TY - JOUR
T1 - STGBench
T2 - sequencing-level spatial DNA–RNA simulation for multimodal and virtual cell-oriented benchmarking of genomic alterations
AU - Wang, Shenjie
AU - Li, Yuhang
AU - Wang, Xiaonan
AU - Wang, Xuwen
AU - Wang, Tianci
AU - Yang, Shuanying
AU - Wang, Jiayin
N1 - Publisher Copyright:
© The Author(s) 2026. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
PY - 2026/7
Y1 - 2026/7
N2 - Spatially resolved genomics and transcriptomics are reshaping our understanding of tumor evolution and therapeutic resistance, yet benchmarking spatial copy number variation (CNV), single-nucleotide variant (SNV), and spatial mutation-burden proxy analyses is constrained by the scarcity of datasets with known ground truth. Existing simulators often produce only count matrices, lack matched DNA–RNA outputs, or do not propagate genomic variation to sequencing-level signals, limiting end-to-end benchmarking of multi-omics pipelines, including virtual cell-oriented multimodal benchmarks. Here, we present STGBench, a sequencing-level spatial DNA–RNA simulator that generates paired DNA-seq alignments (BAM files) and matched gene expression matrices on a user-defined 2D tissue grid. STGBench builds tissue masks from geometric templates or image-derived masks, overlays spatial CNV landscapes and SNV/VAF fields in boundary, gradient, and nested modes, and synthesizes DNA and RNA readouts by coupling copy number states to expression under a negative binomial model with spatially correlated technical effects; outputs are directly consumable by downstream tools. Using AneuFinder on simulated DNA data, spatial CNV profiles are recovered with Pearson r up to 0.855. Applying InferCNV to simulated transcriptomes, CNV-driven expression signatures are reproduced (r up to 0.996) and support unsupervised structure consistent with clonal organization; DNA-derived and RNA-inferred CNVs show concordance (r ≈ 0.77). CellSNP recovers diverse spatial SNV patterns from simulated reads, and IGV inspection confirms realistic allelic balance and CNV-associated coverage shifts at nucleotide resolution. Collectively, STGBench provides a controllable benchmark generator for spatial CNV/SNV and mutation-burden analyses with explicit ground truth across paired DNA–RNA modalities. STGBench is open source at https://github.com/Icarus200110/STGBench.
AB - Spatially resolved genomics and transcriptomics are reshaping our understanding of tumor evolution and therapeutic resistance, yet benchmarking spatial copy number variation (CNV), single-nucleotide variant (SNV), and spatial mutation-burden proxy analyses is constrained by the scarcity of datasets with known ground truth. Existing simulators often produce only count matrices, lack matched DNA–RNA outputs, or do not propagate genomic variation to sequencing-level signals, limiting end-to-end benchmarking of multi-omics pipelines, including virtual cell-oriented multimodal benchmarks. Here, we present STGBench, a sequencing-level spatial DNA–RNA simulator that generates paired DNA-seq alignments (BAM files) and matched gene expression matrices on a user-defined 2D tissue grid. STGBench builds tissue masks from geometric templates or image-derived masks, overlays spatial CNV landscapes and SNV/VAF fields in boundary, gradient, and nested modes, and synthesizes DNA and RNA readouts by coupling copy number states to expression under a negative binomial model with spatially correlated technical effects; outputs are directly consumable by downstream tools. Using AneuFinder on simulated DNA data, spatial CNV profiles are recovered with Pearson r up to 0.855. Applying InferCNV to simulated transcriptomes, CNV-driven expression signatures are reproduced (r up to 0.996) and support unsupervised structure consistent with clonal organization; DNA-derived and RNA-inferred CNVs show concordance (r ≈ 0.77). CellSNP recovers diverse spatial SNV patterns from simulated reads, and IGV inspection confirms realistic allelic balance and CNV-associated coverage shifts at nucleotide resolution. Collectively, STGBench provides a controllable benchmark generator for spatial CNV/SNV and mutation-burden analyses with explicit ground truth across paired DNA–RNA modalities. STGBench is open source at https://github.com/Icarus200110/STGBench.
KW - multimodal benchmarking
KW - probabilistic generative model
KW - sequencing-level simulation
KW - spatial clonal heterogeneity
KW - tumor mutation burden
UR - https://www.scopus.com/pages/publications/105044251393
U2 - 10.1093/bib/bbag354
DO - 10.1093/bib/bbag354
M3 - 文章
AN - SCOPUS:105044251393
SN - 1467-5463
VL - 27
JO - Briefings in Bioinformatics
JF - Briefings in Bioinformatics
IS - 4
M1 - bbag354
ER -