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.
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.
Bug reports, feature requests, and software questions
For bug reports, please open a GitHub issue. For questions about sceptre functionality, documentation, or how to apply it to your data, please start a discussion under Q&A.