Lollipop Plot Example¶

This notebook demonstrates the lollipop_plot() function, which visualizes somatic mutations mapped onto a protein structure, revealing mutation hotspots and their relationship to functional domains.

For detailed documentation, see the Lollipop Plot API Reference.

Load Data¶

Load the MAF file and create a PyMutation object. Using consolidate_variants=False preserves all original mutation events for accurate counting.

In [1]:

from pyMut.input import read_maf

maf_path = "../../../src/pyMut/data/examples/MAF/tcga_laml.maf.gz"
print(f'📂 Loading file: {maf_path}')

py_mutation = read_maf(maf_path, assembly="37", consolidate_variants=False)

from pyMut.input import read_maf maf_path = "../../../src/pyMut/data/examples/MAF/tcga_laml.maf.gz" print(f'📂 Loading file: {maf_path}') py_mutation = read_maf(maf_path, assembly="37", consolidate_variants=False)

2025-11-13 00:05:18,592 | INFO | pyMut.input | Starting MAF reading: ../../../src/pyMut/data/examples/MAF/tcga_laml.maf.gz
2025-11-13 00:05:18,593 | INFO | pyMut.input | Loading from cache: ../../../src/pyMut/data/examples/MAF/.pymut_cache/tcga_laml.maf_bec334f3b1d679b0.parquet
2025-11-13 00:05:18,593 | INFO | pyMut.input | Loading from cache: ../../../src/pyMut/data/examples/MAF/.pymut_cache/tcga_laml.maf_bec334f3b1d679b0.parquet
2025-11-13 00:05:18,619 | INFO | pyMut.input | Cache loaded successfully in 0.03 seconds
2025-11-13 00:05:18,619 | INFO | pyMut.input | Cache loaded successfully in 0.03 seconds

📂 Loading file: ../../../src/pyMut/data/examples/MAF/tcga_laml.maf.gz

Configure Plot Settings¶

Enable high-quality rendering settings for publication-ready figures.

In [2]:

py_mutation.configure_high_quality_plots()

py_mutation.configure_high_quality_plots()

Lollipop Plot - DNMT3A¶

DNMT3A is one of the most frequently mutated genes in acute myeloid leukemia (AML). This example uses a specific transcript ID for reproducibility.

Parameters:

• gene: Gene symbol to visualize (required)
• aa_col: Column containing protein change annotation. Default: "HGVSp_Short"
• transcript_id: Specific transcript ID for isoform selection. Default: None
• label_top_n: Number of top mutated positions to label. Default: 20
• count_by: Count mode ("mutations" or "samples"). Default: "mutations"
• show_lollipops: Show mutation lollipops. Default: True
• figsize: Figure size. Default: (16, 6)

In [6]:

py_mutation.lollipop_plot(
    gene='DNMT3A',
    aa_col='Protein_Change',
    transcript_id='NM_022552',
    label_top_n=1,
)

py_mutation.lollipop_plot( gene='DNMT3A', aa_col='Protein_Change', transcript_id='NM_022552', label_top_n=1, )

2025-11-13 00:05:39,793 | INFO | pyMut.visualizations.lollipop_plot | Lollipop plot for DNMT3A created in 0.48s (54 mutations, 26 AA changes)

Out[6]:

Show Only Protein Domains¶

Setting show_lollipops=False displays only the protein backbone with PFAM domain annotations, without mutation markers.

In [8]:

py_mutation.lollipop_plot(
    gene='DNMT3A',
    aa_col='Protein_Change',
    transcript_id='NM_022552',
    label_top_n=1,
    show_lollipops=False
)

py_mutation.lollipop_plot( gene='DNMT3A', aa_col='Protein_Change', transcript_id='NM_022552', label_top_n=1, show_lollipops=False )

2025-11-13 00:07:56,535 | INFO | pyMut.visualizations.lollipop_plot | Protein domain plot for DNMT3A created in 0.44s

Out[8]:

Lollipop Plot - TP53¶

TP53 is a tumor suppressor gene frequently mutated across many cancer types. This example demonstrates the same visualization approach with a different gene.

In [7]:

py_mutation.lollipop_plot(
    gene='TP53',
    aa_col='Protein_Change',
    transcript_id='NM_000546',
    label_top_n=1,
)

py_mutation.lollipop_plot( gene='TP53', aa_col='Protein_Change', transcript_id='NM_000546', label_top_n=1, )

2025-11-13 00:05:45,161 | INFO | pyMut.visualizations.lollipop_plot | Lollipop plot for TP53 created in 0.54s (19 mutations, 17 AA changes)

Out[7]: