Raymond Jaihyun Cho

This paper introduces a statistical framework for disease classification using scRNA-seq data, achieving better interpretability and performance compared to existing methods.

This study uses multi-omic profiling to trace molecular changes in skin cells during carcinogenesis, identifying key mutations and spatial gene expression patterns in keratinocytes, actinic keratoses, and squamous cell carcinomas.

The paper develops software to infer copy number and detect single nucleotide variants in spatial transcriptomics data, applied to skin cancer to reveal genetic patterns and clonal populations.

This work introduces SCGP, a method for unsupervised identification of tissue structures from spatial omics data, and demonstrates its application to spatial transcriptomics and proteomics datasets.

This paper introduces two spectral clustering algorithms for multi-omic single-cell data and shows they produce results comparable to Seurat's WNN method on a CITE-seq dataset.