', 'ENSMUSG00000076258',
       'ENSMUSG00000076036', 'ENSMUSG00000076138', 'ENSMUSG00000093264'],
      dtype='str')

We can plot the gene expression for each of these genes across all of the samples using a heatmap. A heatmap is a graphical representation of data where individual values are represented as colors. In this case, we can use a heatmap to visualize the expression levels of our important genes across all of our samples.

# Plot a heatmap of the important genes across all samples
import seaborn as sns
import matplotlib.pyplot as plt

# Subset the data to include only the important genes
important_genes_data = rpkm_ordered.loc[important_genes]

# Create the heatmap
sns.clustermap(important_genes_data, 
            cmap="viridis", z_score=0)

Figure 1: Heatmap of the RPKM values for the top 6 most highly expressed genes across all samples.

Figure 2: Heatmap of the RPKM values for the top 6 most highly expressed genes across all samples.

But we have no idea which samples are which in this heatmap! We can add some extra information to our heatmap by adding a color bar that indicates the age of the mouse for each sample. To do this, we need to load in our metadata data frame that contains the age information for each sample:

# Load the metadata data frame
new_metadata = pd.read_csv("data/new_metadata.csv", index_col=0)
new_metadata.head()

	genotype	celltype	replicate	mean_expression	age_in_days
sample1	Wt	typeA	1	10.266102	40
sample2	Wt	typeA	2	10.849759	32
sample3	Wt	typeA	3	9.452517	38
sample4	KO	typeA	1	15.833872	35
sample5	KO	typeA	2	15.590184	41

Now we can add a color bar to our heatmap that indicates the age of the mouse for each sample:

# Create the heatmap with a color bar indicating the age of the mouse for each sample
sns.clustermap(important_genes_data, 
            cmap="viridis", z_score=0,
            col_colors=new_metadata["age_in_days"].map({3: "blue", 18: "orange"}))

Figure 3: Heatmap of the RPKM values for the top 6 most highly expressed genes across all samples with a color bar indicating the age of the mouse for each sample.

Figure 4: Heatmap of the RPKM values for the top 6 most highly expressed genes across all samples with a color bar indicating the age of the mouse for each sample.

Subsection 1A

Subsection 1B

Exercise 1

1. A question to evaluate Learning Objective 1
2. A followup question to question #1
3. …

Topic 2

Subsection 2A

Subsection 2B

Exercise 2

1. A question to evaluate Learning Objective 2
2. A followup question to question #1
3. …

Topic 3

Subsection 3A

Subsection 3B

Exercise 3

1. A question to evaluate Learning Objective 3
2. A followup question to question #1
3. …

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