Guoqiang Yu

AQuA2 quantifies molecular spatiotemporal signals from live imaging data by decomposing complex datasets into signaling events, demonstrating applications across biosensors and cell types.

This study identifies microglial subpopulations and shows their origin and transitions through transcriptomic and epigenomic analysis.

The paper surveys data science practices in neuroscience collaborations to identify challenges and propose improvements in data management and training.

The paper shows norepinephrine induces astroglial purinergic signaling that suppresses behavior through adenosine receptor activation in zebrafish.

The paper introduces CMC, a model that efficiently learns multifactorial effects in biological data and outperforms existing methods in several applications.

This paper develops an inducible genetic tool to study microglial states in development and disease, revealing their plasticity and functional roles.

ABDS provides tools for analyzing biologically diverse samples by addressing missing value imputation, signature gene detection, and expression pattern visualization.

This paper introduces an unsupervised deconvolution method, CAM3.0, to determine molecular archetype composition and expression from bulk tissues, showing its effectiveness through simulations and case studies.

This paper introduces OVE-FC and OVE-sFC tests to detect subtype-specific and differentially expressed genes, showing improved accuracy in deconvolution compared to existing methods.

The paper uses cell-type deconvolution to analyze gene expression in schizophrenia and finds upper-layer neurons in the DLPFC are associated with SCZ risk.

This study identifies R47H-TREM2 mutation-induced proinflammatory microglial states in human AD and tauopathy mice, showing Akt pathway involvement and potential therapeutic targets.

This study shows that sevoflurane exposure disrupts astrocyte function, leading to synaptic overgrowth and dysfunction in developing brains.

This paper proposes two computational methods to integrate multi-omics data and identify disease-specific pathways, demonstrating their effectiveness on muscular dystrophy data.

This study uses scRNA-seq to analyze microglia and myeloid cells across brain regions and developmental stages, revealing heterogeneity in postnatal microglia and their roles in disease.

The paper introduces AQuA, an event-based analysis tool for astrocyte imaging data, revealing novel single-cell and population-level physiological phenomena.

The paper analyzes the proteomic architecture of human coronary and aortic atherosclerosis using mass spectrometry data from 200 specimens, identifying key protein networks and regulatory features associated with early disease.