End-to-end pooled and single-cell CRISPR screen analysis from FASTQ to hit genes. Orchestrates library design QC, guide counting, six-stage screen QC (plasmid Gini, replicate Pearson, CEGv2 PR-AUC, copy-number artifact), method-appropriate hit calling across MAGeCK RRA/MLE, BAGEL2, drugZ, JACKS, and Chronos, cancer-cell-line copy-number correction (CRISPRcleanR / Chronos), batch correction for multi-batch screens, and the specialized branches for combinatorial paralog screens, single-cell Perturb-seq, base-editor variant-function screens, prime-editor screens, and in vivo bottleneck-aware screens. Use when analyzing any pooled CRISPR screen end-to-end, matching the hit-calling method to the experimental design, integrating copy-number correction into the pipeline, or branching the workflow for single-cell, combinatorial, base-editor, prime-editor, or in vivo variants.
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End-to-end pooled and single-cell CRISPR screen analysis from FASTQ to hit genes. Orchestrates library design QC, guide counting, six-stage screen QC (plasmid Gini, replicate Pearson, CEGv2 PR-AUC, copy-number artifact), method-appropriate hit calling across MAGeCK RRA/MLE, BAGEL2, drugZ, JACKS, and Chronos, cancer-cell-line copy-number correction (CRISPRcleanR / Chronos), batch correction for multi-batch screens, and the specialized branches for combinatorial paralog screens, single-cell Perturb-seq, base-editor variant-function screens, prime-editor screens, and in vivo bottleneck-aware screens. Use when analyzing any pooled CRISPR screen end-to-end, matching the hit-calling method to the experimental design, integrating copy-number correction into the pipeline, or branching the workflow for single-cell, combinatorial, base-editor, prime-editor, or in vivo variants.
[{"after_counting":">65% mapping rate; <0.5% zero-count in plasmid; Gini <0.1 on plasmid"},{"after_qc":"Replicate Pearson on log-counts >=0.8 (MAGeCK-VISPR floor; >0.85 acceptable, >0.95 ideal); Spearman >0.7; CEGv2 PR-AUC >0.7"},{"after_cn_correction":"Spearman ρ between CN and gene LFC abs <0.10 post-correction (literature 'significant bias' band; <0.05 is a stricter target). Requires a matched CN profile, which the unsupervised CRISPRcleanR path never loads -- compute in crispr-screens/copy-number-correction, or use Chronos, which takes CN as input"},{"after_hit_calling":"Tier-1 hits = 3-method consensus; Tier-2 = 2 of 3; Tier-3 = single-method exploratory"}]
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
CRISPR Screen Pipeline
"Analyze my pooled or single-cell CRISPR screen end-to-end" -> Pick the screen design branch, run guide counting, audit six QC stages, apply copy-number and batch correction as needed, run the design-matched hit-calling method, and consolidate across methods for high-confidence hits.
This is a workflow skill: it owns the chaining decisions and hand-offs, not the internals of any one step.
The governing principle
Every LFC, QC gate, and hit call is computed against a reference that is committed once at library-order time; a wrong-but-silent commitment invalidates the endpoint with no error thrown.
The guide LIBRARY definition (guide->gene map + control classes) is the denominator, the calibrator, and the training reference — committed once. The library must carry non-targeting controls (NTCs, ~1%, the null distribution) AND CEGv2 reference essentials + NEGv1 non-essentials (the positive/negative calibrators for PR-AUC and BAGEL2/Chronos priors). NTCs calibrate the null/FDR; CEGv2/NEGv1 calibrate PR-AUC — swapping or dropping a class silently breaks FDR or QC.
The baseline choice has a right answer and rescales every hit. Dropout/enrichment LFC is against a baseline: plasmid pool for the cloning-bottleneck baseline, Day-0/T0 for the biology baseline, and vehicle (NOT Day-0) for drug screens — drug-vs-Day-0 conflates drug effect with normal proliferation.
Copy-number correction MUST precede hit calling in cancer cell lines. Multiple simultaneous Cas9 cuts at amplified loci trigger a gene-independent DNA-damage/G2 arrest (Aguirre 2016; Munoz 2016; the effect appears in both TP53-mutant and TP53-wild-type lines, though Aguirre 2016 found TP53 status correlates with its magnitude -- separately, Ihry 2018 / Haapaniemi 2018 report p53-dependent toxicity of Cas9 cutting generally), so amplified regions look essential regardless of gene function; calling hits first yields false essentials at ERBB2/MYC/FGFR1. Run CRISPRcleanR/Chronos BEFORE hit calling, or use CRISPRi to bypass the DSB. This is a pipeline step, not a post-hoc interpretation. Verifying afterwards needs a matched CN profile: CRISPRcleanR corrects unsupervised without one, so the check happens in crispr-screens/copy-number-correction (or use Chronos, which consumes CN directly).
abs(rho(LFC,CN)) < 0.1
CEGv2 essential-gene depletion is the screen's built-in positive control. If known essentials do not deplete (CEGv2 PR-AUC below ~0.7), the screen failed selection and NO novel hit is trustworthy regardless of its p-value — the seam analog of a spike-in. Normalize -> QC -> (CN correct) -> hit-call, never hit-call first; add batch as an MLE covariate, never pre-corrected with ComBat on counts (distorts the NB mean-variance the caller assumes).
Made-once commitments
Commitment
Consequence inherited downstream
Guide library (guide->gene map + NTC/CEGv2/NEGv1 control classes)
The counting denominator, the QC calibrator, the hit-calling priors; a missing/misassigned class breaks FDR or PR-AUC
Baseline (plasmid pool / Day-0 / vehicle)
Every LFC; drug-vs-Day-0 conflates drug effect with proliferation
Screen type (dropout / enrichment / FACS / drug-modifier)
Which hit-calling method is even valid
Copy-number profile (cancer lines)
Whether amplicon artifacts are removed before hit calling; residual rho(LFC,CN) is the tell
Pipeline Branches by Screen Design
Library Design ([[library-design]])
|
v
FASTQ Files -> mageck count -> count matrix
|
v
Six-Stage QC ([[screen-qc]])
|
+---------------------+---------------------+
| |
v v
Cancer cell line? Non-cancer?
Apply CN correction No CN correction needed
([[copy-number-correction]])
| |
+---------------------+---------------------+
v
Multi-batch? Apply batch covariate
([[batch-correction]])
|
v
Pick hit-calling method by design ([[hit-calling]])
|
+-----------+---------+---------+-----------+-----------+
| | | | | |
v v v v v v
2-cond Time Drug Essential Multi- Specialized
MAGeCK RRA MAGeCK drugZ BAGEL2 screen (PE/BE/SC/
MLE JACKS or in vivo/
Chronos combinat)
| | | | | |
+-----------+---------+---------+-----------+-----------+
v
Tier-based consensus
v
Orthogonal validation
Step 1: Library Design and Pre-Screen Validation
Reference [[library-design]] for full library composition. Verify before sequencing:
Plasmid pool Gini <0.1 (Li W et al 2015 MAGeCK-VISPR, Genome Biol 16:281)
=99% guides detected at >25 reads/guide
Skew (p90/p10) <2
NTCs comprise ~1% of library; CEGv2 reference essentials + NEGv1 non-essentials included
Step 2: Guide Counting
Goal: Turn raw FASTQ into a per-guide count matrix with consistent sample labels.
Approach: Run mageck count with the library CSV, sample labels in column order, the vector adapter trimmed off the 5' end, and median normalization.
mageck count \
--list-seq library.csv \
--sample-label Plasmid,Day0,Veh_r1,Veh_r2,Drug_r1,Drug_r2 \
--fastq Plasmid.fq.gz Day0.fq.gz Veh_r1.fq.gz Veh_r2.fq.gz Drug_r1.fq.gz Drug_r2.fq.gz \
--norm-method median \
--output-prefix experiment \
--trim-5 5 # integer base-count (or AUTO), NOT an adapter sequence; 5 trims the CACCG scaffold
For Cas12a libraries (Inzolia, in4mer): see [[combinatorial-screens]]. For 10X single-cell direct capture: use cellranger-arc or pertpy-aware counting; see [[perturb-seq-analysis]].
Step 3: Six-Stage Quality Control
Goal: Decide whether the screen is analyzable before calling any hits, using six orthogonal QC stages.
Approach: Load the count matrix, compute per-sample Gini, zero-fraction, and depth plus replicate correlation against the hard gates below. Essential-gene recovery (CEGv2 PR-AUC) is a separate check computed once endpoint-vs-baseline LFCs exist (it needs CEGv2/NEGv1 labels) -- see screen-qc.
If screening in a cancer cell line, apply CRISPRcleanR (unsupervised, no CN profile needed) or Chronos (joint with CN profile). Required to remove Aguirre 2016 / Munoz 2016 amplicon artifact.
Goal: Strip the copy-number amplicon artifact that makes amplified regions look essential in cancer lines.
Approach: Run CRISPRcleanR unsupervised genome-wide LFC correction (no CN profile needed), then feed the corrected counts downstream; for DepMap-scale panels with matched CN, use Chronos instead.
library(CRISPRcleanR)
data(KY_Library_v1.0)
norm <- ccr.NormfoldChanges('experiment.count.txt', min_reads =30, EXPname ='screen',
libraryAnnotation = KY_Library_v1.0)# arg 1 is the file PATH
gw_lfc <- ccr.logFCs2chromPos(norm$logFCs, KY_Library_v1.0)# $logFCs, not $norm_fold_changes
cleaned <- ccr.GWclean(gw_lfc, display =TRUE, label ='screen')
corrected_counts <- ccr.correctCounts('screen', norm$norm_counts, cleaned,
KY_Library_v1.0)# (CL, normalised_counts, correctedFCs, libraryAnnotation)# ccr.correctCounts returns an in-memory frame; it does NOT write this file. Persist it, because the# hit callers below read a count TABLE from disk -- the CN-correction commitment in rule 3 is only# honored if that file, not the raw experiment.count.txt, is what MAGeCK / BAGEL2 / drugZ consume.
write.table(corrected_counts,'screen_cleanr_corrected_counts.txt',
sep ='\t',quote=FALSE, row.names =FALSE)
For DepMap-scale panels with longitudinal data + matched CN, use Chronos. See [[copy-number-correction]].
Step 5: Batch Correction (Multi-Batch Screens)
For multi-batch screens, add batch as a covariate in MAGeCK MLE rather than pre-correcting with ComBat. See [[batch-correction]] for full decision tree.
Step 6: Method-Matched Hit Calling
Goal: Call hits with the method that matches the experimental design, plus at least one orthogonal method for consensus.
Approach: Pick by design - RRA or BAGEL2 for two-condition essentiality, MLE for time course, drugZ for drug-modifier, JACKS for multi-screen, Chronos for cancer panels - and run two methods so the consensus step has something to reconcile.
6a. Two-condition essentiality (MAGeCK RRA or BAGEL2)
Cancer cell lines: pass the CRISPRcleanR-corrected count file (screen_cleanr_corrected_counts.txt from Step 4) as --count-table/-i below, NOT the raw experiment.count.txt — the CN-correction commitment is only honored if the corrected counts are what the hit caller reads.
The Day0/Day14_r* columns below illustrate a time-course dropout design; they must match the count step's --sample-label (the drug-screen count above uses Plasmid,Day0,Veh_r*,Drug_r*).
mageck test \
--count-table experiment.count.txt \
--treatment-id Day14_r1,Day14_r2,Day14_r3 \
--control-id Day0 \
--norm-method median \
--output-prefix essentiality_rra
BAGEL.py fc -i experiment.count.txt -o experiment -c Day0 --min-reads 30 # -o is an output LABEL; fc writes experiment.foldchange
BAGEL.py bf -i experiment.foldchange -o bayes_factor.txt -e CEGv2.txt -n NEGv1.txt \
-c Day14_r1,Day14_r2,Day14_r3 # add -b -NB 1000 for bootstrapping; -k is not a bf option
import chronos
# All three inputs must be dicts of DataFrame keyed by library name, not bare DataFrames.
model = chronos.Chronos(sequence_map={'screen': sequence_map},
guide_gene_map={'screen': guide_gene_map},
readcounts={'screen': counts_df}) # readcounts=, not reads=
model.train(nepochs=301) # nepochs (default 301), not n_steps
gene_effects = model.gene_effect # attribute, not a method call# Copy-number correction is a separate post-hoc step (chronos.alternate_CN(gene_effect, copy_number) / a CN matrix), not a constructor arg
Goal: Consolidate the per-method calls into confidence tiers.
Approach: Merge each method's gene-level result, threshold each to a per-method hit flag, and tier by how many methods agree (Tier 1 = all three, Tier 2 = two of three).
MAGeCKFlute R package provides one-shot FluteRRA / FluteMLE dashboards with KEGG/Reactome enrichment.
Output Files
File
Source step
Description
experiment.count.txt
mageck count
Raw count matrix
experiment.countsummary.txt
mageck count
Per-sample Gini, mapping, % zero
screen_cleanr_corrected_counts.txt
CRISPRcleanR
CN-corrected counts (cancer lines)
essentiality_rra.gene_summary.txt
mageck test
Gene-level RRA scores
bayes_factor.txt
BAGEL2
Per-gene Bayes factors
drugz_output.txt
drugZ
sumZ, normZ, per-direction FDR
jacks_out_gene_JACKS_results.txt
JACKS
Gene effect + sgRNA efficacy
tier_consensus.csv
Custom aggregation
Tier-1/2/3 hits across methods
Common Errors
Symptom
Cause
Fix
False essentials at ERBB2/MYC/FGFR1
Hit calling before copy-number correction (gene-independent DNA-damage arrest at amplicons, regardless of p53 status)
Run CRISPRcleanR/Chronos BEFORE hit calling; verify abs(rho(LFC,CN)) < 0.1; or use CRISPRi to bypass the DSB
FDR broken or PR-AUC uncomputable
NTC (null) and CEGv2 essential (positive control) classes swapped or one absent
Keep both classes; NTCs calibrate the null/FDR, CEGv2/NEGv1 calibrate PR-AUC and BAGEL2/Chronos priors
Every hit rescaled / drug effect confounded
Wrong baseline (Day-0 for a drug screen)
Drug screen -> vehicle control; plasmid pool for the cloning-bottleneck baseline
"Everything significant at FDR<0.01"
Heavy selection breaks median normalization (>40% guides change)
Switch to --norm-method control on NTCs, or BAGEL2
Underpowered / method mismatch
RRA on a time course; single-line Chronos
Pick method by design (fork table); RRA fails multi-condition, Chronos is overkill single-line
Distorted NB mean-variance
Batch pre-corrected with ComBat on counts
Add batch as a MAGeCK MLE covariate instead
Novel hits from a failed screen
CEGv2 essentials did not deplete (PR-AUC < 0.7)
The screen failed selection; no hit is trustworthy regardless of p-value
References
Li W, Xu H, Xiao T, et al (2014) MAGeCK enables robust identification of essential genes from genome-scale CRISPR/Cas9 knockout screens. Genome Biology 15:554. DOI 10.1186/s13059-014-0554-4.
Aguirre AJ, Meyers RM, Weir BA, et al (2016) Genomic copy number dictates a gene-independent cell response to CRISPR/Cas9 targeting. Cancer Discovery 6:914-929. DOI 10.1158/2159-8290.CD-16-0154. (the amplicon artifact.)
Munoz DM, Cassiani PJ, Li L, et al (2016) CRISPR screens provide a comprehensive assessment of cancer vulnerabilities but generate false-positive hits for highly amplified genomic regions. Cancer Discovery 6:900-913. DOI 10.1158/2159-8290.CD-16-0178.
Hart T, Moffat J (2016) BAGEL: a computational framework for identifying essential genes from pooled library screens. BMC Bioinformatics 17:164. DOI 10.1186/s12859-016-1015-8.
Iorio F, Behan FM, Goncalves E, et al (2018) Unsupervised correction of gene-independent cell responses to CRISPR-Cas9 targeting (CRISPRcleanR). BMC Genomics 19:604. DOI 10.1186/s12864-018-4989-y.
Joung J, Konermann S, Gootenberg JS, et al (2017) Genome-scale CRISPR-Cas9 knockout and transcriptional activation screening. Nature Protocols 12:828-863. DOI 10.1038/nprot.2017.016. (library QC: skew, zero-count and coverage conventions.)
Related Skills
crispr-screens/library-design - Library composition and design rules
