Author

Xiaoxia Liu

Stanford UniversityORCID 0000-0002-7693-4665Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Organism:human· 2 Approach:statistical model· 2 Research Focus:benchmarking· 1 Research Focus:differential expression· 1 Research Focus:fine-mapping· 1 Research Focus:GWAS / association mapping· 1 Assay:GWAS summary statistics· 1 Assay:scRNA-seq· 1

Frequent Co-Authors

Chiara SabattiiD· 2 shared Emmanuel J. CandèsiD· 2 shared Hua TangiD· 2 shared Iuliana Ionita‐LazaiD· 2 shared Linxi LiuiD· 2 shared Zihuai HeiD· 2 shared Aaron SossiniD· 1 shared Benjamin B. ChuiD· 1 shared

Activity by Year

2021-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 42 mutual co-author links

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2 shared papersBioinformatics

Chiara SabattiORCID

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2 papers

Recent·Most cited

Uncovering Heterogeneous Effects via Localized Feature Selection

Xiaoxia Liu, Jiaqi Gu, Zhaomeng Chen, Benjamin B. Chu, Linxi Liu, Tim Morrison, Robert R. Butler, Jacob L Edelson, Jinzhou Li, Frank M. Longo, Hua Tang, Iuliana Ionita‐Laza, Chiara Sabatti, Emmanuel J. Candès, Zihuai He

This paper introduces a framework for identifying disease-associated features tailored to individual profiles using localized test statistics, applied to single-cell RNA sequencing data for Alzheimer's disease.

Proceedings of the National Academy of Sciences · Sep 15, 2026Peer-reviewedBioinformatics

Summary statistics knockoff inference empowers identification of putative causal variants in genome-wide association studies

Zihuai He, Linxi Liu, Michaël E. Belloy, Yann Le Guen, Aaron Sossin, Xiaoxia Liu, Xinran Qi, Shiyang Ma, Tony Wyss‐Coray, Hua Tang, Chiara Sabatti, Emmanuel J. Candès, Michael D. Greicius, Iuliana Ionita‐Laza

This paper introduces GhostKnockoff, a method to improve causal variant identification in GWAS by increasing statistical power and reducing proxy variants.

bioRxiv · Dec 6, 2021 · PreprintGenetics