TargetSage

Computational pipeline for CRISPR/RNAi screening analysis — arrayed and pooled.

TargetSage is a Python library and command-line toolkit for analyzing high-throughput CRISPR and RNAi screening data. It supports both:

• Arrayed screens — well-level readouts from plate readers (384/96-well format)
• Pooled screens — guide-level readouts from sequencing (dropout/enrichment)

The pipeline covers the full analysis workflow: data validation → normalization → QC → hit calling → gene aggregation → pathway enrichment → visualization.

📦 Installation

pip install targetsage

Requirements: Python ≥3.11

Optional dependencies for full functionality:

pip install targetsage[notebooks]   # Jupyter support
pip install targetsage[plots]       # Enhanced plotting backends

🚀 Quick Start

Arrayed Screen — One Function

import pandas as pd
from targetsage.stats.batch_correction import run_arrayed_analysis

df = pd.read_csv("my_arrayed_screen.csv")

results = run_arrayed_analysis(
    df,
    rep1_col="Raw_rep1",
    rep2_col="Raw_rep2",
    plate_col="Plate",
    control_type_col="SpotType",
    norm_method="genes and all Non-targeting",
    p_cutoff=0.05,
    lfc_cutoff=1.0,
)

# results is a DataFrame with well-level hits, fold-changes, SSMD, p-values
hits = results[results["is_hit"] == True]
print(f"Found {len(hits)} hits")

Arrayed Screen — Step-by-Step Workflow

from targetsage.pipeline.arrayed_workflow import ArrayedScreenWorkflow

wf = ArrayedScreenWorkflow(
    df,
    screen_name="my_screen",
    rep_cols=["Raw_rep1", "Raw_rep2"],
    plate_col="Plate",
    well_col="Well",
    gene_col="gene_symbol",
    norm_method="genes and all Non-targeting",
    gene_hit_method="moderated_t",
    p_cutoff=0.05,
    lfc_cutoff=1.0,
)

# Run the full pipeline
wf.run_all()

# Access results
print(wf.results_summary())

# Gene-level hits
gene_hits = wf.get_step("hit_calling")

Pooled Screen

from targetsage.pipeline.pooled_intensity_workflow import PooledIntensityWorkflow

wf = PooledIntensityWorkflow(
    df,
    screen_name="my_pooled_screen",
    reference_cols=["Baseline_R1", "Baseline_R2", "Baseline_R3"],
    treatment_cols=["Treatment_R1", "Treatment_R2", "Treatment_R3"],
    gene_col="gene_symbol",
    p_cutoff=0.05,
    log2fc_cutoff=0.3,
)

wf.run_all()

🖥️ Command Line Interface

Three entry points are installed:

# Main dispatcher
targetsage array <command> [options]
targetsage pool  <command> [options]

# Direct aliases
targetsage-array <command> [options]
targetsage-pool  <command> [options]

Arrayed CLI Example

targetsage array run-all \
    -i data/my_screen.csv \
    -o results/arrayed_out \
    --rep1-col Raw_rep1 \
    --rep2-col Raw_rep2 \
    --plate-col Plate \
    --gene-col gene_symbol \
    --norm-method "genes and all Non-targeting" \
    --gene-hit-method moderated_t

Pooled CLI Example

targetsage pool run-all \
    -i data/my_pooled_screen.csv \
    -o results/pooled_out \
    --reference-cols Baseline_R1 Baseline_R2 Baseline_R3 \
    --treatment-cols Treatment_R1 Treatment_R2 Treatment_R3 \
    --gene-col gene_symbol \
    --pvalue-method welch

Get help for any command:

targetsage array --help
targetsage array run-all --help

📊 Analysis Methods

Normalization

Hit Calling (Arrayed)

Hit Calling (Pooled)

QC Metrics

• SSMD — Strictly Standardized Mean Difference (well-level effect size)
• Z′-factor — Plate quality score
• Replicate correlation — Pearson r between replicates (configurable threshold)
• Hit-rate estimation — Per-plate hit-rate for B-score guardrails

Pathway Enrichment

• Over-Representation Analysis (ORA) via gseapy/Enrichr
• Custom GMT gene-set support
• Cached offline mode for reproducibility

📓 Notebooks & Examples

Example notebooks are available in the notebooks/ directory:

Run locally:

git clone https://github.com/your-org/targetsage.git
cd targetsage
pip install -e ".[notebooks]"
jupyter notebook notebooks/

🧪 Testing

# Clone the repository
git clone https://github.com/your-org/targetsage.git
cd targetsage

# Install in development mode
pip install -e ".[dev]"

# Run the test suite
pytest

🏗️ Architecture

targetsage/
├── data/              # Data loaders, schema, validation
├── normalization/     # Normalization methods (B-score, LOESS, Z-score, etc.)
├── hits/              # Hit scoring (well-level + gene-level aggregation)
├── stats/             # Statistical tests, QC, batch correction
├── pipeline/          # Workflow runners (ArrayedScreenWorkflow, PooledIntensityWorkflow)
│   ├── steps/         # Individual step implementations
│   ├── config.py      # Configuration dataclasses
│   ├── arrayed_workflow.py
│   └── pooled_intensity_workflow.py
├── qc/                # QC engine and report generation
├── utils/             # DataFrame helpers, well coordinates, etc.
├── visualization/     # Plotting utilities
├── network/           # Network analysis and visualization
└── cli.py             # Command-line entry points

🔬 Citation

If you use TargetSage in your research, please cite:

TargetSage: A computational pipeline for CRISPR/RNAi screening analysis. Package version X.Y.Z, https://pypi.org/project/targetsage/

Key methods implemented in TargetSage are based on established literature:

• B-score normalization: Brideau et al., J Biomol Screen 2003
• LOESS normalization: Cleveland et al., J Am Stat Assoc 1979
• LME for arrayed CRISPR: PLOS ONE 2024 (simulation-guided method selection)
• SSMD: Zhang et al., J Biomol Screen 2007
• RRA: Kolde et al., Bioinformatics 2012

📄 License

MIT License — see LICENSE file.

🤝 Contributing

Contributions are welcome! Please open an issue or pull request on GitHub.

For the full-stack web application (TargetSage Cloud), see the separate documentation.