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

Nighat Noureen, Xiaojing Wang, Siyuan Zheng

Research output: Contribution to journalArticlepeer-review

Abstract

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

Original languageEnglish (US)
Article number101877
JournalSTAR Protocols
Volume3
Issue number4
DOIs
StatePublished - Dec 16 2022

Keywords

  • Bioinformatics
  • Cancer
  • RNAseq

ASJC Scopus subject areas

  • General Immunology and Microbiology
  • General Biochemistry, Genetics and Molecular Biology
  • General Neuroscience

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