Abstract
Motivation Pooled single cell CRISPR screens have emerged as a powerful tool in functional genomics to probe the effect of genetic interventions at scale. A crucial step in the analysis of the resulting data is the assignment of cells to gRNAs corresponding to a specific genetic intervention. However, this step is challenging due to a lack of systematic benchmarks and accessible software to apply and compare different guide assignment strategies. To address this, we here propose crispat (CRISPR guide assignment tool), a Python package to facilitate the choice of a suitable guide assignment strategy for single cell CRISPR screens.
Results We demonstrate the package on four single cell CRISPR interference screens at low multiplicity of infection from two studies, where crispat identifies strong differences in the number of assigned cells, downregulation of the target genes and number of discoveries across different guide assignment strategies, highlighting the need for a suitable guide assignment strategy to obtain optimal power in single cell CRISPR screens.
Availability and Implementation crispat is implemented in python and the source code, installation instructions and tutorials can be found at https://github.com/velten-group/crispat. Code to reproduce all findings in this paper is available at https://github.com/velten-group/crispat_analysis.
Contact Britta Velten, britta.velten{at}cos.uni-heidelberg.de
Supplementary Information Supplementary Information can be found in the Supplementary Information file.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
Extended the manuscript text to include a more detailed description and discussion of our findings, as well as some minor updates in the analyses.
