Dongjun Chung

The paper provides guidance on analyzing circular data in spatial omics, using case studies to illustrate key considerations and methods.

RKMR identifies predictive marker panels in spatial omics data using a scalable framework that integrates kernel modeling and spatial dependence.

spaCraft provides calibrated power analysis and sample-size planning for multi-sample spatial transcriptomics by integrating domain recovery and clustering uncertainty.

FusedFCR identifies marker genes associated with developmental transitions in time-course scRNA-seq data by combining lasso and fusion penalties for interpretable gene selection.

MSGPCA improves spatial omics analysis by decomposing molecular variation into shared and slice-specific factors, enhancing the detection of tissue structure and microenvironmental variation.

CN-RNN is a deep learning framework for detecting copy number variations from exome sequencing data, showing improved performance over existing methods.

The paper develops TOPAZ, a tool that predicts cancerous cells/glands and analyzes immune cell spatial relationships in prostate cancer, finding that immune cell distance from cancer glands correlates with recurrence risk.

This paper introduces DeepSAS, a deep graph framework to identify senescent cells and their associated genes, revealing cell-type-specific senescence features in aging and disease.

The paper presents a step-by-step pipeline for processing and analyzing RRBS data to study DNA methylation in cancer.

This paper introduces foundational steps for analyzing spatial transcriptomic data from 10X Visium, focusing on clustering, spatially variable gene discovery, and cell-cell communication.

This study identifies genetic loci associated with opioid use disorder-related behaviors in rats using a genome-wide association approach and network-based clustering.

This study uses a non-linear clustering approach in rats to identify distinct behavioral and neuronal profiles associated with heroin vulnerability and resiliency, reflecting human opioid use disorder heterogeneity.

This study identifies cell-type-specific druggable targets in Alzheimer's disease by integrating GWAS and eQTL data across multiple cell types and datasets.

The paper identifies TAP2 protein variants resistant to HSV1 ICP47 inhibition, which could lead to new antiviral therapies.

The paper functionally screens TAP2 variants to show PLK1 overexpression reduces MHC-I surface abundance by phosphorylating TAP2, impairing antigen presentation in cancer cells.

spaDesign provides a statistical framework to optimize sequencing depth in spatial transcriptomics experiments by simulating data and estimating required sequencing depth for detecting spatial domains.

This study shows that long-access heroin self-administration in rats reduces grey matter volume in specific brain regions and alters microglia morphology, suggesting a link between neuroinflammation and structural brain changes.

This paper introduces IDAM, a method for inferring disease-associated microbial gene modules from metagenomic and metatranscriptomic data, showing superior performance and reproducibility in inflammatory bowel disease datasets.

LRT integrates scTCR-seq and scRNA-seq data to infer T cell trajectories by combining clonal relationships with transcriptomic profiles.

The paper introduces BANYAN, a Bayesian model for analyzing community connectivity in spatial transcriptomics data, revealing cell interactions in tumor microenvironments and adipose tissue.

The paper uses spatial transcriptomics and the MAPLE algorithm to analyze fibrotic lung tissue, identifying spatial biomarkers and fibrotic programs in IPF.

RESEPT uses deep learning to visualize and characterize tissue architecture from spatial transcriptomics data, revealing pathological structures in Alzheimer's and glioblastoma samples.

The paper introduces a data purification algorithm to improve detection of disease-associated microbes in microbiome data by reducing noise and heterogeneity.

This paper introduces SPRUCE, a Bayesian spatial multivariate mixture model for spatial transcriptomics data, which identifies cell sub-populations while accounting for skewness and spatial correlation.

IRIS-FGM is an R package for analyzing scRNA-Seq data to identify functional gene modules, cluster cells, and perform enrichment analysis, with support for Seurat objects.