Runs JACKS (Joint Analysis of CRISPR/Cas9 Knockout Screens; Allen et al 2019 Genome Research) which models per-sgRNA log-fold-change as the product of a treatment-dependent gene-essentiality term and a treatment-independent guide-efficacy term. Covers the Bayesian decomposition math, the hierarchical efficacy prior shared across screens performed with the same library, when JACKS outperforms MAGeCK (multi-screen joint analysis, libraries with broad efficacy variance) and when it does not (single screen, novel libraries with no prior efficacy), library-reuse efficacy transfer, downstream essentiality interpretation, and the 2.5x sample-size reduction enabled by efficacy-aware testing. Use when running multiple screens with the same library, when guide-level noise is suspected to dominate per-gene signal, when reusing published essentiality reference screens for efficacy priors, or when comparing screens performed across cell lines that share library but differ biologically.
Runs JACKS (Joint Analysis of CRISPR/Cas9 Knockout Screens; Allen et al 2019 Genome Research) which models per-sgRNA log-fold-change as the product of a treatment-dependent gene-essentiality term and a treatment-independent guide-efficacy term. Covers the Bayesian decomposition math, the hierarchical efficacy prior shared across screens performed with the same library, when JACKS outperforms MAGeCK (multi-screen joint analysis, libraries with broad efficacy variance) and when it does not (single screen, novel libraries with no prior efficacy), library-reuse efficacy transfer, downstream essentiality interpretation, and the 2.5x sample-size reduction enabled by efficacy-aware testing. Use when running multiple screens with the same library, when guide-level noise is suspected to dominate per-gene signal, when reusing published essentiality reference screens for efficacy priors, or when comparing screens performed across cell lines that share library but differ biologically.
Before using code patterns, verify installed versions match. If versions differ:
CLI: python run_JACKS.py --help (run_JACKS.py at the JACKS repo root after clone)
Python: from jacks.jacks_io import runJACKS; help(runJACKS)
GitHub: install via git clone https://github.com/felicityallen/JACKS && cd JACKS && pip install .
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
JACKS CRISPR Screen Analysis
"Analyze CRISPR screens with guide-level efficacy modeling" -> Jointly model per-sgRNA log-fold-change across one or more screens as the product of gene essentiality and guide efficacy, sharing efficacy across screens with the same library so that low-quality guides are down-weighted automatically.
where i is sgRNA index, c is screen condition, g(i) is the gene targeted by sgRNA i. Gene effect varies by condition (different cell lines, different treatments) but guide efficacy is intrinsic to the sgRNA sequence and is treated as constant across screens. The model fits both parameters via variational Bayes with hierarchical priors:
guide_efficacy[i] ~ Normal(1, 1) (Gaussian prior, mean 1, variance 1), shared across all sgRNAs
gene_effect[g, c] ~ Normal(0, sigma_c^2) per condition
The variational posterior gives expected guide efficacy and gene effect; log-likelihood-ratio tests against a null (zero gene effect) provide gene-level significance.
Critical assumption: Guide efficacy is treated as cell-line independent within the same chemistry. Allen 2019 reports per-sgRNA Cas9 KO efficacy is consistent across randomly selected batches of cell lines (within-chemistry), supporting library-shared efficacy. However, efficacy is NOT shareable across chemistries: Cas9 KO efficacy != CRISPRi knockdown efficiency != CRISPRa activation efficiency. JACKS must be run separately per chemistry; use only within the same chemistry on the same library.
When JACKS Outperforms MAGeCK and BAGEL2
Scenario
Advantage
Expected gain (Allen 2019)
Multi-screen joint analysis (>=3 screens with same library)
Efficacy shared; noise averaged
~21% lower error vs MAGeCK; 9% vs original BAGEL; 91-99% of cell lines improved (method-dependent)
Reusing public reference screens (DepMap, Project Score) as efficacy prior
Transfer learning
New screens can be smaller; efficacy priors transfer across same-library screens
Libraries with broad efficacy variance (e.g. older GeCKOv2)
Down-weights known weak guides
Larger gain than on Brunello (already efficacy-filtered)
Heterogeneous quality (mixed plasmid quality across screens)
Per-screen noise estimation
Cleaner per-condition gene effects
When JACKS Is Not the Right Tool
Single screen, no prior efficacy: JACKS has nothing to leverage; MAGeCK or BAGEL2 work as well.
Single timepoint / two-condition essentiality: RRA or BAGEL2 simpler and equivalent.
Heavy-selection drug screens: drugZ explicit for chemogenomic; JACKS less sensitive.
Cross-chemistry sharing (e.g. CRISPRi + Cas9): Efficacy is chemistry-specific; do not share.
Run JACKS Joint Analysis
Goal: Jointly analyze multiple CRISPR screens performed with the same library and chemistry.
Approach: Provide a count matrix with all samples across all screens, a replicate map identifying which samples belong to which screen and condition, and a sgRNA-to-gene map. JACKS learns guide efficacy shared across screens and gene effects per screen.
# Programmatic invocationfrom jacks.jacks_io import runJACKS
# Input file paths
counts_path = 'counts.txt'# rows=sgRNA; first cols 'sgRNA' (or custom), then sample counts
replicate_map_path = 'replicatemap.txt'# tab-separated with header: Replicate, Sample, Control
guide_map_path = 'guidemap.txt'# tab-separated with header: sgRNA, Gene# Replicate map format (tab-separated WITH header; column names match flags below)# Replicate Sample Control# Screen1_T1 Screen1_T Screen1_C# Screen1_T2 Screen1_T Screen1_C# Screen1_C1 Screen1_C Screen1_C# Screen2_T1 Screen2_T Screen2_C# Screen2_T2 Screen2_T Screen2_C# Screen2_C1 Screen2_C Screen2_C
runJACKS(
countfile=counts_path,
replicatefile=replicate_map_path,
guidemappingfile=guide_map_path,
rep_hdr='Replicate',
sample_hdr='Sample',
ctrl_sample_hdr='Control', # per-sample control specification
sgrna_hdr='sgRNA',
gene_hdr='Gene',
outprefix='jacks_out',
apply_w_hp=True, # hierarchical prior on the gene effect w (the JACKS help notes: not recommended)
)
# Equivalent CLI run (run_JACKS.py is at the JACKS repo root after clone)
python run_JACKS.py \
counts.txt \
replicatemap.txt \
guidemap.txt \
--rep_hdr Replicate \
--sample_hdr Sample \
--ctrl_sample_hdr Control \ # per-sample control (or --common_ctrl_sample <name>)
--sgrna_hdr sgRNA \
--gene_hdr Gene \
--outprefix jacks_out \
--apply_w_hp # hierarchical prior on the gene effect w (not recommended by the tool's own help)# Outputs:# jacks_out_gene_JACKS_results.txt gene effect: header `Gene` + one column per cell line# jacks_out_gene_std_JACKS_results.txt matching posterior std per gene per cell line# jacks_out_gene_pval_JACKS_results.txt p-values (written only when --ctrl_genes is supplied)# jacks_out_grna_JACKS_results.txt sgRNA-level: header `sgrna`, `X1`, `X2`# jacks_out_JACKS_results_full.pickle full posterior for downstream
Lower = more confident; combine as effect/std for a z-like statistic
X1 (sgRNA file)
Posterior mean of guide efficacy
Centred near 1 and unbounded; the reference Avana set spans negative values to >100
X2 (sgRNA file)
Second moment E(X^2) of efficacy; std = sqrt(X2 - X1^2)
Confidence in the efficacy estimate
Interpretation rule: A gene is essential if its effect is negative and large relative to its posterior std (effect/std well below zero); supply --ctrl_genes to also get a p-value file. The X1/X2 ratio gives a z-like statistic; |X1/X2| > 2 corresponds to ~95% credible deviation from zero. Sort by X1 (most negative first) for essentiality rank.
Build Library-Wide Efficacy Prior from Reference Screens
Goal: Transfer learned efficacy from a large public screen panel to a new small screen.
Approach: Run JACKS on the reference panel (e.g. DepMap CRISPR screens with TKOv3 or Brunello), extract per-sgRNA efficacy posterior, and supply it as the prior for a new screen.
defextract_efficacy_prior(reference_jacks_results):
'''Build per-sgRNA efficacy prior (mean + std) from a large reference screen.'''
df = pd.read_csv(reference_jacks_results, sep='\t')
prior = df[['sgrna', 'X1', 'X2']] # --reffile requires these exact column names; do not renamereturn prior
# Use in new JACKS run via --reffile <path># Reference: Allen 2019 Genome Research 29:464; efficacy-aware testing enables ~2.5x smaller screens (fewer replicates/guides)
Per-sgRNA Efficacy Diagnostics
Goal: Identify low-efficacy guides for library refinement.
Approach: Examine the distribution of inferred efficacies; guides below 0.3 are likely non-functional and should be excluded from re-designed libraries.
Critical: Genes where every guide is low-efficacy will show no signal regardless of biology. Filter from interpretation; flag for re-design with updated rules (Brunello / TKOv3).
Comparing JACKS, MAGeCK, BAGEL2
Property
JACKS
MAGeCK
BAGEL2
Statistical framework
Variational Bayes
NB GLM + alpha-RRA / MLE
Bayes factor on per-sgRNA fold change
Models guide efficacy
Yes (jointly)
No (optional fixed input)
No
Multi-screen joint
Yes (native)
Limited (MLE design matrix)
No (per-screen)
Speed
Slow (variational inference)
Fast
Fast
Output
gene effect + sgRNA efficacy
beta or RRA score
Bayes Factor
Best for
Multi-screen joint analyses, library calibration
General-purpose, single screen
Essentiality classification
Quantified accuracy gain (Allen 2019)
~21% lower error vs MAGeCK; 9% vs BAGEL v1
Reference
Not benchmarked (Allen 2019 compared BAGEL v1)
Reconciliation: Hits identified by JACKS AND MAGeCK are high confidence. JACKS-only hits typically reflect strong gene signals where one or two guides were dragging down MAGeCK; verify the up-weighted high-efficacy guides have the expected sign. MAGeCK-only hits at FDR <0.05 may be single-guide outliers; check sgrna_summary for guide-level dispersion.
Failure Modes
Efficacy collapsed near zero for all guides
Trigger: Screen used a chemistry the model doesn't support (e.g., CRISPRi screen analyzed with JACKS defaults).
Mechanism: CRISPRi efficacy is fundamentally different from Cas9-KO efficacy; the Beta-prior hyperparameters fit on Cas9 data don't transfer.
Symptom: Median efficacy <0.2; almost no significant gene effects.
Fix: Train per-chemistry priors separately; for CRISPRi/a, current JACKS recommends --apply_w_hp with manually set hyperparameters from a CRISPRi reference dataset.
Cross-cell-line efficacy disagreement
Trigger: Pooling screens across cell lines with very different Cas9 expression / chromatin / fitness baselines.
Mechanism: Efficacy depends on Cas9 expression and chromatin accessibility; sharing across lines averages real per-line differences.
Symptom: Per-line gene effects look noisier than per-line MAGeCK results.
Fix: Use Chronos for multi-cell-line screens with screen-quality modeling; reserve JACKS for screens with matched chemistry + cell type / culture conditions.
MCMC / variational convergence failure
Trigger: Too few iterations relative to library size (10k iters for 100k-guide library is sometimes insufficient).
Mechanism: Variational lower bound has not plateaued; estimates noisy.
Symptom: Repeated runs produce different gene effects.
Fix: JACKS exposes no iteration flag on the CLI (internally n_iter=50); instead increase guides per gene or add screens, and verify the result is stable across re-runs (JACKS exposes no seed flag).
sgRNA-to-gene map mismatch
Trigger: Guide map and count matrix use different sgRNA naming conventions (e.g. BRCA1_1 vs BRCA1.1).
Mechanism: JACKS reads the map as a join; mismatched rows give NaN gene effects.
Symptom: Many genes missing from output.
Fix: Standardize naming; sanity check len(jacks_output) == n_genes_expected.
Reference efficacy prior from wrong library
Trigger: Using DepMap Brunello efficacy as prior for a screen with a custom TKOv3-style library.
Mechanism: Per-sgRNA efficacy is sequence-specific; sgRNAs in one library map to different gene contexts than another.
Symptom: Worse gene-effect estimation than no prior.
Fix: Match library exactly; if no matched reference exists, run without prior.
Reconciliation: When JACKS and Other Tools Disagree
Pattern
Likely cause
Action
JACKS significant, MAGeCK not
One low-efficacy guide dragged MAGeCK; JACKS down-weighted it
Trust JACKS if 3+ high-efficacy guides agree
MAGeCK significant, JACKS not
All guides have similar efficacy; JACKS prior shrinks signal
Verify per-guide LFC consistency in MAGeCK sgrna_summary
JACKS efficacy ~0.5 for all guides
Hierarchical prior over-shrinkage
Run with --apply_w_hp false; refit hyperparameters
Gene effect different sign from MAGeCK
Multi-screen pooling created mean effect different from single-screen
