David Ming Lin

This paper introduces an integrated microfluidic workflow for Oxford Nanopore sequencing that reduces hands-on time and sample loss while enabling high-quality library preparation from small sample volumes.

BIDCell improves subcellular spatial transcriptomics segmentation by combining self-supervised learning with biologically informed loss functions, outperforming existing methods across multiple tissue types.

The paper characterizes cell-cell communication patterns in COVID-19 patients to identify differences between moderate and severe disease outcomes using single-cell RNA-seq data.

Cepo identifies cell identity by analyzing differentially stable genes from scRNA-seq data, improving cell-type characterization and spatial mapping.

The paper introduces scHOT, a method to detect changes in higher order gene interactions in single-cell data, demonstrating its application in liver development and spatial gene correlation patterns.

This study evaluates stably expressed genes (SEGs) in single cells using a computational framework and finds they are more stable than traditional housekeeping genes across species.