sctrial 0.3.0

Participant-level differential analysis for longitudinal single-cell experiments: DiD, paired comparisons, module scoring, pseudobulk, and power analysis.

Participant-Level Differential Analysis for Longitudinal Single-Cell Experiments

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Overview

sctrial is a Python package for performing rigorous statistical inference on single-cell RNA-seq data from clinical trials and longitudinal studies. Built on top of AnnData and Scanpy, it provides specialized tools for:

• Difference-in-Differences (DiD) analysis with participant fixed effects
• Paired within-arm pre→post contrasts
• Between-arm comparisons at fixed timepoints
• Cell-type abundance change testing
• Gene set enrichment analysis (GSEA) on DiD rankings
• Power analysis and sample size calculations
• Effect size estimation with confidence intervals

Key Features

Feature	Description
Trial-Aware Design	Define participant, visit, arm, and cell type columns once
Robust Statistics	Wild cluster bootstrap, participant-level aggregation
Multiple Comparisons	Built-in FDR correction across features and cell types
Power Analysis	Two-arm DiD and single-arm paired power/sample size calculations
Single-Arm Support	arm_col=None for studies without a control arm
Publication-Ready Plots	Forest plots, interaction plots, GSEA heatmaps
Scalable	Efficient processing of large single-cell datasets

Installation

pip install sctrial

For development:

git clone https://github.com/TheOmarLab/sctrial.git
cd sctrial
pip install -e ".[dev]"

Quick Start

pip install "sctrial[plots]"   # includes dataset loaders and visualization

import sctrial as st

# 1. Load a real immunotherapy trial dataset (auto-downloads on first use)
adata = st.load_sade_feldman()
adata = st.harmonize_response(adata)  # majority-vote response labels

# 2. Define trial design
design = st.TrialDesign(
    participant_col="participant_id",
    visit_col="visit",
    arm_col="response_harmonized",
    arm_treated="Responder",
    arm_control="Non-responder",
    celltype_col="cell_type",
)

# 3. Score gene sets (dataset ships pre-normalized with log1p_tpm layer)
gene_sets = {
    "Cytotoxicity": ["GZMA", "GZMB", "PRF1", "GNLY", "NKG7"],
    "Exhaustion":   ["PDCD1", "CTLA4", "HAVCR2", "LAG3", "TIGIT"],
}
adata = st.score_gene_sets(adata, gene_sets, layer="log1p_tpm", method="zmean", prefix="ms_")

# 4. Run Difference-in-Differences on CD8 T cells
features = [c for c in adata.obs.columns if c.startswith("ms_")]
results = st.did_table(adata, features, design, visits=("Pre", "Post"), celltype="CD8 T cell")
print(results[["feature", "beta_DiD", "se_DiD", "p_DiD", "FDR_DiD"]])

Or use the one-liner convenience wrapper for a quick multi-cell-type scan:

results = st.quick_did(
    adata,
    module_scores=gene_sets,
    visits=("Pre", "Post"),
    arm_col="response_harmonized",
    arm_treated="Responder",
    arm_control="Non-responder",
    celltype_col="cell_type",
)

Supported Study Designs & Datasets

sctrial ships with five real clinical trial datasets, accessible via built-in loaders (st.load_*()). Each demonstrates a different study design:

Design	Description	Dataset	Source	Tutorial
Two-arm paired DiD	Pre/post × treatment/control interaction	Sade-Feldman et al., Cell 2018 — melanoma immunotherapy	GSE120575	Immunotherapy
Single-arm pre/post	Paired within-arm contrasts over time	ImmPort GSE171964 — PBMC vaccine response	GSE171964	Vaccine
Single-arm pre/post	Paired within-arm, multi-timepoint	van Galen et al., Cell 2019 — AML chemotherapy	GSE116256	—
Single-arm multi-timepoint	Longitudinal tracking across 4 visits	GSE290722 — CAR-T cell therapy (ZUMA-1)	GSE290722	—
Cross-sectional between-arm	Between-group comparison at one timepoint	Stephenson et al., Nature Medicine 2021 — COVID-19 severity	E-MTAB-10026	COVID-19

Additional tutorial: Scalability Benchmark — performance testing on the Sade-Feldman dataset.

Documentation

Full documentation: https://sctrial.readthedocs.io

• API Reference
• Tutorials
• FAQ

Citation

If you use sctrial in your research, please cite:

Vasanthakumari P, Valencia I, Aghmiouni MR, Magana B, Omar MN. sctrial: Participant-Level Differential Analysis for Longitudinal Single-Cell Experiments. bioRxiv (2026).

@article{vasanthakumari2026sctrial,
  title = {sctrial: Participant-Level Differential Analysis for Longitudinal Single-Cell Experiments},
  author = {Vasanthakumari, Priyanka and Valencia, Itzel and Aghmiouni, Maryam R. and Magana, Bryan and Omar, Mohamed N.},
  journal = {bioRxiv},
  year = {2026},
  url = {https://github.com/TheOmarLab/sctrial}
}

License

MIT License - see LICENSE for details.