SeqOptPlot.mutation_map

SeqOptPlot.mutation_map(df_pareto, ax=None, figsize=(8, 4), front_only=True, cmap='Reds')

Heatmap of substitution enrichment across the Pareto front (position x amino acid).

Each cell counts how often a given substitution (target amino acid at a 1-based position) appears among the front’s variants — the directed-evolution view of which mutations the optimization converged on.

Parameters:

• df_pareto (pd.DataFrame) – Output of SeqOpt.run() (its variant labels are parsed).

• ax (matplotlib.axes.Axes, optional) – Axes to draw on. A new figure is created when None.

• figsize (tuple, default=(8, 4)) – Figure size when ax is None.

• front_only (bool, default=True) – If True, count only the first (rank=0) front.

• cmap (str, default="Reds") – Matplotlib colormap name for the enrichment counts.

Returns:

• fig (matplotlib.figure.Figure) – The figure.

• ax (matplotlib.axes.Axes) – The axes the heatmap was drawn on.

Examples

import numpy as np, pandas as pd
import matplotlib.pyplot as plt
from sklearn.ensemble import RandomForestClassifier
import aaanalysis as aa
aa.options["verbose"] = False
df_feat = aa.load_features(name="DOM_GSEC")
df_seq  = aa.load_dataset(name="DOM_GSEC", n=50)
labels  = df_seq["label"].to_list()
sf = aa.SequenceFeature()
X = np.asarray(sf.feature_matrix(features=df_feat["feature"],
                                 df_parts=sf.get_df_parts(df_seq=df_seq),
                                 df_scales=aa.load_scales()), dtype=float)
model = RandomForestClassifier(n_estimators=100, random_state=0).fit(X, labels)
wt = df_seq[df_seq["label"] == 0].iloc[[0]].reset_index(drop=True)
objectives = [("substrate", "max", "delta_pred"), ("parsimony", "min", "n_mut")]
seqopt = aa.SeqOpt(mode="importance", model=model, target_class=1, random_state=42)
df_pareto = seqopt.run(df_seq=wt, df_feat=df_feat, objectives=objectives,
                       pop_size=40, n_gen=20, n_mut_max=5, region="tmd")
aa.display_df(df_pareto, n_rows=10, show_shape=True)

DataFrame shape: (7, 8)

	entry	variant	n_mut	sequence_mut	substrate	parsimony	rank	crowding
1	Q14802		0	MQKVTLGLLVFLAGF...PGETPPLITPGSAQS	0.000000	0.000000	0	inf
2	Q14802	L37T+I55T+V56L+S58Q+A59R	5	MQKVTLGLLVFLAGF...PGETPPLITPGSAQS	37.000000	5.000000	0	inf
3	Q14802	L37G+A59R	2	MQKVTLGLLVFLAGF...PGETPPLITPGSAQS	29.000000	2.000000	0	0.429730
4	Q14802	A59R	1	MQKVTLGLLVFLAGF...PGETPPLITPGSAQS	16.000000	1.000000	0	0.316216
5	Q14802	L37G+V56L+A59R	3	MQKVTLGLLVFLAGF...PGETPPLITPGSAQS	33.000000	3.000000	0	0.281081
6	Q14802	A59K	1	MQKVTLGLLVFLAGF...PGETPPLITPGSAQS	16.000000	1.000000	0	0.275676
7	Q14802	L37G+V56L+S58Q+A59R	4	MQKVTLGLLVFLAGF...PGETPPLITPGSAQS	35.000000	4.000000	0	0.254054

aa.plot_settings()
aa.SeqOptPlot().mutation_map(df_pareto=df_pareto, front_only=True)
plt.tight_layout(); plt.show()

# cmap sets the enrichment colormap; figsize sets the figure size in inches
aa.plot_settings()
aa.SeqOptPlot().mutation_map(df_pareto=df_pareto, cmap="Blues", figsize=(9, 5))
plt.tight_layout(); plt.show()