Author

Xinru Qiu

University of California, RiversideORCID 0000-0003-1391-252XUnclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 2 Immunology· 1

Signature Assays, Organisms & Methods

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Organism:human· 3 Research Focus:cell type annotation· 2 Research Focus:differential expression· 2 Research Focus:cell-cell communication· 1 Approach:experimental assay· 1 Research Focus:gene regulatory network inference· 1 Approach:machine learning (classical)· 1 Assay:protein structure· 1

Frequent Co-Authors

Adam GodzikiD· 3 shared Lukasz Jaroszewski· 2 shared Abraham Takkouche· 1 shared Charles WangiD· 1 shared Chirag NepaliD· 1 shared Craig William ZuppaniD· 1 shared Gary YuiD· 1 shared Keita IchiiiD· 1 shared

Activity by Year

2024-2025 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 27 mutual co-author links

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3 shared papersBioinformatics

Adam GodzikORCID

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3 papers

Recent·Most cited

Spatial Transcriptomics Using Archived Formalin-Fixed Paraffin-Embedded Core Needle Biopsy Tissues Revealed Unique Transcriptomic Signatures in Kidney Transplant Rejections

Yan Chen Wongworawat, Chirag Nepal, Mark Duhon, Wanqiu Chen, Minh‐Tri Nguyen, Adam Godzik, Xinru Qiu, Wei Vivian Li, Gary Yu, Rafael Villicana, Craig William Zuppan, Michael E. de Vera, Mark Haas, Charles Wang

This study uses spatial transcriptomics on FFPE kidney biopsy tissues to identify distinct transcriptomic signatures for different types of kidney transplant rejections.

bioRxiv · Apr 8, 2025 · PreprintImmunology

Divergence of the Individual repeats in the leucine-rich repeat domains of human Toll-like receptors explain their diversity and functional adaptations.

Abraham Takkouche, Keita Ichii, Xinru Qiu, Lukasz Jaroszewski, Adam Godzik

This study shows how small variations in LRR repeats of human TLRs lead to structural and functional diversity, enabling recognition of different ligands and immune responses.

bioRxiv · Sep 30, 2024 · PreprintBioinformatics

Deciphering Abnormal Platelet Subpopulations in Inflammatory Diseases through Machine Learning and Single-Cell Transcriptomics

Xinru Qiu, Meera Goh Nair, Lukasz Jaroszewski, Adam Godzik

The paper uses single-cell transcriptomics and machine learning to analyze platelet subpopulations in sepsis, COVID-19, and SLE, identifying disease-associated transcriptional signatures and potential therapeutic targets.

International Journal of Molecular Sciences · May 29, 2024Peer-reviewedBioinformatics