bio-crispr-screens-perturb-seq-analysis
Analyzes single-cell pooled CRISPR screens (Perturb-seq, CROP-seq, Perturb-CITE-seq, ECCITE-seq, multiome) where each cell carries an sgRNA and a scRNA-seq / surface-protein / chromatin readout. Covers experimental design (direct-capture Perturb-seq Dixit 2016 vs CROP-seq 3'UTR-barcoded Datlinger 2017 vs ECCITE-seq vs Multiome), MOI for sgRNA assignment, escaper-cell filtering (Mixscape, Papalexi 2021), SCEPTRE NB GLM + permutation for low-MOI (Barry 2024 Genome Biol 25:124), the Pertpy framework, factor decomposition, genome-scale Perturb-seq (Replogle 2022 Cell, 2.5M cells), and per-perturba
npx skills add BioTender-max/awesome-bio-agent-skills --skill perturb-seq-analysis --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
## Version Compatibility Reference examples tested with: Pertpy 0.6+, SCEPTRE 0.10+ (R / katsevich-lab/sceptre), Mixscape via Seurat 4.3+ or Pertpy, scanpy 1.10+, anndata 0.10+, pandas 2.2+, numpy 1.26+, scipy 1.12+. Before using code patterns, verify installed versions match. If versions differ: - Python: `pip show pertpy scanpy anndata` - R: `packageVersion('sceptre')`; `?sceptre`; `?Seurat::PrepLDA` If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying. ## Single-Cell Perturb-Seq Analysis **"Analyze a single-cell pooled CRISPR perturbation screen"** -> Assign sgRNAs to cells, filter unperturbed escapers, normalize counts, fit per-gene differential expression conditioned on perturbation, and rank perturbations by their molecular effect. - Python: `pertpy` unified framework for Mixscape + SCEPTRE-via-R + differential expression - R: `sceptre` for low-MOI NB GLM + permutation testing - Python/R: `Seurat::MixscapeLDA` and downstream ## Experimental Architecture Comparison | Method | Year | Architecture | Readout | MOI | Single-cell sgRNA detection | |--------|------|--------------
- Version Compatibility
- Single-Cell Perturb-Seq Analysis
- Experimental Architecture Comparison
- MOI and sgRNA Assignment
- Escaper Cell Filtering (Mixscape)
- SCEPTRE for Low-MOI Differential Expression
- Pertpy Unified Framework
- Genome-Wide Perturb-Seq (Replogle 2022)
- Factor-Based Analysis
- Multiomic Perturb-seq (RNA + ATAC)
- Failure Modes
- Low sgRNA detection per cell
- Mixscape filters too many cells as escapers
- Doublet contamination drives apparent multi-perturbation cells
What does the bio-crispr-screens-perturb-seq-analysis skill do?
Analyzes single-cell pooled CRISPR screens (Perturb-seq, CROP-seq, Perturb-CITE-seq, ECCITE-seq, multiome) where each cell carries an sgRNA and a scRNA-seq / surface-protein / chromatin readout. Covers experimental design (direct-capture Perturb-seq Dixit 2016 vs CROP-seq 3'UTR-barcoded Datlinger 2017 vs ECCITE-seq vs Multiome), MOI for sgRNA assignment, escaper-cell filtering (Mixscape, Papalexi 2021), SCEPTRE NB GLM + permutation for low-MOI (Barry 2024 Genome Biol 25:124), the Pertpy framework, factor decomposition, genome-scale Perturb-seq (Replogle 2022 Cell, 2.5M cells), and per-perturba
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill perturb-seq-analysis --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.