crispr-screens-drugz-chemogenomic

name	crispr-screens-drugz-chemogenomic
description	Analyzes CRISPR drug-modifier (chemogenomic) screens with drugZ (Li & Hart 2019 Genome Med), a bidirectional Z-score method that identifies synthetic-lethal sensitizing genes and resistance-conferring suppressor genes from vehicle vs drug comparisons. Covers vehicle-anchored design (not Day-0), the bidirectional Z math giving 2-3x sensitivity over MAGeCK / STARS / edgeR / RIGER on drug screens, per-gene sumZ and normZ, synth (sensitizer) vs supp (suppressor) FDR, multi-dose handling, integration with control sgRNAs, and comparison with MAGeCK MLE with dose covariate. Use when running a drug-modifier CRISPR screen, identifying sensitizing or resistance genes for a drug candidate, choosing drugZ vs MAGeCK MLE for chemogenomic analysis, troubleshooting low-effect drug screens where MAGeCK lacks sensitivity, or designing a drug-screen layout (vehicle vs drug arms).
tool_type	cli
primary_tool	drugZ

Version Compatibility

Reference examples tested with: drugZ Aug-2019+ (hart-lab/drugz; Python 3.6+), MAGeCK 0.5.9+, pandas 2.2+, numpy 1.26+, scipy 1.12+, statsmodels 0.14+, matplotlib 3.8+.

Before using code patterns, verify installed versions match. If versions differ:

• CLI: drugz --version; python drugz.py --help
• GitHub: install via git clone https://github.com/hart-lab/drugz

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

drugZ Chemogenomic Analysis

"Identify genes that sensitize or confer resistance to my drug in a CRISPR screen" -> Compare drug-treated vs vehicle-treated arms (NOT Day-0 baseline) using bidirectional Z-scores per sgRNA, sum to per-gene normalized Z, and rank genes for sensitizer (synthetic lethal) vs suppressor (resistance) phenotype.

• CLI: python drugz.py -i counts.txt -o drugz.txt -c Vehicle_r1,Vehicle_r2 -x Drug_r1,Drug_r2
• Python: programmatic via drugz.drugz_analysis() (internal Python module)
• Workflow: vehicle-anchored counts -> Z-scoring -> per-gene summation -> direction-specific FDR

Why drugZ for Drug Screens (not MAGeCK)

Property	drugZ	MAGeCK RRA	MAGeCK MLE
Bidirectional sensitivity	YES (sensitizer + resistance same scale)	Asymmetric (neg/pos separately)	Asymmetric
Drug-anchored baseline	YES (drug vs vehicle)	Either (drug vs vehicle or vs Day 0)	Either
Sensitivity to small effects	2-3x higher (Li & Hart 2019 benchmark)	Lower	Lower
Statistical framework	Z-score from per-sgRNA NB residuals	NB + alpha-RRA	NB GLM with design matrix
Handles guide-level noise	sgRNA-level z aggregation	Rank-based aggregation	Built-in guide-efficacy term (optional)
Best for	Drug-modifier / chemogenomic screens	General essentiality / standard 2-condition	Time course / multi-condition

Why MAGeCK is suboptimal for drug screens: MAGeCK's RRA was designed for two-condition essentiality; drug-vs-vehicle screens often have small effect sizes (10-30% sgRNA shift) that RRA rank-based aggregation under-detects. drugZ uses parametric Z-scoring tuned for these small effects.

Quantified gain (Li & Hart 2019): On DNA damage response chemogenomic screens, drugZ identified 2-3x more hits than STARS, MAGeCK, edgeR, or RIGER at the same FDR threshold; the additional hits were enriched in the expected pathway (DDR).

The drugZ Algorithm (under the hood)

1. For each sgRNA, compute log2-fold-change drug vs vehicle: LFC_drug_vs_veh
2. Compute the empirical Z-score from a fitted Gaussian over the bulk distribution: Z = (LFC - median(LFC)) / MAD(LFC)
3. Per gene, sum Z across all sgRNAs targeting it: sumZ = sum(Z_sgRNA)
4. Normalize for the number of sgRNAs: normZ = sumZ / sqrt(N_sgrna)
5. Compute two-sided p-value per gene: synth (sensitizer = negative normZ) and supp (resistance = positive normZ)
6. Benjamini-Hochberg FDR correction per direction

Critical: Vehicle vs drug, NOT Day 0 vs drug. Day-0 baseline conflates proliferation effects with drug effects.

Run drugZ on a Drug-Modifier Screen

Goal: Quantify per-gene sensitizing and suppressor effects from a chemogenomic screen.

Approach: Run drugz.py with vehicle and drug sample columns; output per-gene sumZ, normZ, and direction-specific p-values + FDR.

git clone https://github.com/hart-lab/drugz
cd drugz

# Standard drug screen comparison:
# Vehicle (DMSO or carrier) replicates: Veh_r1, Veh_r2, Veh_r3
# Drug-treated replicates: Drug_r1, Drug_r2, Drug_r3

python drugz.py \
    -i counts.txt \                       # input read-count file (tab-separated)
    -o drugz_output.txt \                  # output file
    -c Veh_r1,Veh_r2,Veh_r3 \              # control samples (comma-separated)
    -x Drug_r1,Drug_r2,Drug_r3 \           # treated samples (comma-separated)
    -r control_genes.txt \                 # OPTIONAL: genes to exclude
    -p 5                                   # pseudocount (default 5)

# Output: drugz_output.txt with columns:
#   GENE, numObs, sumZ, normZ, pval_synth, rank_synth, fdr_synth, pval_supp, rank_supp, fdr_supp

Output columns:

Column	Meaning
GENE	Gene symbol
numObs	Number of sgRNAs contributing
sumZ	Summed per-sgRNA Z-score
normZ	Normalized Z = sumZ / sqrt(N)
pval_synth	One-sided p-value for sensitizer (negative effect; gene KO sensitizes to drug)
rank_synth	Rank for sensitizers
fdr_synth	BH-corrected FDR for sensitizers
pval_supp	One-sided p-value for suppressor (positive effect; gene KO confers resistance)
rank_supp	Rank for suppressors
fdr_supp	BH-corrected FDR for suppressors

Interpretation:

• Sensitizers (synthetic lethal): fdr_synth < 0.05 -- loss of these genes makes cells more sensitive to drug. Examples: PARPi targets BRCA1/2; cisplatin sensitizes ERCC.
• Suppressors (resistance): fdr_supp < 0.05 -- loss of these genes confers resistance. Examples: drug-efflux genes; drug target itself paradoxically.

Vehicle vs Day-0 Reference: Critical Decision

Why this matters: Drug screen analysis can compare drug to:

1. Vehicle (DMSO / carrier) -- isolates drug-specific effect; correct anchor.
2. Day 0 (initial library) -- conflates proliferation, drug, and vehicle effects.

counts at Day 0          (no perturbation; cloning baseline)
    |
    v
counts at Day 7 - Vehicle (proliferation only; what survives in normal culture)
counts at Day 7 - Drug    (proliferation + drug effect)
    |
    v
Drug effect = LFC(Drug vs Vehicle)         # CORRECT
Wrong:       LFC(Drug vs Day 0)            # confounds drug with general proliferation

drugZ specifically requires --control-samples to be vehicle samples. Always include matched vehicle controls in drug screens.

Drug-Dose and Time-Course Designs

drugZ for dose-response: Not natively designed for dose; instead, run drugZ separately at each dose vs vehicle, then look for genes with consistent direction across doses.

for DOSE in low mid high; do
    python drugz.py \
        -i counts.txt \
        -o drugz_${DOSE}.txt \
        -c Veh_r1,Veh_r2 \
        -x Drug${DOSE}_r1,Drug${DOSE}_r2
done

# Then aggregate: genes significant at high dose AND consistent direction at mid/low dose

For multi-condition drug-screens (time × drug × cell-line), use MAGeCK MLE with explicit design matrix instead -- MLE handles multi-factorial; drugZ does not.

Comparison: drugZ vs MAGeCK MLE for Drug Screen

Goal: When to use each method.

Question	drugZ	MAGeCK MLE
Single drug, single dose, vehicle vs drug	YES (preferred)	Acceptable
Multiple doses, drug response curve	Per-dose drugZ + meta	YES (preferred with dose covariate)
Time course at single dose	Per-timepoint drugZ + meta	YES (preferred with time covariate)
Drug + cell-line panel	Per-line drugZ + meta	YES (or Chronos)
Combinatorial drug pairs	Per-pair drugZ + meta	YES (preferred with interaction)
Synergy / antagonism detection	Limited (per-drug calling only)	YES (interaction term in MLE)
Small effect sizes (LFC <0.5)	Highest sensitivity	Lower sensitivity
Heavy selection (>40% guides change)	OK	Norm needs control sgRNAs

Reconciliation: For simple drug-modifier screens with one drug and one vehicle, run both drugZ and MAGeCK MLE; hits called by both are high confidence; drugZ-only hits at low LFC need orthogonal validation (drug + arrayed validation).

Removing Genes from Null Distribution

Goal: Exclude reference essential or control genes from the Z-score null distribution.

Approach: Provide -r with a file listing gene symbols whose sgRNA-level Z scores should not influence the null. Useful when CEGv2 essentials would otherwise inflate the null distribution.

# Pass a file with one gene per line
cat > remove_essential.txt <<EOF
RPS3
RPL11
EIF3A
POLR2A
CDK1
EOF

python drugz.py \
    -i counts.txt \
    -o drugz_clean.txt \
    -c Veh_r1,Veh_r2 \
    -x Drug_r1,Drug_r2 \
    -r remove_essential.txt

When to use: If pilot drugZ runs show many essential genes appearing as "sensitizers" purely because they drop out under any condition, removing them gives a cleaner drug-specific signal.

Failure Modes

drugZ shows no synthetic-lethal hits despite known sensitizing genes

Trigger: Comparing drug vs Day-0 instead of drug vs vehicle. Mechanism: Day-0 comparison conflates drug effect with normal-culture proliferation; essential genes drop in both conditions, masking drug-specific sensitization. Symptom: PARPi screen shows no sensitization at BRCA1/BRCA2 despite expected biology. Fix: Re-run with vehicle samples as --control-samples. The drug-vs-vehicle is the canonical comparison.

High false-positive rate among essential genes

Trigger: Essential genes drop out in both vehicle and drug arms; small relative shift gives misleadingly high Z. Mechanism: drugZ's Z-score is symmetric; essential genes drop in both arms but slightly more in drug -> "synthetic lethal" call. Symptom: Hit list dominated by RPS, RPL, EIF essentials. Fix: Use --remove-genes-file to exclude CEGv2 essentials; or filter the output post-hoc.

Inconsistent results between repeats of drugZ

Trigger: Insufficient sgRNAs per gene; small effect sizes. Mechanism: drugZ's per-gene sumZ depends on enough sgRNAs to be stable; with 3-4 sgRNAs/gene, single-guide noise drives variation. Symptom: Same data produces different top hits across repeated runs. Fix: Use a 6+ sgRNAs/gene library (Avana, Dolcetto); or aggregate multiple drugZ runs with different bootstrap seeds; or use MAGeCK MLE for stability.

drugZ ignores dose information

Trigger: Multi-dose screen analyzed at highest dose only. Mechanism: drugZ doesn't model dose; running at one dose loses the dose-response information. Symptom: Hits at high dose may be dose-specific (not true responders). Fix: Run drugZ at each dose; require consistency across doses for high-confidence hits.

Drug-target gene appears as "suppressor"

Trigger: Loss of drug target reduces drug binding, increasing drug resistance. Mechanism: Real biology -- drug target itself is a resistance gene from a KO perspective. Symptom: Drug-target gene like PARP1 appears in suppressor list for PARPi screen. Fix: Expected biology. Annotate the drug target separately. The suppressor list is correct.

Quantitative Thresholds

Threshold	Value	Source / Rationale
Sensitizer hit	fdr_synth < 0.05	Li & Hart 2019; BH-corrected
Suppressor hit	fdr_supp < 0.05	Same
High-confidence sensitizer	fdr_synth < 0.01 AND normZ < -3	Conservative
Pseudocount default	5	Li & Hart 2019
Min sgRNAs per gene for stable Z	4-6	Below this, Z varies between runs
Vehicle replicates needed	3+	For stable Z null distribution
Drug replicates needed	3+	For per-gene sumZ stability

Common Errors

Error / symptom	Cause	Solution
No hits	Wrong control samples (Day-0 instead of vehicle)	Re-run with vehicle
Hits dominated by essentials	Essentials inflate null	Use --remove-genes-file with CEGv2
Unstable hits across runs	Too few sgRNAs/gene	Use 6+ sgRNAs/gene library
Drug-target appears in suppressor	Real biology	Annotate separately
MAGeCK and drugZ disagree	Different statistical sensitivity	drugZ more sensitive; trust for chemogenomic
Inconsistent between doses	Real dose effect	Require consistency across doses

References

• Li G & Hart T. 2019. Genome Medicine 11:52. drugZ algorithm and benchmark.
• Aregger M et al. 2020. Mol Cell 80:577. Original chemogenomic screens with TKOv3 library.
• Olivieri M et al. 2020. Cell 182:481. DDR chemogenomic screens with drugZ.
• Behan FM et al. 2019. Nature 568:511. Sanger Score; drug-modifier panels.
• Pacini C et al. 2021. Cell Syst 12:1132. Benchmark of drug-modifier methods.

Related Skills

• crispr-screens/mageck-analysis - MAGeCK MLE alternative for multi-condition drug screens
• crispr-screens/bagel-essentiality - BAGEL2 alternative; sensitive to tumor-suppressor / drug-target
• crispr-screens/hit-calling - Cross-method decision tree including drugZ
• crispr-screens/screen-qc - Pre-drugZ QC including replicate concordance
• crispr-screens/library-design - 6+ sgRNAs/gene library for stable Z
• crispr-screens/copy-number-correction - Pre-correction for cancer-line drug screens
• crispr-screens/base-editing-analysis - Variant-function drug-modifier screens
• pathway-analysis/go-enrichment - Functional analysis of drug-modifier hits
• clinical-databases/clinvar-lookup - Clinical interpretation of drug targets