ArticleNature communications2024
Simulating multiple variability in spatially resolved transcriptomics with scCube.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- DiSCO: deconvoluting spatial transcriptomics via combinatorial optimization with a foundational diffusion model.Briefings in bioinformatics · 2026Article
- BEASTsim-a benchmarking and analysis platform for spatial transcriptomics simulations.Briefings in bioinformatics · 2026Article
- Robust characterization and interpretation of rare pathogenic cell populations from spatial omics using GARDEN.Nature communications · 2026Article
- Spider: a flexible and unified framework for simulating spatial transcriptomics data.Bioinformatics (Oxford, England) · 2026Article
- From features to slice: parameter-cloud modeling of spatial transcriptomics for simulation and 3D interpolatory augmentation.bioRxiv : the preprint server for biology · 2025Article
- Article
- LARIS enables accurate and efficient ligand and receptor interaction analysis in spatial transcriptomics.bioRxiv : the preprint server for biology · 2025Article
- scSpatialSIM: a simulator of spatial single-cell molecular data.SoftwareX · 2025Article
- spVelo: RNA velocity inference for multi-batch spatial transcriptomics data.Genome biology · 2025Article
- POPARI: Modeling multisample variation in spatial transcriptomics.bioRxiv : the preprint server for biology · 2025Article
- Review
- CITEgeist: Cellular Indexing of Transcriptomes and Epitopes for Guided Exploration of Intrinsic Spatial Trends.bioRxiv : the preprint server for biology · 2025Article
- Identification and characterization of cell niches in tissue from spatial omics data at single-cell resolution.Nature communications · 2025Article
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Authors and funding
13 authors.
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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.
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