Upload kmer_predict.py

kmer_predict.py

@@ -0,0 +1,485 @@
+#!/usr/bin/env python3
+"""
+K-mer-based group prediction for unknown sequences.
+
+Inputs:
+- Unknown sequences: a FASTA file or a directory of FASTA files
+- Unique k-mers: either
+  * a directory containing unique_k{k}_{group}.tsv/.txt files (from script #1), OR
+  * a ZIP file containing those files
+
+Modes:
+- fast: exact substring matching only (very fast)
+- full: alignment-based matching (slower, more tolerant) + Fisher + FDR
+
+Outputs:
+- predictions_by_alignment.csv
+- predicted_results_summary.png
+
+Example:
+  python kmer_predict.py \
+    --unknown unknown_fastas/ \
+    --kmer-input kmer_results.zip \
+    --outdir pred_out \
+    --seqtype dna \
+    --mode fast
+
+"""
+
+from __future__ import annotations
+
+import argparse
+import os
+import re
+import shutil
+import tempfile
+import zipfile
+from dataclasses import dataclass
+from typing import Dict, Iterable, List, Optional, Sequence, Tuple
+
+import pandas as pd
+import matplotlib
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt
+
+from scipy.stats import fisher_exact
+from statsmodels.stats.multitest import multipletests
+
+from Bio import Align
+from Bio.Align import substitution_matrices
+
+
+FASTA_EXTS = (".fasta", ".fa", ".fas", ".fna")
+KMER_FILE_EXTS = (".tsv", ".txt")
+DEFAULT_GROUP_REGEX = r"unique_k\d+_(.+)\.(tsv|txt)$"
+
+BLOSUM62 = substitution_matrices.load("BLOSUM62")
+
+
+# ----------------------------
+# FASTA utilities
+# ----------------------------
+
+def read_fasta(filepath: str) -> Tuple[List[str], List[str]]:
+    headers, seqs, seq = [], [], []
+    with open(filepath, "r", encoding="utf-8") as fh:
+        for line in fh:
+            line = line.rstrip("\n")
+            if not line:
+                continue
+            if line.startswith(">"):
+                if seq:
+                    seqs.append("".join(seq))
+                    seq = []
+                headers.append(line[1:].strip())
+            else:
+                seq.append(line.strip().upper())
+        if seq:
+            seqs.append("".join(seq))
+    return headers, seqs
+
+
+def clean_protein(seq: str) -> str:
+    return re.sub(r"[^ACDEFGHIKLMNPQRSTVWY]", "", seq.upper())
+
+
+def clean_dna(seq: str) -> str:
+    # allow U and N like your original
+    return re.sub(r"[^ACGTUN]", "", seq.upper())
+
+
+def iter_unknown_sequences(unknown: str, is_protein: bool) -> List[Tuple[str, str, str]]:
+    """
+    Returns list of (source_file, header, cleaned_seq).
+    unknown can be a fasta file or a directory with fasta files.
+    """
+    seq_index: List[Tuple[str, str, str]] = []
+
+    if os.path.isdir(unknown):
+        files = [
+            os.path.join(unknown, f)
+            for f in os.listdir(unknown)
+            if f.lower().endswith(FASTA_EXTS)
+        ]
+    else:
+        files = [unknown]
+
+    files = [f for f in files if os.path.isfile(f)]
+    for fp in sorted(files):
+        headers, seqs = read_fasta(fp)
+        if is_protein:
+            seqs = [clean_protein(s) for s in seqs]
+        else:
+            seqs = [clean_dna(s) for s in seqs]
+
+        for h, s in zip(headers, seqs):
+            if s:  # drop empty after cleaning
+                seq_index.append((fp, h, s))
+
+    return seq_index
+
+
+# ----------------------------
+# ZIP utilities (safe extract)
+# ----------------------------
+
+def safe_extract_zip(zip_path: str, dst_dir: str) -> None:
+    """Extract ZIP safely (prevents zip-slip)."""
+    with zipfile.ZipFile(zip_path, "r") as z:
+        for member in z.infolist():
+            if member.is_dir():
+                continue
+            target = os.path.normpath(os.path.join(dst_dir, member.filename))
+            if not target.startswith(os.path.abspath(dst_dir) + os.sep):
+                continue  # skip suspicious paths
+            os.makedirs(os.path.dirname(target), exist_ok=True)
+            with z.open(member) as src, open(target, "wb") as out:
+                shutil.copyfileobj(src, out)
+
+
+# ----------------------------
+# Load unique kmers
+# ----------------------------
+
+@dataclass
+class KmerDB:
+    group_kmers: Dict[str, List[str]]
+    source_dir: str
+
+
+def parse_group_from_filename(fname: str, group_regex: str) -> str:
+    m = re.search(group_regex, fname, re.IGNORECASE)
+    if m:
+        return m.group(1)
+    # fallback: remove extension
+    return os.path.splitext(fname)[0]
+
+
+def load_unique_kmers_from_dir(
+    kmer_dir: str,
+    is_protein: bool,
+    group_regex: str = DEFAULT_GROUP_REGEX,
+) -> KmerDB:
+    """
+    Loads k-mers from files like:
+      unique_k15_group1.tsv
+      unique_k20_groupA.txt
+    Accepts TSV or TXT; ignores comment/header lines.
+    """
+    group_kmers: Dict[str, List[str]] = {}
+
+    for fname in sorted(os.listdir(kmer_dir)):
+        if not fname.lower().endswith(KMER_FILE_EXTS):
+            continue
+
+        fpath = os.path.join(kmer_dir, fname)
+        if not os.path.isfile(fpath):
+            continue
+
+        group = parse_group_from_filename(fname, group_regex)
+        group = group.strip()
+
+        group_kmers.setdefault(group, [])
+
+        with open(fpath, "r", encoding="utf-8") as fh:
+            for line in fh:
+                line = line.strip()
+                if (not line) or line.startswith("#"):
+                    continue
+                if line.lower().startswith(("kmer", "total")):
+                    continue
+
+                token = line.split()[0].upper()
+                token = clean_protein(token) if is_protein else clean_dna(token)
+                if token:
+                    group_kmers[group].append(token)
+
+    # Deduplicate while preserving order
+    for g in list(group_kmers.keys()):
+        group_kmers[g] = list(dict.fromkeys(group_kmers[g]))
+        if len(group_kmers[g]) == 0:
+            # drop empty groups
+            del group_kmers[g]
+
+    if not group_kmers:
+        raise FileNotFoundError(f"No k-mer files found in: {kmer_dir}")
+
+    return KmerDB(group_kmers=group_kmers, source_dir=kmer_dir)
+
+
+def load_unique_kmers(kmer_input: str, is_protein: bool, group_regex: str) -> KmerDB:
+    """
+    kmer_input can be a directory OR a .zip file containing k-mer output files.
+    """
+    if os.path.isdir(kmer_input):
+        return load_unique_kmers_from_dir(kmer_input, is_protein, group_regex=group_regex)
+
+    if os.path.isfile(kmer_input) and kmer_input.lower().endswith(".zip"):
+        tmp = tempfile.mkdtemp(prefix="kmerdb_")
+        safe_extract_zip(kmer_input, tmp)
+        # find a directory inside that actually contains kmer files; simplest: use tmp itself
+        return load_unique_kmers_from_dir(tmp, is_protein, group_regex=group_regex)
+
+    raise FileNotFoundError(f"--kmer-input must be a directory or a .zip file: {kmer_input}")
+
+
+# ----------------------------
+# Matching
+# ----------------------------
+
+def align_kmer_to_seq(
+    kmer: str,
+    seq: str,
+    is_protein: bool,
+    identity_threshold: float = 0.9,
+    min_coverage: float = 0.8,
+    gap_open: float = -10,
+    gap_extend: float = -0.5,
+    nuc_match: float = 2,
+    nuc_mismatch: float = -1,
+    nuc_gap_open: float = -5,
+    nuc_gap_extend: float = -1,
+) -> bool:
+    if not kmer or not seq:
+        return False
+
+    # Fast exact substring path
+    if identity_threshold == 1.0 and min_coverage == 1.0:
+        return kmer in seq
+    if len(kmer) <= 3:
+        return kmer in seq
+
+    try:
+        aligner = Align.PairwiseAligner()
+        if is_protein:
+            aligner.substitution_matrix = BLOSUM62
+            aligner.open_gap_score = gap_open
+            aligner.extend_gap_score = gap_extend
+        else:
+            aligner.match_score = nuc_match
+            aligner.mismatch_score = nuc_mismatch
+            aligner.open_gap_score = nuc_gap_open
+            aligner.extend_gap_score = nuc_gap_extend
+
+        alns = aligner.align(kmer, seq)
+        if not alns:
+            return False
+
+        aln = alns[0]
+        aligned_query = aln.aligned[0]
+        aligned_target = aln.aligned[1]
+
+        aligned_len = sum(e - s for s, e in aligned_query)
+        if aligned_len == 0:
+            return False
+
+        matches = 0
+        for (qs, qe), (ts, te) in zip(aligned_query, aligned_target):
+            subseq_q = kmer[qs:qe]
+            subseq_t = seq[ts:te]
+            matches += sum(1 for a, b in zip(subseq_q, subseq_t) if a == b)
+
+        identity = matches / aligned_len
+        coverage = aligned_len / len(kmer)
+        return (identity >= identity_threshold) and (coverage >= min_coverage)
+
+    except Exception:
+        return False
+
+
+# ----------------------------
+# Prediction core
+# ----------------------------
+
+def predict(
+    unknown: str,
+    kmer_input: str,
+    output_dir: str,
+    seqtype: str,
+    mode: str,
+    identity_threshold: float,
+    min_coverage: float,
+    fdr_alpha: float,
+    group_regex: str,
+) -> pd.DataFrame:
+    is_protein = (seqtype.lower() == "protein")
+    mode = mode.lower().strip()
+    if mode not in {"fast", "full"}:
+        raise ValueError("--mode must be 'fast' or 'full'")
+
+    # Load kmers (dir or zip)
+    db = load_unique_kmers(kmer_input, is_protein=is_protein, group_regex=group_regex)
+    group_kmers = db.group_kmers
+
+    print(f"Loaded k-mer counts: { {g: len(group_kmers[g]) for g in group_kmers} }")
+
+    # Unknown sequences
+    seq_index = iter_unknown_sequences(unknown, is_protein=is_protein)
+    if not seq_index:
+        raise FileNotFoundError("No sequences found in --unknown (file/dir).")
+
+    # Mode parameters
+    if mode == "fast":
+        eff_identity = 1.0
+        eff_coverage = 1.0
+        compute_stats = False
+    else:
+        eff_identity = float(identity_threshold)
+        eff_coverage = float(min_coverage)
+        compute_stats = True
+
+    results: List[dict] = []
+
+    total_seqs = len(seq_index)
+    for i, (srcfile, header, seq) in enumerate(seq_index, start=1):
+        print(f"Processing sequence {i}/{total_seqs} ({os.path.basename(srcfile)})")
+
+        group_found_counts = {g: 0 for g in group_kmers}
+        total_found = 0
+
+        for g, kmers in group_kmers.items():
+            for kmer in kmers:
+                if align_kmer_to_seq(
+                    kmer, seq, is_protein=is_protein,
+                    identity_threshold=eff_identity,
+                    min_coverage=eff_coverage,
+                ):
+                    group_found_counts[g] += 1
+                    total_found += 1
+
+        predicted = max(group_found_counts, key=group_found_counts.get)
+        conf_present = (group_found_counts[predicted] / total_found) if total_found else 0.0
+        conf_vocab = group_found_counts[predicted] / max(1, len(group_kmers[predicted]))
+
+        row = {
+            "Source_file": os.path.basename(srcfile),
+            "Sequence": header,
+            "Predicted_group": predicted,
+            "Matches_total": total_found,
+            **{f"Matches_{g}": group_found_counts[g] for g in group_kmers},
+            "Confidence_by_present": conf_present,
+            "Confidence_by_group_vocab": conf_vocab,
+        }
+
+        if compute_stats:
+            fisher_p = {}
+            # total vocabulary size of "other groups" for contingency table
+            other_vocab_total = {g: sum(len(group_kmers[og]) for og in group_kmers if og != g) for g in group_kmers}
+
+            for g in group_kmers:
+                a = group_found_counts[g]
+                b = len(group_kmers[g]) - a
+                c = total_found - a
+                d = other_vocab_total[g] - c
+                if d < 0:
+                    d = 0
+                table = [[a, b], [c, d]]
+                _, p = fisher_exact(table, alternative="greater")
+                fisher_p[g] = p
+            row.update({f"FisherP_{g}": fisher_p[g] for g in group_kmers})
+
+        results.append(row)
+
+    df = pd.DataFrame(results)
+
+    # FDR correction (full mode)
+    if mode == "full":
+        fisher_cols = [c for c in df.columns if c.startswith("FisherP_")]
+        if fisher_cols:
+            all_pvals = df[fisher_cols].values.flatten()
+            _, qvals, _, _ = multipletests(all_pvals, alpha=float(fdr_alpha), method="fdr_bh")
+            qval_matrix = qvals.reshape(df[fisher_cols].shape)
+            for j, col in enumerate(fisher_cols):
+                grp = col.split("_", 1)[1]
+                df[f"FDR_{grp}"] = qval_matrix[:, j]
+
+    # Save
+    os.makedirs(output_dir, exist_ok=True)
+    out_csv = os.path.join(output_dir, "predictions_by_alignment.csv")
+    df.to_csv(out_csv, index=False)
+    print(f"Saved predictions to {out_csv}")
+
+    # Plot
+    save_summary_plot(df, output_dir)
+
+    return df
+
+
+def save_summary_plot(df: pd.DataFrame, output_dir: str) -> None:
+    """
+    Matplotlib-only summary figure:
+    - Left: predicted group counts
+    - Right: confidence distribution (boxplot)
+    """
+    fig, axes = plt.subplots(1, 2, figsize=(12, 5))
+
+    # Left: bar counts
+    counts = df["Predicted_group"].value_counts()
+    axes[0].bar(counts.index.astype(str), counts.values)
+    axes[0].set_xlabel("Predicted Group")
+    axes[0].set_ylabel("Number of Sequences")
+    axes[0].set_title("Predicted Group Counts")
+    axes[0].tick_params(axis="x", rotation=45)
+
+    # Right: boxplot confidence_by_present by group
+    groups = sorted(df["Predicted_group"].unique().tolist())
+    data = [df.loc[df["Predicted_group"] == g, "Confidence_by_present"].values for g in groups]
+    axes[1].boxplot(data, labels=groups, showfliers=False)
+    axes[1].set_title("Prediction Confidence (by Present)")
+    axes[1].set_xlabel("Predicted Group")
+    axes[1].set_ylabel("Confidence")
+    axes[1].tick_params(axis="x", rotation=45)
+
+    fig.tight_layout()
+    fig.savefig(os.path.join(output_dir, "predicted_results_summary.png"), dpi=300)
+    plt.close(fig)
+
+
+# ----------------------------
+# CLI
+# ----------------------------
+
+def build_parser() -> argparse.ArgumentParser:
+    p = argparse.ArgumentParser(
+        description="Predict group membership of unknown sequences using unique k-mers.",
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter,
+    )
+    p.add_argument("--unknown", required=True, help="Unknown FASTA file OR directory of FASTA files.")
+    p.add_argument("--kmer-input", required=True, help="Directory of unique_k*.tsv/txt OR a ZIP containing them.")
+    p.add_argument("--outdir", default="prediction_results", help="Output directory.")
+    p.add_argument("--seqtype", choices=["dna", "protein"], default="dna", help="Sequence type.")
+    p.add_argument("--mode", choices=["fast", "full"], default="fast", help="fast=substring only; full=alignment+Fisher+FDR.")
+    p.add_argument("--identity", type=float, default=0.9, help="Alignment identity threshold (full mode only).")
+    p.add_argument("--coverage", type=float, default=0.8, help="Alignment coverage threshold (full mode only).")
+    p.add_argument("--fdr", type=float, default=0.05, help="FDR alpha (full mode only).")
+    p.add_argument(
+        "--group-regex",
+        default=DEFAULT_GROUP_REGEX,
+        help="Regex to extract group name from k-mer filenames (1st capture group = group).",
+    )
+    return p
+
+
+def main() -> None:
+    args = build_parser().parse_args()
+
+    # Validate unknown
+    if not os.path.exists(args.unknown):
+        raise FileNotFoundError(f"--unknown not found: {args.unknown}")
+
+    # Run
+    predict(
+        unknown=args.unknown,
+        kmer_input=args.kmer_input,
+        output_dir=args.outdir,
+        seqtype=args.seqtype,
+        mode=args.mode,
+        identity_threshold=args.identity,
+        min_coverage=args.coverage,
+        fdr_alpha=args.fdr,
+        group_regex=args.group_regex,
+    )
+
+
+if __name__ == "__main__":
+    main()