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jacks-analysis

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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.

When to use

• Multi-screen joint analysis with same library
• Guide-level noise reduction
• Reusing published essentiality reference screens for efficacy priors
• Comparing screens across cell lines sharing library
• Libraries with broad efficacy variance
• Heterogeneous quality mixed plasmid quality

When not to use

• Single screen with no prior efficacy
• Single timepoint or two-condition essentiality
• Heavy-selection drug screens
• Cancer-cell-line copy-number screens
• Cross-chemistry sharing (e.g. CRISPRi + Cas9)

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