Brian Neelon

RKMR: A Rapid Kernel Machine Regression Framework for Optimal Marker Detection in Spatial Omics Data

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

FusedFCR: A Fused Forward Continuation-Ratio model for marker selection along cell-fate trajectories

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: Multi-Slice Graph PCA for replicate-aware Spatial Omics analysis

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

SpaceBF: Spatial coexpression analysis using Bayesian Fused approaches in spatial omics datasets

SpaceBF identifies spatially varying co-expression patterns in spatial omics data using a Bayesian fused modeling framework, outperforming existing methods in specificity and power.

CBKMR: A Copula-based Bayesian Kernel Machine Regression Framework for Optimal Marker Detection in Omics Data

CBKMR improves marker detection in omics data by capturing nonlinear gene-outcome relationships and interactions, outperforming existing methods in accuracy and uncertainty quantification.

GRASS-NB: Group-structured variable selection for spatial negative binomial data with applications to cancer registry and spatial omics

The paper introduces a Bayesian negative binomial regression model with group-structured priors for spatially correlated count data, applied to cancer registry and spatial omics datasets.

S3R: Modeling spatially varying associations with Spatially Smooth Sparse Regression

S3R models spatially varying associations in spatial transcriptomics data by estimating location-specific coefficients with structured sparsity and smoothness penalties, showing effectiveness in synthetic and real datasets.

SpaceANOVA: Spatial co-occurrence analysis of cell types in multiplex imaging data using point process and functional ANOVA

SpaceANOVA analyzes spatial co-occurrence of cell types in multiplex imaging data across tissue or disease groups using point process and functional ANOVA, revealing novel insights in colorectal cancer precursor lesions.

A Bayesian Multivariate Mixture Model for Spatial Transcriptomics Data

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.