Analyzes CRISPR drug-modifier (chemogenomic) screens with drugZ (Colic et al. 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 greater sensitivity to small-effect hits than 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).
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name
bio-crispr-screens-drugz-chemogenomic
description
Analyzes CRISPR drug-modifier (chemogenomic) screens with drugZ (Colic et al. 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 greater sensitivity to small-effect hits than 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).
Before using code patterns, verify installed versions match. If versions differ:
CLI: python drugz.py --help (the repo has no setup.py, so there is no drugz console script)
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.
Empirical-Bayes windowed Z-score on guide-level log fold change
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.
Benchmark (Colic et al. 2019): On DNA-damage-response chemogenomic screens, drugZ hits were far more strongly enriched for the expected pathway (DDR) than STARS, MAGeCK, edgeR or RIGER hits across FDR thresholds, reflecting better sensitivity to the moderate fitness defects typical of drug-gene interactions. Compare methods on expected-pathway enrichment, not raw hit count.
The drugZ Algorithm (under the hood)
For each sgRNA, compute log2-fold-change drug vs vehicle: LFC_drug_vs_veh
Compute an empirical-Bayes Z per sgRNA: Z = LFC / eb_std, where eb_std is the standard deviation of a sliding window of guides with similar control abundance (--half_window_size, default 500), smoothed monotonically
Per gene, sum Z across all sgRNAs targeting it: sumZ = sum(Z_sgRNA)
Normalize and re-standardize across genes: normZ = zscore(sumZ / sqrt(numObs))
Compute a one-sided p-value per direction: synth (sensitizer = negative normZ) and supp (resistance = positive normZ)
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.
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:
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 -c to name the 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 linecat > 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 passed to -c. 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 -r with a comma-delimited list of essential gene names to exclude; 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
Colic et al. 2019; BH-corrected
Suppressor hit
fdr_supp < 0.05
Same
High-confidence sensitizer
fdr_synth < 0.01 AND normZ < -3
Conservative
Pseudocount default
5
Colic et al. 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 -r with a comma-list of 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
Colic M et al. 2019. Genome Medicine 11:52. drugZ algorithm and chemogenomic-interaction benchmark.
Olivieri M et al. 2020. Cell 182:481. DDR chemogenomic screens with drugZ.
Behan FM et al. 2019. Nature 568:511. Project Score; genome-wide cancer-dependency screens for target prioritization.
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
