scDesign3 generates realistic in silico data for multimodal single-cell and spatial omics

Dongyuan Song; Qingyang Wang; Guanao Yan; Tianyang Liu; Tianyi Sun; Jingyi Jessica Li

doi:10.1038/s41587-023-01772-1

scDesign3 generates realistic in silico data for multimodal single-cell and spatial omics

Nat Biotechnol. 2024 Feb;42(2):247-252. doi: 10.1038/s41587-023-01772-1. Epub 2023 May 11.

Authors

Dongyuan Song¹, Qingyang Wang², Guanao Yan², Tianyang Liu², Tianyi Sun², Jingyi Jessica Li^{3

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Affiliations

¹ Bioinformatics Interdepartmental Ph.D. Program, University of California, Los Angeles, CA, USA.
² Department of Statistics, University of California, Los Angeles, CA, USA.
³ Bioinformatics Interdepartmental Ph.D. Program, University of California, Los Angeles, CA, USA. jli@stat.ucla.edu.
⁴ Department of Statistics, University of California, Los Angeles, CA, USA. jli@stat.ucla.edu.
⁵ Department of Human Genetics, University of California, Los Angeles, CA, USA. jli@stat.ucla.edu.
⁶ Department of Computational Medicine, University of California, Los Angeles, CA, USA. jli@stat.ucla.edu.
⁷ Department of Biostatistics, University of California, Los Angeles, CA, USA. jli@stat.ucla.edu.
⁸ Radcliffe Institute for Advanced Study, Harvard University, Cambridge, MA, USA. jli@stat.ucla.edu.

PMID: 37169966
DOI: 10.1038/s41587-023-01772-1

Abstract

We present a statistical simulator, scDesign3, to generate realistic single-cell and spatial omics data, including various cell states, experimental designs and feature modalities, by learning interpretable parameters from real data. Using a unified probabilistic model for single-cell and spatial omics data, scDesign3 infers biologically meaningful parameters; assesses the goodness-of-fit of inferred cell clusters, trajectories and spatial locations; and generates in silico negative and positive controls for benchmarking computational tools.

MeSH terms

Benchmarking*
Models, Statistical*
Research Design

Abstract

MeSH terms

Grants and funding