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, the source code, installation instructions and tutorials can be found at https://github.com/velten-group/crispat and it can be installed from PyPI (https://pypi.org/project/crispat/). Code to reproduce all findings in this paper is available at https://github.com/velten-group/crispat_analysis, as well as at https://zenodo.org/records/13373265.

MOTIVATION: Factor analysis is a widely used tool for unsupervised dimensionality reduction of high-throughput datasets in molecular biology, with recently proposed extensions designed specifically for spatial transcriptomics data. However, these methods expect (count) matrices as data input and are therefore not directly applicable to single molecule resolution data, which are in the form of coordinate lists annotated with genes and provide insight into subcellular spatial expression patterns. To address this, we here propose FISHFactor, a probabilistic factor model that combines the benefits of spatial, non-negative factor analysis with a Poisson point process likelihood to explicitly model and account for the nature of single molecule resolution data. In addition, FISHFactor shares information across a potentially large number of cells in a common weight matrix, allowing consistent interpretation of factors across cells and yielding improved latent variable estimates. RESULTS: We compare FISHFactor to existing methods that rely on aggregating information through spatial binning and cannot combine information from multiple cells and show that our method leads to more accurate results on simulated data. We show that our method is scalable and can be readily applied to large datasets. Finally, we demonstrate on a real dataset that FISHFactor is able to identify major subcellular expression patterns and spatial gene clusters in a data-driven manner. AVAILABILITY AND IMPLEMENTATION: The model implementation, data simulation and experiment scripts are available under https://www.github.com/bioFAM/FISHFactor.