SequenceFeature.dipeptide_composition

SequenceFeature.dipeptide_composition(df_seq, list_parts=None, return_df=False)[source]

Create the dipeptide-composition (DPC) baseline matrix for given sequences.

Builds the no-positional-split dipeptide-composition baseline featurization: for each sequence the requested Parts are concatenated into one span and the fraction of each of the 400 ordered adjacent canonical amino-acid pairs over that span is computed, yielding the (n_seq, 400) matrix X. The 400 columns are the ordered pairs AA, AC, ..., YY in ut.LIST_CANONICAL_AA order (pair xy = first residue x followed by second residue y) so column order is stable, and each row with at least two canonical residues sums to 1. Unlike SequenceFeature.feature_matrix(), which averages scales over a specific Part-Split, this method carries no positional information — it is a plain adjacent-pair frequency count.

Application. Use this to build a baseline feature set for a prediction model: fit the same classifier on this composition X and on a CPP feature_matrix() and compare the scores to show how much the positional Part-Split-Scale features add over a plain dipeptide-frequency encoding (the “DPC baseline vs CPP” comparison). DPC captures local sequential order (which residue follows which) that plain amino-acid composition discards, while still being position-agnostic — reach for CPP when you need where-along-the-sequence information.

Added in version 1.1.0.

Parameters:

  • df_seq (pd.DataFrame, shape (n_samples, n_seq_info)) – DataFrame containing an entry column with unique protein identifiers and sequence information in a distinct format: Position-based, Part-based, Sequence-based, or Sequence-TMD-based (the same input accepted by SequenceFeature.get_df_parts()).

  • list_parts (str or list of str, optional) – Names of the sequence parts to count pairs over (see SequenceFeature for valid parts). If None (default), the whole TMD-JMD span jmd_n + tmd + jmd_c (the tmd_jmd part) is used. Multiple parts are concatenated per sequence before pairing, so an adjacent pair may cross a part boundary (see Notes).

  • return_df (bool, default=False) – If True, return a labeled pd.DataFrame (rows indexed like df_parts, one column per ordered amino-acid pair) instead of a plain numpy array.

Returns:

X – Dipeptide-composition matrix containing, per sequence, the fraction of each of the 400 ordered adjacent canonical amino-acid pairs over the span (columns AA, AC, ..., YY in ut.LIST_CANONICAL_AA order). Returned as a pd.DataFrame (pair strings as columns) when return_df=True.

Return type:

array-like, shape (n_samples, 400)

Notes

  • Missing / non-canonical residues: only the 20 canonical amino acids are counted. Gap symbols ('-') and any other non-canonical symbol (e.g. 'X') are dropped before pairing per the package convention, so the remaining canonical residues form one contiguous chain — an adjacent pair therefore spans over a dropped residue rather than being broken by it.

  • Part boundaries: parts are concatenated into one span (matching SequenceFeature.scale_composition()), so when multiple list_parts are given an adjacent pair does cross the boundary between two concatenated parts (the last residue of one part pairs with the first residue of the next).

  • A sequence whose span has fewer than two canonical residues (empty, a single residue, or all non-canonical) has no adjacent pair and yields an all-NaN row; a UserWarning naming the count is emitted when verbose=True.

  • This is a no-positional-split composition only; positional splits remain the job of CPP.

See also

Examples

SequenceFeature().dipeptide_composition() builds a baseline feature set for a prediction model. For each sequence it counts the fraction of each of the 400 ordered adjacent canonical amino-acid pairs over a span, giving the (n_seq, 400) matrix X — the sequence’s dipeptide composition (DPC). DPC captures local sequential order (which residue follows which) that plain amino-acid composition discards, while still being position-agnostic. Its purpose is comparison: fit the same classifier on this X and on a :class:CPP feature_matrix, and compare the scores to see how much CPP’s positional Part-Split-Scale features add over a plain dipeptide-frequency encoding. Here we load the DOM_GSEC example dataset (see [Breimann25]):

import aaanalysis as aa
aa.options["verbose"] = False
df_seq = aa.load_dataset(name="DOM_GSEC")
sf = aa.SequenceFeature()

By default (list_parts=None) the whole TMD-JMD span (jmd_n + tmd + jmd_c) is used. Only the 20 canonical amino acids are counted; gap symbols ('-') and other non-canonical residues are dropped before pairing, so each row with at least two canonical residues sums to 1:

X = sf.dipeptide_composition(df_seq=df_seq)
print(f"n samples: {X.shape[0]}")
print(f"n dipeptides: {X.shape[1]}")
print(f"Shape of X: {X.shape}")
print(f"Row sums (first 3): {X[:3].sum(axis=1)}")

n samples: 126
n dipeptides: 400
Shape of X: (126, 400)
Row sums (first 3): [1. 1. 1.]

With return_df=True the matrix is returned as a labeled pd.DataFrame (rows indexed by protein entry, one column per ordered amino-acid pair AA, AC, ..., YY):

df_dipeptide_composition = sf.dipeptide_composition(df_seq=df_seq, return_df=True)
aa.display_df(df_dipeptide_composition, n_rows=10, show_shape=True)

DataFrame shape: (126, 400)
  AA AC AD AE AF AG AH AI AK AL AM AN AP AQ AR AS AT AV AW AY CA CC CD CE CF CG CH CI CK CL CM CN CP CQ CR CS CT CV CW CY DA DC DD DE DF DG DH DI DK DL DM DN DP DQ DR DS DT DV DW DY EA EC ED EE EF EG EH EI EK EL EM EN EP EQ ER ES ET EV EW EY FA FC FD FE FF FG FH FI FK FL FM FN FP FQ FR FS FT FV FW FY GA GC GD GE GF GG GH GI GK GL GM GN GP GQ GR GS GT GV GW GY HA HC HD HE HF HG HH HI HK HL HM HN HP HQ HR HS HT HV HW HY IA IC ID IE IF IG IH II IK IL IM IN IP IQ IR IS IT IV IW IY KA KC KD KE KF KG KH KI KK KL KM KN KP KQ KR KS KT KV KW KY LA LC LD LE LF LG LH LI LK LL LM LN LP LQ LR LS LT LV LW LY MA MC MD ME MF MG MH MI MK ML MM MN MP MQ MR MS MT MV MW MY NA NC ND NE NF NG NH NI NK NL NM NN NP NQ NR NS NT NV NW NY PA PC PD PE PF PG PH PI PK PL PM PN PP PQ PR PS PT PV PW PY QA QC QD QE QF QG QH QI QK QL QM QN QP QQ QR QS QT QV QW QY RA RC RD RE RF RG RH RI RK RL RM RN RP RQ RR RS RT RV RW RY SA SC SD SE SF SG SH SI SK SL SM SN SP SQ SR SS ST SV SW SY TA TC TD TE TF TG TH TI TK TL TM TN TP TQ TR TS TT TV TW TY VA VC VD VE VF VG VH VI VK VL VM VN VP VQ VR VS VT VV VW VY WA WC WD WE WF WG WH WI WK WL WM WN WP WQ WR WS WT WV WW WY YA YC YD YE YF YG YH YI YK YL YM YN YP YQ YR YS YT YV YW YY
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Use list_parts to count adjacent pairs only over selected sequence parts. For example, restrict the baseline to the TMD:

X_tmd = sf.dipeptide_composition(df_seq=df_seq, list_parts="tmd")
print(f"Shape of X (TMD only): {X_tmd.shape}")

Shape of X (TMD only): (126, 400)

Multiple parts are concatenated into one span before pairing, so an adjacent pair may cross the boundary between two parts (the last residue of one part pairs with the first residue of the next):

X_jmd = sf.dipeptide_composition(df_seq=df_seq, list_parts=["jmd_n", "jmd_c"])
print(f"Shape of X (JMD_N + JMD_C): {X_jmd.shape}")

Shape of X (JMD_N + JMD_C): (126, 400)