Note

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Plot a similarity heatmap of a XCoR along the phylogenetic tree

Here we present how you can plot a heatmap of sequence similarity ordered following a phylogenetic tree.

Import necessary packages

from cocoatree.io import load_MSA, load_tree_ete3
from cocoatree.datasets import load_S1A_serine_proteases
from cocoatree.visualization import update_tree_ete3_and_return_style
from cocoatree.msa import compute_seq_identity, compute_seq_similarity, \
    compute_normalized_seq_similarity
import pandas as pd
import matplotlib.pyplot as plt

Import metadata

serine_dataset = load_S1A_serine_proteases('halabi')
df_annot = serine_dataset["metadata"]
print(df_annot)

        Seq_ID  ...         Family
0      4139558  ...        Muridae
1      7546312  ...         Suidae
2       230004  ...      Hominidae
3      4139720  ...      Hominidae
4     10835843  ...        Muridae
...        ...  ...            ...
1465  54641874  ...  Drosophilidae
1466  55236641  ...      Culicidae
1467  55238595  ...      Culicidae
1468  66772117  ...  Drosophilidae
1469  24660124  ...  Drosophilidae

[1470 rows x 14 columns]

To use your own metadata file, import a csv file as a pandas dataframe.

The dataframe must have a ‘Seq_ID’ column, which corresponds to the sequence identifiers used in the fasta and in the phylogenetic tree. The other columns can contain qualitative data that will be displayed as categories alongside the phylogenetic tree. In this example, we will use the last 3 columns ‘Protein_type’, ‘Subphylum’, and ‘Class’.

For more details on the S1A serine proteases dataset, go to S1A serine proteases.

We will also use a personnalized colormap defined as follows:

halabi_cmap = {
    'vertebrate': '#798e87',
    'invertebrate': '#c27d38',
    'fungi': '#ccc591',
    'bacteria': '#29211f',
    'Chymotrypsin': '#00a08a',
    'Trypsin': '#ff0000',
    'Tryptase': '#f2ad00',
    'Kallikrein': '#f98400',
    'Granzyme': '#5bbcd6',
    'Mammalia': '#C969A1',
    'Actinopterygii': '#CE4441',
    'Amphibia': '#EE8577',
    'Malacostraca': '#EB7926',
    'other': 'lightgrey',
    'Insecta': '#FFBB44',
    'Actinobacteria': '#859B6C',
    'Arachnida': '#62929A',
    'Oligochaeta': '#004F63'
}

Import tree file

The file must be in Newick format and can include confidence scores such as bootstrap or jack-knife (only one type of confidence score at a time).

tree_file = 'data/halabi_82_seqs.txt'
tree_ete3 = load_tree_ete3(tree_file)
print(tree_ete3)

                  /-2462649
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Import XCoR sequences

Load the sequences you wish to visualize with cocoatree.io.load_MSA() as a fasta file. The sequence names must correspond to Seq_ID and to the leaf names in the tree file.

xcor_file = 'data/halabi_xcor_1_SCA.fasta'
data = load_MSA(xcor_file, 'fasta')
xcor_id = data["sequence_ids"]
xcor_seq = data["alignment"]

Plot tree with sequence similarity heatmap

Generate the tree style that will be applied to your tree.

Here, we will show various elements:

  • the tree without its leaf names (show_leaf_name=False)

  • metadata as colored columns, in order: Protein_type, Subphylum,

and Class.

  • XCoR sequences colored by amino acid physico-chemical properties

(t_xcor_seq=True)

  • a heatmap of pairwise sequence similarity computed on the XCoR

sequences (t_xcor_heatmap=True, matrix_type=’similarity’) and using the GnBu colormap (colormap=’GnBu’).

tree_style, _ = update_tree_ete3_and_return_style(
    tree_ete3, df_annot, xcor_id, xcor_seq,
    meta_data=('Protein_type', 'Subphylum', 'Class'),
    show_leaf_name=False,
    fig_title='Heatmap of sequence similarity',
    linewidth=3,
    linecolor="#000000",
    bootstrap_style={},
    tree_scale=200,
    metadata_colors=halabi_cmap,
    t_xcor_seq=True,
    t_xcor_heatmap=True,
    matrix_type='similarity',
    colormap='GnBu'
    )

Save the image file

tree_ete3.render("xcor_phylogeny.png", tree_style=tree_style)
plot tree similarity heatmap
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As the similarity score that is computed here is not normalized, it is difficult to see variations on the heatmap. We will thus use a normalized sequence similarity score.

Plot tree with normalized similarity heatmap

You first need to reload the tree in order to clean the tree style defined above

tree_ete3 = load_tree_ete3(tree_file)
tree_style, _ = update_tree_ete3_and_return_style(
    tree_ete3, df_annot, xcor_id, xcor_seq,
    meta_data=('Protein_type', 'Subphylum', 'Class'),
    show_leaf_name=False,
    fig_title='Heatmap of normalized sequence similarity',
    linewidth=3,
    linecolor="#000000",
    bootstrap_style={},
    tree_scale=200,
    metadata_colors=halabi_cmap,
    t_xcor_seq=True,
    t_xcor_heatmap=True,
    matrix_type='norm_similarity',
    colormap='GnBu'
    )
tree_ete3.render("xcor_phylogeny.png", tree_style=tree_style)
plot tree similarity heatmap
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442.95894000000004, 1501.0], 151: [406.77714000000003, 1501.0, 496.38306, 1601.0], 152: [413.73818000000006, 1501.0, 462.02186000000006, 1541.0], 153: [420.87366000000003, 1501.0, 461.68106, 1521.0], 154: [420.87366000000003, 1521.0, 462.02186000000006, 1541.0], 155: [413.73818000000006, 1541.0, 496.38306000000006, 1601.0], 156: [437.05986000000007, 1541.0, 496.38306000000006, 1561.0], 157: [437.05986000000007, 1561.0, 489.48912000000007, 1601.0], 158: [480.30426000000006, 1561.0, 488.86678000000006, 1581.0], 159: [480.30426000000006, 1581.0, 489.48912000000007, 1601.0], 160: [54.90806, 1601.0, 293.38026, 1641.0], 161: [92.82826, 1601.0, 284.16206, 1621.0], 162: [92.82826, 1621.0, 293.38026, 1641.0]}}

Compare similarity and identity matrices

Let’s compare the matrices obtained by computing sequence identity versus sequence similarity.

Start by filtering and reordering the MSA sequences so that they follow the phylogenetic tree

leaves_id = tree_ete3.get_leaf_names()
sequences = pd.DataFrame(index=xcor_id, data={"seq": xcor_seq})
reordered_sequences = sequences.loc[leaves_id, "seq"].values

Compute the matrices

norm_sim_matrix = compute_normalized_seq_similarity(reordered_sequences)
sim_matrix = compute_seq_similarity(reordered_sequences)
id_matrix = compute_seq_identity(reordered_sequences)

Plot the heatmaps:

plt.figure(figsize=(20, 8))
plt.subplot(1, 3, 1)
plt.imshow(id_matrix, cmap='GnBu')
plt.xlabel('sequences')
plt.ylabel(None)
plt.title('Identity')
plt.colorbar(shrink=0.4)

plt.subplot(1, 3, 2)
plt.imshow(sim_matrix, cmap='GnBu')
plt.xlabel('sequences')
plt.ylabel(None)
plt.title('Similarity')
plt.colorbar(shrink=0.4)

plt.subplot(1, 3, 3)
plt.imshow(norm_sim_matrix, cmap='GnBu')
plt.xlabel('sequences')
plt.ylabel(None)
plt.title('Normalized similarity')
plt.colorbar(shrink=0.4)
Identity, Similarity, Normalized similarity
<matplotlib.colorbar.Colorbar object at 0x7f5e71f41610>

In this case, there is not much difference between sequence identity and sequence similarity.

Total running time of the script: (0 minutes 7.675 seconds)

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