Protocol to benchmark gene expression signature scoring techniques for single-cell RNA sequencing data in cancer

Scoring gene signatures is common for bulk and single-cell RNA sequencing (scRNAseq) data. Here, using cancer as a data model, we describe steps to benchmark signature scoring techniques for scRNAseq data in the context of uneven gene dropouts. These steps include identifying and comparing deregulated signatures, generating gold standard signatures for specificity and sensitivity tests, and simulating the impact of dropouts using down sampling. The protocol provides a framework for benchmarking scRNAseq algorithms in such context. For complete details on the use and execution of this protocol, please refer to Noureen et al. (2022).¹