Author

Zixiao Lu

Southern Medical UniversityORCID 0000-0003-0809-8703Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Research Focus:cell type annotation· 3 Research Focus:cell-cell communication· 2 Research Focus:clustering· 2 Approach:deep learning· 2 Approach:statistical model· 2 Assay:ATAC-seq· 1 Research Focus:batch correction / integration· 1 Assay:bulk RNA-seq· 1

Frequent Co-Authors

Jie ZhangiD· 2 shared Kun HuangiD· 2 shared Siwen XuiD· 2 shared Wei ShaoiD· 2 shared Bryan R. HelmiD· 1 shared Chao Zhang· 1 shared Dong NiiD· 1 shared Jin YangiD· 1 shared

Activity by Year

2019-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 19 mutual co-author links

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2 shared papersBioinformatics

Jie ZhangORCID

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4 papers

Recent·Most cited

Spatialsnake: a flexible workflow for spatial transcriptomics analysis across platforms

Zhenghao Lin, Zhihao Zhang, Nan Huang, Zixiao Lu, Siwen Xu

Spatialsnake provides a flexible workflow for spatial transcriptomics analysis across multiple platforms, enabling analysis of selected populations and tissue regions with adjustable parameters.

bioRxiv · Oct 29, 2026 · PreprintBioinformatics

Linkage: an interactive shiny app and R package for linking of DNA regulatory peaks to genes

Zenghui Liu, Shaodong Chen, Tianting Li, Chao Zhang, Yuyan Luo, Junxi Zheng, Zixiao Lu, Jin Yang, Siwen Xu

Linkage is an R Shiny app and package that links ATAC-seq peaks to genes to identify cis-regulatory elements and transcription factor networks.

bioRxiv · Apr 24, 2024 · PreprintBioinformatics

A deep learning approach for tissue spatial quantification and genomic correlations of histopathological images

Zixiao Lu, Xiaohui Zhan, Yi Wu, Jun Cheng, Wei Shao, Dong Ni, Zhi Han, Jie Zhang, Qianjin Feng, Kun Huang

The paper develops a CNN model to classify epithelial and stromal tissues in histopathological images and correlates these with gene expression data to identify biological processes.

Genomics Proteomics & Bioinformatics · Jul 17, 2021Peer-reviewedBioinformatics

BERMUDA: A novel deep transfer learning method for single-cell RNA sequencing batch correction reveals hidden high-resolution cellular subtypes

Tongxin Wang, Travis S. Johnson, Wei Shao, Zixiao Lu, Bryan R. Helm, Jie Zhang, Kun Huang

BERMUDA corrects batch effects in scRNA-seq data and reveals hidden cellular subtypes by transferring information across batches.

Genome biology · Aug 12, 2019Peer-reviewedBioinformatics