221 lines
7.4 KiB
Python
221 lines
7.4 KiB
Python
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#!/usr/bin/env python3
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"""
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TF Family analysis among cross-stress DEGs.
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Uses InterProScan annotations + pfam mapping to identify TF families.
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"""
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import os, csv, re, json
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from collections import defaultdict, Counter
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from pathlib import Path
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PROJECT_ROOT = Path(__file__).resolve().parents[1]
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WORKSPACE = str(PROJECT_ROOT / "workspace")
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OUTPUT = os.path.join(WORKSPACE, "cross_stress_analysis")
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ANNO_FILE = os.path.join(
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WORKSPACE,
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"job_1780143735966_0e7710/final/Os.rnaseq_candidate_family.gene_annotations.tsv",
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)
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VENN_DIR = os.path.join(OUTPUT, "time_lrt_venn_gene_lists")
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# Key stress-related TF families to check
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TF_FAMILIES = {
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"WRKY": ["PF03106", "WRKY"],
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"NAC": ["PF02365", "NAC domain"],
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"MYB": ["PF00249", "PF13921", "MYB"],
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"AP2_ERF": ["PF00847", "AP2", "ERF", "DREB"],
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"bZIP": ["PF00170", "PF07716", "bZIP"],
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"bHLH": ["PF00010", "bHLH", "helix-loop-helix"],
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"HSF": ["PF00447", "HSF"],
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"MADS": ["PF00319", "PF01486", "MADS"],
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"TCP": ["PF03634", "TCP"],
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"ARF": ["PF06507", "ARF"],
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"GRAS": ["PF03514", "GRAS"],
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"HD-ZIP": ["PF00046", "PF02183", "homeobox", "HD-ZIP"],
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"Dof": ["PF02701", "Dof"],
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"GATA": ["PF00320", "GATA"],
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"Trihelix": ["PF13837", "Trihelix"],
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"SPL_SBP": ["PF03110", "SBP"],
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"GRF": ["PF06850", "GRF"],
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"LBD": ["PF03195", "LBD"],
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"NF-Y": ["PF02045", "PF00808", "NF-Y", "CBF"],
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"E2F_DP": ["PF02319", "E2F"],
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"B3": ["PF02362", "B3"],
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"CPP": ["PF03641", "CPP"],
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"YABBY": ["PF04690", "YABBY"],
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"TALE": ["PF05920", "TALE"],
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"VOZ": ["PF07878", "VOZ"],
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"STAT": ["PF02864", "STAT"],
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"ZF_HD": ["PF04770", "ZF-HD"],
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}
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def parse_candidate_families(family_str):
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"""Parse candidate_families column and return list of detected TF families"""
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if not family_str or family_str == "NA":
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return []
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family_str = family_str.upper()
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detected = []
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for tf_name, keywords in TF_FAMILIES.items():
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for kw in keywords:
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if kw.upper() in family_str:
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detected.append(tf_name)
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break
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return detected
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def read_gene_list(filepath):
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"""Read gene IDs from a file (one per line)"""
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genes = set()
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with open(filepath) as f:
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for line in f:
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gene = line.strip()
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if gene and not gene.startswith("gene_id"):
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genes.add(gene)
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return genes
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def main():
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# Read annotation file - build gene → TF families mapping
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print("Reading annotation file...")
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gene_to_tf = defaultdict(set)
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tf_gene_count = Counter()
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with open(ANNO_FILE) as f:
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reader = csv.DictReader(f, delimiter="\t")
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for row in reader:
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gene = row.get("gene_id", "")
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families_str = row.get("candidate_families", "")
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sig_str = row.get("signatures", "")
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domain_desc = row.get("domain_descriptions", "")
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# Combine all text fields for searching
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combined = f"{families_str} {sig_str} {domain_desc}"
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detected = parse_candidate_families(combined)
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if detected:
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gene_to_tf[gene].update(detected)
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for tf in detected:
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tf_gene_count[tf] += 1
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print(f" Total annotated genes with TF: {len(gene_to_tf)}")
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print(f" Total TF assignments: {sum(tf_gene_count.values())}")
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# Read Venn gene lists
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print("\nReading Venn gene lists...")
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venn_categories = {}
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if os.path.isdir(VENN_DIR):
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for fname in sorted(os.listdir(VENN_DIR)):
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if fname.endswith(".txt"):
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key = fname.replace(".txt", "")
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venn_categories[key] = read_gene_list(os.path.join(VENN_DIR, fname))
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print(f" {key}: {len(venn_categories[key])} genes")
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# Analyze TF families in each Venn category
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print("\n" + "=" * 80)
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print("TF FAMILY DISTRIBUTION ACROSS CROSS-STRESS CATEGORIES")
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print("=" * 80)
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results = {}
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# Also do the early Venn
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for analysis_name, venn_dir_base in [
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("time_lrt_venn", "time_lrt_venn_gene_lists"),
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("early_venn", "early_venn_gene_lists"),
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]:
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venn_dir = os.path.join(OUTPUT, venn_dir_base)
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if not os.path.isdir(venn_dir):
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continue
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print(f"\n{'#' * 70}")
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print(f" Analysis: {analysis_name}")
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print(f"{'#' * 70}")
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category_genes = {}
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for fname in sorted(os.listdir(venn_dir)):
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if fname.endswith(".txt"):
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key = fname.replace(".txt", "")
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category_genes[key] = read_gene_list(os.path.join(venn_dir, fname))
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# Count TF families in each category
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tf_counts = {}
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for cat_name, genes in category_genes.items():
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tf_in_cat = Counter()
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for gene in genes:
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if gene in gene_to_tf:
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for tf in gene_to_tf[gene]:
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tf_in_cat[tf] += 1
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tf_counts[cat_name] = tf_in_cat
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# Print as table
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categories_ordered = [
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"all_three",
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"salt_drought",
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"salt_heat",
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"drought_heat",
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"only_salt",
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"only_drought",
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"only_heat",
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]
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# Header
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header = f"{'TF Family':<15}"
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for cat in categories_ordered:
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if cat in tf_counts:
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total = len(category_genes.get(cat, set()))
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header += f" {cat:<18}"
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print(header)
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print("-" * len(header))
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# TF families sorted by total prevalence
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all_tfs = sorted(TF_FAMILIES.keys())
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tf_row_data = {}
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for tf in all_tfs:
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row = f"{tf:<15}"
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row_data = {}
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for cat in categories_ordered:
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if cat in tf_counts:
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count = tf_counts[cat].get(tf, 0)
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total = len(category_genes.get(cat, set()))
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pct = (count / total * 100) if total > 0 else 0
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row += f" {count:<5}({pct:.1f}%) "
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row_data[cat] = count
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tf_row_data[tf] = row_data
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print(row)
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# Summary
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print(f"\n{'─' * 70}")
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print(f"{'Category':<25} {'Total Genes':<15} {'TF Genes':<15} {'TF %':<10}")
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print(f"{'─' * 70}")
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for cat in categories_ordered:
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if cat in tf_counts:
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total = len(category_genes.get(cat, set()))
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tf_genes = set()
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for gene in category_genes.get(cat, set()):
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if gene in gene_to_tf:
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tf_genes.add(gene)
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pct = len(tf_genes) / total * 100 if total > 0 else 0
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print(f"{cat:<25} {total:<15} {len(tf_genes):<15} {pct:<10.1f}%")
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# Save to file
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outfile = os.path.join(OUTPUT, f"{analysis_name}_tf_summary.txt")
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with open(outfile, "w") as f:
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f.write(f"TF Family Analysis: {analysis_name}\n")
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f.write("=" * 70 + "\n\n")
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f.write(f"{'TF Family':<15}")
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for cat in categories_ordered:
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if cat in tf_counts:
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f.write(f"\t{cat}")
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f.write("\n")
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for tf in all_tfs:
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f.write(f"{tf:<15}")
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for cat in categories_ordered:
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if cat in tf_counts:
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count = tf_counts[cat].get(tf, 0)
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f.write(f"\t{count}")
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f.write("\n")
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print(f"\n Saved: {outfile}")
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if __name__ == "__main__":
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main()
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