Star Protocols: Protocol to benchmark gene expression signature scoring techniques for single-cell RNA sequencing data in cancer (Zheng Lab)

STAR Protocols

Protocol for benchmarking signature scoring techniques in scRNAseq data analysis
Comparing single-cell and bulk-based approaches in sensitivity and specificity
Using down sampling to simulate the impact of dropouts on signature scoring techniques


Scoring gene signatures are 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 downsampling. The protocol provides a framework for benchmarking scRNAseq algorithms in such a context.

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Article Categories: Research Paper