Visualizing with plotly

In this section we will use Plotly to make interactive plots. Please let us know if you have suggestions for new figures that could be made with Plotly.

%matplotlib inline
import numpy as np
from mutagenesis_visualization.main.demo.demo_objects import DemoObjects
from mutagenesis_visualization.main.utils.data_paths import PDB_5P21

DEMO_OBJECTS:DemoObjects = DemoObjects()
hras_rbd = DEMO_OBJECTS.hras_rbd
hras_gapgef = DEMO_OBJECTS.hras_gapgef

Heatmap

Classes reviewed in this section:

• mutagenesis_visualization.main.plotly.heatmap.HeatmapP

Plot an interactive heatmap. Hopover individual pixels to get their characteristics.

hras_rbd.plotly_heatmap(
    title='H-Ras Heatmap',
    figsize=(6, 2.5),
)

Mean

Classes reviewed in this section:

• mutagenesis_visualization.main.plotly.enrichment_bar.EnrichmentBarP

Analogous function to object.mean but rendered using plotly. Will plot the mean enrichment score for every position on a bar chart. It will be colored blue for loss of function and red for gain of function. Additionally, setting the parameter mode to an amino acid (using the one letter code) will plot the enrichment for that particular amino acid along the protein. In this example, we are showing the mean enrichment scores (top) and an alanine scan (bottom)

hras_rbd.plotly_enrichment_bar(
    title='Mean',
    figsize=(6, 2.5),
)

hras_rbd.plotly_enrichment_bar(
    title='A scan',
    mode='A',
    figsize=(6, 2.5),
)

Histogram

Classes reviewed in this section:

• mutagenesis_visualization.main.plotly.histogram.HistogramP

Plot a histogram.

hras_rbd.plotly_histogram(
    title='Histogram',
    figsize=(3, 2.5),
)

Rank

Classes reviewed in this section:

• mutagenesis_visualization.main.plotly.rank.RankP

Create an interactive rank figure that displays each mutant. The default mode is set to pointmutant to provide the ranking on the mutation level. You can download the plot as a png file by clicking the camera icon which appears on the far left when our cursor is over the plot. You can export to an html file by giving a path to the variable output_html.

hras_rbd.plotly_rank(
    title='Rank of pointmutants',
)

Now display the rank of the positional mean.

hras_rbd.plotly_rank(mode='mean',title='Rank of positions')

Scatter

Classes reviewed in this section:

• mutagenesis_visualization.main.plotly.scatter.ScatterP

If you have two datasets, you can create a scatter plot. The advantage of using plotly over matplotlib is that you can visually check each data point by hovoring your cursor over a point. By setting show_results = True, the OLS regression results will also be printed as output. The mode = ‘pointmutant’ is default which shows a comparison as mutation by mutation.

hras_rbd.plotly_scatter(
    hras_gapgef,
    show_results=False,
    title='Scatter Point Mutants',
    x_label='hras_rbd',
    y_label='hras_gapgef',
)

Now we just look at the positional average.

hras_rbd.plotly_scatter(
    hras_gapgef,
    mode='mean',
    title='Scatter Positional Average',
    x_label='hras_rbd',
    y_label='hras_gapgef',
)

3D scatter plot

Classes reviewed in this section:

• mutagenesis_visualization.main.plotly.scatter_3d.Scatter3D

If there is an available PDB structure, you can input it and the software will plot a 3d plot of the C-alpha atoms, colored by their enrichment score.

The method object.plotly_scatter_3d will take as an input either a PDB file (pdb_path=/path/to/file) or the x,y,z coordinates (df_coordinates).

hras_rbd.plotly_scatter_3d(
    mode='mean',
    pdb_path=PDB_5P21,
    title='Scatter 3D',
    squared=False,
    x_label='x',
    y_label='y',
    z_label='z',
)

By setting up mode=‘V’, we can evaluate the impact of valine substitutions. Mode can be set up to any residue. In this example, residues in the core are tolerant to valine substitutions.

hras_rbd.plotly_scatter_3d(
    mode='V',
    pdb_path=PDB_5P21,
    title='Scatter 3D - Valine substitution',
    squared=False,
    x_label='x',
    y_label='y',
    z_label='z',
)

When we set mode=‘D’, the core of the protein turns completely blue.

hras_rbd.plotly_scatter_3d(
    mode='D',
    pdb_path=PDB_5P21,
    title='Scatter 3D - Aspartate substitution',
    squared=False,
    x_label='x',
    y_label='y',
    z_label='z',
)

By setting squared = True, we plot the distance to the center of the protein of each residue. In this example, we see that residues in the core of the protein are blue, indicating a sensitivity to mutations.

hras_rbd.plotly_scatter_3d(
    mode='mean',
    pdb_path=PDB_5P21,
    title='Scatter 3D - Distance to center',
    squared=True,
    x_label='x',
    y_label='y',
    z_label='z',
)

PDB properties

Classes reviewed in this section:

• mutagenesis_visualization.main.plotly.scatter_3d_pdb.Scatter3DPDB

From the PDB, properties such as B-factor or SASA can be extracted. Using plotly we allow the user to have a 3-D scatter plot colored by the enrichment scores. You can additionally include other properties to include such as the conservation scores using the parameter custom.

# Plot 3-D SASA, log B-factor and Shannon Entropy
hras_rbd.plotly_scatter_3d_pdbprop(
    plot = ['Distance', 'SASA', 'log B-factor'],
    pdb_path=PDB_5P21,
    title='Scatter 3D - PDB properties',
)