scDesign2: a transparent simulator that generates high-fidelity single-cell gene expression count data with gene correlations captured

A pressing challenge in single-cell transcriptomics is to benchmark experimental protocols and computational methods. A solution is to use computational simulators, but existing simulators cannot simultaneously achieve three goals: preserving genes, capturing gene correlations, and generating any number of cells with varying sequencing depths. To fill this gap, we propose scDesign2, a transparent simulator that achieves all three goals and generates high-fidelity synthetic data for multiple single-cell gene expression count-based technologies. In particular, scDesign2 is advantageous in its transparent use of probabilistic models and its ability to capture gene correlations via copulas.

• Publication year

  2021

• Venue

  Genome Biology

• Publication date

  2021-05-25

• Fields of study

  Biology, Medicine, Computer Science

• Identifiers
  DOI 10.1186/s13059-021-02367-2PMID 34034771PMCID 8147071
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

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