SeqMut.eval

SeqMut.eval(df_scan=None, th=None)[source]

Evaluate a mutational scan: tag mutations stable/disruptive and summarize per region.

A mutation is disruptive when its |ΔCPP| reaches the threshold; the per-region disruptive rate shows where in the sequence (JMD-N / TMD / JMD-C) substitutions move the CPP profile most.

Parameters:

df_scan (pd.DataFrame) – Mutational landscape produced by SeqMut.scan().
th (float, optional) – |ΔCPP| threshold above which a mutation is disruptive. If None, the upper tertile (2/3 quantile) of the observed delta_cpp distribution is used.

Returns:

df_eval – One row per entry x region with n_mut, n_disruptive, frac_disruptive, and mean_delta_cpp.

Return type:

pd.DataFrame, shape (n_entry_region, 6)

Examples

:meth:SeqMut.eval tags each scanned mutation stable / disruptive by an |ΔCPP| threshold (default: the upper tertile) and summarizes disruptive rates per entry x region.

import aaanalysis as aa
aa.options["verbose"] = False

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()
cpp = aa.CPP(df_parts=df_parts, split_kws=split_kws, verbose=False)
df_feat = cpp.run(labels=labels, n_filter=25)

seqmut = aa.SeqMut()
df_scan = seqmut.scan(df_seq=df_seq, df_feat=df_feat, region="tmd")
aa.display_df(seqmut.eval(df_scan=df_scan), n_rows=10, show_shape=True)

[94mCPP 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.[0m
DataFrame shape: (20, 6)

	entry	region	n_mut	n_disruptive	frac_disruptive	mean_delta_cpp
1	P16070	tmd	437	150	0.343249	0.251832
2	P09803	tmd	437	152	0.347826	0.195534
3	Q03157	tmd	437	149	0.340961	0.200584
4	P05556	tmd	437	142	0.324943	0.265178
5	Q06481	tmd	437	141	0.322654	0.191197
6	P05067	tmd	437	147	0.336384	0.211840
7	P70180	tmd	437	124	0.283753	0.201154
8	P01135	tmd	437	147	0.336384	0.253857
9	P35070	tmd	437	154	0.352403	0.213124
10	P16234	tmd	437	155	0.354691	0.218947