Evidence map›Paper›PMID 41120648›Full record

ArticleInterdisciplinary sciences, computational life sciences2026

DiTSim: A Diffusion-Transformers Based Single-Cell ATAC-seq Data Simulator.

Shengze Dong, Songming Tang, Ding Liu, Shengquan Chen

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Article in Interdisciplinary sciences, computational life sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

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5 · Who and what money

Authors and funding

4 authors.

Shengze Dong *School of Computer Science and Technology, Tiangong University, Tianjin, 300387, People's Republic of China.
Songming Tang *School of Mathematical Sciences and LPMC, Nankai University, Tianjin, 300071, People's Republic of China.
Ding LiuSchool of Computer Science and Technology, Tiangong University, Tianjin, 300387, People's Republic of China. liuding@tiangong.edu.cn.
Shengquan ChenSchool of Mathematical Sciences and LPMC, Nankai University, Tianjin, 300071, People's Republic of China. chenshengquan@nankai.edu.cn.ORCID http://orcid.org/0000-0002-3503-9306

Funding

the National Natural Science Foundation of China 62203236the National Natural Science Foundation of China 62473212the Science & Technology Development Fund of Tianjin Education Commission for Higher Education 2018KJ217the Tianjin Natural Science Foundation of China 20JCYBJC00500
6 · The paper itself

Abstract

Single-cell assay for transposase-accessible chromatin sequencing (scATAC-seq) allows for deciphering the epigenetic landscape at single-cell resolution. The inaccuracies in annotations and the scarcity of available real datasets hinder the unbiased and comprehensive evaluation of computational methods designed for scATAC-seq data analysis, which underscores the importance of scATAC-seq data simulation methods. Existing scATAC-seq data simulation methods impose strict requirements on the prior distribution of the data and fail to generate simulated data with a consistent manifold structure aligned with real data. In this study, we propose DiTSim, a scATAC-seq data simulation method based on diffusion transformers. DiTSim efficiently fits the global distribution of real scATAC-seq datasets and stably synthesizes samples with known cell type annotations for assessing scATAC-seq data analysis pipelines. Through comprehensive experiments on multiple datasets, DiTSim has demonstrated its outstanding performance in achieving consistency with real data and robustness to datasets with diverse characteristics. Moreover, extensive enrichment analysis demonstrates that DiTSim has the capability to imbue simulated data with biological significance, a critical aspect often overlooked in prior studies.

Indexed as

Chromatin Immunoprecipitation SequencingComputer SimulationSingle-Cell AnalysisSoftwareAlgorithmsAnimalsComputational BiologyDiffusionHumansData simulationDenoising diffusion modelscATAC-seqTransformer

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.