Daeseung Lee

This paper introduces PGL, a large language model that generates synthetic single-cell RNA-seq data from metadata, enabling virtual cohort construction and reference-on-demand analysis.

IAMSAM is a web-based tool for analyzing spatial transcriptomics data, enabling tissue segmentation and downstream analysis like differential expression and cell type prediction.

This study uses spatial transcriptomics to analyze cancer-associated fibroblasts in lung squamous cell carcinoma, linking their spatial organization to tumor progression and patient outcomes.

CELLama transforms single-cell and spatial transcriptomics data into embeddings using language model abilities, enabling flexible analysis without manual reference data.

SpatialSPM compares gene expression patterns across spatial transcriptomic datasets by aligning and analyzing multi-dimensional image matrices to identify differentially expressed regions.

This paper introduces SuperST, a deep learning method that reconstructs high-resolution spatial gene expression images from low-resolution barcode-based spatial transcriptomics data, improving alignment with immunofluorescence images.

GeneDART extends gene coverage in imaging-based spatial transcriptomics by combining it with barcode-based data using deep learning for domain adaptation.