Boyi Guo

LabWeaver improves agentic data analysis by organizing experimental context around laboratories, enhancing reliability in single-cell RNA-seq analysis of synovial sarcoma.

ClustSIGNAL improves spatial transcriptomics clustering by adapting smoothing based on neighbourhood complexity, enabling scalable analysis of large datasets.

This study maps molecular and genetic signatures of schizophrenia in human prefrontal cortex across spatial scales, revealing altered BDNF-TrkB signaling and inhibitory circuit dysfunction.

spaTransfer transfers cell type or spatial domain labels between single-cell and spatial transcriptomics datasets using non-negative matrix factorization and a multinomial model.

The paper provides an open-source online book for analyzing spatial transcriptomics data using Bioconductor in R, with reproducible code and datasets.

The paper introduces spatialMNN, an algorithm that integrates spatial transcriptomics data to identify spatial domains by incorporating 2D spatial information into mutual nearest neighbors analysis.

SpotSweeper improves spatial transcriptomics QC by identifying local outliers and regional artifacts, enhancing downstream analysis.

This paper introduces spoon, a statistical framework to correct the mean-variance relationship in spatial transcriptomics data, improving the identification of spatially variable genes.

The paper investigates ligand-receptor interactions (LRIs) in neurological diseases and finds that 96.8% of disease-associated LRIs are interconnected, highlighting their role in disease comorbidity and potential drug targets.

The paper generates a molecular neuroanatomical map of the human prefrontal cortex using spatial transcriptomics and single nucleus RNA-sequencing, revealing novel spatial domains and cell-cell interactions linked to psychiatric disorders.

escheR improves 2D data visualization for multi-dimensional data using Gestalt principles, with applications in genomics and spatial transcriptomics.