Author

Duanchen Sun

Oregon Health & Science UniversityORCID 0000-0002-2802-6347Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 2 Genomics· 1

Signature Assays, Organisms & Methods

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Approach:deep learning· 2 Research Focus:differential expression· 2 Organism:human· 2 Approach:statistical model· 2 Research Focus:cell type annotation· 1 Research Focus:cell-cell communication· 1 Research Focus:clustering· 1 Assay:DNA methylation· 1

Frequent Co-Authors

Andrew AdeyiD· 2 shared Zheng XiaiD· 2 shared Alexey DaniloviD· 1 shared Amy E. MoraniD· 1 shared Andrew J. FieldsiD· 1 shared Brian J. O’RoakiD· 1 shared Cole TrapnelliD· 1 shared David Z. QianiD· 1 shared

Activity by Year

2018-2022 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 30 mutual co-author links

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2 shared papersBioinformatics

Andrew AdeyORCID

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3 papers

Recent·Most cited

Single-Cell ATAC-seq analysis via Network Refinement with peaks location information

Jiating Yu, Duanchen Sun, Zhichao Hou, Ling‐Yun Wu

SCARP improves scATAC-seq analysis by incorporating peak location information to better characterize co-accessibility and cell heterogeneity.

bioRxiv · Nov 18, 2022 · PreprintBioinformatics

Phenotype-guided subpopulation identification from single-cell sequencing data

Duanchen Sun, Xiangnan Guan, Amy E. Moran, David Z. Qian, Pepper S. Schedin, Mu‐Shui Dai, Alexey Danilov, Joshi Alumkal, Andrew Adey, Paul T. Spellman, Zheng Xia

Scissor identifies cell subpopulations from single-cell data correlated with specific phenotypes using clinical information, revealing biologically relevant subpopulations in lung and melanoma cancers.

bioRxiv · Jun 5, 2020 · PreprintBioinformatics

Scalable and efficient single-cell DNA methylation sequencing by combinatorial indexing.

Ryan M. Mulqueen, Dmitry Pokholok, Steven J Norberg, Kristof A. Torkenczy, Andrew J. Fields, Duanchen Sun, John R. Sinnamon, Jay Shendure, Cole Trapnell, Brian J. O’Roak, Zheng Xia, Frank J. Steemers, Andrew Adey

The paper introduces sci-MET, a scalable single-cell DNA methylation sequencing method, achieving 69% read alignment rates and successfully deconvolving a mixture of three human cell lines.

Nature Biotechnology · Apr 9, 2018Peer-reviewedGenomics