Peggi M. Angel

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

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.

The paper identifies ECM proteomic signatures associated with disease-free survival in DCIS and IBC, showing reduced collagen peptides correlate with poorer outcomes.

The paper uses spatial proteomics to show age-related delays in fracture healing by analyzing extracellular matrix composition in young and aged mice.

This study uses spatial mass spectrometry imaging to identify ECM peptide markers distinguishing OA-affected subchondral bone from healthy tissue and less affected regions.

This study identifies sialic acid linkage profiles in liver cancers to differentiate between cholangiocarcinoma subtypes and hepatocellular carcinoma.

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.

This study analyzes the spatial collagen proteome in normal breast tissue, linking it to BI-RADS categories and BMI through proteomic and imaging analyses.

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.