Haoran Zhu

Feb 16, 2025 | nature.com | Yi Fan |Zhuohan Yu |Yanchi Su |Haoran Zhu |Fuzhou Wang |Xingjian Chen | +5 more

Single-cell ATAC-seq technology advances our understanding of single-cell heterogeneity in gene regulation by enabling exploration of epigenetic landscapes and regulatory elements. However, low sequencing depth per cell leads to data sparsity and high dimensionality, limiting the characterization of gene regulatory elements. Here, we develop scAGDE, a single-cell chromatin accessibility model-based deep graph representation learning method that simultaneously learns representation and clustering through explicit modeling of data generation. Our evaluations demonstrated that scAGDE outperforms existing methods in cell segregation, key marker identification, and visualization across diverse datasets while mitigating dropout events and unveiling hidden chromatin-accessible regions. We find that scAGDE preferentially identifies enhancer-like regions and elucidates complex regulatory landscapes, pinpointing putative enhancers regulating the constitutive expression of CTLA4 and the transcriptional dynamics of CD8A in immune cells. When applied to human brain tissue, scAGDE successfully annotated cis-regulatory element-specified cell types and revealed functional diversity and regulatory mechanisms of glutamatergic neurons. Single-cell ATAC-seq reveals gene regulation at individual cell levels but struggles with data sparsity. Here, authors introduce scAGDE, a deep graph learning framework that improves cell embedding and clustering, outperforming existing methods and uncovering key regulatory mechanisms.

Nov 13, 2024 | onlinelibrary.wiley.com | Qiang Wu |Yutang Li |Haoran Zhu |Lu Zhang

Conflict of Interest The authors declare no conflict of interest. References 1 State Administration for Market Regulation, Valve steel and superalloy bars for internal combustion engines, S. GB/T 12773-2021, 2021. 2 , , , , , , Arch. Metall. Mater. 2021, 66, 145. 3 , , , , J. Mater. Res. Technol. 2019, 8, 2011. 4 , , Mater. Des. 2011, 32, 2429. 5 , , , , , , Materials 2019, 12, 1893. 6 , , , , , , Metalurgija 2019, 58, 83. 7 , , , , , , Ironmaking Steelmaking 2019, 47, 51.

Aug 7, 2024 | nature.com | Haoran Zhu |Masako Narita |Sarah Gough |Lars Zender |Sarah Aitken |Matthew Hoare | +2 more

AbstractOncogenic RAS-induced senescence (OIS) is an autonomous tumour suppressor mechanism associated with premalignancy1,2. Achieving this phenotype typically requires a high level of oncogenic stress, yet the phenotype provoked by lower oncogenic dosage remains unclear. Here we develop oncogenic RAS dose-escalation models in vitro and in vivo, revealing a RAS dose-driven non-linear continuum of downstream phenotypes.

May 6, 2024 | arxiv.org | Haoran Zhu

arXiv:2405.03215 (cs) View PDF Subjects: Distributed, Parallel, and Cluster Computing (cs.DC) Cite as: arXiv:2405.03215 [cs.DC] (or arXiv:2405.03215v1 [cs.DC] for this version) Submission history From: Haoran Zhu [ view email] [v1] Mon, 6 May 2024 07:26:32 UTC (21 KB) Bibliographic Tools Bibliographic Explorer Toggle Bibliographic Explorer () Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Links to Code Toggle...

Apr 11, 2024 | mdpi.com | Zhuo Han |Lihui Ma |Xiaowei Li |Haoran Zhu

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