Bio Crispr Screens Bagel Essentiality

Version Compatibility

Reference examples tested with: BAGEL2 1.0.5+ (hart-lab/bagel), pandas 2.2+, numpy 1.26+, scipy 1.12+, matplotlib 3.8+.

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

• CLI: BAGEL.py fc --help; BAGEL.py bf --help; BAGEL.py pr --help
• Python: BAGEL2 is distributed via git clone (no canonical PyPI release); confirm python BAGEL.py --version after checkout.

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

BAGEL2 Essentiality Analysis

"Identify essential genes from my CRISPR fitness screen using BAGEL2" -> Compute per-sgRNA fold changes from counts, derive per-gene log-likelihood ratios against reference essential and non-essential gene sets, sum to Bayes Factor, and apply BF threshold calibrated by precision-recall against the reference.

• CLI: BAGEL.py fc to compute fold changes
• CLI: BAGEL.py bf to compute Bayes Factors
• CLI: BAGEL.py pr for precision-recall curves
• Reference sets: CEGv2 (essentials) and NEGv1 (non-essentials); both at https://github.com/hart-lab/bagel

The BAGEL2 Bayesian Framework (under the hood)

Why this matters for postdoc-level use: BAGEL2 uses a Bayes-factor classifier trained on known essential and non-essential genes. The chain:

• For each sgRNA, compute log-fold-change (LFC) treatment vs control.
• For each gene, look up per-sgRNA LFCs.
• For each sgRNA, compute the log-likelihood ratio: log( P(LFC | gene is essential) / P(LFC | gene is non-essential) ). The numerator and denominator are KDEs (kernel density estimates) of LFC distributions from CEGv2 and NEGv1 reference sgRNAs.
• Sum per-gene log-likelihood ratios across all sgRNAs targeting the gene -> per-gene Bayes Factor.
• Bootstrap (default 1000 iterations) for confidence interval; BF >6 corresponds to ~FDR 0.05 (Hart 2017 G3 calibration).

Critical BAGEL2 improvements over BAGEL1:

• Linear extrapolation: BAGEL1 truncated the LLR at the edges of its KDE; BAGEL2 fits a linear regression in the stable region and extrapolates, giving wider dynamic range. This recovers tumor suppressors (highly positive LFC) that BAGEL1 missed.
• Multi-target correction: For sgRNAs targeting multiple genomic loci (off-targets), BAGEL2 down-weights their contribution. The original BAGEL counted off-target hits as essentiality signal.
• Tumor suppressor sensitivity: BAGEL2 correctly identifies positive selection (enrichment) genes -- not possible in BAGEL1.

Calibration to CEGv2 / NEGv1

Why these reference sets matter: BAGEL2's discriminative power depends on KDEs of LFCs from known essential vs known non-essential genes. CEGv2 (Hart 2017) is 684 core essential genes shared across cell lines; NEGv1 (Hart 2014) is 927 non-essential genes verified across multiple screens. These act as positive and negative controls within every screen.

Reference set integrity:

• CEGv2: pan-cancer essentials -- common dropouts across most cancer cell lines
• NEGv1: confidently non-essential -- genes without expression or genes with verified neutral status

Critical pitfall: Using a custom essentiality reference (e.g., a single-cell-line CRISPR screen) instead of CEGv2 biases the BAGEL2 model toward that line's specific biology. Always use the standardized references unless you have a specific reason for custom training.

Compute Per-Sample Fold Changes

Goal: Generate per-sgRNA fold-change matrix as input for Bayes-factor calculation.

Approach: Take normalized counts, compute log-fold-change vs a control (Day 0 or plasmid baseline) per sgRNA.

# BAGEL2 installation: distributed via git clone (no canonical PyPI release).
git clone https://github.com/hart-lab/bagel
cd bagel
# Some forks publish to PyPI (e.g. `bagel-cas9`) but the official distribution is the GitHub repo.

# Inputs:
# counts.txt: tab-separated with columns: sgRNA, GENE, Sample1, Sample2, ...
# Control column(s): typically Day 0 or plasmid sample(s)
# Treatment column(s): screen endpoint

BAGEL.py fc \
    -i counts.txt \
    -o foldchange.txt \
    -c Plasmid \                           # control sample (or Day 0)
    --min-reads 30                         # minimum reads/sgRNA in control
# Output: foldchange.txt - per-sgRNA LFCs

Compute Bayes Factors

Goal: Score per-gene essentiality as a Bayes Factor.

Approach: Run BAGEL.py bf with the fold-change matrix and reference gene sets; specify number of bootstrap iterations.

BAGEL.py bf \
    -i foldchange.txt \
    -o bayes_factor.txt \
    -e CEGv2.txt \                         # essentials reference (CEGv2)
    -n NEGv1.txt \                          # non-essentials reference
    -c Sample1,Sample2,Sample3 \            # treatment samples to score
    -k 1000                                # bootstrap iterations (1000 default)
# Output: bayes_factor.txt - per-gene Bayes Factor + CI

Output columns:

| Column | Meaning |

|--------|---------|

| GENE | Gene symbol |

| BF | Per-gene Bayes Factor (log-likelihood ratio summed across sgRNAs) |

| STD | Standard deviation from bootstrap |

| NumObs | Number of sgRNAs contributing |

Interpretation rule: BF >6 corresponds to FDR ~0.05 against CEGv2; BF >12 corresponds to FDR ~0.005. Higher BF = stronger evidence the gene is essential. BAGEL2 also reports negative BFs which can indicate tumor suppressors (positive selection).

Precision-Recall Curve

Goal: Empirically select BF threshold for a given precision/recall tradeoff.

Approach: Run BAGEL.py pr to compute precision and recall at every BF level against CEGv2; pick the BF that gives desired precision.

BAGEL.py pr \
    -i bayes_factor.txt \
    -o precision_recall.txt \
    -e CEGv2.txt \
    -n NEGv1.txt
# Output: precision_recall.txt - precision/recall at each BF threshold

Practical thresholds (Kim & Hart 2021):

| BF threshold | Precision | Recall | Use case |

|--------------|-----------|--------|----------|

| 0 | 0.85 | 0.95 | Exploratory; high recall |

| 6 | 0.95 | 0.85 | Standard; corresponds to FDR 0.05 |

| 12 | 0.99 | 0.65 | High-confidence; corresponds to FDR 0.005 |

| 30 | 1.00 | 0.20 | Ultra-stringent; near-certain essentials |

Pick threshold based on application: For exploratory hit calling, BF >0 with low precision is acceptable; for clinical-grade essentiality calls, BF >12 or higher.

Interpret BAGEL2 Results

Goal: Stratify genes into essential, non-essential, and tumor-suppressor categories.

Approach: Apply BF threshold to classify; flag negative BF as candidate tumor suppressors.

import pandas as pd

def interpret_bagel(bf_path, bf_essential=6, bf_tumor_suppressor=-6):
    '''Classify genes from BAGEL2 BF output.'''
    df = pd.read_csv(bf_path, sep='\t')
    df['call'] = 'neutral'
    df.loc[df['BF'] > bf_essential, 'call'] = 'essential'
    df.loc[df['BF'] < bf_tumor_suppressor, 'call'] = 'tumor_suppressor'
    return df.sort_values('BF', ascending=False)

Tumor suppressor identification: Genes with significantly negative BF (e.g., <-6) are enriched in the screen, indicating fitness advantage from their loss. This is biologically distinct from "non-essential" and may indicate tumor-suppressor function. BAGEL1 could not detect this; BAGEL2's linear extrapolation enables it.

Bayesian Reasoning Per Sgrna

Why this matters: BAGEL2 computes per-sgRNA contributions; a gene with 4 sgRNAs each contributing +5 to BF gets +20 total. A gene with 3 sgRNAs contributing +5 and 1 sgRNA contributing -3 (off-target or low-efficacy) gets +12 net.

# Per-sgRNA contributions for diagnosis
# Output table: each sgRNA's LLR contribution to gene-level BF
# Useful for identifying low-efficacy guides

Critical: When per-sgRNA contributions are very heterogeneous (one sgRNA dominates BF), the gene is "guide-of-one"; verify with JACKS efficiency analysis or apply the second-best-sgRNA rule from [[hit-calling]].

Comparing BAGEL2, MAGeCK, drugZ

| Property | BAGEL2 | MAGeCK | drugZ |

|----------|--------|--------|-------|

| Statistical framework | Bayes factor with reference sets | NB GLM | Bidirectional Z-score |

| Calibrated against | CEGv2 / NEGv1 | Internal null | Vehicle distribution |

| Tumor suppressor detection | YES | Limited (RRA positive-selection score) | YES |

| Best for | Essentiality classification | General hit calling | Chemogenomic drug screens |

| Output | Bayes factor + CI | FDR + LFC | Z-score + FDR per direction |

| Hit threshold | BF >6 (≈FDR 0.05) | FDR <0.05 | FDR <0.05 |

| Library calibration | Indirect (reference set) | None | None |

Reconciliation: BF >6 ≈ MAGeCK FDR 0.05 (Hart 2017 G3 calibration). BAGEL2 hits absent from MAGeCK suggest weak signal that BAGEL2's reference anchoring detects but MAGeCK's null-based test misses; verify by inspecting per-sgRNA contributions.

Failure Modes

BAGEL2 returns no hits despite known essentials

Trigger: Wrong reference gene set file; CEGv2 or NEGv1 file may have wrong format or be missing genes.

Mechanism: BAGEL2 trains KDEs on the reference; if references are not representative, KDE separation is poor and no gene has BF >6.

Symptom: Median BF near zero; no genes >6 even at low FDR.

Fix: Re-download CEGv2 / NEGv1 from https://github.com/hart-lab/bagel. Verify gene symbols match the screen's annotation.

BAGEL2 calls negative-LFC genes "tumor suppressors"

Trigger: Heavy dropout screen where many genes drop out; the dropout signal is captured as positive BF but the *enriched* genes (negative BF) are noise.

Mechanism: BAGEL2's symmetric distribution treats deeply enriched genes as significant; in a dropout-only screen, the enrichment signal is purely noise.

Symptom: Many genes with negative BF; these don't validate as tumor suppressors.

Fix: Restrict tumor-suppressor calling to screens specifically expecting enrichment (e.g., drug-resistance, GoF screens); for dropout screens, only interpret positive BF.

Bootstrap CI is wide; BF estimates unstable

Trigger: Per-gene number of sgRNAs too low (e.g., <4 in some libraries).

Mechanism: Bootstrap of LLR over very few sgRNAs creates wide CI.

Symptom: STD column larger than BF; many genes have CI spanning zero.

Fix: Use a library with at least 4-6 sgRNAs/gene; or increase bootstrap iterations to 5000+; or filter out genes with <3 sgRNAs.

Low BF for known essential despite high LFC

Trigger: One sgRNA per gene is contributing very low LLR (off-target or low-efficacy).

Mechanism: BAGEL2 sums LLR; one weak guide drags total down.

Symptom: Known essential like RPS3 has BF <6 despite 3 of 4 guides showing -5 LFC.

Fix: Inspect per-sgRNA LLR; identify the dragging guide; verify whether to exclude or to use JACKS for efficacy-aware analysis.

Non-cancer cell-line screen with custom essentials

Trigger: Iurine embryonic kidney HEK293T or iPSC-derived neurons where standard essentials may not be essential.

Mechanism: CEGv2 is calibrated for cancer cell lines; some essentials in tumor cells are not essential in iPSC.

Symptom: PR curve against CEGv2 shows poor separation; many CEGv2 essentials don't drop out.

Fix: Use cell-type-specific essentialome (e.g., Dempster 2019 *Nat Commun* defined essentials in various cell types); or use MAGeCK / Chronos which doesn't depend on reference sets.

Quantitative Thresholds

| Threshold | Value | Source / Rationale |

|-----------|-------|--------------------|

| BF for FDR ~0.05 | >6 | Hart 2017 G3 calibration; Kim & Hart 2021 |

| BF for FDR ~0.005 | >12 | Empirical from PR curve |

| BF for FDR ~0.001 | >30 | Empirical |

| BF for tumor-suppressor candidate | <-6 | Empirical; verify with orthogonal screen |

| Bootstrap iterations | 1000 default; 5000+ for tight CI | Hart-lab convention |

| Min reads per sgRNA in control | 30 | Joung 2017; BAGEL2 default |

| Min sgRNAs per gene for stable BF | 4-6 | Wider with library convention |

Common Errors

| Error / symptom | Cause | Solution |

|-----------------|-------|----------|

| No hits despite essentials present | Wrong reference set | Re-verify CEGv2 / NEGv1 files |

| Wide bootstrap CI | Too few sgRNAs/gene | Increase library coverage; more iterations |

| Negative BF for known essentials | Confounding factor (e.g., CN amplification) | Pre-correct with CRISPRcleanR / Chronos |

| Tumor suppressor calls don't validate | Pure dropout screen; enrichment is noise | Restrict tumor suppressor calls to expected design |

| Per-sgRNA LLR dominated by one guide | Outlier or off-target | Apply second-best-sgRNA rule |

References

• Kim E & Hart T. 2021. *Genome Medicine* 13:2. BAGEL2 algorithm and improvements.
• Hart T & Moffat J. 2016. *BMC Bioinformatics* 17:164. BAGEL Bayes factor framework.
• Hart T et al. 2017. *G3* 7:2719. CEGv2 / NEGv1 calibration; FDR-BF relationship.
• Hart T et al. 2014. *Mol Syst Biol* 10:733. Original essential gene reference set.
• Dempster JM et al. 2019. *Nat Commun* 10:5817. Cell-type-specific essentialomes.
• Pacini C et al. 2021. *Cell Syst* 12:1132. Reference essentiality benchmarks.

Related Skills

• crispr-screens/mageck-analysis - MAGeCK RRA/MLE alternative
• crispr-screens/jacks-analysis - JACKS for per-guide efficacy
• crispr-screens/drugz-chemogenomic - drugZ for drug screens
• crispr-screens/hit-calling - Cross-method decision tree
• crispr-screens/screen-qc - Pre-BAGEL QC including CEGv2 PR-AUC
• crispr-screens/library-design - 4-6 sgRNAs/gene library standard
• crispr-screens/copy-number-correction - Pre-correction for cancer-line screens
• pathway-analysis/go-enrichment - Downstream functional analysis