Cross-method decision tree for calling hits in pooled CRISPR screens. Catalogs statistical models (MAGeCK RRA, MAGeCK MLE, BAGEL2, drugZ, JACKS, Chronos, CERES), experimental designs each is built for, failure modes outside design domain, reconciliation when methods disagree, multiple-testing and effect-size thresholds, the order of operations (count -> QC -> CN-correct -> hit-call -> validate), the second-best-sgRNA conservative rule, and consensus-hit strategy. Use when choosing among MAGeCK / BAGEL2 / drugZ / JACKS / Chronos for a given design, reconciling disagreement across two or three methods on the same screen, deciding whether to require consensus, gating downstream validation by hit-confidence tier, or interpreting unstable hit lists across reruns.
Cross-method decision tree for calling hits in pooled CRISPR screens. Catalogs statistical models (MAGeCK RRA, MAGeCK MLE, BAGEL2, drugZ, JACKS, Chronos, CERES), experimental designs each is built for, failure modes outside design domain, reconciliation when methods disagree, multiple-testing and effect-size thresholds, the order of operations (count -> QC -> CN-correct -> hit-call -> validate), the second-best-sgRNA conservative rule, and consensus-hit strategy. Use when choosing among MAGeCK / BAGEL2 / drugZ / JACKS / Chronos for a given design, reconciling disagreement across two or three methods on the same screen, deciding whether to require consensus, gating downstream validation by hit-confidence tier, or interpreting unstable hit lists across reruns.
Python: pip show crispr_chronos (JACKS installs from GitHub, not PyPI)
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
Hit Calling Decision Tree
"Identify significant hits in my CRISPR screen" -> Choose the analysis method that matches the experimental design, statistical assumptions, and quality grade of the screen. Reconcile across methods when high-stakes hits must be validated.
The primary hit-calling methods cover non-overlapping niches; the decision is not "which is best" but "which matches the design."
Design / question
Primary method
Why
Secondary check
Two-condition essentiality, one cell line, no CN concerns
MAGeCK RRA
Robust, fast, gold-standard for ranked analysis
BAGEL2 (Bayes factor on same data)
Time course (3+ timepoints)
MAGeCK MLE
RRA cannot model multi-condition
JACKS (efficacy-aware)
Multi-cell-line panel (cancer dependency)
Chronos
Models CN bias + screen quality jointly
MAGeCK MLE per line + meta-analysis
Drug screen (vehicle vs drug)
drugZ
Bidirectional Z; vehicle-anchored
MAGeCK MLE with dose covariate
Multi-screen joint, same library
JACKS
Shared efficacy; enables ~2.5x smaller screens
MAGeCK MLE; results should converge
Essentiality classification with reference sets
BAGEL2
Bayes factor with CEGv2/NEGv1 calibration
MAGeCK RRA
Combinatorial / paired guide
MAGeCK MLE with GI scoring
Models interaction term; see [[combinatorial-screens]]
Custom GI scoring
Single-cell perturbation (Perturb-seq)
SCEPTRE
NB GLM + permutation; see [[perturb-seq-analysis]]
Mixscape pre-filter
Cancer-line copy-number screen
Chronos (preferred) or CERES
Joint CN-bias + gene-effect modeling; see [[copy-number-correction]]
CRISPRcleanR pre-hoc + MAGeCK
Statistical Models Compared
Method
Year
Statistical model
Tests
Best for
Fails when
MAGeCK RRA
2014
NB per-sgRNA -> alpha-RRA per gene
Two-sided
General two-condition
>40% guides change (median norm breaks); time course; cancer-line CN
MAGeCK MLE
2015
NB GLM with design matrix; per-gene beta
Wald per condition
Multi-condition / time course
Cell-line specific essentiality; CN bias
BAGEL2
2021
Bayes factor from log-likelihood ratio
Essential vs non-essential
Essentiality classification
Non-essentiality screens; drug screens
drugZ
2019
Bidirectional Z-score on guide-level LFC
Sensitizer vs suppressor
Drug-modifier / chemogenomic
Essentiality (no biological prior); time-course
JACKS
2019
Variational Bayes: LFC = gene * efficacy
Per-gene posterior
Multi-screen joint, library calibration
Single screen; cross-chemistry
Chronos
2021
Cell-population dynamics ODE + NB
Gene effect adjusted for screen quality
Cancer-line panels, longitudinal
Single screen; non-cancer applications
CERES
2017
Nonlinear model decoupling CN-bias from gene effect
Per-gene effect
Cancer-line panel with CN profile
Superseded by Chronos at DepMap
RRA vs MLE Within MAGeCK
Property
RRA (mageck test)
MLE (mageck mle)
Conditions supported
2
Multiple (design matrix)
Statistical test
Robust rank aggregation
Wald on beta from NB GLM
Output
neg/pos score, FDR per direction
beta per condition
sgRNA efficiency
Not modeled (optional fixed input)
Modeled via --sgrna-efficiency
Outlier robustness
High (rank-based)
Lower (likelihood-based)
Best for
Standard 2-condition screen
Time course, drug screen, multi-cell-line, paired
Speed
Fast
Slow (per-gene optimization)
Algorithmic Taxonomy: Why Each Was Built
Method
Designed to solve
MAGeCK RRA
First robust statistical framework for CRISPR-screen ranking; alpha-RRA borrowed from RRA in microarray meta-analysis
MAGeCK MLE
Extend MAGeCK to multi-condition; explicit beta scores allow direct LFC interpretation
BAGEL2
Reference-set-anchored Bayesian classification; precision-recall calibrated; tumor-suppressor sensitivity (BAGEL1 was uni-directional)
drugZ
Drug-modifier screens have low effect sizes and need bidirectional sensitivity; STARS/MAGeCK miss synthetic-lethal hits
JACKS
Sample-size reduction via library-shared efficacy; library calibration as side product
Goal: Reduce false positives from single outlier sgRNAs by requiring the second-most-extreme guide per gene to also be a hit.
Approach: For each gene, sort sgRNAs by LFC; require the second-best LFC to exceed a threshold. Rejects genes that depend on one extreme guide.
defsecond_best_lfc(sgrna_lfc_df, genes_series, direction='neg'):
'''Return per-gene LFC of the second-best sgRNA in the direction of interest.
For dropout (direction="neg"), second-most-negative LFC.'''
results = []
for gene in genes_series.unique():
gene_lfc = sgrna_lfc_df[genes_series == gene].sort_values()
if direction == 'neg':
second = gene_lfc.iloc[1] iflen(gene_lfc) >= 2else gene_lfc.iloc[0]
else:
second = gene_lfc.iloc[-2] iflen(gene_lfc) >= 2else gene_lfc.iloc[-1]
results.append({'gene': gene, 'second_best_lfc': second})
return pd.DataFrame(results)
Rule: A high-confidence hit has second-best LFC also passing the threshold. A guide-of-one hit has only one extreme guide and should be flagged for orthogonal validation. This rule predates JACKS and is implicit in MAGeCK RRA but explicit elsewhere.
Reconciliation: BF >6 in BAGEL2 corresponds to ~90% posterior probability (Hart 2017 G3, by overlap with CEGv2) and is commonly used as a stringent cutoff roughly comparable to MAGeCK FDR 0.05. Treat that equivalence as an approximate convention, not an exact calibration. drugZ FDR is per-direction; the fdr_synth and fdr_supp columns are independent BH corrections.
Order of Operations
1. Library design (see library-design) <- design quality dictates hit calling
2. Plasmid pool sequencing <- baseline; non-negotiable
3. Run screen at MOI 0.3, 500x coverage
4. Sequence endpoint
5. Run mageck count <- generates raw + normalized counts
6. Screen QC (see screen-qc) <- gates downstream method choice
7. Copy-number correction if cancer line <- CRISPRcleanR or Chronos; see copy-number-correction
8. Batch correction if multi-batch <- see batch-correction
9. Hit calling (this skill) <- choose method by design
10. Consensus across 2-3 methods <- for high-stakes hits
11. Orthogonal validation <- arrayed; different chemistry
12. Pathway analysis <- see pathway-analysis/gsea
Custom z-score Hit Calling (when standard tools don't fit)
Goal: Compute gene-level z-scores when neither MAGeCK nor BAGEL2 fits the experimental design.
Approach: RPM-normalize, compute per-sgRNA log2 fold-changes, aggregate to gene level, derive z-score from the null distribution of non-targeting controls (cleanest) or all genes (assumes <40% changing), apply BH correction.
MAGeCK and BAGEL2 disagree by 200+ hits at FDR 0.05
Trigger: Heavy-selection screen (>40% guides change) or cancer-line CN bias.
Mechanism: MAGeCK median normalization breaks; BAGEL2 is robust due to reference-set anchoring.
Symptom: MAGeCK hit list inflated; BAGEL2 list closer to expected size.
Fix: Run MAGeCK with --norm-method control; apply CN correction; trust BAGEL2 for essentiality.
Chronos and MAGeCK disagree at the top 10 in a cancer line
Trigger: Top hits are at amplified loci.
Mechanism: Chronos models CN bias; MAGeCK does not.
Symptom: ERBB2 in HER2+, MYC in MYC-amplified, etc. are top hits in MAGeCK but not Chronos.
Fix: Apply [[copy-number-correction]] before MAGeCK or switch to Chronos.
drugZ and MAGeCK disagree on small-effect drug-modifier screen
Trigger: Effect size is small; MAGeCK rank-based test is less sensitive than drugZ bidirectional Z.
Mechanism: drugZ specifically optimized for small effects in drug screens (Colic et al. 2019); MAGeCK RRA loses sensitivity at small effects.
Symptom: At matched FDR, drugZ calls small-effect chemogenomic interactions (e.g. DDR genes) that MAGeCK RRA misses, with stronger expected-pathway enrichment.
Fix: Use drugZ as primary for chemogenomic; MAGeCK as confirmatory. See [[drugz-chemogenomic]].
Trigger: A gene has one or two strong sgRNAs and 2-3 weak ones; MAGeCK averages them, JACKS down-weights the weak.
Mechanism: JACKS variational Bayes correctly identifies low-efficacy guides; MAGeCK aggregates without this prior.
Symptom: Gene is JACKS hit but not MAGeCK.
Fix: Inspect per-sgRNA LFC; if strong guides are consistent, JACKS is correct. Validate gene orthogonally.
Consensus across 3 methods is empty (no hits)
Trigger: Either no real biology, or each method has different failure mode being triggered.
Mechanism: Screen quality is low; signal-to-noise across all methods is poor.
Symptom: Tier 1 consensus list is empty.
Fix: Re-audit QC. Check Cas9 selection, MOI, timepoint, library positioning. Re-run screen if QC fails.
Quantitative Thresholds
Threshold
Value
Source / Rationale
MAGeCK RRA FDR (gene-level)
<0.05
Li 2014; standard publication
MAGeCK RRA LFC
abs(LFC) >1
2-fold; biological
BAGEL2 Bayes Factor
>6 standard; >12 stricter
Hart 2017; BAGEL convention
drugZ FDR
<0.05 per direction
Colic et al. 2019
JACKS fdr_log10
<-1 (FDR <0.1); <-2 (FDR <0.01)
Standard FDR convention
Chronos dependency probability
>0.5
DepMap convention (dependency-probability cutoff)
Tier 1 consensus (3 methods)
100% agreement
High confidence; minimal validation needed
Tier 2 consensus (2 of 3)
67% agreement
Arrayed validation required
Tier 3 (1 method only)
Hypothesis; flag for follow-up
Multiple screens or arrayed required
Second-best sgRNA rule
Second-best LFC also passes threshold
Reduces single-guide outliers
Common Errors
Error / symptom
Cause
Solution
All genes significant in MAGeCK RRA
Heavy selection breaks median norm
--norm-method control; or use BAGEL2
BAGEL2 returns no hits despite known essentials
Wrong reference gene set
Verify CEGv2/NEGv1 files match library
drugZ output empty
Used Day 0 as control instead of vehicle
Re-run with vehicle as control
Chronos errors out
Missing CN profile for cell line
Use CRISPRcleanR (unsupervised) instead
Methods disagree by orders of magnitude
Quality issue or design mismatch
Re-audit QC; reconcile via tier consensus
Empty tier 1 consensus
No real biology OR QC failure
Re-audit QC
Single-guide-driven hits
Outlier sgRNA
Apply second-best rule; orthogonal validate
References
Li W et al. 2014. Genome Biol 15:554. MAGeCK alpha-RRA.
Li W et al. 2015. Genome Biol 16:281. MAGeCK MLE.
Kim E & Hart T. 2021. Genome Med 13:2. BAGEL2.
Colic M et al. 2019. Genome Med 11:52. drugZ.
Allen F et al. 2019. Genome Res 29:464. JACKS.
Dempster J et al. 2021. Genome Biol 22:343. Chronos.
Meyers R et al. 2017. Nat Genet 49:1779. CERES.
Hart T & Moffat J. 2016. BMC Bioinformatics 17:164. BAGEL Bayes factor framework.
Hart T et al. 2017. G3 7:2719. CEGv2/NEGv1 calibration.
Related Skills
crispr-screens/mageck-analysis - Full MAGeCK RRA + MLE detail
crispr-screens/bagel-essentiality - Full BAGEL2 detail
crispr-screens/drugz-chemogenomic - Full drugZ detail for drug screens
crispr-screens/jacks-analysis - Full JACKS detail and library calibration
