SeqMutPlot.variant_impact

SeqMutPlot.variant_impact(df_variant, entry=None, n=None, ax=None, figsize=(8, 5))

Plot a ranked bar chart of combined variants by their impact.

One horizontal bar per combined variant (from SeqMut.combine()), its length the variant’s delta_pred (model prediction shift) when present, otherwise shift_score; bars are colored red/blue by sign. This is the variant-level view of stacking 2-3 mutations — complementary to the single-mutation SeqMutPlot.mutation_landscape().

Parameters:

• df_variant (pd.DataFrame) – Combined-variant table produced by SeqMut.combine().

• entry (str, optional) – Protein entry to plot. If None, all variants are shown.

• n (int, optional) – Plot only the top n variants by impact. If None, all are shown.

• ax (Axes, optional) – Pre-defined Axes object to plot on. If None, a new one is created.

• figsize (tuple, default=(8, 5)) – Figure dimensions (width, height) in inches (used when ax is None).

Returns:

• fig (Figure) – Figure object containing the plot.

• ax (Axes) – Axes object of the ranked-variant bar chart.

Notes

• Returned as a (fig, ax) pair (see SeqMutPlot for the shared return contract).

Examples

:meth:SeqMutPlot.variant_impact ranks the combined variants from :meth:SeqMut.combine as a horizontal bar chart, each bar the variant’s delta_pred (model prediction shift) colored by sign.

import itertools
import pandas as pd
import matplotlib.pyplot as plt
import aaanalysis as aa
aa.options["verbose"] = False

# Data, CPP features, and a fitted TreeModel that scores each sequence
df_seq = aa.load_dataset(name="DOM_GSEC", n=10)
labels = df_seq["label"].to_list()
sf = aa.SequenceFeature()
df_parts = sf.get_df_parts(df_seq=df_seq)
split_kws = sf.get_split_kws()
df_scales = aa.load_scales()
cpp = aa.CPP(df_parts=df_parts, split_kws=split_kws, df_scales=df_scales, verbose=False)
df_feat = cpp.run(labels=labels, n_filter=25)
X = sf.feature_matrix(features=list(df_feat["feature"]), df_parts=df_parts, df_scales=df_scales)
tm = aa.TreeModel().fit(X, labels=labels)
entry = df_seq["entry"].iloc[0]
ts = int(df_seq.set_index("entry").loc[entry, "tmd_start"])

seqmut = aa.SeqMut(model=tm)
variants = pd.DataFrame({
    "entry": [entry] * 5,
    "variant": ["double", "double", "triple", "triple", "triple"],
    "pos": [ts, ts + 1, ts, ts + 2, ts + 4],
    "to_aa": ["A", "P", "A", "K", "L"],
})
df_variant = seqmut.combine(df_seq=df_seq, variants=variants, df_feat=df_feat)
aa.plot_settings()
aa.SeqMutPlot().variant_impact(df_variant=df_variant, entry=entry, n=10, figsize=(8, 4))
plt.tight_layout()
plt.show()

/Users/stephanbreimann/Programming/1Packages/wt-seqmut-ml-guided/aaanalysis/feature_engineering/_backend/cpp_run.py:163: UserWarning: CPP is using the Python kernel fallback — the compiled Cython extension is not available in this install. Output is bit-exact with the Cython path but ~2x slower. Reinstall via pip install --force-reinstall aaanalysis to fetch a prebuilt wheel.
  warnings.warn(