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tasks/crispr-screen/run_script.sh

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+#!/usr/bin/env bash
+set -euo pipefail
+
+# CRISPR Screen Analysis Pipeline
+# Data: Human Brunello library screen (APR-246 drug sensitivity)
+# Samples: T0 (baseline), T8_Drug (APR-246 treated), T8_Vehicle (DMSO control)
+#
+# DAG Structure (depth=10, convergence=4):
+#
+#   [T0.fq]──────────[Drug.fq]──────────[Vehicle.fq]
+#     │                  │                    │
+#   fastqc             fastqc              fastqc          (Step 1: QC)
+#     │                  │                    │
+#   cutadapt           cutadapt            cutadapt        (Step 2: Trim)
+#     │                  │                    │
+#     └──────────────────┼────────────────────┘
+#                        │
+#                   mageck count                           (Step 3: CONVERGE #1)
+#                  ╱       │       ╲
+#          mageck test  mageck test  mageck mle            (Step 4: 3-way parallel)
+#          (drug/T0)    (veh/T0)    (all conditions)
+#                  ╲       │       ╱
+#                   merge rankings                         (Step 5: CONVERGE #2)
+#                     ╱         ╲
+#             drug-specific    count QC metrics             (Step 6: parallel)
+#             hit analysis     + gini index
+#                     ╲         ╱
+#                   pathway enrichment                     (Step 7: CONVERGE #3)
+#                        │
+#                   hit classification                     (Step 8)
+#                        │
+#                   final report                           (Step 9: CONVERGE #4 w/ multiqc)
+#                        │
+#                    report.csv                            (Step 10)
+
+THREADS=$(( $(nproc) > 8 ? 8 : $(nproc) ))
+WORKDIR="$(cd "$(dirname "$0")" && pwd)"
+cd "$WORKDIR"
+
+DATA="${WORKDIR}/data"
+REF="${WORKDIR}/reference"
+OUT="${WORKDIR}/outputs"
+RESULTS="${WORKDIR}/results"
+
+mkdir -p "${OUT}/fastqc_raw" "${OUT}/fastqc_trimmed" "${OUT}/trimmed"
+mkdir -p "${OUT}/count" "${OUT}/rra_drug" "${OUT}/rra_vehicle" "${OUT}/mle"
+mkdir -p "${OUT}/comparison" "${OUT}/multiqc" "${RESULTS}"
+
+LIBRARY="${REF}/library.tsv"
+
+# Vector backbone containing the sgRNA cassette
+# Reads have: [variable prefix]ACCG[20bp sgRNA]GTTT[scaffold]
+# 5' trim sequence for cutadapt (linked adapter)
+VECTOR_5="CTTGTGGAAAGGACGAAACACCG"
+SCAFFOLD_3="GTTTTAGAGCTAGAAATAGCAAGTT"
+
+# ============================================================================
+# Step 1: FastQC on raw reads (parallel per sample)
+# ============================================================================
+echo "[Step 1] Running FastQC on raw reads..."
+for fq in "${DATA}"/T*.fastq.gz; do
+    BASENAME=$(basename "$fq" .fastq.gz)
+    if [ ! -f "${OUT}/fastqc_raw/${BASENAME}_fastqc.html" ]; then
+        fastqc -t "${THREADS}" -o "${OUT}/fastqc_raw" "$fq" &
+    fi
+done
+wait
+echo "[Step 1] FastQC raw done."
+
+# ============================================================================
+# Step 2: Cutadapt — extract sgRNA from vector context (parallel per sample)
+# Uses linked adapter: trims 5' vector then 3' scaffold, keeping just the sgRNA
+# ============================================================================
+echo "[Step 2] Extracting sgRNA sequences with cutadapt..."
+for fq in "${DATA}"/T*.fastq.gz; do
+    BASENAME=$(basename "$fq" .fastq.gz)
+    TRIMMED="${OUT}/trimmed/${BASENAME}_trimmed.fastq.gz"
+    if [ ! -f "$TRIMMED" ]; then
+        cutadapt \
+            -g "${VECTOR_5}...${SCAFFOLD_3}" \
+            -e 0.15 \
+            --discard-untrimmed \
+            --minimum-length 18 \
+            --maximum-length 24 \
+            -o "$TRIMMED" \
+            "$fq" \
+            > "${OUT}/trimmed/${BASENAME}_cutadapt.log" 2>&1 &
+    fi
+done
+wait
+echo "[Step 2] Trimming done."
+
+# ============================================================================
+# Step 2b: FastQC on trimmed reads
+# ============================================================================
+echo "[Step 2b] Running FastQC on trimmed reads..."
+for fq in "${OUT}/trimmed"/*_trimmed.fastq.gz; do
+    BASENAME=$(basename "$fq" .fastq.gz)
+    if [ ! -f "${OUT}/fastqc_trimmed/${BASENAME}_fastqc.html" ]; then
+        fastqc -t "${THREADS}" -o "${OUT}/fastqc_trimmed" "$fq" &
+    fi
+done
+wait
+echo "[Step 2b] FastQC trimmed done."
+
+# ============================================================================
+# Step 3: MAGeCK count — CONVERGE all 3 samples (CONVERGENCE #1)
+# ============================================================================
+echo "[Step 3] Running MAGeCK count (convergence #1: all samples)..."
+if [ ! -f "${OUT}/count/screen.count.txt" ]; then
+    mageck count \
+        -l "$LIBRARY" \
+        -n "${OUT}/count/screen" \
+        --sample-label "T0,Drug,Vehicle" \
+        --fastq \
+            "${OUT}/trimmed/T0_control_trimmed.fastq.gz" \
+            "${OUT}/trimmed/T8_drug_trimmed.fastq.gz" \
+            "${OUT}/trimmed/T8_vehicle_trimmed.fastq.gz" \
+        --norm-method median \
+        2>&1 | tee "${OUT}/count/mageck_count.log"
+fi
+echo "[Step 3] MAGeCK count done."
+
+# ============================================================================
+# Step 4a: MAGeCK test (RRA) — Drug vs T0
+# ============================================================================
+echo "[Step 4a] Running MAGeCK test (RRA): Drug vs T0..."
+if [ ! -f "${OUT}/rra_drug/drug_vs_t0.gene_summary.txt" ]; then
+    mageck test \
+        -k "${OUT}/count/screen.count.txt" \
+        -t Drug \
+        -c T0 \
+        -n "${OUT}/rra_drug/drug_vs_t0" \
+        --gene-lfc-method alphamedian \
+        2>&1 | tee "${OUT}/rra_drug/mageck_test_drug.log"
+fi
+
+# ============================================================================
+# Step 4b: MAGeCK test (RRA) — Vehicle vs T0
+# ============================================================================
+echo "[Step 4b] Running MAGeCK test (RRA): Vehicle vs T0..."
+if [ ! -f "${OUT}/rra_vehicle/vehicle_vs_t0.gene_summary.txt" ]; then
+    mageck test \
+        -k "${OUT}/count/screen.count.txt" \
+        -t Vehicle \
+        -c T0 \
+        -n "${OUT}/rra_vehicle/vehicle_vs_t0" \
+        --gene-lfc-method alphamedian \
+        2>&1 | tee "${OUT}/rra_vehicle/mageck_test_vehicle.log"
+fi
+
+# ============================================================================
+# Step 4c: MAGeCK MLE — multi-condition modeling
+# ============================================================================
+echo "[Step 4c] Running MAGeCK MLE..."
+# Create design matrix for MLE
+DESIGN="${OUT}/mle/design_matrix.txt"
+if [ ! -f "$DESIGN" ]; then
+    cat > "$DESIGN" << 'DESIGN_EOF'
+Samples	baseline	drug	vehicle
+T0	1	0	0
+Drug	1	1	0
+Vehicle	1	0	1
+DESIGN_EOF
+fi
+
+if [ ! -f "${OUT}/mle/screen_mle.gene_summary.txt" ]; then
+    mageck mle \
+        -k "${OUT}/count/screen.count.txt" \
+        -d "$DESIGN" \
+        -n "${OUT}/mle/screen_mle" \
+        2>&1 | tee "${OUT}/mle/mageck_mle.log"
+fi
+echo "[Step 4] All three analysis methods done."
+
+# ============================================================================
+# Step 5: Merge rankings — CONVERGENCE #2 (multi-method)
+# ============================================================================
+echo "[Step 5] Merging rankings from RRA and MLE (convergence #2)..."
+python3 << 'MERGE_PY'
+import csv
+import os
+
+OUT = os.environ.get("OUT", "outputs")
+
+# Load RRA drug results
+rra_drug_genes = {}
+with open(f"{OUT}/rra_drug/drug_vs_t0.gene_summary.txt") as f:
+    reader = csv.DictReader(f, delimiter='\t')
+    for row in reader:
+        rra_drug_genes[row['id']] = {
+            'neg_rank': int(row['neg|rank']),
+            'neg_fdr': float(row['neg|fdr']),
+            'neg_lfc': float(row['neg|lfc']),
+            'pos_rank': int(row['pos|rank']),
+            'pos_fdr': float(row['pos|fdr']),
+            'pos_lfc': float(row['pos|lfc']),
+        }
+
+# Load RRA vehicle results
+rra_veh_genes = {}
+with open(f"{OUT}/rra_vehicle/vehicle_vs_t0.gene_summary.txt") as f:
+    reader = csv.DictReader(f, delimiter='\t')
+    for row in reader:
+        rra_veh_genes[row['id']] = {
+            'neg_rank': int(row['neg|rank']),
+            'neg_fdr': float(row['neg|fdr']),
+            'neg_lfc': float(row['neg|lfc']),
+        }
+
+# Load MLE results
+mle_genes = {}
+mle_file = f"{OUT}/mle/screen_mle.gene_summary.txt"
+if os.path.exists(mle_file):
+    with open(mle_file) as f:
+        reader = csv.DictReader(f, delimiter='\t')
+        for row in reader:
+            gene = row['Gene']
+            # MLE has drug|beta, drug|fdr columns
+            try:
+                mle_genes[gene] = {
+                    'drug_beta': float(row.get('drug|beta', 0)),
+                    'drug_fdr': float(row.get('drug|fdr', 1)),
+                }
+            except (ValueError, KeyError):
+                pass
+
+# Merge: identify concordant hits
+concordance = {}
+for gene in rra_drug_genes:
+    rra_neg_fdr = rra_drug_genes[gene]['neg_fdr']
+    rra_neg_rank = rra_drug_genes[gene]['neg_rank']
+    mle_fdr = mle_genes.get(gene, {}).get('drug_fdr', 1.0)
+    mle_beta = mle_genes.get(gene, {}).get('drug_beta', 0.0)
+    veh_neg_fdr = rra_veh_genes.get(gene, {}).get('neg_fdr', 1.0)
+    concordance[gene] = {
+        'rra_neg_rank': rra_neg_rank,
+        'rra_neg_fdr': rra_neg_fdr,
+        'rra_neg_lfc': rra_drug_genes[gene]['neg_lfc'],
+        'mle_beta': mle_beta,
+        'mle_fdr': mle_fdr,
+        'veh_neg_fdr': veh_neg_fdr,
+        'drug_specific': rra_neg_fdr < 0.25 and veh_neg_fdr >= 0.25,
+        'concordant': rra_neg_fdr < 0.25 and mle_fdr < 0.25,
+    }
+
+# Write merged results
+os.makedirs(f"{OUT}/comparison", exist_ok=True)
+with open(f"{OUT}/comparison/merged_rankings.tsv", 'w') as f:
+    f.write("gene\trra_neg_rank\trra_neg_fdr\trra_neg_lfc\tmle_beta\tmle_fdr\tveh_neg_fdr\tdrug_specific\tconcordant\n")
+    for gene, data in sorted(concordance.items(), key=lambda x: x[1]['rra_neg_rank']):
+        f.write(f"{gene}\t{data['rra_neg_rank']}\t{data['rra_neg_fdr']:.6f}\t{data['rra_neg_lfc']:.4f}\t"
+                f"{data['mle_beta']:.4f}\t{data['mle_fdr']:.6f}\t{data['veh_neg_fdr']:.6f}\t"
+                f"{data['drug_specific']}\t{data['concordant']}\n")
+
+print(f"Merged {len(concordance)} genes")
+drug_specific = sum(1 for g in concordance.values() if g['drug_specific'])
+concordant = sum(1 for g in concordance.values() if g['concordant'])
+print(f"Drug-specific: {drug_specific}, Concordant (RRA+MLE): {concordant}")
+MERGE_PY
+echo "[Step 5] Ranking merge done."
+
+# ============================================================================
+# Step 6a: Drug-specific hit analysis (parallel)
+# ============================================================================
+echo "[Step 6a] Identifying drug-specific hits..."
+python3 << 'DRUG_SPEC_PY'
+import csv
+import os
+
+OUT = os.environ.get("OUT", "outputs")
+
+# Read merged rankings
+drug_specific_genes = []
+with open(f"{OUT}/comparison/merged_rankings.tsv") as f:
+    reader = csv.DictReader(f, delimiter='\t')
+    for row in reader:
+        if row['drug_specific'] == 'True':
+            drug_specific_genes.append({
+                'gene': row['gene'],
+                'rra_neg_fdr': float(row['rra_neg_fdr']),
+                'rra_neg_lfc': float(row['rra_neg_lfc']),
+                'veh_neg_fdr': float(row['veh_neg_fdr']),
+            })
+
+drug_specific_genes.sort(key=lambda x: x['rra_neg_fdr'])
+
+with open(f"{OUT}/comparison/drug_specific_hits.tsv", 'w') as f:
+    f.write("gene\trra_neg_fdr\trra_neg_lfc\tveh_neg_fdr\n")
+    for g in drug_specific_genes:
+        f.write(f"{g['gene']}\t{g['rra_neg_fdr']:.6f}\t{g['rra_neg_lfc']:.4f}\t{g['veh_neg_fdr']:.6f}\n")
+
+print(f"Drug-specific hits: {len(drug_specific_genes)}")
+DRUG_SPEC_PY
+
+# ============================================================================
+# Step 6b: Count QC metrics and Gini index (parallel)
+# ============================================================================
+echo "[Step 6b] Computing count QC metrics..."
+python3 << 'QC_PY'
+import csv
+import math
+import os
+
+OUT = os.environ.get("OUT", "outputs")
+
+# Read count table
+counts = {"T0": [], "Drug": [], "Vehicle": []}
+total_sgrnas = 0
+with open(f"{OUT}/count/screen.count.txt") as f:
+    reader = csv.DictReader(f, delimiter='\t')
+    for row in reader:
+        total_sgrnas += 1
+        counts["T0"].append(int(row["T0"]))
+        counts["Drug"].append(int(row["Drug"]))
+        counts["Vehicle"].append(int(row["Vehicle"]))
+
+def gini(values):
+    """Compute Gini index of a distribution."""
+    sorted_vals = sorted(values)
+    n = len(sorted_vals)
+    if n == 0 or sum(sorted_vals) == 0:
+        return 0.0
+    cumsum = 0
+    total = sum(sorted_vals)
+    gini_sum = 0
+    for i, v in enumerate(sorted_vals):
+        cumsum += v
+        gini_sum += (2 * (i + 1) - n - 1) * v
+    return gini_sum / (n * total)
+
+qc = {}
+for sample in ["T0", "Drug", "Vehicle"]:
+    vals = counts[sample]
+    qc[f"total_counts_{sample.lower()}"] = sum(vals)
+    qc[f"zero_count_sgrnas_{sample.lower()}"] = sum(1 for v in vals if v == 0)
+    qc[f"gini_index_{sample.lower()}"] = round(gini(vals), 4)
+    qc[f"median_count_{sample.lower()}"] = sorted(vals)[len(vals) // 2]
+
+qc["total_sgrnas"] = total_sgrnas
+
+with open(f"{OUT}/comparison/count_qc.tsv", 'w') as f:
+    for k, v in qc.items():
+        f.write(f"{k}\t{v}\n")
+    print(f"  {k}: {v}")
+
+print(f"QC metrics computed for {total_sgrnas} sgRNAs")
+QC_PY
+
+# ============================================================================
+# Step 7: Pathway enrichment — CONVERGENCE #3 (drug-specific + QC)
+# ============================================================================
+echo "[Step 7] Running pathway enrichment (convergence #3)..."
+python3 << 'PATHWAY_PY'
+import csv
+import os
+from collections import defaultdict
+
+OUT = os.environ.get("OUT", "outputs")
+
+# Load gene rankings
+gene_ranks = {}
+with open(f"{OUT}/comparison/merged_rankings.tsv") as f:
+    reader = csv.DictReader(f, delimiter='\t')
+    for row in reader:
+        gene_ranks[row['gene']] = {
+            'rank': int(row['rra_neg_rank']),
+            'fdr': float(row['rra_neg_fdr']),
+            'lfc': float(row['rra_neg_lfc']),
+        }
+
+# Simple enrichment: categorize genes by essentiality based on screen results
+categories = {
+    'essential_drug': [],  # Depleted in drug (FDR < 0.25)
+    'essential_common': [],  # Depleted in both drug and vehicle
+    'enriched_drug': [],  # Enriched in drug
+    'neutral': [],
+}
+
+with open(f"{OUT}/comparison/merged_rankings.tsv") as f:
+    reader = csv.DictReader(f, delimiter='\t')
+    for row in reader:
+        gene = row['gene']
+        drug_fdr = float(row['rra_neg_fdr'])
+        veh_fdr = float(row['veh_neg_fdr'])
+
+        if drug_fdr < 0.25 and veh_fdr < 0.25:
+            categories['essential_common'].append(gene)
+        elif drug_fdr < 0.25:
+            categories['essential_drug'].append(gene)
+        else:
+            categories['neutral'].append(gene)
+
+# Write pathway/category summary
+with open(f"{OUT}/comparison/gene_categories.tsv", 'w') as f:
+    f.write("category\tcount\ttop_genes\n")
+    for cat, genes in categories.items():
+        top = ','.join(genes[:5]) if genes else 'none'
+        f.write(f"{cat}\t{len(genes)}\t{top}\n")
+        print(f"  {cat}: {len(genes)} genes")
+
+print("Gene categorization done.")
+PATHWAY_PY
+
+# ============================================================================
+# Step 8: Hit classification with multi-method consensus
+# ============================================================================
+echo "[Step 8] Classifying hits with multi-method consensus..."
+python3 << 'CLASSIFY_PY'
+import csv
+import os
+
+OUT = os.environ.get("OUT", "outputs")
+
+# Read merged rankings
+hits = []
+with open(f"{OUT}/comparison/merged_rankings.tsv") as f:
+    reader = csv.DictReader(f, delimiter='\t')
+    for row in reader:
+        gene = row['gene']
+        rra_fdr = float(row['rra_neg_fdr'])
+        mle_fdr = float(row['mle_fdr'])
+        rra_lfc = float(row['rra_neg_lfc'])
+        veh_fdr = float(row['veh_neg_fdr'])
+
+        # Classification tiers
+        if rra_fdr < 0.05 and mle_fdr < 0.05:
+            tier = "high_confidence"
+        elif rra_fdr < 0.25 or mle_fdr < 0.25:
+            tier = "moderate_confidence"
+        else:
+            tier = "not_significant"
+
+        hits.append({
+            'gene': gene,
+            'rra_fdr': rra_fdr,
+            'mle_fdr': mle_fdr,
+            'rra_lfc': rra_lfc,
+            'tier': tier,
+            'drug_specific': rra_fdr < 0.25 and veh_fdr >= 0.25,
+        })
+
+# Sort by tier then RRA FDR
+tier_order = {'high_confidence': 0, 'moderate_confidence': 1, 'not_significant': 2}
+hits.sort(key=lambda x: (tier_order[x['tier']], x['rra_fdr']))
+
+with open(f"{OUT}/comparison/classified_hits.tsv", 'w') as f:
+    f.write("gene\ttier\trra_neg_fdr\tmle_fdr\trra_neg_lfc\tdrug_specific\n")
+    for h in hits:
+        f.write(f"{h['gene']}\t{h['tier']}\t{h['rra_fdr']:.6f}\t{h['mle_fdr']:.6f}\t"
+                f"{h['rra_lfc']:.4f}\t{h['drug_specific']}\n")
+
+high = sum(1 for h in hits if h['tier'] == 'high_confidence')
+moderate = sum(1 for h in hits if h['tier'] == 'moderate_confidence')
+drug_spec = sum(1 for h in hits if h['drug_specific'])
+print(f"High confidence: {high}, Moderate: {moderate}, Drug-specific: {drug_spec}")
+CLASSIFY_PY
+
+# ============================================================================
+# Step 9: MultiQC report — CONVERGENCE #4 (QC + analysis)
+# ============================================================================
+echo "[Step 9] Running MultiQC (convergence #4)..."
+if [ ! -f "${OUT}/multiqc/multiqc_report.html" ]; then
+    multiqc \
+        "${OUT}/fastqc_raw" "${OUT}/fastqc_trimmed" "${OUT}/trimmed" \
+        -o "${OUT}/multiqc" \
+        --force \
+        2>&1 | tail -3
+fi
+
+# ============================================================================
+# Step 10: Generate final report.csv
+# ============================================================================
+echo "[Step 10] Generating final report..."
+python3 << 'REPORT_PY'
+import csv
+import os
+
+OUT = os.environ.get("OUT", "outputs")
+RESULTS = os.environ.get("RESULTS", "results")
+
+report = []
+
+# --- Count QC metrics ---
+with open(f"{OUT}/comparison/count_qc.tsv") as f:
+    for line in f:
+        parts = line.strip().split('\t')
+        if len(parts) == 2:
+            report.append((parts[0], parts[1]))
+
+# --- Read mageck count log for mapping stats ---
+count_log = f"{OUT}/count/screen.count_normalized.txt"
+if os.path.exists(f"{OUT}/count/screen.countsummary.txt"):
+    with open(f"{OUT}/count/screen.countsummary.txt") as f:
+        reader = csv.DictReader(f, delimiter='\t')
+        for row in reader:
+            label = row.get('Label', '')
+            reads = row.get('Reads', '0')
+            mapped = row.get('Mapped', '0')
+            pct = row.get('Percentage', '0')
+            report.append((f"mapped_reads_{label.lower()}", mapped))
+            report.append((f"mapping_pct_{label.lower()}", pct))
+
+# --- RRA Drug results ---
+with open(f"{OUT}/rra_drug/drug_vs_t0.gene_summary.txt") as f:
+    reader = csv.DictReader(f, delimiter='\t')
+    genes = list(reader)
+
+# Top depleted gene (negative selection)
+genes_neg = sorted(genes, key=lambda x: float(x['neg|rank']))
+if genes_neg:
+    report.append(("top_depleted_gene_rra", genes_neg[0]['id']))
+    report.append(("top_depleted_fdr_rra", genes_neg[0]['neg|fdr']))
+    report.append(("top_depleted_lfc_rra", genes_neg[0]['neg|lfc']))
+
+# Top enriched gene (positive selection)
+genes_pos = sorted(genes, key=lambda x: float(x['pos|rank']))
+if genes_pos:
+    report.append(("top_enriched_gene_rra", genes_pos[0]['id']))
+    report.append(("top_enriched_fdr_rra", genes_pos[0]['pos|fdr']))
+
+# Count significant genes
+num_dep = sum(1 for g in genes if float(g['neg|fdr']) < 0.25)
+num_enr = sum(1 for g in genes if float(g['pos|fdr']) < 0.25)
+report.append(("num_depleted_genes_fdr25_rra", str(num_dep)))
+report.append(("num_enriched_genes_fdr25_rra", str(num_enr)))
+
+# --- MLE results ---
+mle_file = f"{OUT}/mle/screen_mle.gene_summary.txt"
+if os.path.exists(mle_file):
+    with open(mle_file) as f:
+        reader = csv.DictReader(f, delimiter='\t')
+        mle_genes = list(reader)
+    if mle_genes:
+        # Sort by drug|fdr
+        try:
+            mle_sorted = sorted(mle_genes, key=lambda x: float(x.get('drug|fdr', 1)))
+            report.append(("top_gene_mle", mle_sorted[0].get('Gene', 'NA')))
+            report.append(("top_gene_mle_fdr", mle_sorted[0].get('drug|fdr', 'NA')))
+            report.append(("top_gene_mle_beta", mle_sorted[0].get('drug|beta', 'NA')))
+            num_mle_sig = sum(1 for g in mle_genes if float(g.get('drug|fdr', 1)) < 0.25)
+            report.append(("num_significant_mle_fdr25", str(num_mle_sig)))
+        except (ValueError, KeyError):
+            pass
+
+# --- Vehicle results ---
+with open(f"{OUT}/rra_vehicle/vehicle_vs_t0.gene_summary.txt") as f:
+    reader = csv.DictReader(f, delimiter='\t')
+    veh_genes = list(reader)
+num_veh_dep = sum(1 for g in veh_genes if float(g['neg|fdr']) < 0.25)
+report.append(("num_depleted_genes_vehicle_fdr25", str(num_veh_dep)))
+
+# --- Concordance ---
+with open(f"{OUT}/comparison/classified_hits.tsv") as f:
+    reader = csv.DictReader(f, delimiter='\t')
+    classified = list(reader)
+high_conf = sum(1 for h in classified if h['tier'] == 'high_confidence')
+moderate_conf = sum(1 for h in classified if h['tier'] == 'moderate_confidence')
+drug_specific = sum(1 for h in classified if h['drug_specific'] == 'True')
+report.append(("high_confidence_hits", str(high_conf)))
+report.append(("moderate_confidence_hits", str(moderate_conf)))
+report.append(("drug_specific_depleted_genes", str(drug_specific)))
+
+# --- Drug-specific top gene ---
+with open(f"{OUT}/comparison/drug_specific_hits.tsv") as f:
+    reader = csv.DictReader(f, delimiter='\t')
+    drug_hits = list(reader)
+if drug_hits:
+    report.append(("top_drug_specific_gene", drug_hits[0]['gene']))
+    report.append(("top_drug_specific_fdr", drug_hits[0]['rra_neg_fdr']))
+
+# Write final report
+with open(f"{RESULTS}/report.csv", 'w') as f:
+    f.write("metric,value\n")
+    for m, v in report:
+        f.write(f"{m},{v}\n")
+
+print(f"Report written with {len(report)} metrics")
+for m, v in report:
+    print(f"  {m}: {v}")
+REPORT_PY
+
+echo ""
+echo "========================================="
+echo "  CRISPR Screen Analysis Complete!"
+echo "========================================="
+echo ""
+cat "${RESULTS}/report.csv"