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 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. Please download the latest version of
sceptre (see below) and check the updated tutorial and news page for
further details.
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 >=3.5 on macOS and Linux systems.
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 fit into memory or you are using a high-performance cluster or cloud), see the at-scale tutorial here. (Currently under construction; will be available soon.)
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.
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.
We are grateful to Analytics at Wharton for supporting the development of this software.
