scRNA-seq data returns individual molecular reads for each cell representing the expression of each gene in each cell. However, transcript abundances at the individual gene level can be hard to interpret. Another confounding factor with these readouts is the high sparsity of the data. This sparsity acutely affects genes with low mRNA abundance (Figure 1) Mereu et al. (2020) . Transcription factors (TFs) are key players involved in regulating the present and future cell states by binding to regulatory regions in the DNA and driving gene expression programs Baskar et al. (2022). Therefore, they are tightly regulated and are often found at low abundances due to their powerful effects on the cells. Hence, being able to quantify the activity of TFs in a cell can provide very valuable information when characterizing the biological processes underlying a cell type or state. However, due to their low expression they severely suffer from dropout events and their mRNA abundance can’t be accurately quantified by looking at the number of UMIs. To address this issue methods have been developed to quantify their activities by leveraging the expression of the genes they regulate.

Gene signatures are commonly used in routine single cell analysis. Many methods exists but they are not all created equally. In this tutorial we are going to go follow a recent benchmarking paper @badia-i-mompel2022 and follow their guidelines on best practices when scoring gene signatures!