About

Jichun Xie is an Associate Professor of Biostatistics & Bioinformatics and an Associate Professor of Computer Science at Duke University. He is a member of the Duke Cancer Institute and is affiliated with the Division of Integrative Genomics and the Duke Center for Statistical Genetics and Genomics. His research focuses on biostatistics, bioinformatics, and computational biology, contributing to the advancement of statistical methods and their applications in genomics and biomedical research.

Research topics

• Genetics
• Biology
• Medicine
• Immunology
• Gerontology
• Evolutionary biology
• Cancer research

Selected publications

• A transcriptomics-native foundation model for universal cell representation and virtual cell synthesis

  bioRxiv (Cold Spring Harbor Laboratory) · 2026-04-14

  articleOpen accessSenior authorCorresponding

  Abstract Current single-cell foundation models rely on language-model architectures that ignore transcriptomic data distributions, often underperforming specialized methods. We introduce xVERSE, a transcriptomics-native foundation model coupling batch-invariant representation learning with the probabilistic generation of expression profiles. xVERSE outperforms the leading foundation and batch-effect correction methods in representation learning by 17.9% and 11.4% , respectively, successfully preserving biological heterogeneity while diminishing batch effects. Furthermore, xVERSE surpasses the second-best spatial imputation method by 34.3% and uniquely synthesizes virtual cells indistinguishable from biological data (AUROC ≈ 0.5 ). As a powerful data-augmentation engine, xVERSE utilizes these high-fidelity virtual cells to enable accurate clustering and marker detection in tiny datasets—resolving rare cell types with as few as four cells—while improving the generalizability of cross-modality predictions across diverse pathological states. These results establish xVERSE as a transformative framework unlocking analytical capabilities beyond conventional models.

  PublisherOA PDFDOI

• Advancing biological understanding of cellular senescence with computational multiomics

  Nature Genetics · 2025-09-15 · 22 citations

  reviewOpen access
  PublisherOA PDFDOI

• Single-cell and spatial detection of senescent cells using DeepScence

  Cell Genomics · 2025-10-07 · 3 citations

  articleOpen access

  Accurately identifying senescent cells is essential for studying their spatial and molecular features. We developed DeepScence, a method based on deep neural networks, to identify senescent cells in single-cell and spatial transcriptomics data. DeepScence is based on CoreScence, a senescence-associated gene set we curated that incorporates information from multiple published gene sets. We demonstrate that DeepScence can accurately identify senescent cells in single-cell gene expression data collected both in vitro and in vivo, as well as in spatial transcriptomics data generated by different platforms, substantially outperforming existing methods.

  PublisherDOI

• A Spatial Multi-Omic Framework Identifies Gliomas Permissive to TIL Expansion

  Research Square · 2025-04-25 · 1 citations

  preprintOpen access
  PublisherOA PDFDOI

• DART2: a robust multiple testing method to smartly leverage helpful or misleading ancillary information

  arXiv (Cornell University) · 2024-09-05

  preprintOpen accessSenior author

  In many applications of multiple testing, ancillary information is available, reflecting the hypothesis null or alternative status. Several methods have been developed to leverage this ancillary information to enhance testing power, typically requiring the ancillary information is helpful enough to ensure favorable performance. In this paper, we develop a robust and effective distance-assisted multiple testing procedure named DART2, designed to be powerful and robust regardless of the quality of ancillary information. When the ancillary information is helpful, DART2 can asymptotically control FDR while improving power; otherwise, DART2 can still control FDR and maintain power at least as high as ignoring the ancillary information. We demonstrated DART2's superior performance compared to existing methods through numerical studies under various settings. In addition, DART2 has been applied to a gene association study where we have shown its superior accuracy and robustness under two different types of ancillary information.

  PublisherOA PDFDOI

• B-Lightning: using bait genes for marker gene hunting in single-cell data with complex heterogeneity

  Briefings in Bioinformatics · 2024-11-22

  articleOpen accessSenior author

  In single-cell studies, cells can be characterized with multiple sources of heterogeneity (SOH) such as cell type, developmental stage, cell cycle phase, activation state, and so on. In some studies, many nuisance SOH are of no interest, but may confound the identification of the SOH of interest, and thus affect the accurate annotate the corresponding cell subpopulations. In this paper, we develop B-Lightning, a novel and robust method designed to identify marker genes and cell subpopulations corresponding to an SOH (e.g. cell activation status), isolating it from other SOH (e.g. cell type, cell cycle phase). B-Lightning uses an iterative approach to enrich a small set of trustworthy marker genes to more reliable marker genes and boost the signals of the SOH of interest. Multiple numerical and experimental studies showed that B-Lightning outperforms existing methods in terms of sensitivity and robustness in identifying marker genes. Moreover, it increases the power to differentiate cell subpopulations of interest from other heterogeneous cohorts. B-Lightning successfully identified new senescence markers in ciliated cells from human idiopathic pulmonary fibrosis lung tissues, new T-cell memory and effector markers in the context of SARS-COV-2 infections, and their synchronized patterns that were previously neglected, new AD markers that can better differentiate AD severity, and new dendritic cell functioning markers with differential transcriptomics profiles across breast cancer subtypes. This paper highlights B-Lightning's potential as a powerful tool for single-cell data analysis, particularly in complex data sets where SOH of interest are entangled with numerous nuisance factors.

  PublisherOA PDFDOI

• Single-Cell and Spatial Detection of Senescent Cells Using DeepScence

  SSRN Electronic Journal · 2024-01-01 · 6 citations

  preprintOpen access
  PublisherDOI

• Marker gene fishing for single-cell data with complex heterogeneity

  bioRxiv (Cold Spring Harbor Laboratory) · 2024-11-06

  preprintOpen accessSenior authorCorresponding

  In single-cell studies, cells can be characterized with multiple sources of heterogeneity such as cell type, developmental stage, cell cycle phase, activation state, and so on. In some studies, many nuisance sources of heterogeneity (SOH) are of no interest, but may confound the identification of the SOH of interest, and thus affect the accurate annotate the corresponding cell subpopulations. In this paper, we develop B-Lightning, a novel and robust method designed to identify marker genes and cell subpopulations correponding to a SOH (e.g., cell activation status), isolating it from other sources of heterogeneity (e.g., cell type, cell cycle phase). B-Lightning uses an iterative approach to enrich a small set of trustworthy marker genes to more reliable marker genes and boost the signals of the SOH of interest. Multiple numerical and experimental studies showed that B-Lightning outperforms existing methods in terms of sensitivity and robustness in identifying marker genes. Moreover, it increases the power to differentiate cell subpopulations of interest from other heterogeneous cohorts. B-Lightning successfully identified new senescence markers in ciliated cells from human idiopathic pulmonary fibrosis (IPF) lung tissues, new T cell memory and effector markers in the context of SARS-COV-2 infections, and their synchronized patterns which were previously neglected. This paper highlights B-Lightning's potential as a powerful tool for single-cell data analysis, particularly in complex data sets where sources of heterogeneity of interest are entangled with numerous nuisance factors.

  PublisherOA PDFDOI

• SifiNet: a robust and accurate method to identify feature gene sets and annotate cells

  Nucleic Acids Research · 2024-04-14 · 5 citations

  articleOpen accessSenior author

  SifiNet is a robust and accurate computational pipeline for identifying distinct gene sets, extracting and annotating cellular subpopulations, and elucidating intrinsic relationships among these subpopulations. Uniquely, SifiNet bypasses the cell clustering stage, commonly integrated into other cellular annotation pipelines, thereby circumventing potential inaccuracies in clustering that may compromise subsequent analyses. Consequently, SifiNet has demonstrated superior performance in multiple experimental datasets compared with other state-of-the-art methods. SifiNet can analyze both single-cell RNA and ATAC sequencing data, thereby rendering comprehensive multi-omic cellular profiles. It is conveniently available as an open-source R package.

  PublisherOA PDFDOI

• Immune Phenotype and Postoperative Complications After Elective Surgery

  Annals of Surgery · 2023-04-13 · 5 citations

  article

  OBJECTIVES: To characterize and quantify accumulating immunologic alterations, pre and postoperatively in patients undergoing elective surgical procedures. BACKGROUND: Elective surgery is an anticipatable, controlled human injury. Although the human response to injury is generally stereotyped, individual variability exists. This makes surgical outcomes less predictable, even after standardized procedures, and may provoke complications in patients unable to compensate for their injury. One potential source of variation is found in immune cell maturation, with phenotypic changes dependent on an individual's unique, lifelong response to environmental antigens. METHODS: We enrolled 248 patients in a prospective trial facilitating comprehensive biospecimen and clinical data collection in patients scheduled to undergo elective surgery. Peripheral blood was collected preoperatively, and immediately on return to the postanesthesia care unit. Postoperative complications that occurred within 30 days after surgery were captured. RESULTS: As this was an elective surgical cohort, outcomes were generally favorable. With a median follow-up of 6 months, the overall survival at 30 days was 100%. However, 20.5% of the cohort experienced a postoperative complication (infection, readmission, or system dysfunction). We identified substantial heterogeneity of immune senescence and terminal differentiation phenotypes in surgical patients. More importantly, phenotypes indicating increased T-cell maturation and senescence were associated with postoperative complications and were evident preoperatively. CONCLUSIONS: The baseline immune repertoire may define an immune signature of resilience to surgical injury and help predict risk for surgical complications.

  PublisherDOI

Recent grants

• New computational methods to dynamically pinpointing the subregions carrying disease-associated rare variants

  NIH · $1.6M · 2022–2026

Frequent coauthors

• Siyun Yang

  82 shared

• Kouros Owzar

  Duke University

  70 shared

• Shivanand P. Lad

  Duke University Hospital

  67 shared

• Aladine A. Elsamadicy

  Yale University

  51 shared

• Xinru Ren

  44 shared

• Beth Parente

  42 shared

• Amanda R. Sergesketter

  Duke Medical Center

  40 shared

• Promila Pagadala

  Duke University

  37 shared

Education

• PHD, Department of Biostatistics and Epidemiology at Perelman School of Medicine

  University of Pennsylvania

  2011

• BS, Department of Probability and Statistics at School of Mathematics

  Peking University

  2006