sctrial — Design-Aware Participant-Level Inference for Longitudinal Single-Cell Studies#

Tests Python License

sctrial is a Python package for participant-level differential analysis of longitudinal single-cell RNA-seq data. Built on AnnData, it aggregates cell-level measurements to participant-level replicates and applies design-specific statistical methods — difference-in-differences, paired contrasts, and cross-sectional comparisons — so that inference reflects the actual number of independent biological units in the study.

sctrial overview — from scRNA-seq input through trial-aware analysis to statistical outputs

Key Applications#

  • Difference-in-Differences (DiD) — Participant fixed-effects regression with wild cluster bootstrap inference for treatment effect estimation across arms and timepoints.

  • Paired within-arm contrasts — Pre→post comparisons with paired statistical tests and effect sizes (Cohen’s d, Hedges’ g, log₂ fold-change).

  • Between-arm comparisons — Cross-sectional tests between treatment and control arms at fixed timepoints with pseudobulk replication.

  • Cell-type abundance — Composition changes across conditions using proportion-based statistics and participant-level aggregation.

  • Gene Set Enrichment Analysis — GSEA on DiD-ranked gene lists with support for Hallmark, KEGG, Reactome, and custom gene set collections.

  • Power analysis — Sample size calculations, power curves, and minimum detectable effect sizes for two-arm DiD and single-arm paired designs.

  • Effect size estimation — Confidence intervals on all key estimands.

Getting Started#

Install sctrial and run your first analysis:

pip install sctrial

See the Installation guide for optional extras (plotting, GSEA, Bayesian modules), then follow the Quickstart for a complete walkthrough. For end-to-end analyses on real clinical datasets, explore the Tutorials.

Citing sctrial#

If you use sctrial in your research, please cite:

Vasanthakumari P, Valencia I, Aghmiouni MR, Magana B, Omar M. sctrial: Participant-Level Differential Analysis for Longitudinal Single-Cell Experiments. bioRxiv 2026. doi:10.64898/2026.04.02.716219.

@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},
  journal = {bioRxiv},
  year = {2026},
  doi = {10.64898/2026.04.02.716219},
  url = {https://www.biorxiv.org/content/10.64898/2026.04.02.716219v1}
}