Simulating multiple variability in spatially resolved transcriptomics with scCube

Jingyang Qian, Hudong Bao, Xin Shao, Yin Fang, Jie Liao, Zhuo Chen, Chengyu Li, Wenbo Guo, Yining Hu, Anyao Li, Yue Yao, Xiaohui Fan () and Yiyu Cheng ()
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Jingyang Qian: Zhejiang University
Hudong Bao: Zhejiang University
Xin Shao: Zhejiang University
Yin Fang: Zhejiang University
Jie Liao: Zhejiang University
Zhuo Chen: Zhejiang University
Chengyu Li: Zhejiang University
Wenbo Guo: Zhejiang University
Yining Hu: Zhejiang University
Anyao Li: Zhejiang University
Yue Yao: Zhejiang University
Xiaohui Fan: Zhejiang University
Yiyu Cheng: Zhejiang University

Nature Communications, 2024, vol. 15, issue 1, 1-21

Abstract: Abstract A pressing challenge in spatially resolved transcriptomics (SRT) is to benchmark the computational methods. A widely-used approach involves utilizing simulated data. However, biases exist in terms of the currently available simulated SRT data, which seriously affects the accuracy of method evaluation and validation. Herein, we present scCube ( https://github.com/ZJUFanLab/scCube ), a Python package for independent, reproducible, and technology-diverse simulation of SRT data. scCube not only enables the preservation of spatial expression patterns of genes in reference-based simulations, but also generates simulated data with different spatial variability (covering the spatial pattern type, the resolution, the spot arrangement, the targeted gene type, and the tissue slice dimension, etc.) in reference-free simulations. We comprehensively benchmark scCube with existing single-cell or SRT simulators, and demonstrate the utility of scCube in benchmarking spot deconvolution, gene imputation, and resolution enhancement methods in detail through three applications.

Date: 2024
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DOI: 10.1038/s41467-024-49445-0

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