sceptre implements an analysis pipeline for single-cell CRISPR screen (i.e., Perturb-seq) data, including data import, gRNA-to-cell assignment, quality control, and testing association between CRISPR perturbations and changes in gene expression. It is built on three principles:
- Statistical rigor. Association testing is built on a principled resampling-based methodology. Built-in calibration and power checks let you verify that false positives are controlled and that real effects are detectable — on your own data.
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Massive scalability. Optimized C++ routines improve performance. Optional disk-backed data structures allow the analysis of datasets too large to fit in memory. The companion
sceptreNextflow pipeline distributes analyses across hundreds of processors on a cluster or cloud. - Ease of use. A small, pipe-friendly set of functions takes you from raw count matrices to results in a handful of steps.
Installation
Install the development version of sceptre from GitHub:
# install.packages("remotes")
remotes::install_github("Katsevich-Lab/sceptre")Documentation
New to sceptre? Get started offers a brief tour of the pipeline, with plots and explanation at each step. Other resources are the function reference and the comprehensive sceptre manual.
Project roadmap and community
sceptre is maintained as an open-source research-software project. The current roadmap describes the package’s technical and sustainability priorities. Please read the contribution guide, governance model, and Code of Conduct before contributing. The support guide explains where to ask questions, report bugs, and request features.
Featured publications
- Barry et al., 2024. “Robust differential expression testing…”. Genome Biology.
- Barry et al., 2024. “Exponential family measurement error models…”. Biostatistics.
- Morris et al., 2023. “Discovery of target genes and pathways…”. Science.
- Barry et al., 2021. “SCEPTRE improves calibration and sensitivity…”. Genome Biology.
sceptre is also featured in a 10x Genomics analysis guide.