Yichen Si

Oct 31, 2024 | nature.com | Yongsung Kim |Weiqiu Cheng |Chun-Seok Cho |Yongha Hwang |Yichen Si |Anna Park | +1 more

AbstractSpatial transcriptomics technologies aim to advance gene expression studies by profiling the entire transcriptome with intact spatial information from a single histological slide. However, the application of spatial transcriptomics is limited by low resolution, limited transcript coverage, complex procedures, poor scalability and high costs of initial setup and/or individual experiments.

Sep 12, 2024 | nature.com | Yichen Si |Yongha Hwang |Weiqiu Cheng |Chun-Seok Cho |Miguel Quirós |Jun Lee

AbstractSpatial transcriptomics (ST) technologies have advanced to enable transcriptome-wide gene expression analysis at submicron resolution over large areas. However, analysis of high-resolution ST is often challenged by complex tissue structure, where existing cell segmentation methods struggle due to the irregular cell sizes and shapes, and by the absence of segmentation-free methods scalable to whole-transcriptome analysis.

Nov 7, 2023 | biorxiv.org | Yichen Si |Yongha Hwang |Jeong Yun |Weiqiu Cheng

AbstractSpatial transcriptomics (ST) technologies have advanced to enable transcriptome-wide gene expression analysis at submicrometer resolution over large areas. Analysis of high-resolution ST data relies heavily on image-based cell segmentation or gridding, which often fails in complex tissues due to diversity and irregularity of cell size and shape.

Nov 5, 2023 | biorxiv.org | Yichen Si |Yongha Hwang |Jeong Yun |Weiqiu Cheng

AbstractSpatial transcriptomics (ST) technologies have advanced to enable transcriptome-wide gene expression analysis at submicrometer resolution over large areas. Analysis of high-resolution ST data relies heavily on image-based cell segmentation or gridding, which often fails in complex tissues due to diversity and irregularity of cell size and shape.