# `sceptre`: robust single-cell CRISPR screen analysis

Single-cell CRISPR screens provide unprecedented insights into gene regulation and other facets of human genome biology. However, the analysis of these screens poses significant statistical and computational challenges. `sceptre` (pronounced “scepter”) is a methodology and associated R package for rigorously identifying regulatory relationships in single-cell CRISPR screen experiments. `sceptre` tests whether a given perturbation is associated with the change in expression of a given gene using the robust, powerful, and intuitive conditional randomization test.

Update June 2022: We have released a beta version of the `sceptre` Nextflow pipeline. This pipeline greatly facilitates the application of `sceptre` to large-scale data.

Update March 2022: We are excited to release `sceptre` version 0.1.0, a major update that significantly improves the speed and ease-of-use of the software.

# Installation

You can install the development version of the package from Github with the following command:

    install.packages("devtools")
    devtools::install_github("katsevich-lab/sceptre")

You can browse the source code on Github here. `sceptre` has been tested in R versions >=4.1 on macOS and Linux systems.

# Using the software

`sceptre` has several interfaces, which you can choose between based on the size of your analysis.

Small or moderately-sized analysis: If you are running an analysis of small or moderate size (i.e., the data fit into memory and you are using a single computer), see the standard `sceptre` tutorial here.

Large-scale analysis: If you are running a large-scale analysis (i.e., the data do not easily fit into memory or you are using a high-performance cluster or cloud), see the `sceptre` Nextflow pipeline here. The documentation for the `sceptre` Nextflow pipeline currently is sparse; please open a Github issue if you are interested in using this pipeline, and we will provide support.

Note: `sceptre` currently applies to high multiplicity-of-infection (MOI; >5 gRNAs/cell) single-cell CRISPR screen data. `sceptre` has not yet been carefully vetted in low-MOI settings. We are working on developing such an extension, which we expect to be available in 2022.

# References

Please consider starring this repository and citing the following if you find `sceptre` helpful in your research.

Methods papers

T Barry, X Wang, J Morris, K Roeder, E Katsevich. “SCEPTRE improves calibration and sensitivity in single-cell CRISPR screen analysis.” Genome Biology.

T Barry, E Katsevich, K Roeder. “Exponential family measurement error models for single-cell CRISPR screens.” arXiv preprint.

Application paper

J Morris, Z Daniloski, J Domingo, T Barry, M Ziosi, D Glinos, S Hao, E Mimitou, P Smibert, K Roeder, E Katsevich, T Lappalainen, N Sanjana. “Discovery of target genes and pathways of blood trait loci using pooled CRISPR screens and single cell RNA sequencing.” Preprint available on bioRxiv.

# Funding

We are grateful to Analytics at Wharton for supporting the development of this software.